Elfina A. Sambuagaaccounting.feb.ui.ac.id/apjaf/pub/pub5/5 - THE USE OF ECONOMIC.pdf · THE USE OF ECONOMIC VALUE ADDED (EVA) AND ACCOUNTING EARNINGS AS PRIMARY CONSIDERATION FOR

Asia Pacific Journal of Accounting and Finance

Volume 3 (1), December 2014

THE USE OF ECONOMIC VALUE ADDED (EVA) AND ACCOUNTING

EARNINGS AS PRIMARY CONSIDERATION FOR MERGER AND

ACQUISITIONS

Elfina A. Sambuaga

Universitas Indonesia

Email: [email protected]

Abstract

The purpose of this study is to examine the effect of EVA and earnings of the company that

became the target of mergers and acquisitions. This study uses logistic regression method of

listed companies in Indonesian Stock Exchange that became mergers and acquisitions‟ target

during 2008 to 2012. Results of this study indicate that the higher EVA, will increase the

probability of the company being targeted. In contrast, higher earnings will decrease the

probability of the company being targeted. Further analysis reveals that investor‟s evaluation

of EVA is conditional upon company‟s earnings. Results show that high EVA and low

earnings or low EVA and high earnings increase the probability of a company becoming a

target. However, low EVA and earnings has no effect on the probability of being targeted for

mergers or acquisitions.

Keywords: economic value added (EVA), earnings, merger and acquisition’s target.

mailto:[email protected]

60 Asia Pacific Journal of Accounting and Finance Vol. 3 (1), December 2014, 59-87

1. INTRODUCTION

Competitive bussines making bussinesess strive to improve their performance.

Bussines cooperation is one of the strategies. The merger may take the form of mergers and

acquisitions. Martin and Mc Connell (1981) identifies two motives of mergers and

acquisitions, namely (1) to encourage synergy between the acquirer (bidder) and acquisited

company (the target) in the form of efficiency, due to a combination of operations or physical

that can compete in the market, (2) to discipline or control the performance of the manager of

the aquisited company in order to create a product advantage. Thus, with the merger, the

company expects to achieve greater market share, gain access on innovation to reduce the

risk of new product development, achieve economies of scale and enhance the

competitiveness of enterprises (Kumar and Bansal 2008).

To achieve the expected value of the company, the company tried to show good

performance in accordance with the expectations of the shareholders. One source of

information that can be utilized by the shareholders, and potential investors to evaluate and

assess the performance of the company is the financial statements. Information of Financial

statement becomes an absolute must for shareholders and investors for making a decision.

Income is one measure of corporate performance that is often used as the basis of making

decision by the company (Subramanyam 1996). Watts and Zimmerman (1986) provide

empirical evidence on earnings information that can provide clues about the company's cash

flow that could affect investors' expectations.

However, the existence of the flexibility built into the implementation of Generally

Accepted Accounting Principles (GAAP) cause management to select accounting policies of

the various policy options available, making it possible to profit by the management of the

management company (Subramanyam 1996). Management of earnings by the management

can be done efficiently and opportunistic. Efficient improve informatif of communication in

the case with private information, while opportunistic tendency over the management of

reported earnings to maximize their personal interests (Scott 2012). Opportunistic earnings

information can lead to mistakes on decision making for investors.

The existence of asymmetry information that occurs between the company's

management and the user of accounting information has resulted in quite a lot of space,

especially in the use of different methods of accounting in preparing the financial statements

of the company in order to maximize their utility. As in the process of acquisition, the target

company's management has the opportunity to influence the market price in order negoisiasi

acquisition by exploiting its advantages of information, especially in conducting earnings

management (Trueman and Titman 1988, Dye 1988). So that the target company's

shareholders always benefit in the event the acquisition when compared with the acquirer's

shareholders (Roll 1986, Bradley et al. 1988, Frank and Harris 1989)

Based on this trend, along with the increasing need for companies to measure the

performance, the concept of value based management (VBM) is appearing. Economic Value

Added (EVA) is a variant of value based management popularized and patented by a leading

management consultancy Stern Stewart & Co1. EVA is expressed as a measure of

performance that is better than earnings and earnings per share. They argue that EVA is a

method that is more precise and accurate for measuring the wealth of stockholders than other

Sambuaga, The Use of Economic Value Added (EVA) and Accounting .... 61

methods (Stewart 1991). In this concept, the creation of economic value by companies seen

as economic profit generated after calculating capital costs incurred. In other words, the

concept of EVA is more focused on the company's goal to increase the value or the value

added of capital which invested by shareholders in the company's operations.

EVA concept was developed as a measure of value added based on the calculation of

economic profit companies and not accounting profit (Stewart 1991). EVA financial

management system can encourage managers to improve the company's operational, financial

and corporate investment decisions (Biddle et al. 1997). Lehn and Makhija (1996) concluded

that EVA has a better correlation to stock returns compared with other performance measures

such as ROA, ROE, ROS, share returns, and MVA. Bao and Bao (1998) in his analysis of the

stock price and the value of the company also concluded that the value added is more

significant to change a share, compared with net income (earnings) and abnormal earnings.

