Pilot Study - Financial Ratio Benchmarks for Real Estate Companies of the G.C.C. - Mahesh N Butani

PILOT STUDY: FINANCIAL RATIO BENCHMARKS FOR REAL

ESTATE COMPANIES OF THE G.C.C.

Mahesh Narain Butani

Reg. No. 071303114

A dissertation submitted in partial fulfilment of the requirements for the degree

of

Master of Science in Quantity Surveying.

Dissertation supervisor: Prof. Ammar Kaka

Heriot-Watt University

School of the Built Environment

April 2010

Declaration:

I hereby confirm that this dissertation is my own work.

_________________________ __________________

Signature Date:

ii

TABLE OF CONTENTS

Sr. No. Contents Page No.

Title page i

Declaration i

Table of Contents ii

List of Tables v

List of Figures vi

Acknowledgements vii

Abstract viii

Glossary ix

Chapter 1: Introduction 1

1.1 Rationale for the research 1

1.2 Research goals 3

1.3 Outline methodology 4

Chapter 2: Literature review: Financial performance 6

2.1 Introduction 6

2.2 Purpose of financial ratios 6

2.3 Categories of financial ratios 8

2.4 Evaluating financial statements 8

2.5 Evaluating financial ratios 9

2.6 Research trends and opinions 9

2.7 Typical user profiles 11

2.8 Sources of financial ratio statistics 12

2.9 Special financial ratios 13

2.10 Limitations of financial ratio analysis 14

2.11 Benchmarking 16

2.12 Case study: Financial performance of hospitals 16

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2.13 Summary 19

Chapter 3: Literature review: Financial ratios in construction 21

3.1 Introduction 21

3.2 Contractor insolvency models 21

3.3 Construction specific insolvency models 24

3.4 Analysis by surety providers 29

3.5 Construction industry in Malaysia 32

3.6 Health of contractors in Hong Kong 33

3.7 A model for the Egyptian construction market 35

3.8 Analysis of Indonesian construction firms 38

Chapter 4: Research design and methodology 41

4.1 Introduction 39

4.2 Research goals 42

4.3 Data collection 43

4.4 Objective 1: Identification of financial ratios 43

4.5 Objective 2: Creation of benchmarks 46

4.6 Objective 3: Selection of ratio for detailed analysis 55

4.7 Objective 4: Observation of data grouped by year 56

4.8 Objective 5: Observation of data grouped by country 56

4.9 Objective 6: Observation of data grouped by company size 57

4.10 Hypothesis 57

Chapter 5: Analysis of Results 58

5.1 Introduction 58

5.2 Companies included in the study 60

5.3 Collective balance sheet profile of the companies 61

5.4 Objective 1: Identification of financial ratios 64

5.5 Objective 2: Creation of benchmarks 64

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5.6 Objective 3: Selection of ratio for detailed analysis 75

5.7 Objective 4: Observation of data grouped by year 77

5.8 Objective 5: Observation of data grouped by country 79

5.9 Objective 6: Observation of data grouped by company size 86

5.10 Results of Kruskal-Wallis tests 91

5.11 Testing the hypothesis 92

5.12 Summary 93

Chapter 6: Conclusions 94

6.1 Introduction 94

6.2 Objectives 95

6.3 The research hypothesis 98

6.4 Research limitations 98

6.5 Areas of further research 99

6.6 Contact details 99

References 100

Appendix A: BW plot and stat data for financial ratios 106

Appendix B: BW plot and stat data for ROCE 137

Appendix C: KW test results for ROCE 152

Appendix D: Main spreadsheets for financial ratios 154

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LIST OF TABLES

Ref Details Page

Table 2.1 Users of financial ratios 11

Table 3.1 The constituent ratios of the model 28

Table 3.2 Description of variables used in the study 32

Table 3.3 Trend of changing ratios of the selected contractor 34

Table 3.4 Management action required based on grade obtained 37

Table 3.5 Revenue and profitability ratios 38

Table 3.6 Cash flow and liquidity ratios 39

Table 3.7 Leverage ratios 39

Table 3.8 Efficiency ratios 40

Table 4.1 Schedule of financial ratios selected 44

Table 4.2 Template of profit and loss statement 47

Table 4.3 Template of balance sheet 48

Table 4.4 Template of additional items 50

Table 4.5 Sample of data sheet for each financial ratio 54

Table 5.1 List of companies selected for the study 60

Table 5.2 Collective balance sheet profile of the companies 2008 61

Table 5.3 Proposed benchmark values for financial ratios 2006 to 2008 65

Table 5.4 Financial ratio medians for GCC countries 66

Table 5.5 ROCE medians for all countries and benchmarks 2006 to 2008 75

Table 5.6 KW test for ROCE of all companies 3 year data 78

Table 5.7 Anova test for ROCE of all companies 3 year data 78

Table 5.8 KW test results for grouped data for ROCE 91

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LIST OF FIGURES

Ref Details Page

Figure 2.1 Approach to selecting key financial indicators 18

Figure 3.1 The cut-off between non-failed and failed groups 29

Figure 3.2 Construction failure rates 2006 - 2008 30

Figure 3.3 Model development steps 36

Figure 3.4 Performance grades (G) for different construction sectors 36

Figure 3.5 Performance grades of the selected company over a period 37

Figure 4.1 Sample of box - whisker plot 53

Figure 4.2 Sample of box - whisker plot for 3 years with median and mean 54

Figure 5.1 Collective balance sheet profile of all 30 companies 2008 62

Figure 5.2 Country-wise collective balance sheet profile 62

Figure 5.3 Size-wise collective balance sheet profile 63

Figure 5.4 Frequency distribution of ROCE 2006-08 76

Figure 5.5 Expanded frequency distribution of ROCE 2006-08 76

Figure 5.6 BW plot of ROCE for all companies 77

Figure 5.7 BW plot of ROCE group country-wise 2006 79



Figure 5.10 Column chart of ROCE group country-wise 2006-08 81

Figure 5.11 Column chart of ROCE group year-wise 2006-08 82

Figure 5.12 Web chart of ROCE 2006 83



Figure 5.15 Web chart of ROCE 2006-08 84

Figure 5.16 BW plot of ROCE group size-wise 2006 86



Figure 5.19 Column chart of ROCE group size-wise 2006-08 88

Figure 5.20 Column chart of ROCE group year-wise 2006-08 89

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ACKNOWLEDGEMENTS

First and foremost, I wish to thank Dr. Assem Al Haj for his comforting smile,

encouragement and support which has contributed in many ways to my learning.

My sincerest appreciation goes to Dr. Ammar Kaka, for his patience, advice and

guidance as my dissertation supervisor.

This study was inspired by an article by Pamulu Sapri and I am thankful to him for

providing his paper. I am also deeply indebted to all the scholars and authors of the

papers and books which I have referenced and who have contributed so much to my

understanding.

My gratitude goes to Ramakanta Rath, Heriot Watt librarian extraordinaire, who has

been invaluable in suggesting sources and ideas; and Anita Dias, assistant librarian, who

has been most helpful.

Heartfelt thanks to evening manager, John Mathew who has been exceptionally

supportive and the Heriot Watt administration staff; mainly Aisha Albulooshi,

Greeshma Ramesh, and Mahesh Naik who have been most kind and helpful.

Credit goes to my lovely wife, Anita for her endless support and concern; and to Snehal,

my daughter, for her help and prodding; as also to Nalini, Sunita and Madhu, my sisters

for their encouragement and love.

I am grateful to Joseph Medlej, my superior, who has been most considerate. I also

thank my colleague, Omar Mahmoud for his invaluable advice and reference material.

Also, I thank all my fellow students many of whom are now dear friends, in whose

company, studying was a joyful experience. Special thanks to Rupa Appukuttan for

introducing me to the university and initial assistance.

I am obliged to K K Varma for his review; and Priyanka Parkkot for her expert opinions

pertaining to financial analysis.

Special thanks to my office assistant Raju Sarvanna for being around.

I thank Arab Capital Markets Resource Center (ACMRC) the financial research and

consulting company for providing financial data on the companies

I also wish to thank all the professionals who responded to my queries from the

following forums: Allexperts.com: Ronny Fisher; LinkedIn: Asra Islam, Alan

Dibartolomeo, Amit Tandon, Jack Man and Robert Reitman; Finance30.com: Robert,

Brent Wheeler, Bill Wright, Glen Sawyer, Sundaramany, T Venkataraman, Arnel,

Charles Van Tongren, Joseph, Naresh, Gabriel von Bonsdorff, Fayez, Elizabeth

Sampedro, and Satish Singh; Stat-Help.com: Dr. Adrian Gilbert; and American

Statistical Association Professionals: Arthur Aguirre.

All mistakes, of course, are purely mine.

Above all, I dedicate this study to my mother, Duru for her unshaken confidence and

support throughout. Thank you, ma!

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ABSTRACT

The GCC region has been undergoing a transformation due to a surge of construction

activities in recent times. This has led to a mushrooming of real estate developers; some

of whom have gone public, and are listed on the stock exchanges. Real estate is the

driver of the parallel non-oil economy, and therefore it is important for the stakeholders

to have sufficient awareness of the financial performance of these firms. While the

financial ratios of these firms can be easily derived from the financial statements, there

do not exist any official benchmarks to compare the ratios. This pilot study aims to fill

this gap by creating rudimentary benchmarks for 30 financial ratios based on financial

statements of all the 30 companies listed on the GCC bourses. The study was initially

meant to cover UAE firms, but was extended to cover the GCC due to the limited

number of listed firms in the UAE. This extension was made on the assumption that the

GCC countries have similar economies. The benchmarks for 30 financial ratios for

2006-08 are presented and one ratio: the return on capital employed (ROCE), is

analysed in detail. The study confirms the hypothesis that the ROCE data is statistically

similar among the selected firms of the GCC for the three study years 2006-08, and it is

envisaged that the benchmarks created for the other 29 financial ratios may be used

across the GCC with limited prudence. The study shows that there are statistically

significant variances due to size effects; the study does not consider country effects due

to limitations of data. The study also shows that, in terms of ROCE, all the companies

fared better in 2007 than 2006 and poorly in 2008; and that the smaller companies went

through wild swings in terms of ROCE during the study period.

Keywords: financial ratio, benchmark, real estate, construction, GCC, ROCE

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GLOSSARY OF TERMS

ACMRC Arab Capital Markets Research Centre

ANOVA Analysis of Variance (test)

BACH Banks for the Accounts of Companies Harmonised

BTM Book-to-Market

BW Box-Whisker (plot)

BWC Backlog to Working Capital

CA Current Assets

CFMA Construction Financial Management Association, USA

CFROI Cash Flow Return on Investment

CIDB Construction Industry Development Board , Malaysia

CR Current Ratio

CVA Cash Value Added

DER Debt to Equity Ratio

DSS Decision Support Systems

EBIT Earnings Before Interest and Taxes

EPS Earnings Per Share

EVA® Economic Value Added

FA Fixed Assets

GAAP Generally Accepted Accounting Principles

GCC Gulf Cooperative Council

GDP Gross Domestic Product

HRCC Hospital Report Research Collaborative (Toronto)

IQR Inter Quartile Range

IRR Internal Rate of Return

KW Kruskal- Wallis (test)

MDA Multiple Discriminate Analysis

MIS Management Information Systems

NA Net Assets

NAICS North American Industry Classification System

NCE Net Capital Employed

PAT Profit after Tax

PBIT Profits before Interest and Taxes

PER Price Earnings Ratio

PI Performance Index

QR Quick Ratio

QUT Queensland University of Technology

REIT Real Estate Investment Trusts

RMA (The) Risk Management Association

ROA Return on Assets

ROCE Return on Capital Employed

ROE Return on Equity

ROS Return on Sales

ROTA Return on Total Assets

x

SIC (International) Standard Industry Classification

SIO Surety Information Office

SSRN Social Science Research Network

STL Short Term Liabilities

TSO The Stationery Office, UK

TSR Total shareholder Return

UAE United Arab Emirates

UK United Kingdom

US United States of America

UTE Under billings to Equity

WCT Working Capital Turnover

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CHAPTER 1 INTRODUCTION

1.1 Rationale for the research

The GCC economic overview

The Gulf Cooperative Council (GCC) countries comprise of six countries of the Arabian

Gulf, namely United Arab Emirates (UAE), Sultanate of Oman, Kingdom of Saudi

Arabia, State of Qatar, Kingdom of Bahrain, and State of Kuwait. The GCC countries

have the largest proven oil reserves in the world and this has resulted in the region

ranking as the largest producer and exporter of petroleum. For the region, oil & gas

represent 73% of total export earnings and the sectors account for 63% of government’s

revenues and 41% of the GDP’s. The countries follow somewhat similar paths of

economy, development, and governments. Other similarities are the pegging of the

local currencies to the US Dollar (except Kuwait) and the dependence on a large

expatriate workforce and expatriate businesses. The oil and natural gas boom has

created some of the world’s fastest growing economies in this region; which is now

coupled with a construction and real estate boom backed by decades of saved petroleum

revenues (GCC economic overview 2010). The six countries have enjoyed a

spectacular economic boom until late 2008; the GCC economy tripled in size to US

Dollar 1.1 trillion from 2002 to 2008. The region is continuing its economic reform

program, focusing to attract domestic, regional, and foreign private sector investment

into oil & gas, power generation, telecommunications, and real-estate sectors. The

decline in oil prices, stemming from the global financial crisis has slowed the pace of

investment and development projects, but the governments are in a position to use their

considerable financial resources to stabilize the economy if necessary (GCC economic

overview 2010).

Real estate in the UAE and the rest of the GCC countries

The ownership of real estate in the GCC countries was closed to foreigners until

recently. Lately, there have been partial relaxations in some countries, initiated by

Dubai, UAE and followed by others, each with their own laws, regulations, and

restrictions. This had resulted in the construction boom which started in Dubai circa

2005 and slowly spread to other GCC countries. This gave rise to a multitude of real

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estate companies, large and small, both state sponsored, and privately owned, who are

the drivers of the parallel non-oil economy. Some of these companies have gone public

and became listed to enable them to tap funds from the public.

Gap in data on financial performance benchmarks

If an investor wished to appraise or compare the financial performance of these real

estate companies, he would want to look for regional benchmarks of financials; where

he would face a difficult predicament. There does not seem to be any organisation

producing benchmarks (or industry averages as they are also known) of financial ratios

of companies in the UAE. A search on the World Wide Web will confirm that there are

none in any of the GCC countries either. Some regional stock exchanges and some

stock broking companies and analysts do provide information on financial ratios of

individual companies, notably the Arab Capital Markets Research Centre (ACMRC),

there does not seem to be any agency publishing the benchmarks.

Bridging the gap

While working on an assignment on the subject of construction financial management,

the author noticed that the comparative benchmarks given were from the UK. As a

UAE company had been selected by the author for the assignment, it seemed odd to

compare the calculated financial ratios with UK benchmarks. This led to a quest for

similar benchmarks for UAE contractors; whereby it was realised that none existed.

This gap in information is what prompted the topic for the dissertation, which was

initially a proposal to create rudimentary benchmarks for financial ratios for UAE

contractors, based on a similar study of financial ratios of contractors in Indonesia by

Pamulu in 2008. It was then realised that contractors would have to be coaxed into

releasing their financial figures, which seemed a daunting task. Therefore it was

investigated if there was any information on financials on the public domain of the

internet. Only 3 construction companies were found listed on the stock exchanges - a

contractor with manufacturing facilities, an electromechanical contractor, and a fit-out

contractor. It was also noted that there were six real estate companies listed in the 2

stock exchanges in the UAE, which were a mixed bag in terms of size and

diversification, and by themselves too few to do a meaningful study of the benchmarks.

As the GCC countries have similar economic profiles, the search was extended to the

GCC countries, which resulted in finding 30 real estate companies listed in the various

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bourses of the GCC. Therefore the goal was modified and it was then proposed to do a

study of the entire GCC real estate development companies.

This dissertation is an attempt – a pilot study to examine if the financial ratios of a

mixed bag of real estate companies make some meaning and patterns when they are put

together. This is proposed to be done by statistical analysis of variance and extraction

of medians to serve as rudimentary benchmarks.

1.2 Research Goals

This section describes the aims, objectives hypothesis, and assumptions of the research

Aim

To create benchmarks for select financial ratios for three years 2006 to 2008 and

statistically test if sufficient similarities exist in the underlying data to justifiably enable

the usage of data for the whole GCC listed companies to be used together with

confidence as a group.

Objectives

These objectives have emerged for the research through literature review and

examination of analysts’ websites:

1. Identification of financial ratios which would be useful, especially for the real

estate and construction industry.

2. Creation of benchmarks for each of the selected financial ratios for each of the

three study periods 2006-08.

3. Selection of one financial ratio for detailed analysis.

4. Observation of patterns and variance for the selected ratio in year-based group

data for all companies.

5. Observation of patterns and variance for the selected ratio in country-based

group data for each of the years of the study, to test the hypothesis.

6. Observation of patterns and variance for the selected ratio in size-based group

data.

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Hypothesis

One part of this research is designed to test the Hypothesis that:

H0 = There is no difference between financial ratio data across the GCC countries at

α =0.05 level.

The hypothesis is tested for the selected ratio and the results of the hypothesis are

extended to assume that the benchmarks created with the combined data of the selected

GCC companies may be confidently used throughout the region - or not.

1.3 Outline methodology

This section explains the structure and the proposed methodology for achieving the aims

and objectives of the research.

Stage 1: Literature review

In chapter 2, a review of the literature pertinent to the selected topic has been carried out

to gain a wider understanding of the research topic. It also highlights what work has

been carried out earlier by others and the controversies and problems relating to the

research. A detailed and systematic search did not yield any literature on financial

benchmark ratios for real estate developers in the GCC or UAE or anywhere else; none

was either found on the creation aspect of financial ratio benchmarks for construction

industry. Most of the literature on real estate is on topics related to Real Estate

Investment Trusts (REIT’s) and the investment ratios and other literature on financial

ratios in construction relate mostly to insolvency of contractors. Some literature was

found on a study of financial ratios in the construction industries of Indonesia, Hong

Kong, and Turkey. Interesting information was also found on benchmarking of

financial ratios found is a study undertaken by Deloitte and Touché for the Australian

wine industry. Another study undertaken by Toronto Hospitals for benchmarking of

financial performance has also been examined. The literature review was carried out

from books on financial accounting and statistical analysis; scholarly articles and PhD

theses were reviewed online on the Heriot - Watt electronic resources and Jstor,

Elsevier, SSRN, QUT ePrints, CSA Illumina, Springer science, Emerald, and

Routledge- Informaworld; all accessed through Athens. Some other journals which

were inaccessible were made available upon request by the university librarian. The

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websites of some data providing companies and analysts were also reviewed to study

their methods and presentations of financial data.

Stage 2: Research Design and Methodology

In chapter 3, the research uses the information gathered from literature review for

designing the methodology. The first part requires identification of relevant financial

ratios: and 30 have been identified for the study. The second part is the collection of

raw data to calculate the selected financial ratios, which is collected from the websites

of the listed companies; from the regional stock exchanges; and the ACMRC. The

literature review revealed that 30 companies is the minimum requirement by the Risk

Management Association (RMA 2009) to create benchmarks. The ratios for years

2006-8 are calculated, tabulated, statistically analysed and a bench mark values for each

of the 30 ratios are presented. The financial ratios are also presented as box-whisker

plots in the appendix showing the quartiles, median and mean values for each of the 30

ratios. The financial ratio – Return on Capital Employed (ROCE) is selected for

detailed analysis, to validate the hypothesis and accordingly the ROCE data is split into

year based, country based, size based groups and results presented.

Stage 3: Analysis of Results

In chapter 4, the results obtained from the above analysis are presented in a table for all

the 30 ratios showing median values as benchmarks for the GCC. Also presented are

medians for each of the countries for each year. The ROCE analysis is also checked for

variances within the data based on grouping of the data to test the hypothesis. Some

benchmarks have comparative values from other countries and these have also been

shown wherever possible.

Stage 4: Conclusion

Chapter 5 provides conclusions drawn about objectives set out earlier based on analysis

of the results. It then makes a judgement about the validity of the hypothesis. This

chapter also notes the limitations of the research and provides suggestions for further

research which may be carried out.

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CHAPTER 2: LITERATURE REVIEW: FINANCIAL

PERFORMANCE

2.1 Introduction

This chapter aims to provide a general outlook of the literature on financial ratios

starting with the purpose definitions and moves on to explain the types of financial

ratios. It then goes on to explain the compatibility of the raw data obtained from the

financial statements of different firms; which is followed by a section on evaluation of

the financial ratios. A brief history and research trends are covered in the next section

followed by user profiles of the ratios and their requirements. A section also gives the

names of providers of the financial ratios in the UK and US derived from the literature

reviewed. There have also been special ratios, some even branded and patented, and a

selection of these is presented. This is followed by a discussion on limitations of use

and a conclusion on the future of financial ratios. The final section describes the

process of financial benchmarking and a case study of Ontario hospitals.

2.2 Purpose of financial ratios

The Dunn & Bradstreet Corporation reports that over 60,000 businesses fail each year,

on an average. Although many firms also cease to exist due to mergers or sales, a study

by the Small Business Associations showed that 25% of them shut down within 2 years

of operations, 50% within 4 years, and 70 % within 8 years. Out of these, 20 % failures

are contributable to inadequate capital or too much debt. From these findings it is

assumed that financial education is an important factor in determining whether a

business venture will be successful (Melicher 2008).

Can a business meet its financial obligations? Can it pay its debt? Is the firm liquid? Is

the business model successful? Is it making a reasonable profit? Is it utilizing its assets

to the fullest? Is the firm suitable investment for the shareholders? Would the returns

be better elsewhere? Is it a good investment? The answer to all the above lies in the

analysis of financial statements churned out by companies annually.

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Financial statements are used to report the status of the firm at one point in time as well

as the results of its operations of the previous year. However, the real use comes in the

effective analysis to predict the future income and dividends along with the risks

associated with these variables. (Melicher 2008)

There is an assortment of analytical methods - trend analysis, common size analysis,

ratio analysis, segmental analysis and cash flow analysis (Alexander 2007), and out of

the various methods in use, financial ratios are the corner stone of financial statement

analyses (Horngren 2004) as they capture the critical dimensions of the economic

performance of the firm (Horngren 2004).

Financial ratios are the most widely used among other measures of financial

performance of a firm. Ratio analysis can be considered a means to determine a firm’s

strengths and weaknesses (Melicher 2008) and are increasingly being used as a tool by

management to guide, measure and subsequently reward the employees; Hewitt

associates, a compensation consulting firm reports that 60% of the 1941 large firms

have profit sharing programs (Horngren 2004).

Ratios standardize balance sheet and income statement numbers (Melicher 2008);

“business ratios are the guiding stars for the management of enterprises” according to

Walsh (2003) as they guide the management towards the most effective long and short

term strategies. Financial ratios are also used for modelling purposes by practitioners

(analysts) and researchers (Salmi 1994).

Besides the obvious uses, ratios have also been used in forecasting potential corporate

bankruptcies, classifying a potential customer’s credit rating; and lately there is research

into models to identify potential takeovers and also to value shares. This is made

possible because of progress in application of statistical techniques to ratios. This has

resulted in improvement of the quality of a ‘general picture’ of a company through time-

line analysis and line-of-business analysis (Pendelbury 2004).

The four major categories of financial ratios measure liquidity, profitability, leverage,

and efficiency. As a general rule, a higher value in profitability and liquidity and lower

values in leverage indicate a better financial health of a firm (Pamulu 2007).

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2.3 Categories of financial ratios

Different writers have classified the numerous ratios into various categories depending

upon their application.

Horngren (2004) has grouped the most ratios into four categories: short-term liquidity

ratios, long-term solvency ratios, profitability ratios and market price & dividend ratios;

whereas Melicher (2008) has classified them as: liquidity ratios, asset management

ratios, financial leverage ratios, profitability ratios and market value ratios.

Pendelbury (2004) has classified them as: profitability and performance ratios,

efficiency and effectiveness ratios, liquidity and stability ratios, capital structure ratios,

and investment and financial risk ratios.

2.4 Evaluating financial statements

A study of financial ratios would be imprudent before first touching the subject of the

raw data on which they are based; which is the financial statements; these are namely

the profit & loss statement, the balance sheet, the cash flow statement and the auditors

report.

Financial statements identify a multitude of figures for us and these figures do not mean

much until we compare them with something else. They are therefore analysed by

means of horizontal or vertical analyses. The analysis of the performance over a time

period is termed as “horizontal analysis” or “trend analysis,” whereas a comparison with

other firms in the peer group is termed as “vertical analysis” or “common size analysis.”

In trend analysis, a base year is chosen as a ‘benchmark’ and the various elements are

shown as an index of this benchmark. Literature suggests a minimum of 5 year time

frame (Alexander 2007) to check how the various items in a balance sheet have changes

over time. In a common size analysis the financial statements items are compared with

the peer group, and to remove the size effect, the balance sheet items are expressed in

terms of percentage of revenue and the balance sheet items in terms of percentage of

assets (Alexander 2007).

The elements of non-compatibility of financial statements should be carefully reviewed

prior to undertaking an analysis. These pitfalls could include changes relating to the

time span of the financial year of the statements, different balance sheet closing dates,

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changes in the firm’s structure due to mergers, acquisitions or restructuring activities,

differences in valuation rules or accounting methods, and differences in presentation

rules (Alexander 2007).

2.5 Evaluating financial ratios

Financial ratios are calculated from the various figures of the financial statements. As

the data for financial ratios comes from financial statements, it can also be regarded as

an extension of other financial statement analytical techniques (Pamulu 2007).

In order to meaningfully evaluate the performance, a comparison would be needed with

the peer group of other firms in the industry, or the firms own past performance or an

absolute benchmark (Alexander 2007).

It would not make much meaning to judge the performance of a firm based on one or

two year results if there are no benchmarks for similar industries available and if the

results are too short to build an internal benchmark (Alexander 2007). Unfortunately

there is no ‘ideal’ or absolute standard benchmark for the ratios (Pendelbury 2004);

Alexander (2007) and Pendelbury (2004) suggests creation of a benchmark based on the

best performer or the most successful firm in the peer group. It should also be noted

that if the statistics are made based on larger companies, the benchmarks so created

would hold little relevance for the smaller companies in the same industry (Pendelbury

2004).

2.6 Research trends and opinions

A majority of the research literature on financial ratios is centred on several major

themes (Salmi 1994) with some overlaps which include functional form –the

proportionality debate, distribution characteristics, classification, comparability across

industries, time-series properties, bankruptcy prediction models, explanation of other

firm characteristics, stock markets, and forecasting ability – v/s financial models, and

the estimation of Internal Rate of Return (IRR).

According to Pamulu (2007), some of the most relevant research in financial ratio

analysis in construction has been by Fadel (1977), Akintoye (1991), Langford (1993),

10

Edum-Fotwe, (1996), Pilateris (2003), Cheah (2004), Chan (2005), Yee (2006), Singh

(2006), Ocal (2007), and Luu (2008).

In a research on ‘trends in financial ratios analysis research’, a review of the basis of

was conducted by Salmi (1994) of the four areas of financial ratios: function form,

distributional characteristics, classification, and measurement of profitability. The

generic conclusions of the research were that “there are several unexpectedly distinct

lines with research traditions of their own.” They also concluded that “while significant

irregularities can be observed, they are not necessarily stable across different ratios,

industries, and time periods” (Salmi 1994). It was observed that the proportionality was

stronger ‘within’ industries than ‘between’ industries, and the proportionality varied

between ratios and between time periods indicating problems in temporal stability

(Salmi 1994). A follow up review conducted by Salmi, Nikkinen, and Sahlström (2005)

has confirmed that the major conclusions were still valid even after a decade.

A research on ‘country and size effects’ on the financial ratios was conducted by Cinca

(2001) on European companies, which involved 11 countries, with 3 size groups and

over a 14 year period on 15 financial ratios; using multivariate statistical analysis. The

data was used from the harmonised aggregate financial statements published by the

European commission in the BACH (Bank for the Accounts of Companies Harmonised)

database. The results showed that financial ratios ‘did’ reflect the size of the firm; also

there were no significant ‘size related differences’ in profitability, but the differences

appeared when countries were compared. . The research has shown that size is

important in the financial structure of European firms and that its importance has not

varied over time (Cinca 2001). The research also found that strategic groups did exist

and they were related to country and not size. The research suggests as an example –

that a small Italian firm could take example from a small Austrian firm if it wanted to

increase its profitability rather than find benchmarks in a large Italian firm (Cinca

2001).

In another research on high Book-to-Market (BTM) firms, Piotroski (2000) has

examined whether a simple financial-analysis-based strategy, when applied to a broad

portfolio of high BTM firms could increase the returns to an investor through selection

of financially strong high BTM firms. High BTM firms tend to be neglected by the

analysts, suffer from low levels of investor interest, have limited access to the informal

types of disclosures and even these are considered unreliable (given their recent poor

performance), and the only source of their information dissemination is through the

annual financial statements. The evidence suggested that the market did not fully

11

integrate financial information into stock prices in a timely manner (Piotroski 2000).

The paper discusses that the application of a simple financial statement based heuristic

applied to these out-of-favour stocks could pick out firms with strong prospects. The

research was limited to financial statement analysis of small and medium firms with low

share turnovers and no analyst following – the unfavoured stocks (Piotroski 2000). The

research proved that an investment strategy based on the research generated a return of

23 % annually between 1976 and 1996 and in that market scenario of 2000, could

potentially generate an additional 7.5 % annually through the selection of financially

strong high BTM firms (Piotroski 2000).

2.7 Typical user profiles

Table 2.1: Users of financial ratios

Financial ratios are employed widely by all parties interested in an enterprise: the

owners, management, personnel, customers, suppliers, competitors, regulatory agencies,

and academics, each with their own objectives on application (Salmi 1994). All users

will no doubt be interested in the future prospects and different users would have varied

requirements based on the decisions required to be taken (Pendelbury 2004).

Potential shareholders would examine the financial statements as an excellent source of

information about a company (Melicher 2008) before they decide to invest in a

company’s shares. Subsequently they will continually assess their investments and

some of the results may be translated into ‘buy’, ‘hold’, or ‘sell’ decisions. There are

ratios specifically made for investors which focus on the returns to be obtained in the

12

form of dividends or capital appreciation (Alexander 2007). Some companies go the

extra mile to provide additional information for the investors; e.g., the annual report of

Taiwan Semiconductors provides a 5 year history on 30 ratios, which allows an analyst

to quickly access the effectiveness of management in several areas. (Horngren 2004)

While the present shareholders will want to monitor firm performance – mainly the

status of dividends and profitability (Pendelbury 2004), the management would review

if certain company goals are being met (Melicher 2008), whereas the employees would

be interested in information to bargain for better benefits and job security status

(Pendelbury 2004). An excellent example is the Duke Power Company, which decided

that profit may not be the right measure for rewarding employees and decided to use

ROE as one of the new measures; as ROE can be improved both by increasing

profitability and efficiency (Horngren 2004)

Lenders would be interested in the creditworthiness (Pendelbury 2004), and potential

buyers of bonds will want to ensure the timely payback capacity of principal and

interest (Melicher 2008).