Kleiman (1999), and Garvey and Milbourn (2000) find that EVA has a correlation with stock

prices compared with earnings information obtained from company financial statements.

Research in Indonesia, Kurniady (2003) found that EVA can explain changes in the Market

Value of Equity compared to operating income and net income. Wardhani (2004) also

concluded that EVA can explain the variation in returns over the long term than CFROI share

and net income.

On the other hand, the concept of EVA is not superior as Stewart stated. Biddle et al.

(1997) found that EVA may be an effective measurement tool, but earnings (earnings) still

dominate in their association with stock returns. Similar results were also found by Chen and

Dodd (1997), which when compared to EPS, ROI, and ROA, EVA measurements provide

more information, but it still can not replace the traditional measurement. While in Indonesia,

Iqbal (2004) found that EVA is not better explain stock price compared profitability ratios

such as EPS, ROA, and ROE.

If the EVA concept associated with the acquisition process, Stewart (2013) states that

when a company decides to make acquisitions, the company not only 'buy' revenue of the

target company, but also the quality of earnings is the risk, return, growth prospects, and the

value of EVA. The concept of EVA has the potential to identify the business units that can

earn revenue exceeds the cost of capital, and can create the possibility of a more productive

compared to other measuring devices (O'Byrne 1996). Heller (2000) states that the success of

mergers and acquisitions depends on a company's ability to identify targets with a good

strategy.

Several previous studies that examine the factors that may affect the company

becomes a target, such as firm size and profitability ratios, using statistical models such as

discriminant analysis (Simkowitz and Monroe 1971), the binomial logit (Dietrich and

Sorensen 1984; Palepu, 1986; Barnes 1999; Powell 2001), and multinomial logit that

specifically distinguish the nature of the takeover (friendly and hostile) (Powell 2004 ), have

not been getting the results really can be a determinant of the company becomes a target or

not. Economists believe that some takeover activity, triggered by the desire to improve the

company's poor performance (Agrawal and Jaffe 2003). Alcalde and Espita (2003) found that

companies that want to have acquired a low profitability and poor corporate value. Shleifer

and Vishny (1997) also stated that the acquisition of the target company is a company with a

poor performance.


For this reason, the main objective of this study was to determine whether the EVA or

net profit more dominant for investors/potential acquirers as consideration in selecting targets

of mergers and acquisitions. In testing, this study used a sample of nonfinancial companies

listed on the Indonesia Stock Exchange and statistical methods logistics. The selection of the

sample using purposive sampling method, based on the list of targeted companies in 2008-

2012, as well as using financial statement information of company 3 years before the

company became the target of mergers and acquisitions.

2. LITERATURE REVIEW AND DEVELOPMENT HYPOTHESIS

2.1 Agency Theory

Jensen and Meckling (1976) stated that the agency theory describes the owners

(shareholders) as a principal and as an agent manager. The manager is hired by the owner to

work in the interests of the owner. Manager provided in part powers to take decisions for the

benefit of the owner, so that managers are required to account for all his efforts to the owner.

Efficient contract is a contract that meets the two factors, namely (1) the agent and the

principal have symmetric information, meaning that both the agent and the principal have the

quality and quantity of information that is the same so there is no hidden information that can

be used for the benefit of one party only, (2) risks associated with returns taken his services

agency is small, which means that the agent has a high certainty regarding his reward.

Problems will arise when there is asymmetry information, either in the form of hidden

activities (hidden action) as well as hidden information (hidden information) owned by

management acting as an agent or manager. Hidden action will bring up the actions that are

unethical (moral hazard) and hidden information that will bring up the existence of adverse

selection in the selection of policies or rules that benefit the management of a company

(Wondabio 2009). In monitoring the implementation of the contract by the management, and

to determine whether a common goal has been achieved, it is accounting important role

(Watts and Zimmerman 1986). Profit is the accounting numbers are often used as a measure,

thus allowing managers to choose accounting policies to maximize their utility and/or to

maximize the value of the company (Scott 2012).

2.2. Economic Value Added (EVA) dan Earnings

For the acquirer company, one of the factors that can motivate companies to take over

other companies is the economic value owned by the target company (Damodaran 2001).

Research results Bowman and Singh (1993) emphasized the importance of corporate mergers

and acquisitions solely to improve performance. On the other hand, there is the consideration

that the acquisition enables the company to redeem each specific power is the cause of the

failure of the market (Hennart and Park 1993). The existence of the resource base, suggest

that the decision mergers and acquisitions became a way to improve the performance of the

company.

The success of mergers and acquisitions depends on a company's ability to identify

targets with a good strategy (Heller 2000). Haspeslagh and Jemison (1991) analogize the


decision-making process for mergers and acquisitions as the stages of the analysis process

begins with determining goals, possibilities were systematically search and filtering, and

evaluate strategies and finances. Carbonara and Caiazza (2009) adds that mergers and

acquisitions should not be viewed as a business transaction, but as a process which is divided

into three stages: pre combination, integration and assessment.

According to Stewart (2013), the acquisition is not much different than any other

investment decisions. The reason is when a company decides to make acquisitions , the

company not only 'buy' revenue of the target company, but also the quality of earnings is the

risk, return, growth prospects, and the value of his EVA. The results of the calculation of

EVA integrate directly with the creation of shareholder value, while allowing investors to

identify investment opportunities of the company (Stewart 1991). Several empirical studies

such as Lehn and Makhija (1997), O'Byrne (1996), Bao and Bao (1998), Worthington and

West (2004), Kurniady (2003) and Wardhani (2004) supports the existence of EVA as a

performance measure better other than the traditional measure. Thus, the EVA performance

measurement can provide an overview of the performance of the company and the company's

investment opportunities.

On the other hand, mergers and acquisitions will be able to fix the poor performance

of management in companies that have poor performance. EVA can attract investors/potential

acquirers who want to make mergers and acquisitions taking into account the resources and

the expected synergies target company profitable. While companies with low EVA will be

more attractive to investors/prospective acquirers are more interested in companies that are

considered to have poor performance to improve corporate performance targets. So that can

be resold at a higher price. The argument is based on the hypothesis is:

H1a : Company with low EVA has a higher probability of becoming a target for

mergers and acquisitions than other companies.

Basically, the method of investment appraisal considers two things, namely

accounting earnings and/or cash flow. Cash flow information is useful in assessing the

company's ability to generate cash, because cash flow statement reports cash flows based on

the classification of a certain period according to operating, investing and financing activities

(Pradhono and Lewis 2004). Meanwhile, profits according to Dechow et al. (2010) serves as

the company's financial performance measurement. Brown (1987) found that net profit has

content relevant information for investors. Another study conducted by Biddle et al. (1995),

Liu et al. (1997), Francis et al. (2003) also found that income is a major source of firm

specific information

Earnings figures may reflect the profit generated by the company is capable of during

a certain period. However, the rate of profit can be used as a strategy manager with poor

performance to avoid dismissal (DeAngelo 1998, Pourciau 1993). In line with agency theory,

managers have a tendency to perform earnings management. Earnings management was an

attempt by managers to obtain personal gain (Schipper 1989). Healy (1985) stated that the

presence of asymmetric information between investors and management an opportunity for

management to perform earnings management. Earnings management also make the target


company has always benefited in the event the acquisition when compared with the acquirer's

shareholders (Roll 1986, Bradley et al. 1988, Franks and Harris 1989).

However, profit still contains useful information for investors. Companies with low

profits will be assessed failed to create a profit for his company. The failure of these

companies will be judged as a failure of a manager's performance. Perception management

will manage the company's failure can be a motivation for investors/potential acquirers to

take over the company. According to the research Alcalde and Espitia (2003) and Shleifer

and Vishny (1997), companies with poor performance will be more likely to be targets of

mergers and acquisitions. The argument is based on the hypothesis is:

H2a : Companies with a low net income have a higher probability of becoming a

target for mergers and acquisitions than other companies.

Seen from an accounting standpoint, there are two limitations in interpreting the

financial statements of the investor. First, the criteria for the presentation of the elements of

financial statements that are susceptible to policy managers, so managers have the

opportunity to set policy engineering. Secondly, the absence of perfect observation

considering that not the entire policy manager can be observed by investors. The existence of

asymmetry information that occurs between the company's management and the user of

accounting information has resulted in quite a lot of space to use a different method of

accounting in preparing the financial statements in order to maximize their utility. Various

motivations such as contract bonuses (Healy 1985, Holthausen et al. 1985, Gaver and Austin

1995), long-term debt (debt covenants) (Deakin 1979, Dhalival 1980, Bowen et al. 1981,

Defond and Jiambalvo 1994), taxes (Boyton et al. 1992), CEO turnover (DeAngelo 1998,

Pourciau 1993), as well as the initial public offering or IPO (Neil et al. 1995, Richardson

1998, Sutanto 2000, Gumanti 2001) conducted a manager to adjust the level of corporate

profits.

On the other hand, the weakness raises profit of EVA as a performance measurement

company, regardless of your choice of accounting method. Furthermore, one of the

understanding that EVA is superior due to accounting adjustments used in calculating EVA.

The existence of these accounting adjustments made based on the understanding that the

measurement of the performance of the economic value added (EVA) is more effective than

accounting numbers, such as earnings. Some researchers such as Uyamura et al. (1996) and

O'Hanlon and Peasnell (1996) also supports the EVA as a performance measure to minimize

the variations of accounting and provide a framework that is more valuable to convert

improper accounting, which may be performed by managers.

Thus, if a company has a high profit but EVA is low, indicating the company has a

poor performance. Because the resulting profit figures could also be due to management

decisions are made to maximize the performance of the company. Thus, if the

investor/prospective acquirer seen from the viewpoint of EVA, the company has a poor

performance. However, investors/potential acquirers will likely see higher net income as a

good investment opportunity, regardless of the value of his EVA. Or otherwise for

investors/potential acquirers, low EVA actually allowed companies to be targeted mergers


and acquisitions, corporate performance targets that can be repaired. The argument is based

on the hypothesis is:

H3a : Companies that have a low EVA, but high net income have a higher

probability of becoming a target for mergers and acquisitions than other

companies.