Practitioners (analysts) use financial ratios to forecast the future prospects of a firm,

whereas the researchers aim to exploit the ratios for creation of better models (Salmi

1994).

2.8 Sources of financial ratio statistics

There are various private and governmental bodies that provide data on financial ratios;

some agencies in the UK are (Pendelbury 2004):

• Centre for Interfirm Comparison (a non-profit undertaking jointly established by

the British Institute of Management and the British Productivity Council)

• ICC information limited

• Datastream (Thomson Financial)

• Financial Times Interactive Data Limited

• Jordan & Sons Limited

• Dun and Bradstreet ltd

• Standard And Poor Compusat

• The times 1000

• The Stock Exchange Official Year Book

13

• Institute Of Chartered Accountants Of England And Wales

• Office Of The National Statistics

In the US, are (Melicher 2008):

• The Risk Management Association (RMA), (formerly Robert Morris associates.)

• Financial Dynamics

• Standard & Poor’s

• Federal Trade Commission

2.9 Special financial ratios

Of late some researchers and research firms have formulated special ratios, some of

which are even patented; a few of these are explained briefly below.

Shareholder value and total shareholder return

The concept of shareholder value emerged in 1980. Rappaport (1998) is regarded as the

founding father through his publication “creating shareholder value” and the idea was

that a stream of future earnings provided a better indicator to evaluate the potential of a

firm (Alexander 2007).

Total shareholder returns (TSR)

This represents the change in capital value of a company over a one year period plus

dividends expressed as a plus or minus percentage of the opening value (Alexander

2007).

EVA ®:

Economic Value Added is the most well known concept which builds on the residual

value concept and has been trademarked by Stern Stewart & Co. EVA ® is a monetary

tool and was created as a management tool for use within a firm and is less useful for

inter-company analysis (Alexander 2007).

CFROI and CVA

These Stand for Cash Flow Return on Investment and Cash Value Added respectively.

These were developed by the Boston Consulting Group and can be defined as the annual

gross cash flow relative to the invested capital of the firm and CVA is a residual income

14

measure. These are valuable tools for communication to the outside world as well as for

internal management and strategic decisions. (Alexander 2007)

Altman’s Z-score

In 1968, Professor Altman (1968) combined 5 ratios to produce this score; in his

seminal article. He found that companies with Z scores above 2.99 had not failed,

whereas companies with scores below 1.81 had failed. His research was limited to the

manufacturing sector and holds good for firms located in the same region, sector and

time period (Alexander 2007). Another model for prediction of failure was carried out

by Taffler (1982) in the UK, and is also only valid for the region from which the

company data was obtained (Alexander 2007)

Very often company managements create new ratios for the investors which show the

companies in better light; like the ‘like for like sales’, ‘profit before one time

expenditures, before goodwill and impairment’, and many others (Alexander 2007).

2.10 Limitations of financial ratio analysis

The limitations stated below also apply to the analysis of financial statements in general

and limit the usefulness of ratio analysis results as well:

Non-monetary factors

These are factors like labour relations and quality of products which affect a firm’s

prospect, but are not reflected in the statements. Some firms now try to overcome this

limitation by providing non-financial performance ratios in their statements (Alexander

2007).

Accounting standards

It is necessary to check for compatibility in the comparable statements prior to

undertaking a horizontal or vertical analysis (Alexander 2007). As accounting standards

often differ among firms and this can cause confusion (Melicher 2008).

Historic cost accounting

The interpretation of the business is based on historical cost figures and this may not be

the best guide for future performance (Alexander 2007)

15

Short –term fluctuations

Short term fluctuations within the year are not known and the figures represent one

point in time, where figures may present a better view of liquidity than has been

throughout the year (Alexander 2007).

Changes in value of money

Inflation and price changes can make the entire analysis invalid, though the short term

fluctuations are better covered in the interim reports (Alexander 2007).

Diversified companies

The growth by mergers and acquisitions has led to companies having diversified

operations in diverse industries and the calculation of ratios based on this diversification

is of very limited use (Pendelbury 2004). By their very nature, industry ratios are

focussed narrowly on a specific industry, but the operations of large firms such as GE,

Exxon-Mobil, and IBM often cross many industry boundaries (Melicher 2008).

Data mismatch

Comparing firms’ ratios to an average must be done with caution as some sources report

averages based on means, other on medians, yet others cite Interquartile ranges

(Melicher 2008).

Peer group

Caution must be urged in selection of the proper peer group as firms in the same

industry may be with vastly different characteristics with respect to size and turnover,

multinational as well as domestic (Melicher 2008)

Mode of calculations

Analysts and public bodies may have different ways of calculating the same ratios, e.g.

Some may use pre-tax earnings; others may assume debt only as long term debt, while

others may assume all liabilities as debt (Melicher 2008).

Foreign companies

According to Choi (1983), financial ratios are often ‘misused’ when applied to foreign

companies. In a study of Japanese and Korean firms participating in the US stock

16

exchanges, it was noticed that there was considerable misinterpretation of the financial

ratios. This was partly due to explainable differences in international accounting

practices; however even when the ratios were based on US GAAP; they were subject to

misinterpretation, as the US investor would not comprehend the foreign environment

which may influence the financial ratios in those environments. It was concluded that

institutional, cultural, political, and tax consideration between the two countries did

cause their financial ratios to vary considerably from the US industry averages and that

the numbers had very limited significance without an understanding of the

environmental context. (Choi 1983)

Subjectivity of analysts

The concept of best and worst in a benchmarks may not be taken seriously, as different

analysts would have their own assessment of what is good or bad; a very high current

ratio may be considered good by a short term creditor, as it would mean that there are

assets readily available to repay the debt, whereas management may consider higher

levels of inventory than necessary (Horngren 2004).

2.11 Benchmarking

Benchmarking is a process by which a firm or institution compares its own measured

performance with a selected standard known as a benchmark. This benchmark could be

based on its own set thumb rules or past performance or with an industry standard.

Accordingly, it is aptly named a horizontal, cross or vertical benchmark. There are

several performances which can be benchmarked and financial performance is one of

the important metrics for benchmarking.

2.12 Case study: Financial performance of Ontario hospitals

Since 1998, hospitals of Ontario have been voluntarily participating in a performance

reporting initiative, considered to be one of the largest in the world of its kind. This

project is initiated by the HRCC (Hospital Report Research Collaborative), which

consists of researchers to assist the hospitals in performance measurement activities.

17

The financial performance indicators were selected by an informal method, as they were

new to this when it started, and in 2005-06, they underwent a substantial change to

reflect changes in the industry (Pink 2007).

The methodology for selection of key financial indicators is depicted in the figure

appended below

The task group started off with convening of an expert panel to ground the research in

practical financial management, who provided advice on methods, relevance of data,

potential indicators of financial performance, selection of indicators to be produced

using secondary data, precise definitions of the selected indicators, validity of data

analysis and the interpretation of results and data limitations.

The experts went on to review of the existing reporting indicators and procedures based

on the first principles which guided the selection process.

Next, different aspects of financial performance were identified from books on

healthcare financial management, and the expert panel decided to retain the existing

structure. Selections of dimensions financial performances were made based on

financial viability, liquidity, capital, efficiency, and human resources.

A non-systematic review of literature was undertaken, and trade journals and

practitioner journals were referred to, with relevance on literary works post 1990.

A total of 114 indicators were identified, a frequency histogram made and the top 37

were selected which were more frequently referred to and used in the industry.

Finally, the evaluation was done according to the following criteria:

1. Validity: Could the indicator be accurately calculated and would there be any

significant variations in reporting.

2. Importance: Would the indicator be considered to bring about material change

and would the hospitals consider it significant and pay attention to poor

performance based on this indicator.

3. Usefulness: Could benchmarks be developed for the indicator and could it be

used to improve the financial performance?

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Fig. 2.1: Approach for selecting key financial indicators

Source: Longwoods review (2007)

After selection, each of the indicators was precisely defined, and merged with

accounting procedures to determine exactly which items from the accounts statements

would go into the numerator and denominator of the ratios.

The research team then developed software to analyse the indicator values for individual

hospitals. Descriptive statistics, histograms, and scatter plots were used to verify

programme errors and errors in data due to impossible values, with a view to improving

19

the data quality with feedback on the errors in data. The expert panel was consulted at

every stage regarding data validity and outliers’ problems for resolution.

Finally, the documents were issued to all hospitals for their view s on the new system

and to provide feedback regarding the usefulness, validity, and importance of the new

indicators. The feedback was found to be of immense value as they found some

inherent bugs in the system to be resolved and several inconsistencies that required

fixing and quite a few improvements. The comments were analysed in a report that was

reviewed by the experts and as a consequence, some finer tuning was done to the

indicators.

The conclusions put forward by Pink (2007) in his report highlighted some of the great

things about this survey and problem areas.

The approach reaffirmed the value of collaboration of research skills of a university

team with research skills and practitioners with experience of hospital finances. The

literature, expert panel, and survey approach selected a different set of indicators than

those originally proposed. The literature alone provided insufficient basis to select key

financial indicators (Pink 2007).

The last concern of the authors was the data quality, which even after 10 years of

collection, still remained and it was hoped that the use of MIS (Management

Information Systems) would make hospitals aware of data quality problems and lead to

better data in the future.

In his review, Boissannault (2007) states that the HRCC approach was transparent and

reduced the chance of any influence by any pressure from special interest group; it is

scalable and can easily be adapted to other regions of Canada.

2.13 Summary

A reasonable assessment of any firm’s financial status, performance, potential and

position can be made with the discretionary use of several ratios with additional

information relating to trends, common size, industry and accounting analysis and a

good deal of common sense (Alexander 2007); Walsh (2003) adds that “the financial

ratios are only a reflection of what is actually happening and it is the reality and not the

ratios that must be managed.”

Due to the overt importance placed on the financial ratios, it would be reasonable to

assume that ratio analysis would figure a lot in microeconomic theories regarding asset

20

valuation and securities analysis, but apparently literature suggests otherwise

(Pendelbury 2004). Research into financial ratios has revealed that while significant

similarities and trends are observed, they are not constant across ratios, industries, or

time periods (Salmi 1994). This is due to the lack of conceptual founding regarding

financial ratios and decision making (Pendelbury 2004). If absolute values existed, ratio

analysis would be a mechanical procedure and it is because of the lack of absolute

criteria and the imperfection of surrogate ‘standards’ that a considerable lot of skill and

judgement is required to evaluate and interpret the ratios (Pendelbury 2004) .

Therefore a more elaborate research is required to be formulated to improve the

generalize-ability of the financial ratios analysis and the results must be theoretically

consistent to make the results useful for decision makers (Salmi 1994) and until then,

“ratios will be useful without ever establishing their usefulness” (Pendelbury 2004).

21

CHAPTER 3: FINANCIAL RATIOS IN THE CONSTRUCTION

INDUSTRY

3.1 Introduction

The literature on financial ratios in real estate relates to REIT’s, capital structure of real

estate companies, real estate investment financials, corporate real estate, and treatment

of real estate assets in valuation of companies. There is virtually no literature on

financial ratios of property developers. On the other hand, the literature on financial

performance in the construction industry generally relates to contractors’ insolvency,

country specific performance measurements, pre-bid selection of contractors based on

analysis of financial statements, and analyses of contractors by surety bond issuers.

They are based on experiences from Turkey, Egypt, Malaysia, Indonesia, Hong Kong,

U.K., and U.S. and a review of these is presented in this chapter.

3.2 Company insolvency models

Contractor solvency seems to be a popular topic as a lot of literature on construction is

focused around this. There have been research by Edmister (1972), Altman (1983),

Taffler (1983), Keasey and Watson (1986) in relation to all companies. In construction

the research has been carried out by Mason (1979), Kangari (1988), Abidali (1990),

Russell (1992), Langford (1993) Ramsey-Dawber (1993).

These have been critically appraised by Edum-Fotwe (1996) in his paper titled, “A

review of financial ratio tools for predicting contractor insolvency.” There are a variety

of methods used to evaluate the corporate performance of construction companies and

to identify potentially insolvent contractors. Edum-Fotwe (1996) has highlighted the

shortcomings of analysis using financial ratios and has suggested standardising the

assessment criteria of subjective index methods and also the transformation method to

improvise the efficiency of ratio models.

In the British economy, the sector most hit with a high proportion of insolvencies has

been the construction sector (Edum-Fotwe 1996). To survive, the timely evaluation by

of their financial status is required to enable them to take evasive strategies in time and

22

these must be built into their corporate strategy (Edum-Fotwe 1996). To achieve this

objective, some companies take guidance from external analysts which form the basis of

their planning (McNamee 1985). One of the significantly featured analyses is financial

and particularly financial ratios (Cannon 1991).

As the performance of traditional ratios to predict insolvencies has been woefully

inadequate, other models based on a combination of these ratios have emerged over

time to counteract these deficiencies (Inman 1991). The primary motivation for

development of the construction insolvency models has been to protect the clients and

financial institutions from the risks involved in dealing with contractors.

The application of a ratio model to sort out potentially insolvent contractors while

selecting a contractor and refrain from awarding them the contract was suggested by

Mason and Harris (1979). The use of similar models can also be used by contractors to

monitor their continued poor corporate performance and take evasive measures to avoid

eventual insolvency. Ratio analysis provides a quick and effective method to get an

insight of the company performance and by comparing these ratios with the industry

averages, the company’s position with respect to the peer group can also be obtained.

Traditional financial ratios

Traditional ratios are generally categorised as measuring liquidity, profitability,

leverage, and activity (Edum-Fotwe 1996).

Liquidity ratios measure if a company is capable of meeting its current liabilities; this is

one of the leading causes of company failure and the ratios in this category are the

current ratio and the solvency ratio. A value of 1.0 is considered satisfactory for both

ratios according to Harris and McCaffer (1995).

Profitability ratios comprise profit margin which measures how well a company

maximises the sales while keeping costs low; the return on assets measures the

efficiency in asset utilization in profit generation; and the return on equity measures the

profits in relation to the shareholders funds (Edum-Fotwe 1996).

Leverage ratios deal with the capital structure and show the extent of financial risk

exposure of the company. Gearing represents a proportion of the capital which is

borrowed funds; while there is no optimum level for this ratio, the lower levels are

associated with reduced financial risks (Padget 1991). The interest cover shows the

relationship between the interest liability and the profits; a higher cover is associated

with reduced financial risk (Pringle 1980).

23

The activity ratios measure efficiency of the company’s operations; the asset turnover

gives the performance of generating sales from available assets and several variants of

this can be calculated; the stock turnover indicates the times a contractor’s inventory

(work-in-progress plus raw materials) is turned over in a financial year (Edum-Fotwe

1996).

Financial ratio models

Financial ratio models combine a number of ratios in a statistical analysis to determine

an acceptable value beyond which a company can be considered in a safe zone, or a

doubtful zone or high risk zone (Edum-Fotwe 1996). Some of the popular models are

described below.

Altman’s z-score

The most popular of the financial ratio models has been the Z score developed by

Altman in 1968, to develop a corporate failure prediction model. He used data from

large U.S. companies outside construction and performed multiple discriminant analysis

of 22 financial ratios, and combined the weighted factors of 5 of these financial ratios to

provide a single index now famous as the Z score that classified businesses as failing, at

risk or non-failing. The result of these studies produced the composite model:

• “Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6 X4 + 1.0X5

Where:

• X1 = Working capital/Total assets

• X2 = Retained earnings since inception/Total assets

• X3 = Earnings before taxes and interest/Total assets

• X4 = Market value of equity/Book value of total debt; and

• X5 = Turnover/Total assets

Accordingly, companies were classified as:

• A score of < 1.8 implied imminent failure

• A score between 1.8 and 2.7 was regarded as a risk zone

• A score greater than 2.7 indicated a long term solvency potential”

24

Taffler’s Z-score

Taffler (1983) used data from British companies and came up with a four variable Z-

score model as follows:

• “Z = 0.53X1 + 0.13X2 + 0.18X3 + 0.16 X4

Where:

• X1 = Profit before tax/Current liabilities

• X2 = Current assets/Total liabilities

• X3 = Current liabilities/Total assets; and

• X4 = Turnover/Total assets

Accordingly, he suggested:

• A score of < 0.0 showed characteristics similar to a failed company

• A score greater than 0.2 was characteristic of good long term survival prospects”

Taffler did not provide any explanation as to why his model would provide a greater

reliability that Altman; however both models agreed on the ratio turnover/total assets as

a positive indicator of corporate failure.

3.3 Construction specific insolvency models:

As construction industry has a lot of insolvencies, some researchers developed models

for contractor insolvencies, and the more popular ones of these models are discussed

below.

Mason and Harris’s Z-score:

In 1979, Mason and Harris developed a six-variable model specifically for evaluation of

construction companies:

• “Z = 2.54 – 51.2X1 + 87.8X2 – 4.8X3 – 14.5X4 - 9.1X5 – 4.5X6

Where:

• X1 = Profit before tax and interest/Opening balance sheet net assets

• X2 = Profit before tax/Opening balance sheet net capital employed

• X3 = Debtors/Creditors

• X4 = Current liabilities/Current assets

25

• X5 = (Days debtors); and

• X6 = Creditors trend measurement

Accordingly, he suggested:

• A negative Z-score indicated potential insolvency

• A positive Z-score indicated a long term survival prospect”

There have been questions raised on the X1 being negative indicating higher profit

producing a greater tendency towards failure, and also X1 and X2 – both measures of

profit having opposite signs. It has been argued that this model could have been made

simpler by inverting the variables with negative signs.

Abidali’s Z-score

In 1990, Abidali also developed a seven-variable model specifically for evaluation of

construction companies on tender lists:

• “Z = 14.6 + 82.0X1 – 14.5X2 + 2.5X3 – 1.2X4 + 3.55X5 – 3.55X6 – 3.0X7

(4)

Where:

• X1 = Profit after tax and interest/Net capital employed

• X2 = Current assets/Net assets

• X3 = Turnover/Net assets

• X4 = Short term loans/Profit before tax and interest

• X5 = Tax trend over three years

• X6 = Profit after tax trend over three years; and

• X7 = Short term loan trend over three years.

Accordingly, he suggested that

• A Z-score > 2.94 at the least indicated a long term survival prospect.”

In line with the earlier findings of Edmister (1972) and Argenti (1980) Abidali (1990)

realised the fallacy of using single values as absolute measures for solvency and

recommended his model be used along with other analyses of management.

26

Shortcomings of the insolvency models

Single ratios have been criticised for not providing a broad view of the company

performance and required an analysis of a series of ratios to give a meaningful picture

of the health of a company; in addition to some of them giving contradictory

indications.

Evaluation with subjective index has been plagued with different experts having their

own opinions on the importance and weightages to different financial ratio (Edum-

Fotwe 1996).

The success ratings of Z-scores also leave much to be desired; Altman (1983) claimed a

prediction accuracy of 90 % for his model, which was found out by Inman (1991) to be

incorrect (Edum-Fotwe 1996).

Also, the data is derived from the year when failure is imminent, the data of the

preceding years is not considered where the signals could give more time for remedial

actions; therefore these models are useless as monitoring tools. This concentration on

final stage analyses is found to be driven by the clients who endorse these researches as

they are more concerned with the current year insolvency probability of the contractors

under study (Edum-Fotwe 1996).

Improving the models

The attempts of improving the predictability of financial ratio models have been in two

directions. Argenti (1980) has decided to use managerial efficiency factors – the A-

scores to be used in combination with the Z-scores, whereas Abidali (1990) has also

proceeded with development of A-scores for the construction industry.

Inman (1991) has noted that the latest development in financial ratio models depicts

trends rather than single figures and this approach required transformation of the various

financial ratios within the models. Latinen (1993) has indicated that construction

companies do not fail suddenly and in four phases characterised by a starting phase, an

intervening phase, a final phase and the failure phase and a financial ratio model based

on this failure process employing the transformation approach could be more useful to

contractors as well as clients seeking to reduce the risks of employing a potentially

insolvent contractor (Edum-Fotwe 1996).

27

An improved solvency model for contractors

Abidali (1995) describes research aimed at developing an operational system for

identification of construction companies in danger of failure, based on his earlier

research in 1990 described earlier.

In his model, the prime component is a Z-score based on a model with 7 variables

derived from the combination of financial ratio analysis and statistical multivariate

discriminant analysis. The other component is the A-score based on the weighted

factored management performance. By using the two scores in conjunction, it is

possible to predict corporate failure with confidence.

As a company moves towards insolvency, adverse financial and managerial indications

may be observed. It is possible to ascertain the status of liquidity and other measures

through the careful study of financial ratio; but this requires careful interpretation.

Hence a Z-score approach has been adopted with a view to simplifying and the removal

of subjectivity. Practitioners stress that financial ratio information alone is not enough

and the knowledge of past managerial actions is also necessary for proper assessment; it

has been observed that proper management actions have saved companies from the

brink of collapse. Consequently managerial performance related to bad judgements are

identified through losses, high leverage and overdrafts, etc; these factors are weighted

based on importance and converted to a score – the A-Score (Argenti 1983) to be used

in conjunction with the Z-score.

While the sample sizes were small, it has been reported that the method developed was

robust statistically. A total of 24 traditional ratios and 7 tend indicators variables were

initially selected for discriminant analysis and the variables which discriminated the

most between the two groups of failed and solvent companies were selected for the

model.

The constituent variables:

• “X1 = Profit after tax and interest/Net capital employed – This is a profitability

measure better known as ROCE; Return on capital employed. This value is

moves towards the negative in failing companies.

• X2 = Current assets/Net assets – this is a financial leverage measure; failing

firms have low current assets; also some failing firms net assets are also falling

and this ratio can become higher in these cases; as the firm may be liquidating

its long term assets to service its current liabilities.

28

• X3 = Turnover/Net assets – this is a production capacity measure; and is usually

denotes a lack of response to market situation in failing companies; however

some failed firms have a high value of this ratio due to an increased turnover by

over trading, usually at low margins.

• X4 = Short term loans/Profit before tax and interest – this is a measure of

liquidity and shows the relative safety of short-term loans compared to earnings.

• X5 = Tax trend over three years – this can be viewed as a portion of the profits

and as the company heads towards failure, this value will decease and when it in

a loss no tax is paid.

• X6 = Profit after tax trend over three years – this trend turns towards the

negative in failing companies and the trend increases as a company is in

recovery

• X7 = Short term loan trend over three years – this measures the liquidity over a

period; generally failed companies are highly dependent on short term loans and

the trend increases as the company spirals towards collapse.”

Table 3.1: The constituent ratios of the model

Source: Abidali 1995

29

Figure 3.1: The cut-off between non-failed 20 and failed 11 groups

Source: Abidali 1995

The model produced a histogram of Z-scores as shown in Figure 3.1; an overlap occurs

and a region of ± 2.94 has been recommended to deal with the overlap areas where

misclassification can occur. From the above figure it is observed that 100 % of the

failed firms and 90% of the non-failed were classified correctly.

The Z-score model on its own cannot predict the insolvency of a company and merely

indicates that it has similar characteristics of a failed company and therefore has a high

chance of failure.

3.4 Analysis by surety providers

According to the Surety Information Office, construction is a high enterprise, and even

capable and well-established contractors can ultimately fail. According to BizMiner

(2010), out of the 1,155,245 contractors operating in 2006 in the U.S., only 919,848

were still in business in 2008 - a 20.37% failure rate. Despite rigorous prequalification

process and best judgment about the qualifications of the contractor, sometimes

contractor default is unavoidable. However, when a contractor fails on a bonded

project, it is the surety company that remedies the default. (Surety Information Office

(SIO 2010)

30

Figure 3.2: Construction failure rates 2006-2008

Source: http://www.sio.org/html/importance.html

Taha (1995), in his paper presents a development of a Knowledge Based Decision

Support System (KBDSS) to assist in predicting construction contractors’ bond claims

using the contractors’ financial statements.

Surety bonds are used in construction as Bid Bonds, Performance Bonds, etc. They

provide a mechanism whereby clients can encash a certain amount in case the contractor

defaults on his obligations. These are provided by financial institutions which may be

backed by insurance or surety underwriters. To enable the underwriters to access a

contractor and predict his ability to perform, it requires the underwriters to make

judgements about the current state of the contracting company. These judgements may

be based on financial statement analysis by means of ratio analysis and also other

parameters of management performance.

Currently the evaluation of financial ratio analysis is done subjectively with a large

dependence on the accumulated experience of the underwriter; as the relationships

between the various data elements are not clearly understood (Taha 1995).

Taha (1995) has provided a means to develop a logical system of making conclusions

based on results of the different systems currently available to reduce the dependence on

31

only one measurement by using artificial intelligence techniques like induction of

decision trees or production rules and neural networks to help in decision making.

Relevant existing statistical models

A review of past studies reveals that Multiple Discriminate Analysis (MDA),

regression, and logistic regression models have been employed in the analysis of

financial ratio data by Fillippone in 1976, Kangari, in 1992 and in 1993 by Severson.

Taha (1995) has collected data from five surety companies on 57 claim and 71 non

claim contractors, these were categorised by construction types as per the Standard

Industrial Classification Manual (SIC 1987). He has used 12 financial ratio (based on

14 financial ratio commonly used by Dun and Bradstreet) and one variable to check if

the contractor had a cost monitoring system.

Table 3.2: Description of variables used in the study

Source: Taha (1995)

The system integrates fragmented statistical models and knowledge into a DSS so

sureties can analyse the outcome of each model and knowledge in a coordinated manner

rather than relying on a single model and compare the outcomes.

Also, as the DSS is equipped with meta-knowledge to intelligently select most suitable

model it thereby provides peer-opinion too. If the data contains missing values which

are to be predicted or where multiple dependent variables are present, the knowledge

from machine learning has distinct advantages over statistical models. Also, this

32

acquired knowledge is continuously updated to include recent additional info and

augments existing statistic models.

3.5 Construction industry in Malaysia

The construction industry in Malaysia is championed by the Construction Industry

Development Board (CIDB 2007). In 2007, the CIBC formulated a 10 year master plan

to refocus the strategic plan and chart the future direction of the industry. This was due

to the concern that the construction industry, recorded a mere 0.7 % average annual

growth between 2000 and 2007.

The objective of Chan (2009) was to develop performance measures to gauge

performance over a range of activities and for stakeholders to monitor its progress

towards the goals.

An analysis of the 39 publicly listed construction companies on the main board of Bursa

Malaysia (Hiap 2008) reported mean and median Profits before interest and tax (PBIT)

of 9.8% and 9.0 % respectively and the Return on equity (ROE) was 7.7 % for 2006. It

was envisaged that the large companies had healthy profits and the low profits (CIDB

2007) were for smaller companies involved in subcontracting...

In comparison, the U.K. (TSO 2007) median PBIT ranged from 3.2 % to 8.2% between

1999, and 2007 and for Australia (Building Commissions 2009a) the profit margins

were 12.6% in 2007, and an average of 9.5% between 2001 and 2007. Accordingly, a

value of 10 to 15 % was proposed as the 2015 target.

It was also noted that the stock exchange represented a small proportion of the entire

industry. In conclusion, Chan (2009) notes that the measures in financial perspectives

are lagging measures or outcomes and that a performance measurement system is of

little value if it is not used as a guide to management decisions. “Feedback loop and

consequent decision making base on these measurements are necessary to “convert

measurement systems into management systems” (Chan 2009).

33

3.6 Health of contractors in Hong Kong

In Hong Kong, in the aftermath of the 1997 Asian economic crisis, home prices fell by

60% and then in 2003 the government stopped construction through the Home

Ownership Scheme (HOS) and Private Sector Participation Scheme (PSPS) resulting in

drastic reduction in demand in addition to other problems of liquidity of private

developers, over competition, etc. It was foreseen that some local contractors might not

be able to survive the deflation pressures and therefore a need for financial monitoring

was sensed by Chan (2005).

Merwin (1942) concluded three ratios to be very sensitive predictors of discontinuance

up to even 5 years in advance; these were net working capital to total assets, current

ratio, and net worth. Beaver (1966) also suggested that ratio analysis could predict

failure 5 years before; suggested that cash flow to total debt ratio had excellent

discriminatory power; and that the predictive power of liquid assets ratios was much

lower. He also cautioned that the most popular ratios might be the ones most

susceptible to manipulation by management! It is widely reported in literature that

liquidity and net working capital ratios are most important indicators of solvency (Chan

2005).Beaver (1968) found in his later studies that non-liquid asset ratios like net

income over total assets, cash flow over total debts predicted better than liquidity ratios,

even in the years preceding failure.

Gupta (1985) has opined that profitability ratios are better than balance sheet ratios in

reflecting financial health, and that the MDA analysis in monitoring sickness was of

limited value; therefore in his research, Chan (2005) has used all essential ratios

together with the Z-score to access the health of contractors.

The Hong Kong Society of Accountants (2001), in its Statement of Auditing Standard,

lists examples that may indicate that the company has problems in continuing its

business:

• net liability or net current liability;

• excessive reliance on short-term borrowings to finance long-term assets;

• negative operating cash flows;

• adverse key financial ratios;

• substantial operating losses or significant deterioration in the value of assets

used to generate cash flows; and

• arrears or discontinuance of dividend payments

34

The contractor selected for case study was typical of the big six in Hong Kong. The

data for period 1997 to 2004 was collected for analysis of financial ratios and Altman’s

distress models and the results are presented below.

Table 3.3: Trend of changing ratios of the selected contractor

Source: Chan 2005

Chan (2005) reflects that the ranges of Z-scores proposed by Altman (1968) may not

accurately describe the construction industry scenario. In the selected company, the

trend of Z-score did not show deteriorating conditions of financial performance; the

value of 1997 of 1.6 should have shown a warning to the company that financial

problems did exist and early action needed to be taken to prevent collapse.

Other indications were that the profit and return of assets; which is a measure of true

productivity had remained steady, the total assets turnover had a declining trend; this

was due to assets being idle due to reduced business, current and quick ratio were found

to be in the acceptable region of 1.0, and the debt financing were found to be in a

decreasing trend in line with company’s conservative policies, and it had net cash over

equity for the past four years.

Based on the above analysis, the financial health of the selected contractor was found to

be sound with some warning signs in 2001/02 and it was suggested that this was a

critical time for the contractor to review his business strategies in the region.

35

3.7 A model for the Egyptian construction market

The research by Basha (2007) introduces a performance evaluation model for

contracting companies based on financial performance, effects of company size, macro-

economic, and industry related factors. It considers four construction categories:

general building, heavy engineering, special trade, and real estate. The research uses

the Egyptian market as case study, but asserts that it could be used generically on any

company in any market (Basha 2006).