One of the reasons companies prefer doing mergers and acquisitions as a strategy is

due to mergers and acquisitions, the company does not need to start a new business since the

beginning of the business share of the company had been formed earlier, so that will

accelerate the company's goals. In this case, the prospective acquirer can see EVA as a

consideration of whether the target company has been able to create added value or not.

Because the concept of EVA is very clear when used as a benchmark assessment of corporate

performance targets, especially the focus is on value creation performance (value creation).

With the EVA, then the manager can make decisions in accordance with the wishes of

shareholders and to maximize yields and minimize investment costs

Stewart claimed EVA had to look at the performance advantages due to accounting

adjustments. However, research conducted by Young (1999) and Anderson et al. (2004)

found that the accounting adjustment does not affect much. Weaver (2001) also supports this

research by clarifying that no two companies use the same accounting adjustments in

calculating the calculating EVA, NOPAT and capital investment. Young (1999) and Weaver

(2001) denied as Stewart claims about the advantages of EVA from other measurements due

to adjustments to the accounting method used.

On the other hand, the results of empirical tests involving EVA and profits still vary.

O'Byrne (1996) and Bao and Bao (1998) found that value added is more significant to the

company's stock price compared to earnings and abnormal returns. Instead, Biddle et al.

(1997) found that EVA may be an effective tool for performance measurement, internal

decision making, and the issue of compensation, but EVA still can not dominate earnings in

its association with stock returns. Peixoto (2002) also found that EVA does not have a

significant effect on the value of equity, as well as Hidayat (2005) who noticed that the net

profit still have more power in explaining stock returns than EVA.

Companies with a low net income will probably give the perception to investors that

the company has a poor performance. According to the research Alcalde and Espitia (2003),

and Shleifer and Vishny (1997), companies with poor performance can be targeted mergers

and acquisitions for investors/potential acquirers who have the motivation to take over

companies with low profitability. On the other hand, investors/potential acquirers expect the

best synergy. So the decision mergers and acquisitions also consider the perspective of the

resource base of the company's targets (Chatterjee 1991; Barney 1986, 1988). In this case the

investor/prospective acquirer may consider companies EVA, especially if the acquisition is a

strategy that allows each company to exchange resources, to improve the performance of

both.

The argument is based on the hypothesis is:


H4a : Companies that have high EVA but low profits have a higher probability of

becoming a target for mergers and acquisitions than other companies.

Mergers and acquisitions due to the influence of top managers want more power and

status (Mualler 1969). Managerial economics uses agency theory to explain the motives of

mergers and acquisitions, where the manager has the discretion to freedom in accordance

with their own interests (Jensen and Meckling 1976). So that mergers and acquisitions are no

longer driven by the motivation to increase the value of the company, but rather an effort to

boost profits managerial (Roll 1986). In this condition, the manager will deliberately use

resources in an efficient manner; become the target of investor/acquirer which targeting

inefficient companies (Jensen 1984). On the other hand, investors/potential acquirers with the

motivation to buy the company at a low price and sell them at higher prices would prefer a

company with its shares lower value than the value of the assets being sold.

Measurements of performance by comparing the earnings correlation with EVA and

stock price have been conducted by several researchers. EVA based research O'Byrne (1996),

and Bao and Bao (1998) is more significant than the change in stock price earnings (earnings)

and abnormal returns. Tests on the influence of EVA and earnings, both operating income

and net income to Market Value and Market Value Added equity was also performed by

Kurniady (2003). The result is EVA also still exceeds operating cash flow (CFO) and residual

income (RI) in explaining stock return. Instead, Peixoto (2002) found that EVA does not have

a significant effect on equity value. These results are in line with Biddle et al. (1997) and

Hidayat (2005), which examines the relative and incremental information of EVA and EVA

may prove an effective tool for performance measurement, internal decision making and the

issue of compensation. However, net profit can explain stock returns than EVA. The

existence of different results this gives the idea that EVA and earnings (earnings) have their

respective advantages in relation to performance measurement and correlation with stock

prices

If associated with merger and acquisition activity, the company that owns EVA and

low net income can indicate that the company has a poor performance. Companies that have a

poor performance can be reflected in its stock price, so that it will attract investors/potential

acquirers who want to invest in buying the company at a low price and will expect to be able

to sell them at a high price if it managed to improve its performance (Capron and Pastre

2002). Based on the hypothesis that the arguments used are:

H5a : Companies with EVA and low profits have a higher probability of becoming

a target for mergers and acquisitions than other companies.