Previous models by Kangari (1992) used multiple regression analysis to develop a

performance curve in which financial positions of any company satisfying the model

limitations could be determined. Goda (1999) produced a quantitative model with an

objective to develop ‘standard’ financial ratios that reflected the performance of the

entire Egyptian construction industry and to compare these with similar indexes

produced internationally. The results obtained from his regression analysis were more

reliable than ones produced with supervised neural network (Basha 2006).Both of them

focussed on evaluating performance using financial ratios only; also Goda (1999) did

not consider size effects (Basha 2007).

The data for 1992-2000 was collected from Authority for Money Markets; Egyptian

Government for 112 companies registered with authority and involved study of 415

financial statements for the 9 year period.

Financial ratios were selected on the basis of ones which reflected the various aspects of

company management, many were used in previous studies, strongly correlated to the

performance of the construction companies; and also were included in the International

Standard Industry Classification (SIC), to enable comparison of the performance at

international levels.

Normalizing financial data – As some ratios are expressed as times and others in

percentage, mathematical formulation of this mixed type gives bias to larger values of

ratios (Kangari 1992); and to overcome this problem, all data was ‘normalised’ with an

appropriate co-efficient.

Company size effect – Comparing large and small companies with same average

industry value is unsuitable as financial structures and managements are different

(Kangari 1992). This is overcome by adjusting the normalised ratio with a size factor,

obtained by dividing the median of the ratio for the overall industry by its counterpart

median for the subgroup size; the size is based on the total assets rather than turnover.

36

Figure 3.3: Model development steps

Source: Basha 2007

Hasabo (1996) reported that company failure was caused by three factors: macro-

economic (35-40%), industry (10-15%), and company related (40-45%). Accordingly

the research developed a Performance Index (PI) using three scores of Financial(SC),

Economic(Se) and Industrial (Si)scores; also a company Grade Index (G) using

cumulative distribution of PI, which shows the percentage of companies below the

industry average and the selected company situation.

Figure 3.4: Performance Grades (G) for different Construction Sectors

Source: Basha 2007

37

The model was validated and showed that the top position belonged to the heavy

construction sector with 65% of the companies below the median (PI=0). One company

was analysed using the model for the years 1992 to 2000, showing grades obtained for

every year and a list of management actions relating to the grades has been proposed.

Figure 3.5: Performance Grades of the selected company over a period

Source: Basha 2007

Table 3.4: Management action required based on grade obtained

Source: Basha 2007

38

3.8 Analysis of Indonesian construction firms

Pamulu, in 2008 conducted a pilot study to evaluate the financial ratios of the

Indonesian construction industry, which was to be an extension of a larger study aimed

at identification of problems within the industry and to propose areas for strategic

management decisions. Financial ratio analysis was carried out on all the six private

and state owned construction firms listed on Surabaya Stock Exchange, using five years

data from 2003 to 2007.

According to Pamulu (2008), relevant research in financial ratios in construction has

been carried out by Fadel (1977), Akintoye (1991), Langford (1993), Edum-Fotwe

(1996), Pilateris (2003), Cheah (2004), Chan (2005), Yee (2006), Singh (2006), and

Ocal (2007). But one of the issues not covered have been the lack of any specific

benchmark values for each ratio.

The study uses modified traditional ratios adapted from the U.S. based Construction

Financial Management Association’s (CFMA) annual financial survey to support

analysis (Ellis 2006). In 1999, the CFMA has introduced construction industry

benchmarks for nineteen financial ratios used by it. As the specific financial ratios

standards (benchmarks) for Indonesia were not available, the study compares the

derived ratios with the U.S. industry standards.

The results of the analysis are presented as Median values for all the financial ratios for

each year below.

Table 3.5: Revenue and profitability ratios

Source: Pamulu 2008

39

Very profitable firms in the U.S. achieved highest value for Return on Equity (ROE)

47%, and Return on Assets (ROA) of 7.7% and ROE 21.6% (Ellis 2007), whereas

Walsh (2003) considers that 15% is a satisfactory return

Table 3.6: Cash flow and liquidity ratio

Source: Pamulu 2008

A value between 1 & 2 is considered acceptable for the Current Ratio (CR) and Quick

Ratio (QR) (Pamulu 2008) though a value above 1.2 is considered by others as the

upper limit and anything above that means the resources are not being utilized properly.

In the same periods, U.S. firms CR average was 1.3, QR was 1.2, and Working Capital

Turnover (WCT) was between 8.4 and 14.0.

Table 3.7: Leverage ratios

Source: Pamulu 2008

A Debt to Equity Ratio (DER) of 3 to 1 is considered acceptable by most sureties

(Pamulu 2008). UTE shows the unbilled work as a percentage of equity; a ratio in

excess of 20% is considered unusual by most sureties (Pamulu 2008). U.S. companies

40

seem to take lesser financial risks to generate better performance. (Ellis 2006) with an

average DER of 2.4

Table 3.8: Efficiency ratios

Source: Pamulu 2008

A Backlog to Working Capital (BWC) of 20 or less considered acceptable, higher ratio

indicates need for permanent working capital injection (Pamulu 2008); the average

value for U.S. firms is 10 and Indonesian firms is 3.

The analysis revealed firms were financially sound (Pamulu 2008).

41

CHAPTER 4: RESEARCH DESIGN AND METHODOLOGY

4.1 Introduction

This aim of this chapter is to propose a research design and methodology that will

address the objectives of the research, as defined in chapter 1. The literature review has

served to find some interesting papers; some of the literature found outside of

construction but deemed useful for incorporation in this chapter was adoption of

benchmarks for financial performance for Toronto hospitals (Pink 2007), another one

for the Australian wine industry, and research done by Pamulu (2008) on financial ratios

of construction firms of Indonesia.

Data from 30 companies have been used; a figure which is just sufficient to create pilot

benchmarks on the lines of similar industry benchmarks available with data providing

companies. (RMA, 2009)

A literature review was undertaken to identify suitable financial ratios, views on

financial ratios, benchmarking methods, statistical methods within journals, industry

publications, and articles in practitioner journals.

To identify literature, searches of Athens, Jstor, Emerald, and others were undertaken

using keywords such as ‘financial performance’, ‘financial ratios’, ‘real estate

performance’, ‘statistical analysis’, ‘non-parametric analysis’, ‘GCC real estate’.

Articles published prior to 1990 were generally excluded in the searches because of

their lower relevance, except when a history was required to be studied. (Pink 2007).

Selections of financial ratios relevant to real estate was tried, but no data was available –

mostly it was related to REIT’s; some blogs were also accessed and questions posted,

but the results were not very encouraging; most responses were relating to non-financial

parameters relating to performance benchmarks and production and sales benchmarks;

e.g. ‘sq m of production per week’, ‘length of roads per day’, ‘height per day on

buildings’, etc.

42

4.2 Research Goals

The research goals from chapter 1 are redefined here to maintain focus on the

objectives.

Aim

To create benchmarks for select financial ratios for three years 2006 to 2008 and

statistically test if sufficient similarities exist in the underlying data to justifiably enable

the usage of data for the whole GCC listed companies to be used together with

confidence as a group.

Objectives

1. Identification of financial ratios which would be useful, especially for the real

estate and construction industry.

2. Creation of benchmarks for each of the selected financial ratios for each of the

three study periods 2006-08.


4. Observation of patterns and variance for the selected ratio in year-based group

data for all companies.



6. Observation of patterns and variance for the selected ratio in size-based group

data.

Hypothesis

This research is designed to test the Hypothesis that:

H0 = There is no difference between financial ratio data across the GCC countries at

α =0.05 level.

The hypothesis is tested for the selected ratio and the results of the hypothesis are



This was achieved by performing a KW test on the selected ratio and examination of the

results and the P values.

43

4.3 Data collection

The only way to legitimately get financial statements was to get ones available in public

domain. It was envisaged that only the real estate companies listed on the respective

stock exchanges would provide access to their financial statements. Or they could be

available from the bourses or some governmental agencies.

Therefore compilation was made of the companies listed under real estate in all the

bourses of the six GCC countries. These were found to be 30 in all: .6 from the UAE, 6.

from Saudi Arabia, .5 from Qatar, 12 from Kuwait, 1 from Bahrain, and none in Oman.

The number of companies found is just enough to form a base for statistical

calculations. According to RMA, when there are fewer than 30 financial statements, the

composite data are not shown because such a small sample is not considered

representative and could be misleading (RMA, 2009.).

The websites of these firms were found from the respective stock exchanges, and the

financial statements downloaded for the years 2006, 2007 and 2008, where available.

Some statements were available in Arabic language only, and the figures from these

were translated. Some companies had not provided data for certain years, and some

financial data on these companies was downloaded online from the stock exchange or

third party company data providers; mainly Arab Capital Markets Research Centre

(ACMRC).

4.4 Objective 1: Identification of financial ratios

The objective was to define and shortlist a few ratios which are relevant to the topic of

the dissertation among the abundance of financial ratios available in literature and

practice.

Literature Review and Consultation

Different sources were considered, literature, books, professional analysts and database

companies and lists of ratios recommended by them were viewed, notably

• RMA Annual Statement Studies (2009)

• International Financial Reporting and Analysis (Alexander 2007.)

44

• Company Accounts: Analysis, Interpretation, and Understanding (Pendelbury

2004.)

• Key Management Ratios (Walsh 2003).

• Introduction to Financial Accounting (Horngren 2004).

• Introduction to Finance: Markets, Investments, and Financial Management

(Melicher 2008).

• Selection of Key Financial Indicators: A Literature, Panel, and Survey Approach

(Pink 2007).

Various authors and analysts have put forward their own interpretations on the ratios

and the methods of calculations, some require average values for the whole year, taken

at intervals, some require the closing value of a previous year and the current year to be

averaged, etc.

Some professionals in the field of financial analysis, blogs were consulted on which

ratios were really relevant to the real estate companies; many of the responses received

were ratios related not to the financial statements. Conclusive answers were not found

and finally 30 ratios were selected from Walsh (2003) and Pendelbury (2004).

The following is a list of the selected financial ratios and the formulae adopted shown as

numerator and denominator.

Table 4.1 Schedule of Financial Ratios Selected

Sr.

No. Ratio Numerator Denominator

Profitability And Performance Ratios

1

ROCE - Return On Capital

Employed

Earnings Before Interest

And Taxes

Capital

Employed

2 ROTA - Return On Total Assets


And Taxes Total Assets

3 ROE - Return On Equity Earnings Before Taxes Owners Funds

4

ROS - Return On Sales (Profit

Margin) Earnings Before Taxes Sales

Investors Ratios

5 EPS - Earnings Per Share Earnings After Taxes Shares Issued

6 PE Ratio - Price / Earnings Ratio Market Price Per Share

Earning Per

Share

7 Dividend Yield Dividend Per Share

Market Price Per

Share

8 Earnings Yield Earning Per Share

Market Price Per

Share

45

Sr.

No. Ratio Numerator Denominator

9 Market To Book Ratio Market Capitalisation Owners Funds

Efficiency And Effectiveness

10 Net Assets Turnover Sales

Net Assets

(Owners Funds)

11 Fixed Assets Turnover Sales Fixed Assets

12 Debtors Turnover Sales Debtors

13 Average Collection Period Debtors X 365 Sales

14 Creditors Turnover Sales Creditors

15 Stock Turnover Sales Stock

16

Net Working Capital To Sales

Ratio

Stock + Debtors -

Creditors Sales

17 Sales To Working Capital Ratio Sales Working Capital

18 Inventory Days Stock x 365 Sales

Liquidity And Stability Ratios

19

Current Ratio (Working Capital

Ratio) Current Assets

Current

Liabilities

20 Quick Ratio (Acid Test Ratio) Current Assets - Stock

Current

Liabilities

Capital Structure, Investment And Financial Risk Ratios

21 Long-Term Debt To Equity Ratio Long-Term Loans Owners Funds

22

Long-Term Debt To Total Long-

Term Finance Ratio Long-Term Loans

Capital

Employed

23 Total Debt To Total Assets Ratio Interest Bearing Debt Total Assets

24 Interest Cover


And Taxes Interest

25 Dividend Cover Earning Per Share

Dividend Per

Share

26

Fixed Assets To Total Assets

Ratio Fixed Assets Total Assets

27

Long-Term Funds To Total

Assets Ratio Capital Employed Total Assets

28

Total Owing To Total Assets

Ratio Creditors Total Assets

29 Capital Gearing


And Taxes

Earnings Before

Taxes

30 Gearing Ratio Interest Bearing Debts Owners Funds

Source: Created for this research

46

4.5 Objective 2: Creation of benchmarks

Data collection

The balance sheets, profit and loss statements, and cash flow statement of the selected

30 companies were extracted from the financial statements. The data in them was a mix

of different terms, some did not have a breakdown of assets, and liabilities into long-

term and current, others have used Islamic financing and terminologies, each was

prepared by different auditors all certain following local and international standards.

As the various statements did not follow a standard format, therefore the various bits of

data had to be clubbed / split into a standard format (Tables 4.2, 4.3 and 4.4) described

below to enable the ratios to be calculated.

Problems in data

Some of the companies selected had current and fixed assets clubbed as assets and

similarly the liabilities were clubbed together. It was through identifying similarities in

different statements that the bifurcation was made, but some errors could have crept in

here.

Also, some of the land owned by the companies is split up into land for investment, land

for development, and land under development – work in progress. Again various

accounting standards allow for different ways in which these lands, a huge asset are

treated; in some balance sheets, some of these headings appeared in both current as well

as fixed assets. This controversy is an entire subject in itself, and beyond the scope of

this research. Without knowledge of the accounting standards, and the exact bifurcation

details, it was not possible to pinpoint the exact nature of the assets. Best judgement has

been used in collusion with consultation with financial analysts on forums and

necessary assumptions have been made.

There are companies using Islamic finance, and the finance costs have been clubbed

with interest wherever shown clearly as such. In other cases, it may have been clubbed

under operating costs and cannot be easily known without investigating further.

Normalising the data

Therefore all the data from the three sheets was translated into standard template Profit

and Loss Statement (Table 4.2), and Balance Sheet (Table 4.3) as appended below.

Some additional calculations required for the financial ratios along with data on

47

dividends was derived from the Cash Flow Statement and the share prices for the past

year high and low were taken from the stock exchanges and put in a separate table, as

appended below (Table 4.4).

The Profit and Loss Statement

Table 4.2: Template of Profit and Loss statement

CALCULATIONS FOR FINANCIAL RATIO'S

COMPANY :

COUNTRY : UAE

ACTIVITIES :

CURRENCY, UNITS : AED' MILLION

Sr. No. REFERENCE Notes 2008 2007 2006

PROFIT & LOSS STATEMENT

1 Revenue/Turnover / Income / Total Income Sales 15,014 20,001 15,142

2 Cost Of Income Costs (11,850) (13,296) (8,646)

3 Profit Before Interest & Tax / Gross Profit EBIT 3,164 6,705 6,496

4 Interest Int (87) (154) (93)

5 Profit Before Tax EBT 3,077 6,551 6,403

6 Tax Credit Tax 3 (14) (47)

7 Net Profit for the year EAT 3,080 6,537 6,356

8 Minority Interest Min

9 Retained Profit Net Earning 3,080 6,537 6,356

10 Crosscheck Calc With Original Values 3,081 6,536 6,356

11 Basic & Diluted EPS EPS 0.50 1.08 1

EMMAR PROPERTIES PJSC AND SUBSIDIARIES


The details of each of the items of the Profit and Loss statement are explained below:

• Revenue/Turnover / Income / Total Income: Total income from main operations,

interest, and other income has all been clubbed under this head to arrive at the

gross income

• Cost of Income: All the costs relating directly to the operations as well as

overheads and all other costs except interest and tax are clubbed under this head

• Profit before Interest & Tax / Gross Profit: This is the difference of the above

income minus expenses

• Interest: This is the interest, and in certain cases, the finance costs related to

Islamic finance have all been clubbed under this category

• Profits before Tax: This is the PBIT minus the interest

• Tax: There is no income tax and very little corporate tax, and is put here,

wherever shown as such in the statement

48

• Net Profit for the year : This is the balance amount after deduction of tax

• Dividend Distributed: A part of the profit is distributed, wherever shown as

such, is put here

• Retained Profit: This is the profit retained by the company and ploughed back

into the business, which adds to the value of the company and the capital of the

company

• Crosscheck Calc with Original Values: This figure is used to crosscheck whether

the balances calculated tally with the balances in the company’s printed

accounts, and is a reconciliation of the statement.

• Basic & Diluted EPS: This is separately shown in the statement, wherever

shown is put here, or calculated later on.

The Balance Sheet

Table 4.3: Template of Balance Sheet

BALANCE SHEET 2008 2007 2006

1 Fixed / Long Term Assets

2 General 17,158 24,601 18,579

3 Investment Properties These make working capital negative 13,248 5,636 6,971

4 Development Properties 19,178 16,194 11,121

5 Total FA FA 49,584 46,431 36,671

6 Current Assets

7 General 5,393 4,727 2,329

8 Stock - - -

9 Debtors 5,714 3,633 2,690

10 Total CA CA 11,107 8,360 5,019

11 Total Assets 60,691 54,791 41,690

12 Non Current/ Long Term Liabilities LT+C Mixed in 2008

13 General Preference to Long Term 1,115 1,073 888

14 Long Term Loans 9,174 7,704 3,992

15 Total LL LTL 10,289 8,777 4,880

16 Current Liabilities

17 General - - -

18 Creditors 13,839 8,826 6,265

19 Short Term Loans - - -

20 Total CL CL 13,839 8,826 6,265

21 Total Liabilities TL 24,128 17,603 11,145

22 Equity/Shareholders Funds

23 Share Capital 6,091 6,091 6,076

24 General 30,471 31,097 24,470

25 Total Equity OF 36,562 37,188 30,546

26 Crosscheck, Total Equity & Liability = Total Assets 60,690 54,791 41,691


49

The details of each of the items of the Balance Sheet are explained below:

• Fixed / Long Term Assets: This is a heading only – meaning the assets which

are intended to be retained for at least one year or more

• General: Every fixed asset not shown elsewhere is put here

• Investment Properties: These make working capital negative. These are the

properties procured for investment

• Development Properties: These are the properties earmarked for development or

under development, and are transferred to current assets only after partial sale or

state of readiness- again different auditors have different viewpoints on this, but

if this heading is clearly indicated in the statement, it has been put here

• Total FA: This is the sum of all the fixed assets – that is the assets which will

not be disposed off within one year

• Current Assets : This is a heading only – meaning the assets which will be

disposed off within a period of one year / and converted into cash / sold /

sellable properties

• General: All assets not listed under stock and debtors are put here – include

advances paid, etc.

• Stock: The stock is the goods / raw materials / materials supplied to the

contractors/ and included the ready possession properties/ work in progress

shown in some statements, which are involved in contracting too along with real

estate operations

• Debtors: The amounts to be collected from various parties for properties sold or

advances or instalments due are clubbed under this head

• Total CA: This is the total of all the current assets mentioned above

• Total Assets: This is the total of all current and fixed assets. Some companies

have not split the assets into current and fixed. Some of the bifurcation had to

be done using judgement and analysis of other companies’ balance sheets.

• Non Current/ Long Term Liabilities: This is a heading, meaning liabilities,

monies to be paid after a period of one year or more.

• General: Any long term liability not listed in long term loans is added here

• Long Term Loans: All term loans and Sukuks – Islamic bonds which are due for

payment after one year are listed here

• Total LL: This is the total of all long term liabilities listed above

50

• Current Liabilities: This is a heading. Meaning all amounts payable within one

year

• General: All other current liabilities are clubbed here

• Creditors: All amounts payable to suppliers of goods and services, contractors,

consultants

• Short Term Loans: Loans payable within a year, including Islamic loans

• Total CL: This contains the total of all above current liabilities

• Total Liabilities: The total of long term and current liabilities is all the monies

owed by the company

• Equity/Shareholders Funds: Heading. This is the total of all share capital,

retained profits over the years

• Share Capital: This is the amount of money invested by the owners/shareholders.

It is also the face value of the shares issued to date

• General: All other amounts are clubbed here

• Total Equity: Total funds belonging to the shareholders

• Crosscheck, Total Equity & Liability = Total Assets

The total equity and total liabilities should equal the net assets; this is what should

balance in the balance sheet; because the net assets minus net liabilities are the amount

left for the owners/shareholders of the company. In a way, the equity is also a liability

of the company, therefore equity and liability are clubbed together to check with their

net asset value.

Calculation for additional items

Table 4.4: Template of Additional items

Additional Items required for Financial Ratios 2008 2007 2006

1 Capital Employed (Shareholder's Funds) + Long Term Loans) 45,736 44,892 34,538

2 Working Capital (Current Assets - Current Liabilities) (2,732) (466) (1,246)

3 Interest Bearing Debts (Long Term & Current) 9,174 7,704 3,992

4 Dividend Paid 1,199 2,355

5 Dividend Per Share - 0.20 0.39

6 Market Price Per Share (Average of 52 Week High & Low) 8.84 12.68 18

7 52 Week High 15.85 15.60 25

8 52 Week Low 1.82 9.75 10

9 Earnings Per Share (Net Profit / Shares Issued) 0.51 1.07 1.05

10 Total Shares Issued (Assume =ShareCapital @ 1 / Share) No 6,096 6,096 6,081

11 Market Capitalisation (Total Shares * Market Value per share) 53,858 77,267 108,090

12 Net Working Capital (Stock + Debtors - Creditors) (8,125) (5,193) (3,575)


51

These are some additional pre-calculations required to calculate the financial ratios

under consideration.

• Capital Employed – This is the Shareholder's Funds plus Long Term Loans

• Working Capital – This is calculated as Current Assets minus Current Liabilities

• Interest Bearing Debts – This is the total of all loans taken by the company; both

Long Term & Current.

• Dividend per Share – This is the Total Dividend declared divided by the number

of ordinary shares issued

• Market Price per Share – Since the price is dynamic, it is calculated as the

Average of 52 Week High & Low from below.

• 52 Week High – This is found from the relevant stock market data and is the

highest price quoted for the shares of the company in the preceding one year

• 52 Week Low – Similar to the above, but the lowest price quoted

• Earnings per Share (Net Profit / Share Capital)

Calculations for Financial Ratios

As a next step, using the formulae from the above mentioned literature, the 30 Financial

Ratios were calculated for the years 2006, 2007 and 2008 on separate spreadsheets for

each of the 30 companies. This provided a chunk of secondary data of 90 figures for

each Financial Ratio.

Tabulation

A spreadsheet was made for each of the three years with the Financial Ratios as rows

and the company names as columns and all the data was linked from the individual

company calculation sheet to the main spreadsheets. These master spread-sheets

contained approximately 30 bits of data for each ratio for each year.

Statistical calculations

The RMA in their publication Annual Statement Studies (2009) advocate the use of

medians and quartiles as a means to display the Financial Ratios data. Based on other

literature review also, it was decided to use medians and quartiles in lieu of arithmetic

mean and standard deviation as the former is not susceptible to outliers.

52

Calculation of Quartiles, Outliers, Median

Using built in excel formulae, the Mean, Number of Data bits, Standard Deviation,

Quartiles (Q0=Lowest, Q1 = 25th percentile, Q2=Median, Q3=75th percentile &

Q4=Highest) were calculated for each ratio in each of the master spreadsheets. Also

were calculated other values which would be useful in creating of a Box-whisker plot to

graphically represent the data. These were:

• Inter Quartile Range(IQR), which is the difference between the 1st and 3rd

quartile and represents 50% of all the central values; this is different from

Range, which is the difference between the highest and the lowest data bit,

• Step(S), which is 1.5 times the IQR and represents the maximum length of the

whiskers,

• Inner Fence Low , which is Q1 – S,

• Inner Fence High, which is Q3+1,

• Outer Fence Low , being Q1-2S ; some analysts also use 3S as the cut-off value

for Outer fence, and

• Outer Fence High, being Q3+2S

• Calculations to check for Outliers, which could be Low or High, Mild or Severe

depending on their position with respect to the Inner & Outer Fences; this is

described in detail further ahead.

Treatment of Outliers

Outliers are the data bits of abnormal values that do not fit the norm and are extreme

values far away from the chunk of the data. Depending on the field of statistics, these

could be very valuable observation and require detailed investigation or could be some

mistakes in reading and ignored. Accordingly, some analysts tend to ignore these and

there are controversies on how outliers should be treated. In this research, outliers have

been described as Mild or Severe. The outliers falling between the inner and outer

fences on both extremes of Low and High values are termed as Mild outliers and the

ones beyond as severe outliers. In this research all the outliers have been retained;

though extreme outliers have been removed from the graphs for the sake of clarity

Box-whisker plots

For data which is not normally distributed – the nonparametric data of the type

encountered in financial ratios, the statistics textbooks have prescribed descriptive

53

statistics to be presented in the form of a graph known as the Box-Whisker plot. The

sides of the box represent the Q1 and Q3 values, which enclose 50% of the central data

and somewhere between these values is the Median of all data, the Q2 value which is

represented by a line inside the box. From the ends of the box, lines, or whiskers are

drawn on either side up to the Inner Fence as mentioned above. These ends represent

the ‘allowable’ limits of data based on the 1.5 times the value of the IQR, which is 50 %

of the central data, as mentioned above. In many cases the last bit of data within the

limits of the inner fence is made the end point, which is what has been adopted in this

method. Therefore, it was mentioned earlier that the Step is the ‘maximum’ value of the

whisker. Outlier data points beyond the inner fence up to the outer fence are shown as

small dots, and the outliers beyond the outer fence, the severe outliers are ignored in the

plot to present the central data more clearly.

Figure 4.1: Sample of Box-Whisker plot

BOX-WHISKER PLOTS FOR FINANCIAL RATIOS OF GCC REAL ESTATE

COMPANIES

MEDIANS AND MEANS FOR 2006, 2007 & 2008

( Legend : Red = Median; Green = Mean

Note: Extreme Outliers >3*IQR are not shown in this Plot )

14%

16%

20070%

5%

10%

15%

20%

25%

30%

35%

40%

FINANCIAL RATIO : RETURN ON CAPITAL EMPLOYED

VA

LU

ES

_


Presentation of results

Box whisker plots are presented for the 3 years of 2006, 2007, and 2008 along with their

medians as the proposed benchmark values for the particular financial ratios. The

arithmetic means are also shown on the same plot for comparison. A table showing all

the above statistical values including the mean values for every year and standard

deviation are presented to show the differences between the median and the mean.

54

Figure 4.2: Sample of Box Whisker plot for 3 years with median and mean

BOX-WHISKER PLOTS FOR FINANCIAL RATIOS OF GCC REAL ESTATE COMPANIES




0%

10%

2008

14%

16%

2007

11%

15%

2006

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%


VA

LU

ES

_


Table 4.5: Sample of data sheet for each financial ratio

Sr No Statistic 2008 2007 2006

1 Mean 0% 16% 15%

2 N 29 29 27

3 Standard Deviation 35% 9% 13%

Quartiles

4 Q0=Minimum -157% 0% -7%

5 Q1-25% 5% 9% 7%

6 Q2=Median 10% 14% 11%

7 Q3=75% 14% 23% 19%

8 Q4=Max 26% 36% 60%

9 Range 183% 36% 67%

Outliers

10 IQR 9% 14% 13%

11 Step = 1.5 IQR 14% 21% 19%

12 Inner Low Fence -9% -12% -12%

13 Inner High Fence 27% 44% 38%

14 Outer Low Fence -23% -33% -31%

15 Outer High Fence 41% 65% 57%

Whisker Limits

16 Lower Whisker end -6% 0% -7%

17 Upper Whisker end 26% 36% 38%

RETURN ON CAPITAL EMPLOYED

STATISTICAL DATA


55

4.6 Objective 3: Selection of ratio for detailed analysis

One ratio, the Return on Capital Employed (ROCE) is selected for detailed analysis.

The initial assumption was that the economies of the GCC were similar so common

benchmarks for Financial Ratios could be used for the entire region. ROCE seems to be

a good comparator across countries which share the same peg to the US Dollar, thereby

the interest rates for lending would be similar (except Kuwait), and it is assumed that a

large part of the capital is borrowed funds. Therefore the companies would have to

have a minimum return to cover the interest payments.

ROCE has been used by both Mason & Harris (1979) and Abidali (1995) in their

versions of the Z-score as one of the variables for insolvency predictions as X2 and X1

respectively in their models.

ROCE reflects a company’s ability to earn a return on all of the capital that the

company employs. It can help investors see through growth forecasts, and it can often

serve as a reliable measure of corporate performance. But ROCE is also an efficiency

measure of sorts; ROCE doesn’t just gauge profitability as profit margin ratios do, it

measures profitability after factoring in the amount of capital used. Because ROCE

measures profitability in relation to invested capital, ROCE is important for capital-

intensive companies, or firms that require large upfront investments to start producing

goods.

A company’s ROCE should always be compared to the current cost of borrowing.

ROCE is therefore a better measure of the return of a real estate company as it considers

leverage, which is an integral source of funding in this sector. As real estate companies

are traditionally highly leveraged, it is essential to ensure that any company generates

adequate returns to cover its high cost of capital.

Statistical Analysis

To test this theory, the ROCE ratios were subjected to statistics checks for variance. It

was originally proposed to use Anova test, but this was ruled our in favour of the

Kurskal-Wallis (KW) test being more oriented for nonparametric tests. Some authors

propose the use of Anova if the number of data is sufficiently large. .

The Kruskal-Wallis test is a nonparametric test for the comparison of 3 or more

treatment groups, which are independent; it is the nonparametric equivalent to analysis

of variance (Anova). All observations are ranked from smallest to largest, the sum of

56

the ranks is determined for each treatment, and a test statistic H is calculated. For all

combinations of n up to five of 3 treatment groups, testing H requires the use of a

special table; for greater n and more groups (k), H is distributed as c2 with k-1 degrees

of freedom.

The procedure to set up the hypothesis and interpretation is as follows:

H0: There is no difference(s) in medians between the groups.

Ha: There is a difference in at least one of the groups.

The test will produce a p-value. If the p-value is less than 0.05 (assuming a 5 %

significance level), the null hypothesis is rejected and it can be inferred that there is a

difference. If the p-value is greater than 0.05, the null hypothesis is not rejected and it is

concluded that there is no difference between the test groups. (Vassar 2010.)

4.7 Objective 4: Observation of data grouped by year

The ROCE data has been grouped according to the country for each year and the

patterns are discussed. The variances are checked between country groups for each year

with the help of the KW test. As mentioned above, a P value > 0.05 will mean that the

hypothesis is accepted.

The details are presented as column charts and BW plots and tables, showing the

medians as well as the means.

4.8 Objective 5: Observation of data grouped by country

The ROCE data has been grouped according to the data period for all GCC data and

patterns are discussed. The variances are checked between years 2006, 2007 and 2008



The details are presented as column charts, web charts, BW plots, and tables, showing

the medians as well as the means. The trends in yearly medians are discussed.

57

4.9 Objective 6: Observation of data grouped by company size

The company turnovers have been used as measures of size: the companies were

classified as small, medium, big, and large based on a quartile criterion, and checked for

variance between the group sizes for every year.

The ROCE data has been grouped according to the size data for all GCC data and

patterns are discussed. The variances are checked between years 2006, 2007 and 2008



The details are presented as column charts, BW plots and tables, showing the medians

as well as the means. The trends in yearly medians are checked for any observable

pattern.

4.10 Hypothesis

As details and the objectives clearer, the hypothesis is modified as follows:

H0 = There is no difference between ROCE medians of the five GCC countries at α

=0.05 level.

The hypothesis is tested for the ROCE ratio and the results of the hypothesis are



58

CHAPTER 5: ANALYSIS OF RESULTS

5.1 Introduction

The purpose of this chapter is to present the results of the study and analyse the results

in line with the aims, objectives, and hypothesis of the research. As explained earlier,

this study did not involve any questionnaire based survey, but is based on study of the

literature, online discussions with finance and real estate professionals, collection of

data online, calculations of financial ratios including medians and means, and

presentation in data-tables and graphical formats. The format was chosen to be the

Box-whisker format popularised by Tukey (1977), and are attached in the appendix.

The first section provides the list of companies selected for the study followed by a five

box graphic of the balance sheet profiles of all the thirty companies selected, which is

adapted from Walsh (2008).

Further on, each of the 30 Financial Ratios is results are shown in a table and a box-

whisker plot showing the quartiles, median, mean, and mild outliers for each of the

years 2006, 2007, and 2008. It was not possible to include the data for 2009 (a very

crucial year for real estate industry in the GCC, especially Dubai) as the annual results

are just beginning to emerge and the data was collected in August 2008.

The effects of size (Cinca 2001) must not be ruled out as a lot of literature points out,

also the RMA lists a lot of different companies under real estate and each has different

business profiles, these are shown and classified differently as per the NAICS

guidelines and the ratios are presented separately in the RMA and other data providers.

It must be noted that the data in this study and hence the results are for the entire group

of companies, which are really a mixed bag, they are small, medium and large

companies, some are involved in subdivision of land, others in property buy and sell,

others in development and sale, some in renting, some in commercial, others in

residential or retailing. It was not possible to make a separation as the number of

companies is so limited. It was only possible to get 30 companies because that is the

total number of all the companies in the whole GCC listed under real estate in the stock

exchanges. It is possible some other companies aspiring to go public or show

transparency will have the data available on their websites, but this aspect was not

looked into for this study. Collecting financial information for 30 companies for 3 years

59

and presenting 30 ratios already seemed like a daunting task for the purpose of the

dissertation.

No dissertation could be complete without statistical validity and, the assumption made

was that the economies of GCC are similar; therefore the construction industry would

also follow the same patterns. To verify the assumption, one particular popular ratio,

the ROCE was chosen for detailed analysis, to test the hypothesis.

The results for the ratio ROCE have been 90 in number, 30 from each year. The

distribution patterns are presented followed by the box-plots and tables based on groups

made on basis of country, year, and size to study the patterns and an analysis is

presented.

The same groups are analysed for similarity based on country group to support or reject

the null hypothesis of similarity of ratios in the GCC. Analysis is also done for size

groups and results presented. The analyses are done on the K-W test and also a one way

Anova in some cases.

The research objectives have been presented again for focus

1. Identification of financial ratios which would be most useful, especially for

the real estate and construction industry.

2. Creation of benchmarks for each of the selected financial ratios for each of

the three study periods 2006-08.


4. Observation of patterns and variance for the selected ratio in year-based

group data for all companies.



6. Observation of patterns and variance for the selected ratio in size-based

group data.

Hypothesis



=0.05 level.

The hypothesis is tested for the ROCE ratio and the results of the hypothesis are



60

5.2 Companies included in the Study

The entire list of companies listed in the GCC stock exchanges under real estate have

been included in the study and are listed herein.

Table 5.1: List of companies selected for the study

Sr

No. Country

Company

codes used Company Name - Stock Exchange Code - Stock Exchange

1 UNP UNION PROPERTIES PJSC

2 DYR DEYAAR DEVELOPMENT PJSC AND SUBSIDIARIES

3 EMR EMMAR PROPERTIES PJSC AND SUBSIDIARIES

4 SOR SOROUH REAL ESTATE PJSC

5 ALD ALDAR PROPERTIES PJSC

6 RAK RAK PROPERTIES PJSC

7 TIR

TAIBA INVESTMENT & REAL ESTATE DEV. CO.

(TIRECO-TASI)

8 SRE SAUDI REAL ESTATE CO. (SRECO-TASI)

9 MCD

MAKKAH CONSTRUCTION & DEVELOPMENT CO.

(MCDC0-TASI)

10 JBL JABAL OMAR DEVELOPMENT CO. (JABALOMAR-TASI)

11 DAR

DAR AL ARKAN REAL ESTATE DEVELOPMENT

COMPANY (DARALARKAN-TASI)

12 ADC ARRIYADH DEVELOPMENT CO. (ADCO-TASI)

13 UDC UNITED DEVELOPMENT CO (UDCD-QE)

14 SAL SALAM INTERNATIONAL INVESTMENT CO (SIIS-QE)

15 QRE QATAR REAL ESTATE INVESTMENT CO. (QRES-QE)

16 BAR BARWA REAL ESTATE CO (BRES-QE)

17 EZD EZDAN REAL ESTATE (ERES-QE)

18 BA

HR

AIN

SEE SEEF PROPERTIES CO. (SEEF-BSE)

19 GRA

GRAND REAL ESTATE & TOURISTIC DEVELOPMENT

CO. (TOURISTIC-KSE)

20 KRE KUWAIT REAL ESTATE CO. (KRE-KSE)

21 DAM DAMAC KUWAIT HOLDING CO. (DAMACKWT-KSE)

22 ABY ABYAAR REAL ESTATE CO. (ABYAAR-KSE)

23 MAS Al MASSALEH REAL ESTATE CO. (MREC-KSE)

24 KCM KUWAIT COMMERCIAL MARKETS CO. (SHOP-KSE)

25 MAB AL MABANEE CO. (MABANEE-KSE)

26 NRE NATIONAL REAL ESTATE CO. (NRE-KSE)

27 TIJ

THE COMMERCIAL REAL ESTATE CO. (ALTIJARIA-

KSE)

28 MAZ MAZAYA HOLDING CO. (MAZAYA-KSE)

29 TAM TAMDEEN REAL ESTATE CO. (TAM-KSE)

30 GRP GRAND REAL ESTATE PROJECTS CO. (GRAND-KSE)

UA

ES

AU

DI

AR

AB

IAQ

AT

AR

KU

WA

IT

Source: Created for the study

61

5.3 Collective balance sheet profile of the companies

The following is a graphical representation of the collective values of the Balance Sheet

Profile for 2008, of all the Companies included in the study.

The graphic is a basic five box layout of the balance sheet items and is adapted from

Walsh (2008). The five balance sheet headings are fixed assets (FA), current assets

(CA), owner’s funds (OF), long term liabilities (LTL), and current liabilities (CA). All

values in local GCC currencies have been converted into Pounds Sterling (GBP) at the

rate of exchange prevailing on 09 august 2009.

The first chart shows the combined values of all the 30 companies included in the study

for the year 2008. The second chart shows the 5 box layouts for each of the 5 countries

of the GCC to show the representation from each country in the study. The last chart

shows the 5 box layout for the size groups (small, medium, & large based on total

assets) to show the relative representations of each size based group in the study.

Table 5.2: Collective Balance sheet profile of the selected companies for 2008

UAE SAUDI ARABIA QATAR BAHRAIN KUWAIT ALL GCC

AED SAR QAR BHD KWD GBP

Conversion Rate of Exchange 0.162 0.159 0.164 1.580 2.073 1.000

Fixed Assets FA 16,578 5,026 3,479 144 4,531 29,758

Current Assets CA 9,677 1,307 4,269 24 1,155 16,432

Long Term LiabilitiesLTL 6,603 1,031 3,652 - 1,215 12,501

Current Liabilities CL 7,508 531 1,260 13 2,081 11,393

Owners Funds OF 12,144 4,771 2,836 155 2,390 22,296

All figures in Million GBP

BALANCE SHEET PROFILE OF LISTED REAL ESTATE COMPANIES


62

Figure 5.1: Collective Balance sheet profile for 2008 of all 30 companies

GCC Real Estate Companies

Collective Balance Sheet Profile : Five Box Layout of the

30 Listed GCC Real Estate Companies Selected

CACL

FA

LTL

OF

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Va

lue

in M

illi

on

GB

P

Source: Adapted from Walsh, 2008

Figure 5.2: Collective Balance sheet profile of all 30 companies


63

Figure 5.3: Collective Balance sheet profile of all 30 companies


As can be seen in the first graphic of the combined values, the fixed assets are twice the

current assets; the owner’s funds are 50% of the total assets; the current liabilities are as

much as the long term liabilities; and the current liabilities are lower than the current

assets – overall a healthy picture for 2008.

The next graphic is the combined values based on the home countries of the companies

and shows the representation of each country in the study. Based on assets, the highest

representation is from the UAE, followed by Qatar, Saudi Arabia, Kuwait, and Bahrain.

The ratio of fixed to current assets seems highest Kuwait and Saudi Arabia, lower in

UAE and less than one in Qatari companies. Possibly the Qatari companies have valued

more of their land banks under current assets. The proportion of owner’s funds to total

64

assets seems to be highest in Saudi Arabia and lowest in Qatar. The debt equity ratio

seems to be lowest in Saudi Arabia and highest in Qatar with debt greater than equity.

Also, the current asset to current liabilities ratio seems highest in Qatar and lowest in

Kuwaiti companies at 50%.

The next graphic is based on company sizes with respect to total assets and denotes the

total values under each category. The proportion of fixed assets to current assets is

highest in the small companies and lowest in the large companies. The proportion of

owners funds to total assets also follows the same pattern, with small companies having

higher proportion of owners funds than the large companies; possibly the large

companies have easier access to borrowed funds. The current assets to current liabilities

seem to be less than one in small companies, roughly equal in the medium companies

and greater than one in the large companies, exposing this vulnerability of the smaller

firms.

5.4 Objective 1: Identification of financial ratios

The objective was to identify which financial ratios were relevant for analysis of real

estate companies. Literature and books listed in chapter 4 were reviewed; professionals

in the field of financial analysis were consulted on forums, namely LinkedIn,

Finance30.com, and Allexperts.com. The answers were many and vary; many of the

responses received were of performance ratios not related not to the financial

statements. Finally, conclusive answers were not found and 30 ratios were selected

from Walsh (2008), Pendelbury (2008). The financial ratios selected are listed in table

appended in next section.

5.5 Objective 2: Creation of benchmarks

The financial ratios have been calculated, the medians found and proposed as

benchmark values for the study years, as per appended table 5.2. Also appended is

another table 5.3 showing similar medians for all the countries individually, but these

are based on scant data and may be used for reference only.

65

Table 5.3: Proposed benchmark values for financial ratios for 2006 to 2008

2008 2007 2006

Profitability and Performance Ratios

1 ROCE - Return on Capital Employed % 10.1% 13.8% 11.3%

2 ROTA - Return on Total Assets % 5.4% 10.6% 8.7%

3 ROE - Return on Equity % 10.7% 17.1% 14.1%

4 ROS - Return on Sales (Profit Margin) % 47.2% 62.3% 49.1%

Investors Ratios

5 EPS - Earnings Per Share - Local Var. N/A N/A N/A

5a

EPS - Earnings Per Share - Converted to

GBP GBP N/A N/A N/A

6 PE - Price / Earnings Ratio Times 12.24 10.81 11.12

7 Dividend Yield % 4.1% 3.3% 2.3%

8 Earnings Yield % 5.7% 9.0% 8.1%

9 Market to Book Ratio Times 1.47 1.79 2.16

Efficiency and Effectiveness Ratios

10 Net Assets Turnover Times 0.28 0.31 0.28

11 Fixed Assets Turnover Times 0.22 0.25 0.28

12 Debtors Turnover Times 5.53 5.00 6.26

13 Average Collection Period Days 66 73 59

14 Creditors Turnover Times 5.83 3.64 5.52

15 Stock Turnover Times 1.75 2.74 3.40

16 Net Working Capital to Sales Ratio Times 0.23 0.24 0.20

17 Sales to Working Capital Ratio Times 0.62 0.29 0.56

18 Inventory Days Days 209 135 114

Liquidity and Stability Ratios

19 Current Ratio Times 1.25 1.13 1.53

20 Quick Ratio (Acid Test Ratio) Times 0.92 0.86 0.94

Capital Structure, Investment and Financial Risk Ratios

21 Long-Term Debt to Equity Ratio Times 0.38 0.41 0.13

22

Long-Term Debt to Total Long-Term

Finance Ratio Times 0.28 0.29 0.12

23 Total Debt to Total Assets Ratio Times 0.32 0.27 0.13

24 Interest Cover Times 8.97 8.67 19.90

25 Dividend Cover Times 2.74 3.73 1.84

26 Fixed Assets to Total Assets Ratio Times 0.82 0.78 0.68

27 Long-Term Funds to Total Assets Ratio Times 0.72 0.69 0.77

28 Total Owing to Total Assets Ratio Times 0.04 0.03 0.04

29 Capital Gearing Times 1.01 1.02 1.00

30 Gearing Ratio Times 0.73 0.50 0.20

N/A: Data unavailable in order to calculate ratio

Z: Data equals zero in ratio denominator

Sr.

No.Description Of Ratio Unit

Benchmark Values


66

Table 5.4: Financial ratio medians for selected GCC countries

2008 2007 2006 2008 2007 2006 2008 2007 2006 2008 2007 2006 2008 2007 2006

Sr.

No. Description Of Ratio

UAE SAUDI ARABIA QATAR BAHRAIN KUWAIT


1

ROCE - Return on Capital

Employed % 11.4% 14.3% 19.9% 5.2% 6.0% 5.1% 10.1% 10.9% 10.5% 16.3% 26.1% 10.3% 7.7% 20.8% 12.7%

2 ROTA - Return on Total Assets % 8.4% 11.4% 16.6% 4.7% 5.7% 4.5% 6.3% 7.8% 6.7% 15.1% 24.5% 9.6% 2.5% 11.4% 8.1%

3 ROE - Return on Equity % 14.1% 17.3% 21.0% 5.2% 6.0% 5.1% 15.3% 11.9% 14.1% 16.3% 26.1% 10.3% 6.0% 20.3% 12.2%

4

ROS - Return on Sales (Profit

Margin) % 39.0% 44.9% 49.2% 53.9% 62.0% 56.1% 29.2% 55.6% 21.1% 80.0% 88.5% 70.0% 43.8% 70.8% 43.8%

Investors Ratios

5

EPS - Earnings Per Share - Local

Currencies Var.

0.37

0.38

0.37

1.03

1.43

0.95

2.97

2.81

2.41 0.03

0.05

0.02 0.01 0.04

0.03

5a

EPS - Earnings Per Share -

Converted to GBP GBP

0.06

0.06

0.06

0.16

0.23

0.15

0.49

0.46

0.39 0.05

0.08

0.02 0.02 0.09

0.06

6 PE - Price / Earnings Ratio R

9.33

11.44

11.21

24.05

27.91

83.18

13.92

11.08

13.08 5.61

3.20

10.51 9.33 8.41

10.03

7 Dividend Yield % 1.8% 2.2% 2.0% 5.6% 5.1% Z 4.2% 3.3% 3.6% 5.1% 3.8% 3.1% 0.4% 0.5% 4.1%

8 Earnings Yield % 10.7% 8.8% 8.9% 3.6% 3.6% 1.2% 7.2% 9.0% 7.6% 17.8% 31.3% 9.5% 2.9% 10.8% 9.1%

9 Market to Book Ratio Times

1.43

2.17

3.15

1.42

2.18

3.58

2.13

1.49

1.99 0.92

0.84

1.08 1.76 1.31

1.62

67

2008 2007 2006 2008 2007 2006 2008 2007 2006 2008 2007 2006 2008 2007 2006

Sr.




10 Net Assets Turnover Times

0.46

0.49

0.51

0.10

0.10

0.09

0.79

0.33

0.60 0.20

0.30

0.15 0.28 0.34

0.20

11 Fixed Assets Turnover Times

0.29

0.43

0.59

0.11

0.11

0.12

0.62

0.13

0.70 0.22

0.33

0.17 0.12 0.19

0.21

12 Debtors Turnover Times

1.58

1.53

6.26

15.60

10.27

8.18

17.11

5.38

35.66 1.43

1.86

0.83 5.90 4.63

2.80

13 Average Collection Period Days

231

239

59

28

36

45

35

68

16 256

197

438 62 79

130

14 Creditors Turnover Times

1.01

1.31

2.74

24.77

47.34

44.80

15.86

7.45

6.93 2.50

26.00

5.00 5.83 7.57

2.20

15 Stock Turnover Times

1.41

1.17

4.20

3.68

9.39

3.42

1.58

3.04

15.48 Z Z Z 3.33 2.61

1.51

16

Net Working Capital to Sales

Ratio Times

(0.27)

0.44

0.15

0.23

0.13

0.26

0.61

0.42

0.01 0.30

0.50

1.00 0.15 0.13

0.44

17 Sales to Working Capital Ratio Times

0.63

0.40

0.78

0.99

0.56

0.20

1.80

(0.26)

0.99 2.86

2.89

1.25

(0.39)

(0.75)

0.38

18 Inventory Days Days

258

336

87

99

39

181

231

120

24 Z Z Z 141 159

243

Liquidity And Stability Ratios

19 Current Ratio Times

1.47

2.20

2.16

3.16

4.89

1.07

1.61

0.84

1.26 1.88

2.50

2.60 0.39 0.66

1.48

20 Quick Ratio (Acid Test Ratio) Times

1.17

1.10

0.88

2.64

3.64

1.07

1.34

0.81

0.94 1.88

2.50

2.60 0.29 0.53

0.91

68

2008 2007 2006 2008 2007 2006 2008 2007 2006 2008 2007 2006 2008 2007 2006

Sr.



Capital Structure, Investment And Financial Risk Ratios

21 Long-Term Debt to Equity Ratio Times

0.33

0.21

0.02

0.26

0.27

0.03

0.81

0.40

0.31 Z Z Z 0.42 0.49

0.49

22

Long-Term Debt to Total Long-

Term Finance Ratio Times

0.25

0.17

0.02

0.17

0.18

0.03

0.45

0.29

0.23 Z Z Z 0.29 0.33

0.33

23 Total Debt to Total Assets Ratio Times

0.24

0.14

0.10

0.20

0.17

0.03

0.28

0.20

0.12 Z Z Z 0.37 0.29

0.17

24 Interest Cover Times

29.83

91.60

69.85 Z Z Z

9.63

6.56

44.00 Z Z Z 3.38 7.83

6.21

25 Dividend Cover Times

5.95

4.19

4.50

1.45

1.24

1.12

2.26

2.25

3.49 3.48

8.33

3.04

(4.80) 2.20

1.65

26 Fixed Assets to Total Assets Ratio Times

0.55

0.44

0.52

0.81

0.80

0.88

0.58

0.64

0.66 0.86

0.84

0.82 0.87 0.81

0.69

27

Long-Term Funds to Total Assets

Ratio Times

0.72

0.72

0.75

0.90

0.90

0.91

0.72

0.66

0.70 0.92

0.94

0.93 0.53 0.61

0.72

28 Total Owing to Total Assets Ratio Times

0.21

0.15

0.13

0.00

0.01

0.00

0.03

0.03

0.04 0.08

0.01

0.03 0.03 0.02

0.07

29 Capital gearing Times

1.04

1.02

1.00

1.00

1.00

1.00

1.11

1.14

1.01 1.00

1.00

1.00 1.00 1.06

1.03

30 Gearing Ratio Times

0.69

0.21

0.13

0.34

0.28

0.03

0.82

0.40

0.20 Z Z Z 0.78 0.66

0.32




69

Comments on the results

The above financial ratios are presented as box-whisker plots showing the Interquartile

ranges, outliers, median and means; and these are appended in the appendix.

Some financial ratios relating to the real estate in other countries have been procured

from the websites of Bizminer from US under the category “Land Subdivision”; and

from Ventureline, under the category ‘ SIC Code 6552: Real estate – Land sub-dividers

and developers except cemeteries’ and these have been presented where possible in the

analysis on financial ratio benchmarks below.


1 ROCE - Return on capital employed

The GCC Benchmarks proposed are 11 %, 14 %, 10% for 2006, 07, & 08 respectively.

Capital employed represents the total funds employed in the company – the

shareholders funds (equity) plus long term debts. A company in the UK should achieve

a ROCE of 20%. The higher value means efficient usage of assets efficiently; ideally

it should be rising year on year. US companies had a benchmark Return on net worth of

24.85% in 2006 (Bizminer, 2010)

2 ROTA - Return on total assets

The GCC Benchmarks proposed are 9%, 11%, 5% for 2006, 07, & 08 respectively.

These are expected to be lower than other industries due to the high level of investment

properties on real estate company balance sheets. US companies had a benchmark

value of 9.57% in 2006 (Bizminer, 2010)

3 ROE - Return on equity


These values indicate that the industry gives very little scope for the investor to be

worried about opportunity cost. A ROE of 20% or more generally represents a good

performance. US companies had benchmark values of -12%, 3%, & -30.2 % in 2006,

07 & 08 (Ventureline 2010)

70

4 ROS - Return on sales (Profit margin)


US companies had net-profit margins (pre-tax) benchmark values of -3.3%, 7.1%, & -

22% in 2006, 07 & 08 respectively (Ventureline 2010)

Investors Ratios

5 EPS - Earnings per share - Local currencies

5a EPS - Earnings per share - Converted to GBP

These are not ratios and were calculated for use in other ratios.

6 PE ratio - Price earnings ratio

This average could be skewed due to the unrealistic price levels seen in RE stock in the

boom years. The GCC Benchmarks proposed are 11.12, 10.81, 12.24 for 2006, 07, &

08 respectively. A high value indicates a popular share or overvalued. US companies

had benchmark values of 21.2, 87.7 & 5.5 in 2006, 07 & 08 (Ventureline 2010)

7 Dividend yield

The GCC Benchmarks proposed are 2.3%, 3.3%, 4.1% for 2006, 07, & 08 respectively.

This evaluates how investors are rewarded over time. The values show a low yield and

therefore the opportunity cost seems rather high.

8 Earnings yield

The GCC Benchmarks proposed are 8%, 9%, 6 % for 2006, 07, & 08 respectively.

This is the EPC / average share price for the year.

9 Market to book ratio

The GCC Benchmarks proposed are 2.16, 1.79, 1.47 for 2006, 07, & 08 respectively.

This is ratio of market capitalisation / owners funds

71


10 Net assets turnover

This evidently will be low due to the high value fixed assets in the company. The GCC

Benchmarks proposed are 0.28, 0.25, 0.22 for 2006, 07, & 08 respectively. This is the

number of times the assets are covered by sales, and a falling value indicates falling

sales turnover for every Pound invested. US companies had benchmark values of 0.2,

0.1, & 0.2 in 2006, 07 & 08 (Ventureline 2010)

11 Fixed assets turnover


A lower value means reduction in the use of fixed assets and suggests that some assets

be disposed and converted to working capital or repayment of debts. US companies had

benchmark values of 0.6, 0.3 & 0.4 in 2006, 07 & 08 (Ventureline 2010).

12 Debtors turnover


US companies had benchmark values of 5.2, 4.3, & 7.0 in 2006, 07 & 08 (Ventureline

2010). This ratio is not very important for real estate analysts.

13 Average collection period

Their collections are not very large when considered as a percentage of total assets as

such I’m not sure how much we would give importance to the above 2 ratios in an RE

analysis. The GCC Benchmarks proposed are 59, 73, 66 days for 2006, 07 & 08

respectively. US companies had benchmark values of 97, 87, & 57 in 2006, 07 & 08

(Ventureline 2010) .This ratio is not very important for real estate analysts

14 Creditors turnover


Low values indicate late payments.

15 Stock turnover

Much lowers than other industries and is expected to get lower. The GCC Benchmarks

proposed are 3.40, 2.74, 1.75 for 2006, 07, & 08 respectively. US companies had

72

benchmark values of 1.5, 1.1, & 1.1 in 2006, 07 & 08 (Ventureline 2010). This ratio is

not very important for real estate analysts.

16 Net working capital to sales ratio

The GCC Benchmarks proposed are 0.20, 0.24, 0.23 for 2006, 07, & 08 respectively

.RE companies generally have a low NWC only due to the high levels of accounts

payables they carry in comparison to their current assets so how good a benchmark this

would be is debatable.

17 Sales to working capital ratio - Working capital ratio


18 Inventory days

The GCC Benchmarks proposed are 114, 135, 209 days for 2006, 07 & 08 respectively.

US companies had benchmark values of 249, 342 & 328 days in 2006, 07 & 08

(Ventureline 2010). Low values indicate lower sales. This ratio is not useful for real

estate analysts.

Liquidity and Stability Ratios

19 Current ratio

The value is very low and indicates that the companies would always depend on some

form of short term bank financing. The GCC Benchmarks proposed are 1.53, 1.13, 1.25

for 2006, 07, & 08 respectively. An organisation can run into a lot of difficulties if

there is shortage of cash to pay creditors or employees, liquidity ratios are measure of

short term health, used to compare assets which will be turned into cash to pay liabilities

within the same cycle. Ideally this ratio should be 2:1, CA: CL or an organisation will

not be able to meet its current liabilities at a short notice. US companies had benchmark

values of 0.6, 0.5, & 0.8 in 2006, 07 & 08 (Ventureline 2010)

20 Quick ratio (Acid test ratio)


More severe measure of short term liquidity than the current ratio should be 1:1. US

companies had benchmark values of -0.1, -1.4, & -0.8 in 2006, 07 & 08 (Ventureline

2010)

73

Capital Structure, Investment, and Financial Risk Ratios

21 Long-term debt to equity ratio

This seems to be very low in comparison with other industries indicating that they have

high equity levels. It would imply that debt is low, but investment properties in this

region are sometimes highly levered up to 70-80% and depend fully on their revenues

for repayment as these companies are not cash rich or have high liquid funds

immediately available. The GCC Benchmarks proposed are 0.13, 0.41, 0.38 for 2006,

07, & 08 respectively. US companies had benchmark values of 1.3, 1.7 & 2.8 in 2006,

07 & 08 (Ventureline 2010)

22 Long-term debt to total long-term finance ratio


This is another view of the debt equity ratio but with the total long term finance

including equity.

23 Total debt to total assets ratio


This implies that its debt levels are not to be worried about but this ratio is skewed due

to the high level of investment assets bringing it down.

24 Interest cover

Extremely high value indicates that the operating profits are more than sufficient to

cover their interest obligations. The GCC Benchmarks proposed are 19.90, 8.67, 8.97

for 2006, 07, & 08 respectively. This ratio shows the amount of cover available before

interest payments are defaulted. US companies had benchmark values of 0.8, 1.3 & 0.3

in 2006, 07 & 08 (Ventureline 2010)

25 Dividend cover

Due to these companies generally being large cap with generous dividends, coverage

may be stretched even more to keep market sentiments high. The GCC Benchmarks

proposed are 1.84, 3.73, 2.74 for 2006, 07, & 08 respectively. Shows how much of the

profits are removed and how much are ploughed back in. A low ratio of 1:2.5 means

74

that the firm may not be ploughing back enough money for growth, possibly because

the earnings are low or too much is paid out to please investors.

26 Fixed assets to total assets ratio


27 Long-term funds to total assets ratio


28 Total owing to total assets ratio


29 Capital gearing


30 Gearing ratio – debt: equity ratio


A high ratio implies a high financial risk, but can be tolerated if the profitability is high

and consistent. A high profit can support a high debt, but when ROCE starts dropping,

it would be time to restructure debt. US companies had benchmark values of 0.57, 0.63

& 0.73 in 2006, 07 & 08 (Ventureline 2010)

75

5.6 Objective 3: Selection of ratio for detailed analysis

The logic in selecting ROCE has already been described in chapter 4. A company’s

ROCE should always be compared to the current cost of borrowing. ROCE is a better

measure of the return of a real estate company as it considers leverage, which is an

integral source of funding in this sector. As real estate companies are traditionally

highly leveraged, it is essential to ensure that any company generates adequate returns

to cover its high cost of capital.

Table 5.5: ROCE medians for all countries and ROCE benchmarks for the study period

Year UAE SAUDI

ARABIA QATAR BAHRAIN KUWAIT GCC

2008 11.4% 5.2% 10.1% 16.3% 7.7% 10.1%

2007 14.3% 6.0% 10.9% 26.1% 20.8% 13.8%

2006 19.9% 5.1% 10.5% 10.3% 12.7% 11.3%

Source : Created for this study

76

Figure 5.4: Frequency distribution of ROCE in study period

GCC REAL ESTATE COMPANIES


2006 TO 2008

FREQUENCY DISTRIBUTION

0

2

4

6

8

10

12

-160%

-150%

-140%

-130%

-120%

-110%

-100%

-90%

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

ROCE %

FR

EQ

UEN

CY

DIS

TR

IBU

TIO

N_

2008

2007

2006


Figure 5.5: Expanded frequency distribution of ROCE



2006 TO 2008

FREQUENCY DISTRIBUTION

0

1

2

3

4

5

6

7

8

9

10

<=0% 5% 10% 15% 20% 25% 30% 35% >=35%

ROCE %

FR

EQ

UE

NC

Y D

IST

RIB

UT

ION

_

2008

2007

2006


The ROCE data shows a non parametric distribution for all the study years as expected

and therefore the nonparametric statistical testing of variance known as the Kruskal –

Wallis test is proposed to be applied for the analyses.

77

5.7 Objective 4: Observations of data grouped by year

Figure 5.6: BW plot of ROCE for all GCC companies


TREND ANALYSIS OF FINANCIAL RATIO : RETURN ON CAPITAL EMPLOYED

BOX-WHISKER PLOT ( Legend : Red = Median; Green = Mean


2008

10%

0%

2007

16%

14%

2006

15%

11%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

GROUP : ALL SELECTED GCC COMPANIES FOR YEARS 2006 TO 2008

VA

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Source: Created for this study

The BW plot shows the ROCE median rising from 2006 values and coming back in

2008, whereas the mean has slipped considerably between 2007 and 2008.