If plotted in a matrix , then the hypothesis 3 , 4 , and 5 are described (see Appendix) :


3. RESEARCH METHODS

3.1 Sample and Data

This study used sample of firms non financial listed on the Indonesia Stock Exchange

in the year 2008-2012. Samples were taken from companies listed on the Stock Exchange and

the target of mergers and acquisitions by domestic companies are also listed on the Stock

Exchange. A total of 125 companies listed on the Stock Exchange which is the target of

mergers and acquisitions of domestic enterprises in 2008-2012, but as many as 37 companies

were excluded from the sample because the company does not have a complete financial

statement data are required in testing. As for the pair (match paired) of the target company

mergers and acquisitions, the company is taken never became a target during the study

period. Non target selected companies from the same sector classification based JASICA by

IDX (Nisa, 2007) and has the same size company with a non targets. The number of targets

and non-targets has a ratio of 1:1 followed Alcade research and Espitia (2003).

3.2 Hypothesis Testing

Model 1 tested whether EVA and net income affects the probability of becoming a target

company's mergers and acquisitions (hypothesis 1 and 2).

Model 1

Description:

TargetMA : Companies that become targets of mergers and acquisitions‟. Value

is 1 if the company becomes a target, 0 if other.

AvgEVA : The Average of Economic Value Added firm i before it becomes a

target, i.e. in year t - 3 to t - 1t.

AvgEBEI : The Average earnings before extraordinary accounts (earnings

before extraordinary items) in year t - 3 to t - 1.

PBV : The ratio of price to book value of equity (price to book value ratio)

of firm i in year t - 1.

SIZE : Natural logarithm of total assets in year t – 1.

Model 2 is used to test hypotheses 3, 4, and 5, the effect of EVA and return to the

possibility of becoming a target company mergers and acquisitions. Tests on these two

models using three dummy variables, based on a matrix of EVA and net income in the table

(2.1). The main model for testing hypotheses 3, 4, and 5 are:


Model 2

Description:

DUM1 : Dummy variable equal to 1 if the firm would have EVA net

income below the median and above the median, and 0 if other.

DUM2 : Dummy variable equal to 1 if the firm would have EVA net

income above the median and below the median, and 0 if other.

DUM3 : Dummy variable equal to 1 if the firm would have EVA and net

income below the median, and 0 if other.

3.3 Operational research variables

Dummy EVA and Earnings

To determine the numbers 1 and 0 on the dummy variables, the first cut off is determined by

the median of the average value of EVA and the average net profit for 3 years. Based on the

cut off, then determined DUM1, DUM2, and DUM3 using conditional system.

Company Scale (SIZE)

Small companies have a high potential to become acquisition targets. Firm size is proxied by

the natural logarithm daritotal company's assets before becoming the target of mergers and

acquisitions. This proxy election following the research Rege, (1984), Palepu (1986), Walter

(1994), Powell (1997, 2004 ), Alcalde and Espitia (2003), Nisa (2007).

Price to Book Value Ratio (PBV)

PBV describe how big market appreciation to the book value of a stock. Many investors used

PBV to obtain the information level of investment risk caused by the liquidation of the

company. Proxy PBV used by Palepu (1986), and Espahbodi and Espahbodi (2003). PBV is

obtained by dividing the book value of the stock price. PBV calculation is done in the

following way:

(3.9)

4. RESULTS AND DISCUSSION

4.1 Descriptive Statistics Analysis

Based on Table 2, it is known that EVA variable in the target company has a

minimum and maximum value 17.41 and -4.70, respectively. While the non-target companies


have maximum and minimum of 2.29 and -41.51 EVA. Negative values indicate that the

target company mergers and acquisitions, there are companies that have a negative EVA.

EVA is negative or < 0 shows that the company is not able to create an added value for the

company, despite its financial statements reported earnings. While a positive value means the

company has revenues (accounting income that exceeds the cost of capital).

The maximum and minimum values show that there is a sample of the target company

mergers and acquisitions that have a higher value than the EVA sample of non target

companies. However, on average, the value of the second EVA sample companies have a

significant difference (α = 5 %). It is proved that the number of sample firms that become

targets of mergers and acquisitions, has a different value to the company EVA nontargets.

Unlike the case with AvgEBEI variables obtained from the average profit of the

company before the accounts outstanding for 3 years before becoming the target of mergers

and acquisitions, the company's total assets one period before being targeted. Based on Table

2, all companies have an average net profit (AvgEBEI) positive. This is shown by the average

variable Ebei the target company of 0.00 and a non target of 0.05. A positive value indicates

that the average sample firm is able to generate a net profit, but the company has a non

targeted net profit greater than the target company mergers and acquisitions. On average,

there are significant differences (α = 5 %) on the net income component of the target

company with corporate mergers and acquisitions of non targets.

Based on the maximum value of the sample targeted companies and non target, the

target company AvgEBEI variable is greater than the non-targeted companies, amounting to

0.73 and 0.39. This indicates that there is a sample of the target company had a net profit

higher than the non targeted companies. Instead, based on the minimum value, the sample

contained the target company suffered losses greater than the non-targeted companies. This is

indicated by the minimum value of -0.75 for the target company mergers and acquisitions,

and -0.39 for non targets.

Table 2 also shows the distribution of the sample companies on the condition of EVA

valuation and net income simultaneously. Based on the total sample of 179 companies, there

are 64 companies that have below median EVA but net income (EBEI) above the median. Of

the 64 companies, 38 of which are the target company mergers and acquisitions, and 26 non

targets. This indicates that firms with a low EVA looks attractive for investors/potential

acquirers who have the motivation to replace the old management, due to poor performance

despite higher reported net income. On the other hand, there are also investors/potential

acquirers may think of the profits generated in the event of merger capital of the merger and

acquisition activity. Thus, investors/potential acquirers of this kind would be more interested

in companies that have a high profit, despite the low value EVAnya.

On the other hand, EVA high but lower net income also in demand by investors.

Based on the sample of 50 companies that have high EVA but lower net income, there are 28

companies‟ targeted mergers and acquisitions, and 22 are from non-targets. In this condition,

the investor/acquirer candidates who are motivated to take over undervalued companies, has

a tendency to consider income as a measure of performance. Thus, when the company

reported net income was low, investors soon make it as a sign that the company has

management that is less efficient and should be replaced. Especially when investors/potential

acquirers are able to assess the performance of the company based on the value of EVA, the


EVA with high, but lower net income would provide indirect signal to the

investors/prospective acquirers that the company has the ability to create added value for the

company. However, the added value created by the company may result in reported net

income will be lower.

Furthermore, the company that owns EVA and net income (EBEI) below the median.

This condition can only be owned 18 companies, 6 of which are the target company mergers

and acquisitions, and the other 12 are non target companies. The number of targeted

companies in these conditions over a little of everything. This indicates that the selection of

the target company is basically done by taking into account the performance of the company.

When there are no components or performance measure indicates that the company has added

value or capital good ability, then companies are less likely to be targeted mergers and

acquisitions.

Unlike the case with PBV and SIZE variables, both of these variables have no

significant difference (α = 10 %) between the target company mergers and acquisitions, the

company's non targets. Although based on Table 2, the company has a non-target average

PBV higher than the target company. The maximum value of the sample also indicates that

there are companies that have a non-target PBV greater than the target company. As for the

minimum value, the target company has a PBV lower than the non targeted companies.

4.2 Analysis of Different Test Average Variable

Based on the different test results in Table 3, AvgEBEI and AvgEVA variables have

p-value less than 0.05, it can be concluded that the null hypothesis is rejected or accepted

alternative hypotheses. That is, there are significant differences between the average

Economic Value Added (EVA) and the average net income (EBEI) the target company

mergers and acquisitions with companies that do not become targets of mergers and

acquisitions (matched pairs).

PBV and SIZE variables have p values greater than 0.05 then the average enterprise

value (PBV) and firm size (SIZE) has no significant difference between the target company

mergers and acquisitions with companies that do not become targets of mergers and

acquisition (matched pairs). So it can be said that the company's value (PBV) targeted

companies and non targets are the same. Similarly, the size of the company is seen by total

assets, where there is no difference between the size of the target company mergers and

acquisitions with companies that do not become targets of mergers and acquisitions.

4.3 Pearson Correlation Analysis

Table 3 shows that the net profit of the independent variable (AvgEBEI) has a

correlation coefficient of -0.171 (significant at level α = 5 %) of the dependent variable

(TargetMA). Thus it can be said there is a negative correlation between the probability of the

company's net profit to be targets of mergers and acquisitions. This indicates that the

company has a negative net income is likely to be the target of mergers and acquisitions than

other companies.

This correlation shows that the net profit is still seen as an informative source of

information for its users, so it can be used in the decision-making process. Although investors

may realize that the net income figure can be generated from the use of accounting methods


as well as the presence of management decisions. However, the company reported net income

still has a function as a measure of performance during the reporting period, in which the

function of this performance were also applied to represent the company's accounting system.

On the other hand, EVA has positive coeficient correlation but not significant at α =

10 %. This indicates that companies with positive EVA are likely to be targets of mergers and

acquisitions, but it has not been established that the EVA is used investors/potential acquirers

as the basis for target selection consideration. When compared with net income,

investors/potential acquirers prefer evaluations using accounting numbers than the concept of

value added such as EVA. Moreover for investors/potential acquirers in Indonesia, EVA

evaluation may not have been associated with their investment decisions.

Similarly, the PBV and SIZE that have a negative correlation coefficient but is not

significant at α = 10 %. Companies that have enterprise value ( PBV ) is the lower will allow

the company to become the target of mergers and acquisitions, but cannot be certain that the

investors/potential acquirers would take into consideration the value of the target company as

one factor that contributed to affect investment decisions. Similarly, firm size (SIZE), where

a company that has the size of a small company that enables enterprises to be targets of

mergers and acquisitions, but it turns out the size of the company does not have a relationship

with the decision of investors/potential acquirers to determine the target.

Table 4 indicates there is no correlation between the independent variable on the

dependent variable. Early indications based on the results of the Pearson correlation shows

that the variable DUM1, DUM2, DUM3, PBV, and SIZE are not correlated to the probability

of becoming a target company mergers and acquisitions. DUM1, DUM2, DUM3 which is a

combination of EVA and net income may not have a correlation as investors/potential

acquirers in determining the targets of mergers and acquisitions do not see a combination of

EVA and net income as a consideration in the decision. It could be that investors/potential

acquirers only look at one factor alone, without considering other factors.