The KW test conducted to test the similarity of all yearly data against each other gives a

value of 0.041, which is <0.05, and hence the variance is statistically significant. Since

the data is considerable, an Anova test is also conducted in this case only and the results

are a P value of 0.015, which is also <0.05, but the post-hoc Tukey test gives P values

of 0.023 and 0.042 between years 2008/07 and 2008/06, but a very high value of 0.981

for years 2007/06, indicating a low level of variance between the data for these two

years.

78

Table 5.6: KW test for ROCE of all GCC companies 3 years data

Kruskal-Wallis Test Results for: ROCE: ALL GCC 2006, 07, 08

Data Range = Sheet4!$I$2:$K$32

Descriptive Statistics

Value Rank

2008 2007 2006 2008 2007 2006

0.103 0.099 0.128 38.0 35.0 47.0

0.140 0.067 0.211 53.0 22.0 68.0

0.069 0.149 0.188 23.0 54.0 64.0

0.235 0.263 0.277 71.0 76.0 79.0

0.106 0.137 0.380 42.0 51.0 84.0

0.122 0.171 0.185 46.0 60.0 63.0

0.066 0.138 0.038 21.0 52.0 11.0

0.045 0.060 0.040 13.0 20.0 12.0

0.045 0.056 0.080 14.0 18.0 26.0

-0.005 0.120 0.196 7.0 45.0 67.0

0.136 0.053 0.051 50.0 17.0 16.0

0.058 0.082 0.092 19.0 28.0 31.0

0.129 0.094 0.096 48.0 32.0 33.0

0.101 0.191 0.105 36.0 65.0 40.0

0.081 0.194 0.113 27.0 66.0 44.0

0.072 0.109 0.595 24.0 43.0 85.0

0.211 0.261 0.103 69.0 74.0 39.0

0.163 0.106 0.000 57.0 41.0 8.5

-0.255 0.000 -0.072 4.0 8.5 5.0

-0.370 0.357 0.023 3.0 83.0 10.0

-1.571 0.266 0.160 1.0 77.0 56.0

-0.062 0.091 0.097 6.0 30.0 34.0

0.258 0.322 0.170 73.0 82.0 59.0

0.245 0.165 0.157 72.0 58.0 55.0

0.129 0.184 0.273 49.0 62.0 78.0

0.102 0.231 0.046 37.0 70.0 15.0

0.077 0.321 0.284 25.0 81.0 80.0

0.179 0.084 61.0 29.0

-0.594 0.263 2.0 75.0

Median 0.101 0.138 0.113 36.0 52.0 44.0

Sum 1.587 4.635 4.088 991.0 1454.5 1209.5

N 29 29 27 29 29 27

Test Results

Statistic Value DF 1 DF 2 P

Chi-Square 6.290 2 - 0.043

F 3.319 2 81 0.041

Source: Output from software StatistiXL

Table 5.7: Anova test for ROCE for 3 year data

Analysis of Variance results for: ROCE: ALL GCC 2006, 07, 08

Y Variable Range = $I$2:$K$32


Group Mean Std Dev. Std Err N

2008 0.001 0.352 0.065 29

2007 0.160 0.093 0.017 29

2006 0.149 0.133 0.026 27

Analysis of Variance

Source Type III SS Df Mean Sq. F Prob.

Model 0.454 2 0.227 4.458 0.015

Error 4.180 82 0.051

Total 4.634 84

Post Hoc tests

Test Group 1 Group 2 Mean Diff. SE q Prob.

Tukey 2008 2007 -0.159 0.042 3.800 0.023

2006 -0.148 0.043 3.470 0.0422007 2006 0.011 0.043 0.261 0.981

Source: Output from software StatistiXL

79

5.8 Objective 5: Observation of data grouped by country

Fig 5.7: BW plot of ROCE grouped country wise 2006





UAE

20%

23%

SAUDI

8%

5% QATAR

20%

11%

10%

10%

BAHRAIN

11%

13%

KUWAIT

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

GROUP : COUNTRYWISE FOR YEAR 2006

VA

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UAE

14%

15%

SAUDI

9%

6%QATAR

13%

11%

26%

26%

BAHRAIN

20%

21%

KUWAIT

0%

5%

10%

15%

20%

25%

30%

35%

40%


VA

LU

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80






UAE

11%

13%

SAUDI

6%

5%

QATAR

12%

10%

16%

16%

BAHRAIN

-17%

8%

KUWAIT

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%


VA

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The country wise groups shown for each of the 3 study years indicate that in 2006, the

Kuwaiti companies had a large range of 36 %; some companies had negative ROCE;

even though the median and mean are reasonable at 13% and 11 % and the median is

similar to the overall median for 2006. Also the numbers of Kuwaiti companies are

highest in the study, at 13% twice the group sizes from other countries. The lowest

performer is Saudi at median 8%, the Qatar, Bahrain and Kuwait companies are in the

10-11% range, lesser than the overall median of 13% , the top performers are UAE

companies with 23 % median and 20% mean, which by far outweigh the others by 2

times.

In 2007, Kuwaiti companies still have a high range of 36 similar to 2006, the lowest is

improved at zero from negative last year and Saudi companies remain poor performers

at 6% followed by Qatar at 11 and UAE 14, the sole Bahrain company is the best

performer with a median of 26% . Saudi and Qatar companies have smallest whiskers

showing consistency of ROCE values mostly within the central region inter quartile

range.

Come 2008, Kuwaiti companies still show high volatility in the range, 183 from -157 to

26 %this time because they are a good mix of all smell, medium and large. Saudi

81

companies still lowest performance at 5%, UAE and Qatar similar like last year at 10-11

%, which is also the overall median, with the Bahrain company as the top performer

consistent with last year at16 down from 26 of last year .similar to last most of the data

is contained in the box for Saudi and Qatar with one outlier each on the higher side.

Year-wise for countries -

UAE companies show a declining in ROCE steadily from 20 to 14 to 11 in 2008. This

is due to the rising costs during the boom presumably or management problems and

skills shortages in all fields of construction. All data have at least one outlier each

showing some did make exceptional profits between 40 and down to 25% in 2008, these

peaks have also come down. Saudi companies show a slight increase fro m5 to 6 % and

back to 5 % in 2008 - slow and steady is the name of the game.

Qatar companies show large range 14 % in 08, but the medians are consistent at 10-

11%.- the means for 2007 and 2008 are also close to medians but 2006 is far because of

a severe outlier at 60%.

Bahrain co recorded the general trend also of low high low from 2006 to 08 from 10 to

26 to 16 in 2008. It followed the general trends of the Saudi, But with more

fluctuations.

Kuwait too followed the trend of low-high -low from 13 to 20 to 8 % in 2008

Fig 5.10: Column chart ROCE grouped country wise 2006-08



MEDIAN VALUES FOR COUNTRY GROUPS

FOR SAMPLE PERIOD 2006 TO 2008

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

UAE SAUDI QATAR BAHRAIN KUWAIT GCC

GCC COUNTRIES

RO

CE

%

2008

2007

2006


82

Fig 5.11: Column chart ROCE grouped year wise 2006-08



MEDIAN VALUES FOR FOR SAMPLE PERIOD 2006 TO 2008

FOR COUNTRY BASED GROUPS

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

2008 2007 2006

SAMPLE PERIODS

RO

CE

%

UAE SAUDI QATAR BAHRAIN KUWAIT GCC


In the first chart above, the patterns which emerge are that UAE companies show a

steady decline whereas all others have a rise and fall; Saudi is low and steady not much

effected by the 2007/08 rise and fall, Qatar too steady like Saudi but with higher

margins; Bahrain and Kuwait show higher volatility; Qatar is overall closest to the

benchmarks for all the three years.

The next chart shows that in 2006, Kuwait, Bahrain, & Qatar are close to the

benchmark, Saudi underperformed and UAE is almost twice the benchmark. In 2007,

the boom year, all countries have done better than the 2006 except UAE which has

declined to the benchmark level, Kuwait and Bahrain have outperformed, Qatar and

UAE close and Saudi following its low and steady profit. In 2008, all countries are

lower than 2007, Saudi and Qatar have remained steady.

83

Fig 5.12: Web chart ROCE 2006


ROCE 2006

COUNTRIES V/S GCC BENCHMARK

0.0%

5.0%

10.0%

15.0%

20.0%

UAE

SAUDI ARABIA

QATARBAHRAIN

KUWAIT

2006

GCC BM




ROCE 2007


0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

UAE

SAUDI ARABIA

QATARBAHRAIN

KUWAIT

2007

GCC BM


84



ROCE 2008


0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

UAE

SAUDI ARABIA

QATARBAHRAIN

KUWAIT

2008

GCC BM


Fig 5.15: Web chart ROCE 2006-08


ROCE 2006, 2007 & 2008

COUNTRY MEDIANS

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

UAE

SAUDI ARABIA

QATAR

BAHRAIN

KUWAIT

GCC

2008

2007

2006


85

The first 3 web charts show the yearly values with the benchmark, the 2006 chart shows

Saudi at half the benchmark, Qatar, Bahrain and Kuwait close and UAE at twice the

benchmark.

In 2007, Saudi is still close to half the benchmark, Qatar, and UAE close, Kuwait and

Bahrain outside the envelope.

In 2008, Saudi is still halfway, Qatar and UAE close, Kuwait under and Bahrain over

the benchmark.

In the last combined chart, the patterns show 2007 as the outermost envelope, with UAE

breaking it for 2006 and 2008 completely within the envelope of 2007. The Saudi

companies are close every year, and UAE shows the declining trend.

The variances in data based on KW test are discussed separately below.

5.9 Objective 6: Observation of data grouped by company size

Some analysts prefer to use the total assets of a company as a measure of size; which

has been used in the grouping of the balance sheet profiles graphic earlier on.

The company sizes for the current analysis are made with respect to turnover as it is

found to be more relevant here. The turnover figures for 2008 are used and converted to

GBP, this ranged from zero to 2500 million GBP. The separation was done with respect

to quartiles, for want of a better method. The list of 30 companies is sorted according to

turnover, and the quartiles calculated and grouped accordingly into 4 groups which have

companies from zero to q1, q1 to median, median to q3 and q3 to maximum value.

These worked out to the four ranges zero to 25m, 25 to 75m, 75m to 375m, and 375 to

2500 m GBP.

86

Fig 5.16: BW chart ROCE grouped size wise 2006





6%2%

0 to 25 M GBP

10%

11%

25 to 75 M GBP

10%

20%

75 to 375 M GBP

375 to 2500 M GBP

20%

21%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

GROUP : SIZEWISE FOR YEAR 2006

VA

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16%

11%

0 to 25 M GBP

18%

18%

25 to 75 M GBP

11%

15%

75 to 375 M GBP375 to 2500 M GBP

14%

15%

0%

5%

10%

15%

20%

25%

30%

35%

40%


VA

LU

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87






-40%

-26%

0 to 25 M GBP

9%

11%

25 to 75 M GBP

13%

15%

75 to 375 M GBP 375 to 2500 M GBP

11%

12%

-200%

-150%

-100%

-50%

0%

50%


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The 1st graphic shows the overall trend of all the 30 companies; the ROCE was

reasonable at a median of 11% jumped to 14% in the boom of 2007 and slumped to 10

%, possibly due to the sharp increases in costs of all construction materials and

shortage of contractors and manpower.

In the next 3 graphics, the companies are grouped into four size groups based on

quartiles of the turnover. The 2006 graphic shows a pattern of overall increase of

ROCE with respect to the turnover. the low turnover companies have a median ROCE

of just 2 % showing their vulnerability, the medium companies in both ranges 25-to

75 and 75 to 375 M GBP have the same median showing they are from the same

group, these are actually the companies with turnovers in the central 25th to 75th

percentile, though the means are substantially different, mainly due to one High outlier

which is visible at 60%.

The High range has twice the median ROCE of the medium turnover companies at 20%,

showing the strength in size and management structures in a booming economy. The

overall median was 11 % in 2006.

As we progress into 2007, the smaller companies show huge volatility in ROCE values

with a range of 36 % and a central Interquartile range of 19%, the ROCE has jumped to

a healthy 11% from 2% in 2006, which is what the medium companies were making in

88

2006 and they have caught up. the lower-medium companies have fared best at mean

and median both at 18%, showing very reliable figures and they have also a

considerably big range of 27% and an inter quartile range of 12%.the large companies

have also the means and median close at 15% and seem to have the lowest range of 20

% signifying reliability of data and possibility of using these as benchmark values.

The median also compares with the overall median for 2007 of 14%. The overall

median is 10 % in 2008, and mean of zero, signifying volatility of returns due to adverse

market conditions. The small companies have recorded a high volatility and their

ROCE are mostly in the negative from 6 % up to a max of -157%.

The inter quartile range is also fully in the negative from -3 to -48% and this has been a

bad year for this group. the upper medium has done best at In 2008,also shows a

general trend of ROCE increasing with 13% company size except the large group, the

small, medium and high have steadily increasing ROCE medians of -29, 9 and 13 with

large ones at 11 %. The medians are in the range 9 to 13 for all except the small

companies. The means are also in a narrow range of 11 to 15 % for this group.

And all higher then the overall median of 10 which ahs been pushed down by the losses

in the smaller r companies.

Fig 5.19: Column chart ROCE grouped size wise 2006-08



MEDIAN VALUES FOR YEARS 2006 TO 2008

BASED ON COMPANY SIZE GROUP

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

Small - 0 to 25 M GBP Medium - 25 to 75 M GBP Big - 75 to 375 M GBP Large - 375 to 2500 M GBP

SIZE GROUPS

RO

CE

%

2008

2007

2006


89

Fig 5.20: BW chart ROCE grouped year wise 2006-08



MEDIAN VALUES FOR COMPANY SIZE GROUP

FOR SAMPLE PERIOD 2006 TO 2008

-30.0%

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

2008 2007 2006

SAMPLE PERIODS

RO

CE

%

Small - 0 to 25 M GBP Medium - 25 to 75 M GBP Big - 75 to 375 M GBP Large - 375 to 2500 M GBP


The trends which can be noticed for the size groups show that the large, big, and

medium companies exhibited the rise and fall in 2008, whereas the small companies

exhibit a meteoric rise and crash through the floor exposing their vulnerability and the

big and medium show similar values meaning they could be clubbed together, in fact

these two groups form the 50 % inner values, and it is not a wonder they are close.

The next graph shows a pattern of profitability increasing with size, again the medium

and big companies have nearly same values; in 2007, the pattern has totally reversed

remarkably, and all groups are in a close range, the big and medium groups are exactly

the same; in 2008, the large, big, medium are close and the small companies are in the

negative zone.

The variances in data based on KW test are discussed separately below.

90

5.10 Results of Kruskal -Wallis tests

The following was the results of the Kruskal-Wallis test for data for ROCE for 5 GCC

countries in 30 firms for years 2006, 07 & 08.

Table 5.8: Kruskal-Wallis test results

Kruskal-Wallis Test Results Sr.

No. Grouped data Chi-

Square P F P

Remarks

1 Entire GCC firms tested

for similarities between

2006, 2007 & 2008 6.290 0.043 3.319 0.041

P < 0.05

Variance is

Significant

2 All 5 countries tested

against each other for

2008

7.257 0.123 2.130 0.113 P > 0.05

Variance is

Insignificant



2007

6.759 0.149 1.909 0.143 P > 0.05

Variance is

Insignificant



2006

5.160 0.271 1.355 0.280 P > 0.05

Variance is

Insignificant

5 The 4 size based groups

tested for similarities in

2008

8.365 0.039 3.637 0.029 P < 0.05

Variance is

Significant



2007

0.451 0.930 0.136 0.937 P > 0.05

Variance is

Insignificant



2006

16.043 0.001 11.181 0.000 P < 0.05

Variance is

Significant


91

The outputs of the different KW tests are attached in appendix D.

Result 1 is the variance based on yearly data, which has been discussed in section 5.7;

the test result shows significant variance. However, a post-hoc Anova performed on the

same data showed high similarities between 2006 and 2007.

The results 2 to 4 for country-based groups show P > 0.05, for all the study years;

therefore the data for ROCE is similar across the GCC based on country.

The results 5 to 7 show variances between size-based groups as high in 2006 and 2008

and insignificant in 2007, which was the boom year.

5.11 Testing the hypothesis


=0.05 level.

The KW tests on country-based groups have yielded P values of 0.113, 0.143, and

0.280, for the years 2006, 2007, and 2008 respectively. The values are all above 0.05,

and therefore the null hypothesis is not rejected.

Therefore the medians are for all 5 country groups of the GCC are similar statistically;

and it may be inferred that all the ROCE data is from the same population.

The results of the hypothesis are extended to assume that the benchmarks created with

the combined data of the selected GCC companies may be confidently used throughout

the region.

It may also be concluded that the other benchmarks for financial ratios presented in this

study can be used throughout the GCC with reasonable prudence.

92

5.12 Summary

The objectives have been listed and discussed. A list of companies has been selected

for the study and presented. Collective balance sheet profiles of all the companies

included in the study are presented graphically to show the representation from each

country and also each size.

Objective 1 to identify the financial ratios has been achieved with limited success; 30

ratios were selected and a list presented along with the formulae proposed to be used.

Objective 2 to create benchmarks has been achieved with a reasonable degree of

accuracy and the methodology to calculate, tabulate individual company ratios and

calculate the quartiles and present them as a box whisker plot are discussed. The

resulting medians for each year are presented as the benchmarks and also schedule

showing medians for each country for each ratio for the years 2006 to 2008 are

presented. The values of each of the ratios are discussed briefly along with comparison

of medians in the US are also shown where available.

Objective 3 has been achieved with the selection of ROCE as for detailed analysis and a

justification for selection has been provided. The values, frequency distributions, of

ROCE are also presented, which show clearly the non parametric nature of the value set,

and the use of Kruskal-Wallis test, a non-parametric test, is also discussed.

Objective 4 has been to examine the variance in yearly data. The data is presented as

BW plots for each year and results are discussed. The median has moved from 11% in

2006 to 14% in 2007 and 10% in 2008. A KW test is done taking each year data as one

group and significant differences are noted. An Anova post hoc Tukey test shows high

similarities between 2006 and 2007 data.

Objective 5 has been to observe variance in country data and BW plots, column charts

and web charts are presented for each year showing the country-wise plots and the

trends and patterns discussed.

Objective 6 has been to examine variance due to size. Clear differences in size effects

on the ROCE are observed. The data is presented as BW plots and column charts a d

the trends discussed. The large companies have their own trends; the big and medium

generally follow the same trends and the smaller companies show a lot of volatility.

The hypothesis of similarity of ROCE medians across the GCC is tested with the KW

test and the results show P values above 0.05 in all the study years and the results of all

the KW tests are presented.

93

CHAPTER 6: CONCLUSION

6.1 Introduction

The aim of this research was to create benchmarks for select financial ratios during the

period 2006 to 2008 for the selected mix of GCC real estate companies; and to

statistically test if sufficient similarities existed in the underlying data to justify usage of

data as one group, with reasonable confidence. It was also decided to establish the

extent to which similarities exist in the data by examining the variance of one selected

ratio – the ROCE.

In order to achieve this aim, seven objectives emerged for research:

1. Identification of financial ratios which would be useful, especially for the real estate

and construction industry.

2. Creation of benchmarks for each of the selected financial ratios for each of the three

study periods 2006-08.


4. Observation of patterns and variance for the selected ratio in year-based group data

for all companies.

5. Observation of patterns and variance for the selected ratio in country-based group

data for each of the years of the study, to test the hypothesis.

6. Observation of patterns and variance for the selected ratio in size-based group data.

The literature review served to translate these objectives into specific methods relating

to ratio calculations, statistical calculation, and presentation of results as well as the

correct interpretation of the statistical results.

Based on the analysis of the results, the conclusions of the research are presented below

for the objectives.

Thereafter, the chapter considers whether the research hypothesis has been proved or

contradicted, highlights the limitations of the research and identifies areas for further

research.

94

6.2 Objectives

Objective 1: Variable selection

Different sources were considered, journal literature, books, forums of professional

analysts and database companies and lists of ratios recommended by them were viewed,

notably. Conclusive answers were not found and finally 30 ratios were selected from

Walsh (2003) and Pendelbury (2004).

The following is a classification of the selected financial ratios:

• Profitability and performance ratios - 4

• Investors ratios - 5

• Efficiency and effectiveness ratios- 10

• Liquidity and stability ratios - 2

• Capital structure, investment and financial risk ratios - 10

Objective 2: Creating benchmarks

A sheet showing the 30 financial ratios and the medians as benchmarks are proposed for

the GCC. The medians are proposed as rudimentary benchmark values for each of the

ratios for that particular year. Also consolidated sheet detailing the medians for each

country for each ratio for the years 2006, 2007, and 2008 is presented in the earlier

chapter, but these are based on scant data

Objective 3: Selection of one ratio for detailed analysis.

It has been proposed to use Return on Capital Employed (ROCE) for detailed analysis.

ROCE is also an efficiency measure of sorts; it doesn’t just gauge profitability as profit

margin ratios do, it measures profitability after factoring in the amount of capital used.

Because ROCE measures profitability in relation to invested capital, ROCE is important

for capital-intensive companies, or firms that require large upfront investments to start

producing goods. The justifications provided from various opinions seem reasonable to

establish this as a good measure of comparison. A frequency distribution of the ROCE

is presented, which shows the skewness on the data and the non-normality of data.

Hence it was proposed to use the Kruskal-Wallis test for the analysis of variance

between groups based on period, country, and size.

95

Objective 4: Gauging the variance in yearly data for all companies.

The ROCE data was grouped according to the data period for all GCC data and the

variances checked between years 2006, 2007 and 2008 with the help of the KW test.

The KW test gave a P value of 0.041, which is < 0.05 and hence it is confirmed that the

data for the years 2006, 2007, and 2008 have no similarity with each other. An Anova

test with post-hoc test done separately showed that there was a high similarity of 0.98

between the ROCE data for 2006 and 2007.

Objective 5: Variance in country-grouped data

The ROCE data was grouped according to the country for each year and the variances

checked between country groups for each year with the help of the KW test. The details

are presented as BW plots and tables, showing the medians as well as the means. Spider

diagrams and column charts have been provided for clarity and observation of trends

and patterns. The KW tests proved that the country data for all GCC for the study years

is similar. Other interesting observations relating to individual countries are as follows:

• Saudi Arabian companies have the lowest and steadiest values throughout the

study period; a little more than 5 %, and this may be compared with the finance

costs.

• The single Bahraini company has high values throughout the study period and

has had the up-down pattern exhibited by all except the UAE companies.

• Qatari companies have steady returns with all the ups and downs and are

generally closest to the GCC benchmarks proposed through the research.

• UAE companies have shown a steady decline through the period; whereas all the

others have had an upswing in 2007 and back to 2006 or nearby values.

• Kuwaiti companies were close to the benchmark in 2006, have moved up in

2007 and in 2008 are below the 2006 values.

• Generally, all the companies exhibit a rise in 2007 and a fall to 2006 or lower

levels of returns ; except UAE companies, which have steadily declined from

20% to 15% and 11% in 2007 and 2008 respectively.

96

Objective 6: Examination of the variance in data due to size effects.

The company turnover was used as a measure of size: the companies were classified as

Small, Medium, Big and Large based on a quartile criterion, and checked for variance

between the group sizes for every year.

The ROCE data was grouped according to the size data for all GCC data and the

variances checked between years 2006, 2007 and 2008 with the help of the KW test.

The KW analysis showed no similarity in medians in 2006, a very high similarity in

2007, the dizzy period, where everybody did well, and no similarity in 2008 again. This

proves that the size groups follow different patterns, as is exhibited below.

The details are presented as BW plots and tables, showing the medians as well as the

means. The trends in yearly medians are examined for any observable patterns and the

results are:

• In 2006, the large companies were showing highest gains, the big and medium

companies had similar figures lower than large companies, and small companies

had very low returns.

• In 2007, the whole pattern was reversed, albeit with a smaller range and the big

and medium companies again with similar figures with small companies

showing the highest gains with phenomenal increase over 2006 figures.

• In 2008, the large companies had fallen below the 2006 levels, the big and

medium are close and close also to their 2006 levels, whereas the small

companies have gone through the floor in a meteoric rise and fall pattern

exposing their vulnerability to the economic cycles in the region.

• Overall 2007 seems to have brought windfalls to all real estate companies in the

region.

97

6.3 The research hypothesis



=0.05 level.

The KW tests on country-based groups have yielded P values of 0.113, 0.143, and

0.280, for the years 2006, 2007, and 2008 respectively. The values are all above

0.05and therefore the null hypothesis is not rejected.

Therefore the medians are for all 5 country groups of the GCC are similar statistically;

and it may be inferred that all the ROCE data is from the same population.

The results of the hypothesis are extended to assume that the benchmarks created with

the combined data of the selected GCC companies may be confidently used throughout

the region.

It may also be concluded that the other benchmarks for financial ratios presented in this

study can be used throughout the GCC with reasonable prudence.

6.4 Research limitations

The following limitations have been observed during the process of this research:

1. The research is limited by the research sample in terms of number of companies

and the period of study. A minimum 5 year study period has been proposed by

Alexander (2007).

2. There is no comparable data to check if the results are reasonable as there has

been no earlier work in this area for the GCC region.

3. Some outliers which may have affected the values have not been given any

consideration, as medians have been used; this may have led to some level of

inaccuracies, especially in the smaller companies data.

4. Companies selected are not purely real estate developers, some are into other

allied businesses too including rentals, shopping malls, contracting, etc.; some

are semi-government receiving grants and subsidies , others are privately held,

these require to be analysed separately.

5. Some of the data has been adjusted as details were not available. Especially

since confusion prevails over split of land assets into current or long term assets,

98

some companies have clubbed current and long term assets as one item, and

liabilities have also been clubbed; some have had no turnover in one year, and

best judgement has been used in these cases to normalise the data. But no data

has been removed, however strange.

6. Size effects have not been considered due to limitation of data and the

benchmarks proposed are based on a mix of all sizes of companies.

6.5 Areas of further research

Being a pilot study, this research has opened doors for a lot of further investigation and

some of these are listed below:

1. It would be interesting to see the directions the ratios take when the current data

from the 2009 statements is put into the study; as the fortunes of the real estate

and construction have drastically changed as part of a larger cycle.

2. Only one ratio has been examined in detail for variance, others could be

examined and a general view made later for compatibility of data across the

GCC.

3. Research is also required on which financial ratios are really important to

various stakeholders in these companies.

4. Older data from some of the well entrenched companies can be added and the

research enhanced. Finance data may also be procured legitimately from willing

non-listed companies to increase the base for a detailed study.

5. Data can be enhanced and a study of insolvency factors can be created for the

region based on research by Altman (1983), Abidali (1995), and Basha (2007)

and others.

6.6 Contact

This is a pilot study based on scant data and subject to corrections. Any suggestions

will be thankfully acknowledged and may be sent to the author at [email protected]

Or [email protected] or [email protected]

99

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Tukey J W (1977) Exploratory Data Analysis. Reading: Addison-Wesley

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104

Walsh, C (2003) Key Management Ratios, 3rd

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105

APPENDIX A – BOX-WHISKER PLOTS AND STATISTICAL

DATA OF FINANCIAL RATIOS

The box is 50 % of the central values , the last and first value of the box is the 25th and

75th percentile, the difference in values is the IQR, a value considered ok is 1.5 times the

IQR on either sides of the box, and beyond are outliers, the max value for the whiskers is

the 1.5 IQR –step - the last data point just within this step limit is considered the end of

the data within the acceptable limits, this may be a lot lower than the step or just that

value. The outliers are marked as dots – up to a value of 3 IQR, the ones beyond that are

termed severe and not shown in this plots, but they are available in the data in the

appendix. These in our study are mostly due to incorrect or severe data from some of the

balance sheets, some have received grants showing very high turnover without any

corresponding costs, others have very limited or negligible turnover, etc, also a mean

reflecting all these outliers is also shown as a green line, the median is the central data

and shown in red and the values of the median and mean are presented for each year of

the study. The corresponding table also shows similar data for all the quartiles, mean,

SD, number of data points, limits of whiskers, outlier identification data.