4.4 Logistic Regression Analysis

1) Effect of EVA and Profit Against Target Mergers and Acquisitions Probability

Tests on the effect of EVA (hypothesis 1a) and income (hypothesis 2a) against the

possibility of becoming a target company mergers and acquisitions carried out by using the

model 1.

Goodness of Fit Test for Model 1 shows that by including independent variables, the

model becomes better. Similar results were also shown by the percentage of correct

predictions (predicted percentage correct). The model with no independent variables are able

to provide correct predictions of 60% , better than the model with only constants are

predicted correctly by 50 %. Overall coefficient test (omnibus test) showed level of

significance under which means simultaneous independent variables in the model is better at

predicting the dependent variable compared with only one independent variable only. Cox

and Snell's R2 and Nagelkerke's R

2 indicates the number 0.069 and 0.092, while the Hosmer

and Lemeshow Test of significance is 0.493 (greater than the level of = 5 %). It can be

concluded that the model is quite good and interpretation of the coefficient in the model can

be resumed.


Test results on the first model showed that the independent variables EVA (AvgEVA)

and net income (AvgEBEI) significantly affect the dependent variable at level α = 5 % and 10

%. Significant test results show that both EVA and net income also affect the company

became the target of mergers and acquisitions. This is consistent with previous studies stating

that the decision of target‟s company was influenced by the target company performance

which assessed on the profitability of the company (Alcalde and Espita 2003). The results of

this study showed that the measurement of performance based on EVA and net income also

influence the decisions of investors/potential acquirers in determining the target company.

The following are the results of the regression models to test hypotheses 1 H1a and

H2a:

EVA concept is proven to be taken into consideration investors/potential acquirers as

a measure of corporate performance targets, in decision making mergers and acquisitions.

This is consistent with the results of Lehn and Makhija (1996), Bao and Bao (1998), Kleiman

(1999), Garvey and Milbourn (2000), Kurniadi (2003) and Wardhani (2004) who study the

correlation between the performances of the company compared to EVA with other

performance measurements. On the other hand, net income has a significant effect on the

possibility of becoming a target company mergers and acquisitions compared to EVA. This

indicates that conceptually, EVA may be better than accounting figures such as net income.

But in practice, investors/potential acquirers still use net income figures as a consideration in

determining the targets of mergers and acquisitions. These results are consistent with the

findings of Biddle et al. (1997) who still found that net income as a measure of better

performance.

a) Effect of Economic Value Added (EVA) Against Target Mergers and Acquisitions

Probability

Testing the hypothesis 1a was conducted to test the effect of EVA on the probability

of becoming a target company mergers and acquisitions. Based on logistic regression results

in Table 6 shows that the variable has a positive coefficient and AvgEVA significant at α = 1

%. In contrast to the hypothesis, the test results showed that EVA has a positive influence on

the probability of becoming a target company mergers and acquisitions. This means that

companies with high EVA has a greater probability of becoming a target for mergers and

acquisitions

The influence of EVA to the possibility of becoming a target company's mergers and

acquisitions support the statement of Stewart (2013) which can be one of the EVA material

consideration, because EVA is part of the company's earnings quality. According to

Trautwein (1990), according to the theory of efficiency, mergers and acquisitions is

something that has been planned to achieve synergy, so the concept of EVA can be used to

assess the advantages of the above which synergies can be generated by the target company.

Stewart (2013) also added that conceptually, the acquisition decision is not much

different than any other investment decisions. When the company decided to make an

acquisition, the company not only 'buy' revenue of the target company, but also the quality of

earnings is the risk, return, growth prospects, as well as the value of his EVA. Companies that

have EVA positive value means the returns produced by the company exceeds the cost of

capital rate or rate of return expected by investors. On the contrary, if EVA is negative, it

mean that no increase firm value or firm value is reduced due to the resulting rate of return is


lower than investors demanded (Stewart 1991). But Young and O'Byrne (1996) add that

when EVA is negative, not always interpreted negatively as well. Because the company could

have a long -term investment or a large project, but the return of investment is not realized in

the short term.

The result of this study also supports the concept of EVA as a performance measure.

Lehn and Makhija (1996) study the correlation between EVA and better returns compared

with other performance measures such as ROA, ROE, ROS, and MVA. When compared with

a profit, Kleiman (1999), and Garvey and Milbourn (2000) also still supports the EVA as a

performance measurement that correlates better with the return of the company. In its

findings found that EVA is correlated with the share price more than profit. Thus, if a

company with a high EVA has a higher probability of being targeted for mergers and

acquisitions, then perhaps investors/potential acquirers will be considering the benefits

obtained after mergers and acquisitions. Investor/potential acquirer have confidence that by

acquiring companies with high EVA, the company may acquire in the future rate of return

exceeds the cost of capital or the expected rate of return on its shareholders.

b) Influence Of Profit against Target Mergers and Acquisitions Probability.