106

1 RETURN ON CAPITAL EMPLOYED





0%

10%

2008

14%

16%

2007

11%

15%

2006

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%


VA

LU

ES

_


1 Mean 0% 16% 15%

2 N 29 29 27


Quartiles

4 Q0=Minimum -157% 0% -7%

5 Q1-25% 5% 9% 7%

6 Q2=Median 10% 14% 11%

7 Q3=75% 14% 23% 19%

8 Q4=Max 26% 36% 60%

9 Range 183% 36% 67%

Outliers

10 IQR 9% 14% 13%

11 Step = 1.5 IQR 14% 21% 19%



14 Outer Low Fence -23% -33% -31%


Whisker Limits




STATISTICAL DATA

107

2 RETURN ON TOTAL ASSETS





5%

0%

2008

11%

10%

2007

9%

11%

2006

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

FINANCIAL RATIO : RETURN ON TOTAL ASSETS

VA

LU

ES

_


1 Mean 0% 10% 11%

2 N 29 29 27


Quartiles

4 Q0=Minimum -92% 0% -5%

5 Q1-25% 2% 6% 5%

6 Q2=Median 5% 11% 9%

7 Q3=75% 9% 12% 15%

8 Q4=Max 20% 27% 59%

9 Range 111% 27% 64%

Outliers

10 IQR 7% 7% 10%

11 Step = 1.5 IQR 10% 10% 14%



14 Outer Low Fence -18% -14% -24%


Whisker Limits



RETURN ON TOTAL ASSETS

STATISTICAL DATA

108

3 RETURN ON EQUITY





2008

11%

1%

2007

16%

15%

2006

15%

14%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

FINANCIAL RATIO : RETURN ON EQUITY

VA

LU

ES

_


1 Mean 1% 15% 15%

2 N 29 29 27


Quartiles

4 Q0=Minimum -157% 0% -7%

5 Q1-25% 5% 8% 7%

6 Q2=Median 11% 16% 14%

7 Q3=75% 16% 20% 20%

8 Q4=Max 30% 36% 59%

9 Range 187% 36% 66%

Outliers

10 IQR 12% 12% 14%

11 Step = 1.5 IQR 18% 18% 20%

12 Inner Low Fence -13% -10% -13%


14 Outer Low Fence -31% -28% -34%


Whisker Limits

16 Lower Whisker end 0% 0% -7%


RETURN ON EQUITY

STATISTICAL DATA

109

4 RETURN ON SALES – PROFIT MARGIN





47%

21%

2008

62%

59%

2007

49%

47%

2006

-100%

-50%

0%

50%

100%

150%

FINANCIAL RATIO : RETURN ON SALES - PROFIT MARGIN

VA

LU

ES

_


1 Mean 21% 59% 47%

2 N 26 28 27


Quartiles

4 Q0=Minimum -427% 9% -77%

5 Q1-25% 19% 45% 27%

6 Q2=Median 47% 62% 49%

7 Q3=75% 62% 76% 77%

8 Q4=Max 94% 93% 98%

9 Range 521% 84% 175%

Outliers

10 IQR 43% 31% 50%

11 Step = 1.5 IQR 64% 47% 75%



14 Outer Low Fence -110% -49% -123%


Whisker Limits



RETURN ON SALES - PROFIT MARGINS

STATISTICAL DATA

110

6 PRICE EARNING RATIO





2008

1.71

12.24

2007

23.07

10.81

2006

10.21

11.12

-10

0

10

20

30

40

50

60

FINANCIAL RATIO : PRICE EARNINGS

VA

LU

ES

_


1 Mean 1.71 23.07 10.21

2 N 29 28 24

3 Standard Deviation 59.79 34.80 85.39

Quartiles

4 Q0=Minimum (296.43) - (358.16)

5 Q1-25% 5.95 7.20 9.55

6 Q2=Median 12.24 10.81 11.12

7 Q3=75% 17.49 21.68 17.99

8 Q4=Max 48.81 163.51 110.97

9 Range 345.24 163.51 469.12

Outliers

10 IQR 11.54 14.49 8.44

11 Step = 1.5 IQR 17.31 21.73 12.67

12 Inner Low Fence (11.37) (14.53) (3.12)

13 Inner High Fence 34.80 43.41 30.66

14 Outer Low Fence (28.68) (36.26) (15.79)

15 Outer High Fence 52.11 65.14 43.33

Whisker Limits

16 Lower Whisker end (3.02) - 4.04

17 Upper Whisker end 30.44 40.70 18.15

PRICE EARNING RATIO

STATISTICAL DATA

111

7 DIVIDEND YIELD





4.1%

3.4%

2008

3.2%

3.3%

2007

8.1%

7.5%

2006

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

FINANCIAL RATIO : DIVIDEND YIELD

VA

LU

ES

_


1 Mean 3.4% 3.2% 7.5%

2 N 10 11 24


Quartiles

4 Q0=Minimum 0% 0% -5%

5 Q1-25% 1% 1% 2%

6 Q2=Median 4.1% 3.3% 8.1%

7 Q3=75% 5% 4% 10%

8 Q4=Max 7% 10% 25%

9 Range 7% 10% 30%

Outliers

10 IQR 4% 3% 8%

11 Step = 1.5 IQR 5% 4% 12%



14 Outer Low Fence -10% -7% -23%


Whisker Limits

16 Lower Whisker end 0% 0% -5%


DIVIDEND YIELD

STATISTICAL DATA

112

8 EARNINGS YIELD





5.7%

-0.3%

2008

9.0%

11.9%

2007

8.1%

7.5%

2006

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

FINANCIAL RATIO : EARNINGS YIELD

VA

LU

ES

_


1 Mean 0% 12% 8%

2 N 29 29 24


Quartiles

4 Q0=Minimum -71% 0% -5%

5 Q1-25% 3% 4% 2%

6 Q2=Median 5.7% 9.0% 8.1%

7 Q3=75% 11% 12% 10%

8 Q4=Max 18% 60% 25%

9 Range 89% 60% 30%

Outliers

10 IQR 8% 8% 8%

11 Step = 1.5 IQR 12% 12% 12%



14 Outer Low Fence -20% -21% -23%


Whisker Limits



EARNING YIELD

STATISTICAL DATA

113

9 MARKET TO BOOK RATIO





1.47

2.21

2008

1.79

2.23

2007

2.16

2.79

2006

0

1

2

3

4

5

6

7

8

9

10

FINANCIAL RATIO : MARKET TO BOOK RATIO

VA

LU

ES

_


1 Mean 2.21 2.23 2.79

2 N 29.00 29.00 25.00


Quartiles

4 Q0=Minimum 0.77 0.59 0.57

5 Q1-25% 1.15 1.16 1.32

6 Q2=Median 1.47 1.79 2.16

7 Q3=75% 2.46 2.73 3.54

8 Q4=Max 7.55 7.43 9.41

9 Range 6.78 6.84 8.84

Outliers

10 IQR 1.31 1.57 2.22

11 Step = 1.5 IQR 1.96 2.35 3.33

12 Inner Low Fence (0.82) (1.19) (2.01)

13 Inner High Fence 4.42 5.08 6.87

14 Outer Low Fence (2.78) (3.54) (5.34)

15 Outer High Fence 6.39 7.43 10.20

Whisker Limits

16 Lower Whisker end 0.77 0.59 0.57


MARKET TO BOOK RATIO

STATISTICAL DATA

114

10 NET ASSET TURNOVER





0.22

0.31

2008

0.25

0.29

2007

0.28

0.31

2006-

0.20

0.40

0.60

0.80

1.00

1.20

FINANCIAL RATIO : NET ASSET TURNOVER

VA

LU

ES

_


1 Mean 0.31 0.29 0.31

2 N 26.00 28.00 27.00


Quartiles

4 Q0=Minimum 0.01 0.09 0.05

5 Q1-25% 0.11 0.18 0.12

6 Q2=Median 0.22 0.25 0.28

7 Q3=75% 0.40 0.37 0.47

8 Q4=Max 1.38 0.92 0.87

9 Range 1.37 0.83 0.82

Outliers

10 IQR 0.30 0.19 0.35

11 Step = 1.5 IQR 0.44 0.29 0.52

12 Inner Low Fence (0.33) (0.11) (0.40)


14 Outer Low Fence (0.78) (0.40) (0.93)


Whisker Limits



NET ASSET TURNOVER

STATISTICAL DATA

115

11 FIXED ASSET TURNOVER





0.22

0.31

2008

0.25

0.34

2007

0.28

20060.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

FINANCIAL RATIO : FIXED ASSET TURNOVER

VA

LU

ES

_


1 Mean 0.31 0.34 4.25

2 N 26.00 28.00 27.00


Quartiles

4 Q0=Minimum 0.01 0.09 0.05

5 Q1-25% 0.11 0.12 0.13

6 Q2=Median 0.22 0.25 0.28

7 Q3=75% 0.35 0.41 0.61

8 Q4=Max 1.18 1.48 88.08

9 Range 1.17 1.39 88.03

Outliers

10 IQR 0.24 0.28 0.48

11 Step = 1.5 IQR 0.37 0.42 0.72

12 Inner Low Fence (0.26) (0.30) (0.59)


14 Outer Low Fence (0.63) (0.72) (1.31)


Whisker Limits



FIXED ASSET TURNOVER

STATISTICAL DATA

116

12 DEBTORS TURNOVER





5.53

26.59

2008

5.00

10.08

2007

6.26

14.74

20060.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

FINANCIAL RATIO : DEBTORS TURNOVER

VA

LU

ES

_


1 Mean 26.59 10.08 14.74

2 N 20.00 22.00 20.00


Quartiles

4 Q0=Minimum 0.82 1.11 0.83

5 Q1-25% 1.61 1.53 3.73

6 Q2=Median 5.53 5.00 6.26

7 Q3=75% 13.96 10.36 11.29

8 Q4=Max 362.50 60.70 67.94

9 Range 361.68 59.59 67.11

Outliers

10 IQR 12.35 8.82 7.57

11 Step = 1.5 IQR 18.52 13.24 11.35

12 Inner Low Fence (16.91) (11.70) (7.63)

13 Inner High Fence 32.48 23.59 22.65

14 Outer Low Fence (35.43) (24.94) (18.98)

15 Outer High Fence 51.00 36.83 34.00

Whisker Limits



DEBTORS TURNOVER

STATISTICAL DATA

117

13 AVERAGE COLLECTION PERIOD





66.25

122.66

2008

73.41

127.96

2007

58.90

100.44

20060.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

400.00

450.00

500.00

FINANCIAL RATIO : AVERAGE COLLECTION PERIOD

VA

LU

ES

_


1 Mean 122.66 127.96 100.90

2 N 20.00 22.00 20.00


Quartiles

4 Q0=Minimum 1.01 6.01 5.37

5 Q1-25% 28.47 35.25 32.99

6 Q2=Median 66.25 73.41 58.90

7 Q3=75% 226.71 238.23 100.44

8 Q4=Max 447.64 327.90 438.00

9 Range 446.63 321.89 432.63

Outliers

10 IQR 198.24 202.98 67.46

11 Step = 1.5 IQR 297.37 304.47 101.18

12 Inner Low Fence (268.90) (269.23) (68.20)

13 Inner High Fence 524.07 542.70 201.63

14 Outer Low Fence (566.27) (573.70) (169.38)

15 Outer High Fence 821.44 847.18 302.81

Whisker Limits



AVERAGE COLLECTION PERIOD

STATISTICAL DATA

118

14 CREDITORS TURNOVER





5.83

33.13

2008

3.64

22.34

2007

5.52

20.65

20060.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

FINANCIAL RATIO : CREDITORS TURNOVER

VA

LU

ES

_


1 Mean 33.13 22.34 20.65

2 N 21.00 25.00 22.00


Quartiles

4 Q0=Minimum 0.40 0.38 0.86

5 Q1-25% 1.75 2.50 2.36

6 Q2=Median 5.83 3.64 5.52

7 Q3=75% 17.85 18.67 12.67

8 Q4=Max 265.00 139.50 182.00

9 Range 264.60 139.12 181.14

Outliers

10 IQR 16.10 16.17 10.31

11 Step = 1.5 IQR 24.15 24.25 15.47

12 Inner Low Fence (22.40) (21.75) (13.12)

13 Inner High Fence 42.00 42.92 28.14

14 Outer Low Fence (46.55) (46.00) (28.59)

15 Outer High Fence 66.14 67.17 43.61

Whisker Limits



CREDITORS TURNOVER

STATISTICAL DATA

119

15 STOCK TURNOVER





1.75

13.55

2008

2.74

28.36

2007

3.40

18.74

2006

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

FINANCIAL RATIO : STOCK TURNOVER

VA

LU

ES

_


1 Mean 13.55 28.36 18.74

2 N 21.00 22.00 22.00


Quartiles

4 Q0=Minimum 0.47 0.20 0.07

5 Q1-25% 1.08 1.34 1.27

6 Q2=Median 1.75 2.74 3.40

7 Q3=75% 7.23 8.65 11.23

8 Q4=Max 146.00 303.50 130.38

9 Range 145.53 303.30 130.31

Outliers

10 IQR 6.15 7.31 9.95

11 Step = 1.5 IQR 9.22 10.96 14.93

12 Inner Low Fence (8.14) (9.63) (13.66)

13 Inner High Fence 16.45 19.61 26.16

14 Outer Low Fence (17.36) (20.59) (28.59)

15 Outer High Fence 25.66 30.57 41.09

Whisker Limits



STOCK TURNOVER

STATISTICAL DATA

120

16 NET WORKING CAPITAL TO SALES RATIO





0.23

0.37

2008

0.24

0.49

2007

0.20

0.95

2006

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

FINANCIAL RATIO : NET WORKING CAPITAL TO SALES

VA

LU

ES

_


1 Mean 0.37 0.49 0.95

2 N 26.00 28.00 27.00


Quartiles

4 Q0=Minimum (0.55) (0.26) (0.56)

5 Q1-25% - 0.00 (0.05)

6 Q2=Median 0.23 0.24 0.20

7 Q3=75% 0.73 0.63 0.81

8 Q4=Max 1.61 3.05 15.00

9 Range 2.16 3.31 15.56

Outliers

10 IQR 0.73 0.62 0.86

11 Step = 1.5 IQR 1.09 0.93 1.29

12 Inner Low Fence (1.09) (0.93) (1.33)


14 Outer Low Fence (2.18) (1.86) (2.62)


Whisker Limits

16 Lower Whisker end (0.55) (0.26) (0.56)


NET WORKING CAPITAL TO SALES RATIO

STATISTICAL DATA

121

17 SALES TO WORKING CAPITAL RATIO





0.62

1.33

2008

0.29

(5.29)

2007

0.56

(3.86)

2006

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

8.00

10.00

FINANCIAL RATIO : SALES TO WORKING CAPITAL

VA

LU

ES

_


1 Mean 1.33 (5.29) (3.86)

2 N 26.00 28.00 27.00


Quartiles

4 Q0=Minimum (5.50) (67.50) (114.08)

5 Q1-25% (0.24) (0.92) (0.96)

6 Q2=Median 0.6 0.29 0.56

7 Q3=75% 1.74 1.47 1.44

8 Q4=Max 11.43 12.43 13.14

9 Range 16.93 79.93 127.23

Outliers

10 IQR 1.98 2.38 2.40

11 Step = 1.5 IQR 2.98 3.58 3.60

12 Inner Low Fence (3.22) (4.50) (4.56)


14 Outer Low Fence (6.19) (8.07) (8.16)


Whisker Limits

16 Lower Whisker end (1.37) (1.76) (1.85)


SALES TO WORKING CAPITAL RATIO

STATISTICAL DATA

122

18 INVENTORY DAYS





209

231

2008

135

253

2007

114

450

2006

0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

800.00

900.00

FINANCIAL RATIO : INVENTORY DAYS

VA

LU

ES

_


1 Mean 231 253 450

2 N 21 22 22

3 Standard Deviation 220 387 1,150

Quartiles

4 Q0=Minimum 3 1 3

5 Q1-25% 51 43 33

6 Q2=Median 209 135 114

7 Q3=75% 338 274 288

8 Q4=Max 780 1,804 5,475

9 Range 778 1,803 5,472

Outliers

10 IQR 288 231 255

11 Step = 1.5 IQR 431 346 382

12 Inner Low Fence (381) (303) (349)

13 Inner High Fence 769 620 670

14 Outer Low Fence (812) (649) (731)

15 Outer High Fence 1,201 966 1,051

Whisker Limits

16 Lower Whisker end 3 1 3

17 Upper Whisker end 586 589 664

INVENTORY DAYS

STATISTICAL DATA

123

19 WORKING CAPITAL RATIO – CURRENT RATIO





1.25

2.40

2008

1.13

1.77

2007

1.53

2.60

20060.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

FINANCIAL RATIO : CURRENT RATIO

VA

LU

ES

_


1 Mean 2.40 1.77 2.60

2 N 28.00 28.00 27.00


Quartiles

4 Q0=Minimum 0.12 0.14 0.32

5 Q1-25% 0.43 0.68 0.88

6 Q2=Median 1.25 1.13 1.53

7 Q3=75% 1.97 2.51 2.52

8 Q4=Max 22.02 6.58 14.33

9 Range 21.91 6.44 14.01

Outliers

10 IQR 1.55 1.82 1.64

11 Step = 1.5 IQR 2.32 2.73 2.47

12 Inner Low Fence (1.89) (2.05) (1.59)


14 Outer Low Fence (4.21) (4.78) (4.05)


Whisker Limits



CURRENT RATIO

STATISTICAL DATA

124

20 QUICK RATIO – ACID TEST RATIO





0.92

2.03

2008

0.86

1.44

2007

0.94

1.89

20060.00

1.00

2.00

3.00

4.00

5.00

6.00

FINANCIAL RATIO : QUICK (ACID TEST) RATIO

VA

LU

ES

_


1 Mean 2.03 1.44 1.89

2 N 28.00 28.00 27.00


Quartiles

4 Q0=Minimum 0.07 0.07 0.06

5 Q1-25% 0.36 0.52 0.65

6 Q2=Median 0.92 0.86 0.94

7 Q3=75% 1.36 1.75 2.13

8 Q4=Max 22.02 6.36 14.22

9 Range 21.96 6.29 14.16

Outliers

10 IQR 1.00 1.22 1.48

11 Step = 1.5 IQR 1.51 1.83 2.22

12 Inner Low Fence (1.15) (1.31) (1.57)


14 Outer Low Fence (2.65) (3.15) (3.79)


Whisker Limits



QUICK (ACID TEST) RATIO

STATISTICAL DATA

125

21 LONG TERM DEBT TO EQUITY





0.38

0.50

2008

0.41

0.38

2007

0.13

0.22

20060.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

FINANCIAL RATIO : LONG TERM DEBT TO EQUITY

VA

LU

ES

_


1 Mean 0.50 0.38 0.22

2 N 16.00 12.00 14.00


Quartiles

4 Q0=Minimum 0.00 0.00 0.00

5 Q1-25% 0.20 0.14 0.03

6 Q2=Median 0.38 0.41 0.13

7 Q3=75% 0.78 0.48 0.38

8 Q4=Max 1.24 1.27 0.67

9 Range 1.24 1.27 0.67

Outliers

10 IQR 0.58 0.34 0.35

11 Step = 1.5 IQR 0.87 0.51 0.53

12 Inner Low Fence (0.66) (0.37) (0.50)


14 Outer Low Fence (1.53) (0.87) (1.03)


Whisker Limits



LONG TERM DEBT TO EQUITY RATIO

STATISTICAL DATA

126

22 LONG TERM DEBT TO TOTAL LONG TERM FINANCE





2008

0.29

0.28

2007

0.24

0.29

2006

0.16

0.12

0.00

0.10

0.20

0.30

0.40

0.50

0.60

FINANCIAL RATIO : LONG TERM DEBT TO TOTAL LONG TERM FINANCE RATIO

VA

LU

ES

_


1 Mean 0.29 0.24 0.16

2 N 16.00 12.00 14.00


Quartiles

4 Q0=Minimum 0.00 0.00 0.00

5 Q1-25% 0.17 0.12 0.02

6 Q2=Median 0.28 0.29 0.12

7 Q3=75% 0.44 0.32 0.27

8 Q4=Max 0.55 0.56 0.40

9 Range 0.55 0.56 0.40

Outliers

10 IQR 0.27 0.20 0.25

11 Step = 1.5 IQR 0.40 0.30 0.37

12 Inner Low Fence (0.24) (0.18) (0.35)


14 Outer Low Fence (0.64) (0.48) (0.72)


Whisker Limits



LONG TERM DEBT TO TOTAL LONG TERM FINANCE RATIO

STATISTICAL DATA

127

23 TOTAL DEBTS TO TOTAL ASSETS





0.32

0.29

2008

0.27

0.24

2007

0.13

0.15

2006

0.00

0.10

0.20

0.30

0.40

0.50

0.60

FINANCIAL RATIO : TOTAL DEBTS TO TOTAL ASSETS

VA

LU

ES

_


1 Mean 0.29 0.24 0.15

2 N 20.00 21.00 21.00


Quartiles

4 Q0=Minimum 0.03 0.00 0.00

5 Q1-25% 0.15 0.13 0.05

6 Q2=Median 0.32 0.27 0.13

7 Q3=75% 0.43 0.33 0.23

8 Q4=Max 0.53 0.50 0.38

9 Range 0.51 0.50 0.38

Outliers

10 IQR 0.28 0.20 0.19

11 Step = 1.5 IQR 0.42 0.30 0.28

12 Inner Low Fence (0.27) (0.17) (0.23)


14 Outer Low Fence (0.68) (0.46) (0.51)


Whisker Limits



TOTAL DEBTS TO TOTAL ASSETS RATIO

STATISTICAL DATA

128

24 INTEREST COVER





8.97

31.78

2008

8.67

57.56

2007

19.90

42.88

2006

-40.00

-20.00

0.00

20.00

40.00

60.00

80.00

100.00

FINANCIAL RATIO : INTEREST COVER

VA

LU

ES

_


1 Mean 31.78 57.56 42.88

2 N 17.00 17.00 12.00


Quartiles

4 Q0=Minimum (19.00) 2.00 0.50

5 Q1-25% 3.38 5.28 7.98

6 Q2=Median 8.97 8.67 19.90

7 Q3=75% 23.30 26.00 50.46

8 Q4=Max 190.50 420.00 209.33

9 Range 209.50 418.00 208.83

Outliers

10 IQR 19.93 20.72 42.48

11 Step = 1.5 IQR 29.89 31.09 63.72

12 Inner Low Fence (26.51) (25.81) (55.75)

13 Inner High Fence 53.19 57.09 114.19

14 Outer Low Fence (56.40) (56.90) (119.47)

15 Outer High Fence 83.08 88.17 177.91

Whisker Limits

16 Lower Whisker end (19.00) 2.00 0.50


INTEREST COVER

STATISTICAL DATA

129

25 DIVIDEND COVER





1.13

2.74

2008

3.73

4.38

2007

1.84

3.69

2006

-10

-5

0

5

10

15

20

FINANCIAL RATIO : DIVIDEND COVER

VA

LU

ES

_


1 Mean 1.13 4.38 3.69

2 N 10.00 11.00 13.00


Quartiles

4 Q0=Minimum (26.32) 0.74 (1.63)

5 Q1-25% 1.54 2.10 1.55

6 Q2=Median 2.74 3.73 1.84

7 Q3=75% 5.33 4.82 4.50

8 Q4=Max 14.86 14.07 16.45

9 Range 41.17 13.32 18.08

Outliers

10 IQR 3.79 2.72 2.94

11 Step = 1.5 IQR 5.68 4.08 4.42

12 Inner Low Fence (4.14) (1.99) (2.86)


14 Outer Low Fence (9.83) (6.07) (7.28)

15 Outer High Fence 16.70 12.99 13.33

Whisker Limits

16 Lower Whisker end 1.45 0.74 (1.63)


DIVIDEND COVER

STATISTICAL DATA

130

26 FIXED ASSETS TO TOTAL ASSETS





0.82

0.73

2008

0.78

0.69

2007

0.68

0.62

2006

0.00

0.20

0.40

0.60

0.80

1.00

1.20

FINANCIAL RATIO : FIXED ASSET TO TOTAL ASSETS

VA

LU

ES

_

131


1 Mean 0.73 0.69 0.62

2 N 29.00 29.00 27.00


Quartiles

4 Q0=Minimum 0.20 0.16 0.00

5 Q1-25% 0.60 0.52 0.42

6 Q2=Median 0.82 0.78 0.68

7 Q3=75% 0.87 0.85 0.86

8 Q4=Max 0.98 0.99 0.99

9 Range 0.78 0.83 0.99

Outliers

10 IQR 0.27 0.33 0.43

11 Step = 1.5 IQR 0.40 0.50 0.65

12 Inner Low Fence 0.20 0.02 (0.23)


14 Outer Low Fence (0.20) (0.48) (0.88)


Whisker Limits



FIXED ASSET TO TOTAL ASSETS RATIO

STATISTICAL DATA

132

27 LONG TERM FUNDS TO TOTAL ASSETS





0.72

0.67

2008

0.69

0.70

2007

0.76

0.77

2006

0.00

0.20

0.40

0.60

0.80

1.00

1.20

FINANCIAL RATIO : LONG TERM FUNDS TO TOTAL ASSETS

VA

LU

ES

_


1 Mean 0.67 0.70 0.76

2 N 29.00 29.00 27.00


Quartiles

4 Q0=Minimum 0.32 0.27 0.51

5 Q1-25% 0.50 0.58 0.66

6 Q2=Median 0.72 0.69 0.77

7 Q3=75% 0.88 0.89 0.88

8 Q4=Max 0.99 0.99 0.99

9 Range 0.67 0.72 0.48

Outliers

10 IQR 0.38 0.31 0.22

11 Step = 1.5 IQR 0.58 0.47 0.33

12 Inner Low Fence (0.08) 0.11 0.34


14 Outer Low Fence (0.65) (0.36) 0.01


Whisker Limits



LONG TERM FUNDS TO TOTAL ASSETS RATIO

STATISTICAL DATA

133

28 TOTAL OWING TO TOTAL ASSETS





0.04

0.09

2008

0.03

0.06

2007

0.04

0.06

2006

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

FINANCIAL RATIO : TOTAL OWING TO TOTAL ASSETS

VA

LU

ES

_


1 Mean 0.09 0.06 0.06

2 N 22.00 25.00 22.00


Quartiles

4 Q0=Minimum 0.00 0.00 0.00

5 Q1-25% 0.01 0.01 0.01

6 Q2=Median 0.04 0.03 0.04

7 Q3=75% 0.17 0.11 0.11

8 Q4=Max 0.40 0.30 0.18

9 Range 0.40 0.30 0.18

Outliers

10 IQR 0.16 0.10 0.09

11 Step = 1.5 IQR 0.23 0.15 0.14

12 Inner Low Fence (0.22) (0.14) (0.12)


14 Outer Low Fence (0.46) (0.29) (0.26)


Whisker Limits



TOTAL OWING TO TOTAL ASSETS RATIO

STATISTICAL DATA

134

29 CAPITAL GEARING





1.01

2008

1.13

2007

1.13

1.02

2006

0.99

0.60

0.80

1.00

1.20

1.40

1.60

1.80

FINANCIAL RATIO : CAPITAL GEARING

VA

LU

ES

_


1 Mean 1.13 1.13 0.99

2 N 29.00 28.00 26.00


Quartiles

4 Q0=Minimum 0.67 1.00 (1.00)

5 Q1-25% 1.00 1.00 1.00

6 Q2=Median 1.01 1.02 1.00

7 Q3=75% 1.11 1.15 1.03

8 Q4=Max 2.29 2.00 1.71

9 Range 1.62 1.00 2.71

Outliers

10 IQR 0.11 0.15 0.03

11 Step = 1.5 IQR 0.16 0.22 0.04

12 Inner Low Fence 0.84 0.78 0.96


14 Outer Low Fence 0.68 0.56 0.92


Whisker Limits



CAPITAL GEARING RATIO

STATISTICAL DATA

135

30 GEARING RATIO





0.73

0.79

2008

0.51

0.50

2007

0.20

0.28

2006

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

FINANCIAL RATIO : GEARING

VA

LU

ES

_


1 Mean 0.79 0.51 0.28

2 N 20.00 21.00 21.00


Quartiles

4 Q0=Minimum 0.03 0.00 0.00

5 Q1-25% 0.39 0.21 0.06

6 Q2=Median 0.73 0.50 0.20

7 Q3=75% 1.13 0.68 0.37

8 Q4=Max 1.73 1.37 0.78

9 Range 1.70 1.37 0.78

Outliers

10 IQR 0.74 0.47 0.31

11 Step = 1.5 IQR 1.11 0.71 0.46

12 Inner Low Fence (0.72) (0.50) (0.41)


14 Outer Low Fence (1.83) (1.21) (0.87)


Whisker Limits



GEARING RATIO

STATISTICAL DATA

136

APPENDIX B – BOX-WHISKER PLOT AND STATISTICAL

DATA SHEET FOR ROCE ANALYSIS

137





2008

10%

0%

2007

16%

14%

2006

15%

11%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

GROUP : ALL SELECTED GCC COMPANIES FOR YEARS 2006 TO 2008

VA

LU

ES

_

Trend Analysis: ROCE - Return on Capital Employed

Group: All GCC Companies for Years 2006 to 2008

2008 2007 2006

Mean 0% 16% 15%

N 29 29 27

SD 35% 9% 13%

Minimum -157% 0% -7%

First Quartile 5% 9% 7%

Median 10% 14% 11%

Third Quartile 14% 23% 19%

Maximum 26% 36% 60%

Range 183% 36% 67%

IQR 9% 14% 13%

Step 14% 21% 19%

Inner Fence - Low -9% -12% -12%

Inner Fence - High 27% 44% 38%

Outer Fence - Low -23% -33% -31%

Outer Fence - High 41% 65% 57%

SLO NO NO

NO NO SHOCheck for Outliers

Statistic

Sta

tist

ics

Mea

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ua

rtil

esO

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Ch

eck

138





-40%

-26%

0 to 25 M GBP

9%

11%

25 to 75 M GBP

13%

15%

75 to 375 M GBP 375 to 2500 M GBP

11%

12%

-200%

-150%

-100%

-50%

0%

50%


VA

LU

ES

_

Outlier removed for clarity





-40%

-26%

0 to 25 M GBP

9%

11%

25 to 75 M GBP

13%

15%

75 to 375 M GBP 375 to 2500 M GBP

11%

12%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%


VA

LU

ES

_

139





9%

11%

25 to 75 M GBP

13%

15%

75 to 375 M GBP 375 to 2500 M GBP

11%

12%

0%

5%

10%

15%

20%

25%

30%


VA

LU

ES

_


Group: Sizewise for Year 2008

0 to 25 M GBP

25 to 75 M

GBP

75 to 375 M

GBP

375 to 2500

M GBP

Mean -40% 11% 15% 12%

N 7 8 7 7

SD 56% 7% 6% 6%

Minimum -157% 4% 8% 7%

First Quartile -48% 6% 12% 9%

Median -26% 9% 13% 11%

Third Quartile -3% 13% 19% 14%

Maximum 6% 26% 24% 23%

Range 163% 21% 17% 17%

IQR 45% 7% 8% 5%

Step 67% 11% 12% 8%

Inner Fence - Low -116% -5% 0% 1%

Inner Fence - High 64% 24% 31% 21%

Outer Fence - Low -183% -15% -12% -7%

Outer Fence - High 131% 35% 43% 29%

MLO NO NO NO

NO MHO NO MHOCheck for Outliers

Statistic

Sta

tist

ics

Mea

nQ

uarti

les

Ou

tlie

r C

hec

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140





16%

11%

0 to 25 M GBP

18%

18%

25 to 75 M GBP

11%

15%

75 to 375 M GBP375 to 2500 M GBP

14%

15%

0%

5%

10%

15%

20%

25%

30%

35%

40%


VA

LU

ES

_



0 to 25 M GBP

25 to 75 M

GBP

75 to 375 M

GBP

375 to 2500

M GBP

Mean 16% 18% 15% 15%

N 7 8 7 7

SD 13% 9% 9% 6%

Minimum 0% 6% 8% 7%

First Quartile 7% 12% 9% 11%

Median 11% 18% 11% 14%

Third Quartile 26% 24% 17% 17%

Maximum 36% 32% 32% 26%

Range 36% 27% 24% 20%

IQR 19% 12% 9% 6%

Step 29% 18% 13% 9%

Inner Fence - Low -22% -6% -4% 2%


Outer Fence - Low -50% -24% -17% -8%


NO NO NO NO

NO NO MHO NOCheck for Outliers

Statistic

Sta

tist

ics

Mea

nQ

uart

iles

Ou

tlie

r C

hec

k

141





6%2%

0 to 25 M GBP

10%

11%

25 to 75 M GBP

10%

20%

75 to 375 M GBP

375 to 2500 M GBP

20%

21%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%


VA

LU

ES

_



0 to 25 M

GBP

25 to 75 M

GBP

75 to 375 M

GBP

375 to 2500

M GBP

Mean 6% 11% 20% 21%

N 5 8 7 7

SD 13% 6% 19% 9%

Minimum -7% 4% 5% 11%

First Quartile 0% 7% 9% 16%

Median 2% 10% 10% 20%

Third Quartile 5% 16% 22% 24%

Maximum 28% 19% 60% 38%

Range 36% 15% 55% 27%

IQR 5% 9% 13% 9%

Step 8% 13% 19% 13%

Inner Fence - Low -8% -6% -10% 3%


Outer Fence - Low -15% -19% -29% -10%


NO NO NO NO

SHO NO MHO MHOCheck for Outliers

Statistic

Sta

tist

ics

Mea

nQ

uart

iles

Ou

tlie

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142





UAE

11%

13%

SAUDI

6%

5%

QATAR

12%

10%

16%

16%

BAHRAIN

-17%

8%

KUWAIT

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%


VA

LU

ES

_





UAE

11%

13%

SAUDI

6%

5%QATAR

12%

10%

16%

16%

BAHRAIN

-5%

0%

5%

10%

15%

20%

25%

GROUP : COUNTRYWISE FOR YEAR 2008 (WITHOUT KUWAIT COMPANIES)

VA

LU

ES

_

143


Group: Countrywise for Year 2008

UAE SAUDI QATAR BAHRAIN KUWAIT

Mean 13% 6% 12% 16% -17%

N 6 6 5 1 11

SD 6% 5% 6% NA 54%

Minimum 7% 0% 7% 16% -157%

First Quartile 10% 4% 8% 16% -31%

Median 11% 5% 10% 16% 8%

Third Quartile 14% 6% 13% 16% 15%

Maximum 23% 14% 21% 16% 26%

Range 17% 14% 14% 0% 183%

IQR 3% 2% 5% 0% 47%

Step 5% 3% 7% 0% 70%

Inner Fence - Low 6% 2% 1% 16% -101%

Inner Fence - High 18% 9% 20% 16% 85%

Outer Fence - Low 1% -1% -6% 16% -171%

Outer Fence - High 23% 12% 27% 16% 155%

NO MLO NO NO MLO

SHO SHO MHO NO NO

Mea

nQ

ua

rtil

esO

utl

ier

Ch

eck

Check for Outliers

Statistic

Sta

tist

ics

144





UAE

14%

15%

SAUDI

9%

6%QATAR

13%

11%

26%

26%

BAHRAIN

20%

21%

KUWAIT

0%

5%

10%

15%

20%

25%

30%

35%

40%


VA

LU

ES

_




Mean 15% 9% 13% 26% 20%

N 6 5 5 1 12

SD 7% 4% 5% NA 11%

Minimum 7% 5% 8% 26% 0%

First Quartile 11% 6% 9% 26% 10%

Median 14% 6% 11% 26% 21%

Third Quartile 17% 12% 19% 26% 28%

Maximum 26% 14% 19% 26% 36%

Range 20% 9% 11% 0% 36%

IQR 6% 6% 10% 0% 18%

Step 9% 10% 15% 0% 27%

Inner Fence - Low 2% -4% -5% 26% -16%

Inner Fence - High 25% 22% 34% 26% 55%

Outer Fence - Low -6% -14% -20% 26% -43%

Outer Fence - High 34% 31% 48% 26% 81%

NO NO NO NO NO

MHO NO NO NO NO

Mea

nQ

ua

rtil

esO

utl

ier

Ch

eck

Check for Outliers

Statistic

Sta

tist

ics

145





UAE

20%

23%

SAUDI

8%

5% QATAR

20%

11%

10%

10%

BAHRAIN

11%

13%

KUWAIT

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%


VA

LU

ES

_




Mean 23% 8% 20% 10% 11%

N 6 5 5 1 10

SD 9% 7% 22% NA 12%

Minimum 13% 4% 9% 10% -7%

First Quartile 19% 4% 10% 10% 3%

Median 20% 5% 11% 10% 13%

Third Quartile 26% 8% 11% 10% 17%

Maximum 38% 20% 60% 10% 28%

Range 25% 16% 50% 0% 36%

IQR 7% 4% 2% 0% 14%

Step 11% 6% 3% 0% 21%

Inner Fence - Low 7% -2% 7% 10% -18%

Inner Fence - High 37% 14% 14% 10% 38%

Outer Fence - Low -4% -8% 4% 10% -39%

Outer Fence - High48% 20% 17% 10% 58%

NO NO NO NO NO

MHO MHO SHO NO NO

Mea

nQ

ua

rtil

esO

utl

ier

Ch

eck

Check for Outliers

Statistic

Sta

tist

ics

146





13%11%

2008

14%

15%

2007

20%

23%

2006

0%

5%

10%

15%

20%

25%

30%

35%

40%

GROUP : YEARWISE FOR UAE GROUP

VA

LU

ES

_


Group: Yearwise for UAE GROUP

2008 2007 2006

Mean 13% 15% 23%

N 6 6 6

SD 6% 7% 9%

Minimum 7% 7% 13%


Median 11% 14% 20%


Maximum 23% 26% 38%

Range 17% 20% 25%

IQR 3% 6% 7%

Step 5% 9% 11%

Inner Fence - Low 6% 2% 7%


Outer Fence - Low 1% -6% -4%


NO NO NO

SHO MHO MHOCheck for Outliers

Statistic

Sta

tist

ics

Mea

nQ

ua

rtil

esO

utl

ier

Ch

eck

147





6%

5%

2008

6%

9%

2007

5%

8%

2006

-5%

0%

5%

10%

15%

20%

25%

GROUP : YEARWISE FOR SAUDI ARABIA GROUP

VA

LU

ES

_


Group: Yearwise for SAUDI ARABIAN GROUP

2008 2007 2006

Mean 6% 9% 8%

N 6 5 5

SD 5% 4% 7%

Minimum 0% 5% 4%


Median 5% 6% 5%


Maximum 14% 14% 20%

Range 14% 9% 16%

IQR 2% 6% 4%

Step 3% 10% 6%

Inner Fence - Low 2% -4% -2%


Outer Fence - Low -1% -14% -8%


MLO NO NO

SHO NO MHOCheck for Outliers

Statistic

Sta

tist

ics

Mea

nQ

uart

iles

Ou

tlie

r C

hec

k

148





12%

10%

2008

11%

13%

2007

11%

20%

2006

0%

5%

10%

15%

20%

25%

GROUP : YEARWISE FOR QATAR GROUP

VA

LU

ES

_


Group: Yearwise for QATAR GROUP

2008 2007 2006

Mean 12% 13% 20%

N 5 5 5

SD 6% 5% 22%

Minimum 7% 8% 9%


Median 10% 11% 11%


Maximum 21% 19% 60%

Range 14% 11% 50%

IQR 5% 10% 2%

Step 7% 15% 3%

Inner Fence - Low 1% -5% 7%


Outer Fence - Low -6% -20% 4%


NO NO NO

MHO NO SHOCheck for Outliers

Statistic

Sta

tist

ics

Mea

nQ

uart

iles

Ou

tlie

r C

hec

k

149





16%

16%

2008

26%

26%

2007

10%

10%

2006

0%

5%

10%

15%

20%

25%

30%

GROUP : YEARWISE FOR BAHRAIN GROUP (1 COMPANY)

VA

LU

ES

_


Group: Yearwise for BAHRAIN GROUP (1 COMPANY)

2008 2007 2006

Mean 16% 26% 10%

N 1 1 1

SD NA NA NA

Minimum 16% 26% 10%


Median 16% 26% 10%


Maximum 16% 26% 10%

Range 0% 0% 0%

IQR 0% 0% 0%

Step 0% 0% 0%

Inner Fence - Low 16% 26% 10%


Outer Fence - Low 16% 26% 10%


NO NO NO

NO NO NOCheck for Outliers

Statistic

Sta

tist

ics

Mea

nQ

uart

iles

Ou

tlie

r C

hec

k

150





-17%

8%

2008

21%

20%

2007

13%

11%

2006

-80%

-60%

-40%

-20%

0%

20%

40%

60%

GROUP : YEARWISE FOR KUWAIT GROUP

VA

LU

ES

_


Group: Yearwise for KUWAIT GROUP

2008 2007 2006

Mean -17% 20% 11%

N 11 12 10

SD 54% 11% 12%

Minimum -157% 0% -7%

First Quartile -31% 10% 3%

Median 8% 21% 13%


Maximum 26% 36% 28%

Range 183% 36% 36%

IQR 47% 18% 14%

Step 70% 27% 21%

Inner Fence - Low -101% -16% -18%


Outer Fence - Low -171% -43% -39%


MLO NO NO

NO NO NOCheck for Outliers

Statistic

Sta

tist

ics

Mea

nQ

uart

iles

Ou

tlie

r C

hec

k

151

APPENDIX C: KRUSKAL-WALLIS TEST RESULTS FOR ROCE

Kruskal-Wallis Test Results for: 2006

Data Range = Sheet8!$Z$5:$AD$17


Value Rank

UAE SAUDI ARABIA QATAR BAH KUWAIT UAE SAUDI ARABIA QATAR BAH KUWAIT

13.51% 3.78% 11.07% 10.29% 0.00% 13.0 4.0 12.0 11.0 3.0

21.09% 4.01% 10.08% -7.25% 22.0 5.0 10.0 1.0

20.96% 8.48% 14.09% -2.27% 21.0 8.0 14.0 2.0

28.13% 19.64% 18.39% 10.00% 25.0 20.0 18.0 9.0

38.21% 5.09% 58.83% 14.49% 26.0 7.0 27.0 15.0

18.52% 16.51% 19.0 16.0

17.59% 17.0

27.27% 24.0

4.52% 6.0

27.16% 23.0

Median 21.03% 5.09% 14.09% 10.29% 12.25% 21.5 7.0 14.0 11.0 12.0

Sum 140.42% 41.00% 112.46% 10.29% 117.54% 126.0 44.0 81.0 11.0 116.0

N 6 5 5 1 10 6 5 5 1 10

Test Results


Chi-Square 8.254 4 - 0.083

F 2.558 4 21 0.069

152


Data Range = Sheet8!$T$5:$X$17


Value Rank


6.63% 11.34% 7.82% 24.47% 10.40% 10.0 19.0 11.0 28.0 13.0

4.47% 5.69% 6.21% 0.00% 3.0 7.0 9.0 1.0

12.24% 5.00% 8.01% 26.79% 21.0 5.0 12.0 29.0

17.45% 11.09% 5.16% 13.08% 27.0 18.0 6.0 23.0

10.58% 4.63% 10.81% 3.51% 14.0 4.0 17.0 2.0

15.21% 12.75% 25.0 22.0

10.77% 16.0

10.61% 15.0

15.43% 26.0

13.11% 24.0

5.79% 8.0

11.98% 20.0

Median 11.41% 5.69% 7.82% 24.47% 11.38% 17.5 7.0 11.0 28.0 18.0

Sum 66.58% 37.75% 38.00% 24.47% 134.21% 100.0 53.0 55.0 28.0 199.0

N 6 5 5 1 12 6 5 5 1 12

Test Results


Chi-Square 5.414 4 - 0.247

F 1.438 4 23 0.253


Data Range = Sheet8!$N$5:$R$17


Value Rank


10.29% 6.62% 12.90% 16.33% -25.53% 17.0 10.0 20.0 24.0 4.0

14.05% 4.46% 10.10% -37.01% 23.0 7.0 15.0 3.0

6.92% 4.51% 8.13% -157.14% 11.0 8.0 14.0 1.0

23.46% -0.45% 7.20% -6.15% 27.0 6.0 12.0 5.0

10.62% 13.63% 21.12% 25.81% 18.0 22.0 26.0 29.0

12.18% 5.84% 24.46% 19.0 9.0 28.0

12.90% 21.0

10.15% 16.0

7.69% 13.0

17.87% 25.0

-59.38% 2.0

Median 11.40% 5.18% 10.10% 16.33% 7.69% 18.5 8.5 15.0 24.0 13.0

Sum 77.51% 35.05% 59.46% 16.33% -29.18% 115.0 62.0 87.0 24.0 147.0

N 6 6 5 1 11 6 6 5 1 11

Test Results


Chi-Square 5.160 4 - 0.271

F 1.355 4 23 0.280

153

APPENDIX D: SPREADSHEETS OF FINANCIAL RATIOS FOR

2006 TO 2008

Legend:



Conditional format to show outlier data

154

MAIN SCHEDULE OF 30 COMPANIES FIN.A.NCIAL RATIOS FOR 2006

Country UAE SAUDI ARABIA QATAR

Company codes UNP DYR EMR SOR ALD RAK TIR SRE MCD JBL DAR ADC UDC SAL

Sr No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Currency AED AED AED AED AED AED SAR SAR SAR SAR SAR SAR QAR QAR

Turnover in Million Local Currency 2,839 1,057 15,142 1,737 1,678 508 224 182 283 - 4,386 123 1,223 1,200

Rate of Exchange (09/08/09) 0.162 0.162 0.162 0.162 0.162 0.162 0.159 0.159 0.159 0.159 0.159 0.159 0.164 0.164

Equivalent GBP ' 000,000 460 171 2,453 281 272 82 36 29 45 - 697 20 201 197

Rank wrt Turnover 4 10 1 6 7 13 19 21 17 28 2 24 8 9

PROFITABILITY AND PERFORMANCE RATIOSS

1 ROCE - Return on Capital Employed % 13% 21% 19% 28% 38% 19% 4% 4% 8% N.A. 20% 5% 9% 10%

2 ROTA - Return on Total Assets % 9% 11% 16% 22% 25% 18% 4% 4% 7% N.A. 18% 4% 6% 7%

3 ROE - Return on Equity % 14% 21% 21% 28% 38% 19% 4% 4% 8% N.A. 20% 5% 11% 10%

4 ROS - Return on Sales (Profit Margin) % 22% 33% 42% 56% 74% 93% 49% 64% 65% N.A. 48% 56% 21% 11%

INVESTORS RATIOS

5


Currencies Various 0.25 0.34 1.05 0.39 0.72 0.24 0.86 0.95 1.28 N.A. 3.36 0.63 2.41 1.56

5a

EPS - Earnings Per Share - Converted

to GBP GBP 0.04 0.06 0.17 0.06 0.12 0.04 0.14 0.15 0.20 N.A. 0.53 0.10 0.39 0.26

6 PE ratio - Price / Earnings Ratio R 10.27 N.A. 17.01 11.21 9.94 11.67 59.30 77.79 110.97 N.A. N.A. 88.57 17.94 13.08

7 Dividend Yield % N.A. N.A. 2% 2% 1% N.A. N.A. N.A. N.A. N.A. N.A. N.A. N.A. 5%

8 Earnings Yield % 10% N.A. 6% 9% 10% 9% 2% 1% 1% N.A. N.A. 1% 6% 8%

9 Market to Book Ratio Times 1.39 N.A. 3.54 3.15 3.80 2.16 2.16 3.04 9.41 N.A. N.A. 4.12 1.99 1.32

EFFICIENCY AND EFFECTIVENESS

10 Net Assets Turnover Times 0.62 0.65 0.50 0.50 0.51 0.20 0.08 0.06 0.13 N.A. 0.41 0.09 0.52 0.94

11 Fixed Assets Turnover Times 0.56 88.08 0.41 0.96 0.53 0.61 0.12 0.06 0.12 N.A. 17.13 0.09 0.70 0.92

12 Debtors Turnover Times 1.58 4.03 5.63 6.89 9.12 56.44 24.89 4.14 Z N.A. 9.12 7.24 67.94 5.38

13 Average Collection Period Days 231 90 65 53 40 6 15 88 N.A. N.A. 40 50 5 68

14 Creditors Turnover Times 2.23 28.57 2.42 2.33 3.06 23.09 44.80 182.00 8.58 N.A. 97.47 1.92 7.84 6.03

15 Stock Turnover Times 5.12 0.48 Z 4.20 1.96 36.29 5.60 45.50 1.23 N.A. 0.55 Z 15.48 6.98

16 Net Working Capital to Sales Ratio Times 0.38 2.30 (0.24) (0.04) 0.29 0.00 0.20 0.26 0.70 N.A. 1.92 (0.38) (0.05) 0.16

17 Sales to Working Capital Ratio Times 13.14 0.56 (12.15) 1.01 2.24 0.29 0.20 (1.48) (18.87) N.A. 0.42 10.25 0.99 8.63

18 Inventory Days Days 71 762 N.A. 87 186 10 65 8 297 N.A. 664 N.A. 24 52

LIQUIDITY AND STABILITY RATIO'S

19 Current Ratio (Working Capital Ratio) Times 1.10 2.68 0.80 3.08 1.63 14.33 7.53 0.35 0.95 N.A. 10.71 1.07 2.10 1.26

20 Quick Ratio (Acid Test Ratio) Times 0.85 0.72 0.80 2.58 0.91 14.22 7.29 0.32 0.16 N.A. 3.24 1.07 2.03 0.94

CAPITAL STRUCTURE, INVESTMENT AND FIN.A.NCIAL RISK RATIOS

21 Long-term Debt to Equity Ratio Times 0.13 0.00 0.13 0.02 0.01 N.A. 0.00 N.A. 0.06 N.A. N.A. N.A. 0.20 0.08

22

Long-term debt to total long-term

finance ratio NO 0.12 0.00 0.12 0.02 0.01 N.A. 0.00 N.A. 0.05 N.A. N.A. N.A. 0.17 0.07

23 Total debt to total assets ratio Times 0.18 0.00 0.10 0.03 0.13 N.A. 0.00 N.A. 0.05 N.A. N.A. N.A. 0.11 0.12

24 Interest Cover Times 15.30 N.A. 69.85 N.A. 209.33 N.A. N.A. N.A. N.A. N.A. N.A. N.A. N.A. 44.00

25 Dividend Cover Times Z Z 2.70 4.50 16.45 Z Z Z Z N.A. 1.12 Z Z 1.55

26 Fixed assets to total assets ratio Times 0.67 0.00 0.88 0.42 0.62 0.31 0.59 0.98 0.89 N.A. 0.02 0.88 0.43 0.66

27 Long-term Funds to Total Assets Ratio Times 0.68 0.54 0.83 0.81 0.65 0.95 0.94 0.94 0.88 N.A. 0.91 0.88 0.68 0.70

28 Total owing to total assets ratio Times 0.17 0.01 0.15 0.17 0.11 0.01 0.00 0.00 0.01 N.A. 0.00 0.04 0.04 0.10

29 Capital gearing Times 1.07 1.00 1.01 1.00 1.00 1.00 1.00 1.00 1.00 N.A. 1.00 1.00 1.00 1.02

30 Gearing Ratio Times 0.30 0.00 0.13 0.04 0.20 N.A. 0.00 N.A. 0.06 N.A. N.A. N.A. 0.20 0.19

155


BAHKUWAIT STATISTICAL AVERAGE - BENCHMARK CALCULATIONS

QRE BAR EZD SEE GRA KRE DAM ABY MAS KCM MAB NRE TIJ MAZ TAM GRP STANDARD DEV

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Mean N SD Minimum

QAR QAR QAR BHD KWD KWD KWD KWD KWD KWD KWD KWD KWD KWD KWD KWD Q-0

285 3,207 2,738 10 1 13 - - 6 14 12 72 44 27 18 26

0.164 0.164 0.164 1.580 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073

47 526 449 16 2 27 - - 12 29 25 149 91 56 37 54 215 30 459 -

16 3 5 25 27 22 28 28 26 20 23 11 12 14 18 15

11% 11% 60% 10% 0% -7% N.A. N.A. 2% 16% 10% 17% 16% 27% 5% 28% 15% 27 13% -7%

7% 9% 59% 10% 0% -5% N.A. N.A. 1% 8% 8% 13% 13% 14% 3% 18% 11% 27 12% -5%

14% 18% 59% 10% 0% -7% N.A. N.A. -2% 10% 14% 17% 18% 27% 5% 27% 15% 27 13% -7%

83% 14% 98% 70% 0% -77% N.A. N.A. -17% 36% 83% 49% 80% 89% 39% 85% 47% 27 39% -77%

3.95 2.27 5.88 0.02 - (0.01) N.A. N.A. (0.00) 0.04 0.03 0.05 0.03 0.08 0.00 0.09 Not Applicable as these are different Currencies - See below

0.65 0.37 0.96 0.02 - (0.03) N.A. N.A. (0.00) 0.08 0.06 0.11 0.05 0.17 0.01 0.18 Not Applicable as shares have different Face Values

8.31 13.89 8.38 10.51 N.A. (20.18) N.A. N.A. (358.16) 10.03 18.15 11.04 10.10 4.19 97.08 4.04 10.21 24 85.39 (358.16)

N.A. N.A. 2% 3% N.A. 13% N.A. N.A. 0% N.A. N.A. 6% N.A. 14% 1% 2% 4% 12 5% 0%

12% 7% 12% 10% N.A. -5% N.A. N.A. 0% 10% 6% 9% 10% 24% 1% 25% 8% 24 7% -5%

1.17 2.55 4.93 1.08 0.57 1.46 N.A. N.A. 8.14 1.00 2.63 1.82 1.78 1.14 4.38 1.05 2.79 25 2.16 0.57

0.17 1.30 0.60 0.15 0.05 0.09 N.A. N.A. 0.14 0.28 0.17 0.34 0.22 0.31 0.12 0.32 0.35 27 0.30 0.05

0.09 1.44 0.60 0.17 0.14 0.22 N.A. N.A. 0.10 0.21 0.13 0.28 0.20 0.60 0.05 0.31 4.25 27 17.06 0.05

57.00 14.32 Z 0.83 Z Z N.A. N.A. Z 2.80 Z 10.29 1.63 1.13 4.50 Z 14.74 20 20.56 0.83

6 25 N.A. 438 N.A. N.A. N.A. N.A. N.A. 130 N.A. 35 224 324 81 N.A. 101 20 116 5

3.17 14.00 Z 5.00 Z Z N.A. N.A. Z 7.00 Z 3.27 1.13 0.90 0.86 8.67 20.65 22 42.07 0.86

8.91 128.28 130.38 Z 0.07 2.60 N.A. N.A. 0.67 1.40 12.00 Z Z 0.46 2.57 1.63 18.74 22 37.62 0.07

(0.19) 0.01 0.01 1.00 15.00 0.38 N.A. N.A. 1.50 0.93 0.08 (0.21) (0.27) 1.96 (0.56) 0.50 0.95 27 2.91 (0.56)

(0.71) 1.81 (114.08) 1.25 0.07 0.17 N.A. N.A. (1.20) 1.56 1.00 (1.85) 1.33 0.59 (1.64) 2.17 (3.86) 27 22.79 (114.08)

41 3 3 N.A. 5,475 140 N.A. N.A. 548 261 30 N.A. N.A. 798 142 225 450 22 1,150 3

0.61 2.45 0.50 2.60 4.75 2.32 N.A. N.A. 0.81 1.39 1.57 0.32 1.53 1.61 0.66 1.43 2.60 27 3.27 0.32

0.58 2.42 0.06 2.60 1.00 2.24 N.A. N.A. 0.46 0.96 1.52 0.32 1.53 0.83 0.44 0.86 1.89 27 2.85 0.06

0.42 0.63 N.A. N.A. N.A. N.A. N.A. N.A. N.A. N.A. 0.49 N.A. 0.25 N.A. 0.67 N.A. 0.22 14 0.24 0.00

0.30 0.39 N.A. N.A. N.A. N.A. N.A. N.A. N.A. N.A. 0.33 N.A. 0.20 N.A. 0.40 N.A. 0.16 14 0.15 0.00

0.35 0.29 0.01 N.A. N.A. 0.17 N.A. N.A. 0.25 0.16 0.38 0.13 0.23 0.03 0.33 0.16 0.15 21 0.12 0.00

16.80 N.A. 85.00 N.A. N.A. N.A. N.A. N.A. 0.50 2.67 N.A. 36.00 9.75 N.A. 2.40 23.00 42.88 12 58.96 0.50

Z Z 5.43 3.04 Z (0.38) N.A. N.A. (1.63) Z Z 1.59 Z 1.71 1.84 10.00 3.69 13 4.79 (1.63)

0.83 0.43 0.99 0.82 0.27 0.30 N.A. N.A. 0.75 0.68 0.73 0.94 0.69 0.27 0.94 0.68 0.62 27 0.28 0.00

0.63 0.77 0.99 0.93 0.85 0.70 N.A. N.A. 0.52 0.51 0.84 0.76 0.80 0.53 0.74 0.65 0.76 27 0.14 0.51

0.02 0.04 N.A. 0.03 N.A. N.A. N.A. N.A. N.A. 0.02 N.A. 0.08 0.13 0.18 0.06 0.02 0.06 22 0.06 0.00

1.06 1.00 1.01 1.00 N.A. 1.00 N.A. N.A. (1.00) 1.60 1.00 1.03 1.11 1.00 1.71 1.05 0.99 26 0.44 (1.00)

0.78 0.63 0.01 N.A. N.A. 0.25 N.A. N.A. 0.48 0.32 0.68 0.17 0.37 0.06 0.74 0.25 0.28 21 0.25 0.00

156


STATISTICAL AVERAGE - BENCHMARK CALCULATIONS

QUARTILES - FIVE POINT SUMMARY OUTLIERS

First

QuartileMedian

Third

Quartile

Maximu

mRange IQR Step

Q-1 Q-2 Q-3 Q-4 R Fs S Low High

=Q4-Q0 =Q3-Q2 =Fs*1.5 =Q2-S =Q3+S =Q2-2S =Q3+2S Severe Severe

Mild Mild

25 50 200 2,453 2,453 174 261 (236) 461 (497) 722 NO SHO

Box end Box end

7% 11% 19% 60% 67% 13% 19% -12% 38% -31% 57% NO SHO

5% 9% 15% 59% 64% 10% 14% -9% 29% -24% 44% NO SHO

7% 14% 20% 59% 66% 14% 20% -13% 41% -34% 61% NO MHO

27% 49% 77% 98% 175% 50% 75% -48% 152% -123% 227% MLO NO

Not Applicable as these are different Currencies - See below

Not Applicable as shares have different Face Values

9.55 11.12 17.99 110.97 469.12 8.44 12.67 (3.12) 30.66 (15.79) 43.33 SLO SHO

2% 2% 5% 14% 14% 3% 5% -4% 10% -9% 16% NO MHO

2% 8% 10% 25% 30% 8% 12% -11% 22% -23% 35% NO MHO

1.32 2.16 3.54 9.41 8.84 2.22 3.33 (2.01) 6.87 (5.34) 10.20 NO MHO

0.13 0.28 0.51 1.30 1.26 0.37 0.56 (0.43) 1.07 (0.99) 1.63 NO MHO

0.13 0.28 0.61 88.08 88.03 0.48 0.72 (0.59) 1.33 (1.31) 2.04 NO SHO

3.73 6.26 11.29 67.94 67.11 7.57 11.35 (7.63) 22.65 (18.98) 34.00 NO SHO

33 59 100 438 433 67 101 (68) 202 (169) 303 NO SHO

2.36 5.52 12.67 182.00 181.14 10.31 15.47 (13.12) 28.14 (28.59) 43.61 NO SHO

1.27 3.40 11.23 130.38 130.31 9.95 14.93 (13.66) 26.16 (28.59) 41.09 NO SHO

(0.05) 0.20 0.81 15.00 15.56 0.86 1.29 (1.33) 2.10 (2.62) 3.39 NO SHO

(0.96) 0.56 1.44 13.14 127.23 2.40 3.60 (4.56) 5.05 (8.16) 8.65 SLO SHO

33 114 288 5,475 5,472 255 382 (349) 670 (731) 1,051 NO SHO

0.88 1.53 2.52 14.33 14.01 1.64 2.47 (1.59) 4.99 (4.05) 7.46 NO SHO

0.65 0.94 2.13 14.22 14.16 1.48 2.22 (1.57) 4.36 (3.79) 6.58 NO SHO

0.03 0.13 0.38 0.67 0.67 0.35 0.53 (0.50) 0.91 (1.03) 1.44 NO NO

0.02 0.12 0.27 0.40 0.40 0.25 0.37 (0.35) 0.64 (0.72) 1.02 NO NO

0.05 0.13 0.23 0.38 0.38 0.19 0.28 (0.23) 0.51 (0.51) 0.79 NO NO

7.98 19.90 50.46 209.33 208.83 42.48 63.72 (55.75) 114.19 (119.47) 177.91 NO SHO

1.55 1.84 4.50 16.45 18.08 2.94 4.42 (2.86) 8.91 (7.28) 13.33 NO SHO

0.42 0.68 0.86 0.99 0.99 0.43 0.65 (0.23) 1.51 (0.88) 2.16 NO NO

0.66 0.77 0.88 0.99 0.48 0.22 0.33 0.34 1.21 0.01 1.54 NO NO

0.01 0.04 0.11 0.18 0.18 0.09 0.14 (0.12) 0.24 (0.26) 0.38 NO NO

1.00 1.00 1.03 1.71 2.71 0.03 0.04 0.96 1.07 0.92 1.11 SLO SHO

0.06 0.20 0.37 0.78 0.78 0.31 0.46 (0.41) 0.83 (0.87) 1.29 NO NO

Check for

Outliers

Whisker ends

Inner

Fence -

Low

Inner

Fence -

High

Outer

Fence -

Low

Outer

Fence -

High

157

MAIN SCHEDULE OF 30 COMPANIES FINANCIAL RATIOS FOR 2007


Company codes UNP DYR EMR SOR ALD RAK TIR SRE MCD JBL DAR ADC UDC SAL

Sr No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Currency AED AED AED AED AED AED SAR SAR SAR SAR SAR SAR QAR QAR

Turnover in Million Local Currency 3,276 1,040 20,001 2,526 3,464 540 554 297 279 - 4,971 135 2,040 1,430

Rate of Exchange (09/08/09) 0.162 0.162 0.162 0.162 0.162 0.162 0.159 0.159 0.159 0.159 0.159 0.159 0.164 0.164

Equivalent GBP ' 000,000 531 168 3,240 409 561 87 88 47 44 - 790 21 335 235

Rank wrt Turnover 4 8 1 5 3 14 13 21 23 29 2 28 6 7


1 ROCE - Return on Capital Employed % 10% 7% 15% 26% 14% 17% 14% 6% 6% N.A. 12% 5% 8% 9%

2 ROTA - Return on Total Assets % 7% 4% 12% 17% 11% 15% 11% 6% 5% N.A. 11% 5% 8% 6%

3 ROE - Return on Equity % 13% 7% 18% 28% 25% 17% 14% 6% 6% N.A. 19% 5% 12% 10%

4 ROS - Return on Sales (Profit Margin) % 21% 40% 33% 50% 56% 92% 74% 63% 62% N.A. 41% 54% 17% 9%

INVESTORS RATIOS

5


Currencies Various 0.22 0.07 1.07 0.50 0.87 0.25 2.66 1.43 1.01 N.A. 3.72 0.71 3.21 1.56

5a


to GBP GBP 0.04 0.01 0.17 0.08 0.14 0.04 0.42 0.23 0.16 N.A. 0.59 0.11 0.53 0.26

6 PE ratio - Price / Earnings Ratio R 15.84 33.28 11.82 11.06 9.76 7.66 10.56 27.91 40.70 N.A. 15.95 105.99 11.08 9.66

7 Dividend Yield % N.A. 4% 2% 2% 1% 4% N.A. N.A. N.A. N.A. 5% N.A. N.A. N.A.

8 Earnings Yield % 6% 3% 8% 9% 10% 13% 9% 4% 2% N.A. 6% 1% 9% 10%

9 Market to Book Ratio Times 2.08 2.26 2.08 3.11 2.46 1.31 1.41 1.54 2.18 N.A. 2.91 5.46 1.32 0.93

EFFICIENCY AND EFFECTIVENESS

10 Net Assets Turnover Times 0.63 0.17 0.54 0.57 0.45 0.19 0.19 0.10 0.09 N.A. 0.45 0.10 0.71 1.07

11 Fixed Assets Turnover Times 0.42 0.68 0.43 0.91 0.40 0.33 0.29 0.11 0.09 N.A. 0.45 0.10 1.48 0.94

12 Debtors Turnover Times 1.11 1.30 5.51 2.15 1.49 1.57 30.78 6.60 7.54 N.A. 10.27 10.38 13.16 5.38

13 Average Collection Period Days 328 281 66 170 245 233 12 55 48 N.A. 36 35 28 68

14 Creditors Turnover Times 2.50 0.38 2.27 1.16 1.11 1.46 138.50 14.85 139.50 N.A. 47.34 3.07 14.78 7.45

15 Stock Turnover Times 10.30 0.20 Z 1.49 0.86 Z 9.39 99.00 0.91 N.A. 2.21 135.00 2.44 6.41

16 Net Working Capital to Sales Ratio Times 0.60 3.05 (0.26) 0.28 0.94 (0.05) 0.13 0.09 1.23 N.A. 0.53 (0.22) 0.42 0.21

17 Sales to Working Capital Ratio Times 6.51 0.22 (42.92) 1.24 0.37 0.43 0.37 0.56 2.13 N.A. 0.85 (67.50) 1.23 12.43

18 Inventory Days Days 35 1,804 N.A. 245 426 N.A. 39 4 402 N.A. 165 3 149 57

LIQUIDITY AND STABILITY RATIO'S

19 Current Ratio (Working Capital Ratio) Times 1.18 2.57 0.95 1.84 3.06 4.39 6.58 4.89 1.37 N.A. 5.28 0.99 2.22 1.16

20 Quick Ratio (Acid Test Ratio) Times 1.06 0.88 0.95 1.14 2.18 4.39 6.36 4.87 0.50 N.A. 3.64 0.98 1.60 0.85

CAPITAL STRUCTURE, INVESTMENT AND FINANCIAL RISK RATIOS

21 Long-term Debt to Equity Ratio Times 0.42 0.02 0.21 0.07 1.27 N.A. 0.00 N.A. N.A. N.A. 0.55 N.A. 0.46 0.16

22


finance ratio Times 0.30 0.02 0.17 0.07 0.56 N.A. 0.00 N.A. N.A. N.A. 0.35 N.A. 0.31 0.14

23 Total debt to total assets ratio Times 0.30 0.04 0.14 0.08 0.46 N.A. 0.01 N.A. N.A. N.A. 0.33 N.A. 0.31 0.20

24 Interest Cover Times 14.46 139.67 43.54 420.00 5.28 249.00 N.A. N.A. N.A. N.A. N.A. N.A. N.A. 8.17

25 Dividend Cover Times Z 0.74 5.45 4.19 14.07 3.73 Z Z Z N.A. 1.24 Z Z Z

26 Fixed assets to total assets ratio Times 0.70 0.16 0.85 0.38 0.38 0.50 0.52 0.80 0.86 N.A. 0.61 0.88 0.31 0.64

27 Long-term Funds to Total Assets Ratio Times 0.67 0.67 0.82 0.66 0.77 0.89 0.82 0.95 0.90 N.A. 0.93 0.87 0.96 0.66

28 Total owing to total assets ratio Times 0.12 0.30 0.16 0.30 0.14 0.11 0.00 0.01 0.00 N.A. 0.01 0.03 0.03 0.08

29 Capital gearing Times 1.07 1.01 1.02 1.00 1.23 1.00 1.00 1.00 1.00 N.A. 1.00 1.00 1.00 1.14

30 Gearing Ratio Times 0.65 0.07 0.21 0.13 1.37 N.A. 0.01 N.A. N.A. N.A. 0.55 N.A. 0.47 0.35

158


BAHKUWAIT STATISTICAL AVERAGE - BENCHMARK CALCULATIONS

QRE BAR EZD SEE GRA KRE DAM ABY MAS KCM MAB NRE TIJ MAZ TAM GRP STANDARD DEV

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Mean N SD Minimum

QAR QAR QAR BHD KWD KWD KWD KWD KWD KWD KWD KWD KWD KWD KWD KWD Q-0

404 1,011 607 26 14 - 18 24 12 22 30 56 58 38 35 37

0.164 0.164 0.164 1.580 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073

66 166 100 41 29 - 37 50 25 46 62 116 120 79 73 77 255 30 595 -

18 9 12 24 26 29 25 20 27 22 19 11 10 15 17 16

19% 19% 11% 26% 11% 0% 36% 27% 9% 32% 17% 18% 23% 32% 8% 26% 16% 29 9% 0%

8% 5% 11% 24% 10% 0% 27% 13% 4% 13% 11% 11% 15% 13% 6% 12% 10% 29 6% 0%

16% 18% 11% 26% 11% 0% 36% 27% 5% 24% 20% 18% 20% 30% 8% 25% 16% 29 9% 0%

74% 56% 92% 88% 93% N.A. 83% 71% 17% 64% 60% 71% 79% 87% 46% 68% 59% 28 24% 9%

4.17 2.81 1.23 0.05 0.01 - 0.08 0.04 0.00 0.10 0.05 0.05 0.03 0.10 0.01 0.08 Not Applicable as these are different Currencies - See below

0.68 0.46 0.20 0.08 0.03 - 0.17 0.07 0.00 0.21 0.10 0.11 0.06 0.21 0.02 0.17 Not Applicable as shares have different Face Values

9.19 13.37 40.15 3.20 8.41 N.A. 1.66 10.27 163.51 3.00 19.61 10.21 5.81 4.93 48.00 3.54 23.43 28 34.60 1.66

N.A. 3% N.A. 4% N.A. N.A. N.A. N.A. 0% N.A. N.A. N.A. N.A. 10% 1% N.A. 3% 11 3% 0%

11% 7% 2% 31% 12% 0% 60% 10% 1% 33% 5% 10% 17% 20% 2% 28% 12% 29 13% 0%

1.49 2.47 4.39 0.84 0.89 0.97 0.59 2.73 7.43 0.71 3.97 1.79 1.16 1.45 3.98 0.86 2.23 29 1.57 0.59

0.22 0.33 0.12 0.30 0.11 N.A. 0.43 0.38 0.27 0.37 0.34 0.25 0.26 0.35 0.18 0.37 0.34 28 0.22 0.09

0.12 0.13 0.12 0.33 0.12 N.A. 0.33 0.19 0.13 0.27 0.19 0.18 0.20 0.23 0.09 0.32 0.34 28 0.32 0.09

1.41 1.31 60.70 1.86 Z Z Z Z Z 3.14 15.00 Z Z 1.52 35.00 4.63 10.08 22 14.55 1.11

259 278 6 197 N.A. N.A. N.A. N.A. N.A. 116 24 N.A. N.A. 240 10 79 128 22 109 6

3.64 3.35 75.88 26.00 Z Z Z Z 12.00 3.14 15.00 18.67 19.33 1.31 2.92 2.85 22.34 25 39.00 0.38

3.04 0.62 303.50 Z Z Z Z Z 1.71 0.71 30.00 5.60 1.29 4.22 3.50 1.48 28.36 22 70.26 0.20

0.76 2.08 0.01 0.50 - N.A. - - 0.50 1.41 0.03 0.13 0.72 0.13 (0.03) 0.54 0.49 28 0.73 (0.26)

(0.26) (0.29) (60.70) 2.89 3.50 N.A. (1.64) (0.67) (0.33) 3.14 (1.76) (0.98) (8.29) (0.75) (0.90) 2.85 (5.29) 28 18.88 (67.50)

120 589 1 N.A. N.A. N.A. N.A. N.A. 213 514 12 65 283 86 104 247 253 22 387 1

0.38 0.70 0.84 2.50 Z 2.52 0.15 0.14 0.37 1.11 0.69 0.60 0.88 0.66 0.42 1.15 1.77 28 1.68 0.14

0.33 0.56 0.81 2.50 Z 2.52 0.15 0.14 0.25 0.61 0.67 0.53 0.07 0.60 0.27 0.86 1.44 28 1.59 0.07

N.A. 0.40 N.A. N.A. N.A. N.A. N.A. N.A. N.A. N.A. 0.43 N.A. N.A. N.A. 0.54 N.A. 0.38 12 0.34 0.00

N.A. 0.29 N.A. N.A. N.A. N.A. N.A. N.A. N.A. N.A. 0.30 N.A. N.A. N.A. 0.35 N.A. 0.24 12 0.16 0.00

0.50 0.08 0.00 N.A. N.A. 0.13 0.23 0.32 0.35 0.27 0.36 0.29 N.A. 0.21 0.36 N.A. 0.24 21 0.14 0.00

6.56 3.14 N.A. N.A. N.A. N.A. N.A. N.A. 2.00 3.80 7.00 21.00 8.67 17.50 2.78 26.00 57.56 17 113.30 2.00

Z 2.25 Z 8.33 Z Z Z Z 2.20 Z Z Z Z 2.00 4.00 Z 4.38 11 3.86 0.74

0.78 0.50 0.99 0.84 0.97 0.44 0.96 0.95 0.82 0.54 0.81 0.78 0.85 0.63 0.94 0.53 0.69 29 0.22 0.16

0.42 0.27 0.99 0.94 0.98 0.78 0.75 0.49 0.39 0.40 0.65 0.58 0.67 0.41 0.69 0.46 0.70 29 0.21 0.27

0.03 0.02 0.00 0.01 N.A. N.A. N.A. N.A. 0.01 0.05 0.01 0.01 0.01 0.11 0.03 0.06 0.06 25 0.09 0.00

1.18 1.47 1.00 1.00 1.00 N.A. 1.00 1.00 2.00 1.36 1.17 1.05 1.13 1.06 1.56 1.04 1.13 28 0.23 1.00

1.19 0.40 0.00 N.A. N.A. 0.17 0.31 0.66 0.91 0.68 0.80 0.50 N.A. 0.50 0.80 N.A. 0.51 21 0.37 0.00

159



QUARTILES - FIVE POINT SUMMARY OUTLIERS

First

QuartileMedian

Third

Quartile

Maximu

mRange IQR Step

Q-1 Q-2 Q-3 Q-4 R Fs S Low High


Mild Mild

45 78 168 3,240 3,240 123 185 (140) 353 (325) 537 NO SHO

Box end Box end

9% 14% 23% 36% 36% 14% 21% -12% 44% -33% 65% NO NO

6% 11% 13% 27% 27% 7% 10% -5% 23% -15% 34% NO MHO

10% 17% 24% 36% 36% 14% 21% -12% 45% -33% 66% NO NO

45% 62% 76% 93% 84% 31% 47% -2% 122% -49% 169% NO NO



8.22 10.81 21.68 163.51 161.85 13.46 20.19 (11.97) 41.88 (32.16) 62.07 NO SHO

1% 3% 4% 10% 10% 3% 4% -3% 8% -7% 12% NO MHO

4% 9% 12% 60% 60% 8% 12% -9% 24% -21% 37% NO SHO

1.16 1.79 2.73 7.43 6.84 1.57 2.35 (1.19) 5.08 (3.54) 7.43 NO SHO

0.18 0.31 0.43 1.07 0.98 0.25 0.37 (0.19) 0.81 (0.56) 1.18 NO MHO

0.12 0.25 0.41 1.48 1.39 0.28 0.42 (0.30) 0.83 (0.72) 1.25 NO SHO

1.53 5.00 10.36 60.70 59.59 8.82 13.24 (11.70) 23.59 (24.94) 36.83 NO SHO

35 73 238 328 322 203 304 (269) 543 (574) 847 NO NO

2.50 3.64 18.67 139.50 139.12 16.17 24.25 (21.75) 42.92 (46.00) 67.17 NO SHO

1.34 2.74 8.65 303.50 303.30 7.31 10.96 (9.63) 19.61 (20.59) 30.57 NO SHO

0.00 0.24 0.63 3.05 3.31 0.62 0.93 (0.93) 1.56 (1.86) 2.50 NO SHO

(0.92) 0.29 1.47 12.43 79.93 2.38 3.58 (4.50) 5.04 (8.07) 8.62 SLO SHO

43 135 274 1,804 1,803 231 346 (303) 620 (649) 966 NO SHO

0.68 1.13 2.51 6.58 6.44 1.82 2.73 (2.05) 5.24 (4.78) 7.97 NO MHO

0.52 0.86 1.75 6.36 6.29 1.22 1.83 (1.31) 3.58 (3.15) 5.41 NO SHO

0.14 0.41 0.48 1.27 1.27 0.34 0.51 (0.37) 0.99 (0.87) 1.50 NO MHO

0.12 0.29 0.32 0.56 0.56 0.20 0.30 (0.18) 0.63 (0.48) 0.93 NO NO

0.13 0.27 0.33 0.50 0.50 0.20 0.30 (0.17) 0.62 (0.46) 0.92 NO NO

5.28 8.67 26.00 420.00 418.00 20.72 31.09 (25.81) 57.09 (56.90) 88.17 NO SHO

2.10 3.73 4.82 14.07 13.32 2.72 4.08 (1.99) 8.91 (6.07) 12.99 NO SHO

0.52 0.78 0.85 0.99 0.83 0.33 0.50 0.02 1.36 (0.48) 1.86 NO NO

0.58 0.69 0.89 0.99 0.72 0.31 0.47 0.11 1.35 (0.36) 1.82 NO NO

0.01 0.03 0.11 0.30 0.30 0.10 0.15 (0.14) 0.26 (0.29) 0.41 NO MHO

1.00 1.02 1.15 2.00 1.00 0.15 0.22 0.78 1.37 0.56 1.59 NO SHO

0.21 0.50 0.68 1.37 1.37 0.47 0.71 (0.50) 1.38 (1.21) 2.09 NO NO

Check for

Outliers

Whisker ends

Inner

Fence -

Low

Inner

Fence -

High

Outer

Fence -

Low

Outer

Fence -

High

160



Company codes UNP DYR EMR SOR ALD RAK TIR SRE MCD JBL DAR ADC

Sr No. 1 2 3 4 5 6 7 8 9 10 11 12

Currency AED AED AED AED AED AED SAR SAR SAR SAR SAR SAR

Turnover in Million Local Currency 4,249 3,445 15,014 3,923 7,038 424 322 265 273 76 5,638 146

Rate of Exchange (09/08/09) 0.162 0.162 0.162 0.162 0.162 0.162 0.159 0.159 0.159 0.159 0.159 0.159

Equivalent GBP ' 000,000 688 558 2,432 636 1,140 69 51 42 43 12 896 23

Rank wrt Turnover 4 7 1 5 2 17 18 21 20 26 3 23


1 ROCE - Return on Capital Employed % 10% 14% 7% 23% 11% 12% 7% 4% 5% 0% 14% 6%

2 ROTA - Return on Total Assets % 4% 10% 5% 11% 8% 9% 5% 4% 4% 0% 12% 5%

3 ROE - Return on Equity % 13% 15% 8% 30% 22% 12% 7% 4% 5% 0% 21% 6%

4 ROS - Return on Sales (Profit Margin) % 18% 33% 20% 45% 49% 89% 60% 52% 70% -39% 43% 56%

INVESTORS RATIOS

5


Currencies Various 0.23 0.19 0.51 0.71 1.34 0.19 1.15 0.98 1.08 (0.08) 3.27 0.79

5a


to GBP GBP 0.04 0.03 0.08 0.12 0.22 0.03 0.18 0.16 0.17 (0.01) 0.52 0.13

6 PE ratio - Price / Earnings Ratio R 14.08 9.14 17.49 9.51 5.95 8.55 22.00 35.77 30.44 (296.43) 12.24 26.11

7 Dividend Yield % N.A. N.A. N.A. 2% 1% 4% N.A. N.A. N.A. N.A. 6% N.A.

8 Earnings Yield % 7% 11% 6% 11% 17% 12% 5% 3% 3% 0% 8% 4%

9 Market to Book Ratio Times 1.80 1.38 1.47 2.85 1.28 1.04 1.31 1.36 1.28 2.36 2.46 1.47

EFFICIENCY AND EFFECTIVENESS RATIOS

10 Net Assets Turnover Times 0.71 0.47 0.41 0.66 0.44 0.14 0.11 0.09 0.06 0.01 0.48 0.10

11 Fixed Assets Turnover Times 0.27 0.76 0.30 0.80 0.28 0.17 0.12 0.11 0.07 0.01 0.37 0.11

12 Debtors Turnover Times 1.53 3.00 2.63 1.64 1.25 0.82 23.00 22.08 Z Z 5.94 9.13

13 Average Collection Period Days 239 122 139 223 293 448 16 17 N.A. N.A. 61 40

14 Creditors Turnover Times 1.16 1.77 1.08 0.58 0.94 0.40 24.77 265.00 Z 12.67 32.97 5.84

15 Stock Turnover Times 14.02 0.81 Z 1.42 0.99 1.41 7.00 3.68 0.62 Z 2.02 146.00

16 Net Working Capital to Sales Ratio Times (0.14) 1.00 (0.54) (0.40) 0.76 (0.55) 0.15 0.31 1.60 (0.08) 0.63 (0.05)

17 Sales to Working Capital Ratio Times (0.84) 0.72 (5.50) 1.23 0.56 0.70 0.43 0.37 1.56 0.08 2.44 6.64

18 Inventory Days Days 26 449 N.A. 257 370 258 52 99 586 N.A. 181 3

LIQUIDITY AND STABILITY RATIOS

19 Current Ratio (Working Capital Ratio) Times 0.40 3.48 0.80 1.36 2.02 1.57 4.36 9.00 1.46 22.02 1.95 1.12

20 Quick Ratio (Acid Test Ratio) Times 0.36 1.30 0.80 1.05 1.44 1.29 4.16 8.19 0.31 22.02 0.80 1.11

CAPITAL STRUCTURE, INVESTMENT AND FINANCIAL RISK RATIOS

21 Long-term Debt to Equity Ratio Times 0.35 0.11 0.25 0.33 1.24 N.A. 0.001 N.A. N.A. N.A. 0.51 N.A.

22


finance ratio Times 0.26 0.10 0.20 0.25 0.55 N.A. 0.001 N.A. N.A. N.A. 0.34 N.A.

23 Total debt to total assets ratio Times 0.33 0.07 0.15 0.24 0.45 N.A. 0.03 N.A. N.A. N.A. 0.38 N.A.

24 Interest Cover Times 12.56 75.80 36.37 23.30 10.29 190.50 N.A. N.A. N.A. N.A. N.A. N.A.

25 Dividend Cover Times Z Z Z 5.95 14.86 2.75 Z Z Z Z 1.45 Z

26 Fixed assets to total assets ratio Times 0.83 0.40 0.82 0.29 0.50 0.60 0.73 0.75 0.88 0.86 0.77 0.87

27 Long-term Funds to Total Assets Ratio Times 0.42 0.72 0.75 0.47 0.72 0.75 0.81 0.97 0.92 0.99 0.88 0.88

28 Total owing to total assets ratio Times 0.19 0.17 0.23 0.40 0.15 0.25 0.00 0.0003 N.A. 0.001 0.01 0.02

29 Capital gearing Times 1.09 1.01 1.03 1.04 1.11 1.01 1.00 1.00 1.00 1.00 1.00 1.00

30 Gearing Ratio Times 1.07 0.11 0.25 0.69 1.41 N.A. 0.03 N.A. N.A. N.A. 0.65 N.A.

161


QATAR BAHKUWAIT STATISTICAL AVERAGE - BENCHMARK CALCULATIONS

UDC SAL QRE BAR EZD SEE GRA KRE DAM ABY MAS KCM MAB NRE TIJ MAZ TAM GRP

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

QAR QAR QAR QAR QAR BHD KWD KWD KWD KWD KWD KWD KWD KWD KWD KWD KWD KWD

2,356 1,886 442 3,506 1,450 20 - 11 - - 7 21 45 45 35 162 59 -

0.164 0.164 0.164 0.164 0.164 1.580 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073 2.073

386 309 72 575 238 32 - 23 - - 15 44 93 93 73 336 122 -

8 10 16 6 11 22 27 24 27 27 25 19 13 13 15 9 12 27

13% 10% 8% 7% 21% 16% -26% -37% -157% N.A. -6% 26% 24% 13% 10% 8% 18% -59%

9% 6% 6% 2% 20% 15% -24% -26% -92% N.A. -3% 8% 15% 5% 7% 2% 9% -24%

24% 11% 15% 6% 21% 16% -26% -37% -157% N.A. -15% 11% 29% 13% 9% 6% 28% -63%

29% 8% 70% 8% 94% 80% N.A. -427% N.A. N.A. -86% 33% 62% 62% 54% 4% 56% N.A.

6.41 1.70 3.60 1.17 2.97 0.03 (0.02) (0.05) (0.06) N.A. (0.00) 0.04 0.07 0.03 0.01 0.02 0.02 (0.11)

1.05 0.28 0.59 0.19 0.49 0.05 (0.05) (0.11) (0.13) N.A. (0.01) 0.08 0.14 0.07 0.02 0.03 0.04 (0.23)

7.78 8.36 13.92 48.81 16.60 5.61 (3.02) (2.59) (2.26) N.A. (49.11) 9.33 17.41 13.23 13.24 35.03 23.93 (1.41)

N.A. 4% 4% N.A. N.A. 5% 7% N.A. N.A. N.A. 0% N.A. N.A. N.A. N.A. N.A. 0% N.A.

13% 12% 7% 2% 6% 18% -33% -39% -44% N.A. -2% 11% 6% 8% 8% 3% 4% -71%

1.88 0.89 2.13 3.36 3.47 0.92 0.77 0.96 3.55 N.A. 7.55 1.05 4.97 1.76 1.15 2.10 6.60 0.88

0.83 1.28 0.22 0.79 0.22 0.20 N.A. 0.09 N.A. N.A. 0.18 0.34 0.46 0.21 0.16 1.38 0.51 N.A.

0.62 1.18 0.11 0.71 0.22 0.22 N.A. 0.10 N.A. N.A. 0.06 0.28 0.23 0.09 0.10 0.60 0.14 N.A.

27.72 5.17 Z 6.50 362.50 1.43 Z Z Z N.A. Z 1.40 11.25 Z 35.00 3.86 5.90 N.A.

13 71 N.A. 56 1 256 N.A. N.A. N.A. N.A. N.A. 261 32 N.A. 10 95 62 N.A.

17.85 13.87 1.90 Z 241.67 2.50 Z Z Z N.A. 1.75 1.91 45.00 Z 5.83 16.20 Z N.A.

1.58 7.23 0.47 1.08 53.70 Z Z Z Z N.A. 1.75 1.40 22.50 11.25 0.64 Z 4.92 Z

0.61 0.26 1.61 1.08 0.02 0.30 N.A. - N.A. N.A. - 0.90 0.11 0.09 1.43 0.20 0.37 N.A.

0.99 11.43 1.80 0.48 9.73 2.86 N.A. 0.69 N.A. N.A. (0.18) 2.33 (0.87) (0.26) (0.51) (1.37) (0.88) N.A.

231 51 780 338 7 N.A. N.A. N.A. N.A. N.A. 209 261 16 32 574 N.A. 74 N.A.

2.89 1.17 1.18 1.61 3.98 1.88 Z 1.32 0.40 N.A. 0.26 1.08 0.37 0.12 0.48 0.44 0.35 0.22

1.70 0.90 0.50 1.34 3.44 1.88 Z 1.32 0.40 N.A. 0.19 0.95 0.35 0.10 0.07 0.44 0.23 0.22

0.87 0.17 1.11 0.75 N.A. N.A. N.A. N.A. N.A. N.A. 0.67 N.A. 0.42 0.03 0.21 N.A. 1.03 N.A.

0.47 0.15 0.53 0.43 N.A. N.A. N.A. N.A. N.A. N.A. 0.40 N.A. 0.29 0.03 0.18 N.A. 0.51 N.A.

0.34 0.23 0.53 0.14 N.A. N.A. N.A. 0.21 0.42 N.A. 0.37 0.49 0.46 0.31 0.12 0.14 0.45 N.A.

N.A. 10.29 8.97 1.94 171.25 N.A. N.A. N.A. N.A. N.A. (2.00) 1.78 5.67 N.A. 3.38 4.50 4.67 (19.00)

Z 2.72 1.80 Z Z 3.48 (4.80) Z Z N.A. (26.32) Z Z Z Z Z 9.44 Z

0.51 0.58 0.72 0.20 0.97 0.86 0.98 0.63 0.83 N.A. 0.89 0.38 0.87 0.96 0.84 0.75 0.92 0.91

0.72 0.63 0.74 0.32 0.94 0.92 0.94 0.71 0.58 N.A. 0.50 0.32 0.60 0.41 0.67 0.32 0.53 0.41

0.02 0.05 0.04 N.A. 0.00 0.08 N.A. N.A. N.A. N.A. 0.03 0.06 0.00 N.A. 0.02 0.03 N.A. 0.27

1.00 1.11 1.13 2.06 1.01 1.00 1.00 1.00 1.00 N.A. 0.67 2.29 1.21 1.00 1.42 1.29 1.27 0.95

0.88 0.43 1.53 0.75 N.A. N.A. N.A. 0.29 0.71 N.A. 1.26 1.52 1.09 0.78 0.21 0.43 1.73 N.A.

162



STANDARD DEV QUARTILES - FIVE POINT SUMMARY OUTLIERS

Mean N SD MinimumFirst

QuartileMedian

Third

QuartileMaximum Range IQR Step

Inner

Fence -

Inner

Fence -

Outer

Fence -

Outer

Fence -

Check

for

Q-0 Q-1 Q-2 Q-3 Q-4 R Fs S Low High


Mild Mild

300 30 502 - 25 73 374 2,432 2,432 348 523 (497) 896 (1,020) 1,419 NO SHO

Box end Box end Whiske

0% 29 35% -157% 5% 10% 14% 26% 183% 9% 14% -9% 27% -23% 41% SLO NO

0% 29 21% -92% 2% 5% 9% 20% 111% 7% 10% -8% 19% -18% 30% SLO MHO

1% 29 36% -157% 5% 11% 16% 30% 187% 12% 18% -13% 34% -31% 52% SLO NO

21% 26 99% -427% 19% 47% 62% 94% 521% 43% 64% -46% 126% -110% 191% SLO NO



1.71 29 59.79 (296.43) 5.95 12.24 17.49 48.81 345.24 11.54 17.31 (11.37) 34.80 (28.68) 52.11 SLO MHO

3% 10 2% 0% 1% 4% 5% 7% 7% 4% 5% -4% 10% -10% 16% NO NO

0% 29 20% -71% 3% 6% 11% 18% 89% 8% 12% -9% 22% -20% 34% SLO NO

2.21 29 1.68 0.77 1.15 1.47 2.46 7.55 6.78 1.31 1.96 (0.82) 4.42 (2.78) 6.39 NO SHO

0.41 26 0.36 0.01 0.14 0.28 0.50 1.38 1.37 0.36 0.54 (0.40) 1.04 (0.94) 1.58 NO MHO

0.31 26 0.29 0.01 0.11 0.22 0.35 1.18 1.17 0.24 0.37 (0.26) 0.72 (0.63) 1.08 NO SHO

26.59 20 79.69 0.82 1.61 5.53 13.96 362.50 361.68 12.35 18.52 (16.91) 32.48 (35.43) 51.00 NO SHO

123 20 123 1 28 66 227 448 447 198 297 (269) 524 (566) 821 NO NO

33.13 21 74.27 0.40 1.75 5.83 17.85 265.00 264.60 16.10 24.15 (22.40) 42.00 (46.55) 66.14 NO SHO

13.55 21 32.66 0.47 1.08 1.75 7.23 146.00 145.53 6.15 9.22 (8.14) 16.45 (17.36) 25.66 NO SHO

0.37 26 0.61 (0.55) - 0.23 0.73 1.61 2.16 0.73 1.09 (1.09) 1.81 (2.18) 2.90 NO NO

1.33 26 3.39 (5.50) (0.24) 0.62 1.74 11.43 16.93 1.98 2.98 (3.22) 4.72 (6.19) 7.70 MLO SHO

231 21 220 3 51 209 338 780 778 288 431 (381) 769 (812) 1,201 NO MHO

2.40 28 4.25 0.12 0.43 1.25 1.97 22.02 21.91 1.55 2.32 (1.89) 4.29 (4.21) 6.61 NO SHO

2.03 28 4.25 0.07 0.36 0.92 1.36 22.02 21.96 1.00 1.51 (1.15) 2.87 (2.65) 4.38 NO SHO

0.50 16 0.40 0.00 0.20 0.38 0.78 1.24 1.24 0.58 0.87 (0.66) 1.65 (1.53) 2.52 NO NO

0.29 16 0.18 0.00 0.17 0.28 0.44 0.55 0.55 0.27 0.40 (0.24) 0.84 (0.64) 1.25 NO NO

0.29 20 0.15 0.03 0.15 0.32 0.43 0.53 0.51 0.28 0.42 (0.27) 0.84 (0.68) 1.26 NO NO

31.78 17 59.67 (19.00) 3.38 8.97 23.30 190.50 209.50 19.93 29.89 (26.51) 53.19 (56.40) 83.08 NO SHO

1.13 10 10.96 (26.32) 1.54 2.74 5.33 14.86 41.17 3.79 5.68 (4.14) 11.01 (9.83) 16.70 SLO MHO

0.73 29 0.21 0.20 0.60 0.82 0.87 0.98 0.78 0.27 0.40 0.20 1.27 (0.20) 1.67 MLO NO

0.67 29 0.21 0.32 0.50 0.72 0.88 0.99 0.67 0.38 0.58 (0.08) 1.45 (0.65) 2.03 NO NO

0.09 22 0.11 0.00 0.01 0.04 0.17 0.40 0.40 0.16 0.23 (0.22) 0.40 (0.46) 0.64 NO NO

1.13 29 0.32 0.67 1.00 1.01 1.11 2.29 1.62 0.11 0.16 0.84 1.27 0.68 1.43 SLO SHO

0.79 20 0.51 0.03 0.39 0.73 1.13 1.73 1.70 0.74 1.11 (0.72) 2.24 (1.83) 3.36 NO NO

163

End of File

Pilot Study - Financial Ratio Benchmarks for Real Estate Companies of the G.C.C. - Mahesh N Butani

Documents

financial ratio table

study table

liquidity ratios table

leverage ratios table

efficiency ratios table

profitability ratios

year data table

model table