Hypothesis 2a testing was conducted to test the effect of net income (EBEI) against

the probability of becoming a target company mergers and acquisitions. Based on logistic

regression results in Table 6 shows that the AvgEBEI variable has a negative coefficient and

is significant at α = 1 %. The results of these tests show that the income effecting the

probability of the company being targeted. Coefficient sign corresponding with prediction

means companies with higher profit have a lower probability of becoming a target for

mergers and acquisitions than other companies. The regression results support the research

hypothesis 2a, so the hypothesis2a can not be rejected.

The influence of gain or profit on the probability ofcompanies to become targets of

mergers and acquisitions as evidenced in this study supports the results of research Alcalde

and Espita (2003) and Shleifer and Vishny (1997). Alcalde and Espita (2003) found that

firms with low profitability will have a greater probability to be acquired. Similarly,

companies that‟s have poor performance (Shleifer and Vishny 1997). These results are also in

accordance with the motivation of mergers and acquisitions according to Damodaran (2001),

which took over a company that is managed by poor management and replace it.

In line with agency theory, managers also have a tendency to perform earnings

management. Healy (1985) stated that the presence of asymmetric information between

investors and management create an opportunity for management to perform earnings

management. The existence of earnings management also makes the acquisition target

company has always benefited from the acquisition event when compared with the acquirer's

shareholders (Roll 1986, Bradley et al. 1988, Franks and Harris 1989). Based on this

consideration, any one reason investors/potential acquirers are more likely to choose a

company that has a lower net profit is to avoid earnings management by the company with

net income looks good.

c) Effect of PBV and SIZE against Target Merger and Acquisition Probability.

The test results showed that all the control variables do not significantly affect the

likelihood of stock options investment management. PBV variable as a proxy for the value of


the company was negative and not significant at level = 10 % affect the likelihood the

company being targeted. This result is in contrast to results of previous studies conducted on

a sample of companies in Indonesia (Nisa 2007). This may be due to improper proxy used to

measure the value of the company. In another study (Hasbrouck 1985, Bartley and Boardman

1990, Walter 1994) use Tobin's Q as the value of the company. They stated that the q ratio or

Tobin's Q can be explained and demonstrated the ability of the model to better distinguish

between the target company and the non targets.

Firm size (SIZE) is positive and not significant at level = 10 % affect the likelihood

the company being targeted. These results are consistent with previous studies conducted by

Nisa (2007) using samples in Indonesia. Nisa (2007) found that firm size does not affect the

possibility of the company becoming the target of mergers and acquisitions. This might be

because there are big companies in Indonesia who will be the target of mergers and

acquisitions. Thus, both large companies and small companies were equally likely to be

targeted.

2). Effect of EVA and Profit In Simultaneous

The second model is used to test the EVA and net income in the company. There are 3

conditions to see the trend of companies being targeted, namely (1) a company with a low

value of EVA and high income, (2) companies with high EVA and low income, (3)

corporation with EVA and low income.

Goodness of Fit Test for Model 2 shows the -2LL value at Step 1 (after put the

dependent variable) is smaller than Step 0 (only constants), thus it can be said that by

including independent variables, the model becomes better. Similar results were also shown

by the percentage of correct predictions (predicted percentage correct). The model with no

independent variables were able to deliver the correct prediction of 60.2 %, better than the

model with only constants are predicted correctly by 50 %. Significance Hosmer and

Lemeshow test was 0.493 (greater than the level of = 5 %), meanwhile Cox and Snell's R2

and Nagelkerke's R2 indicates the number 0.069 and 0.092.

Although the overall coefficient test (omnibus test) indicates significance at the above

level which means simultaneous independent variables taken together can not account for

the probability of becoming a target company mergers and acquisitions, but these problems

can be solved through the opinion expressed by Gujarati (2009, p.563) which states that in

the binary logistic regression equation, the more attention and priority is the expected

coefficient sign and significance of the coefficients. Goodness of Fit ranks second only to the

significance and expected sign of the coefficient. By the statement, the value of the Omnibus

Test insignificant can be ignored. Thus can be concluded that the model is quite good and the

interpretation of the coefficients in the model can be resumed.

Test results of model 2 in Table 8 showed that DUM1 namely EVA and below the

median net income above the median, significantly affect the dependent variable at level α =

10 %. Likewise DUM2, namely EVA above the median and below the median net income,

significantly affect the dependent variable at level α = 5 %. These results are consistent with

the hypothesis of Alcalde and Espita (2003), and Shleifer and Vishny (1997) that the lower

performance of the company, then the company is likely to become targets of mergers and


acquisitions. The combination between net income and EVA in consideration of target

selection shows that the net profit is still the important information in decision-making

(Subramanyam 1996), but the other net income can not be used independently to evaluate the

performance of the company (Utomo 1999). This is due to the asymmetry of information that

can be used by the target company's management to perform earnings management in order

to influence the market price in order to negotiate the acquisition (Trueman and Titman 1988;

Dye 1988). EVA can be used so that the performance information that is able to identify the

business units that can earn revenue exceeds the cost of capital, and can create the possibility

of a more productive compared to other measuring devices (O'Byrne 1996).

The following are the results of the regression models to test hypotheses H3a 1 , H4A

and H5A :

Unlike DUM3 variables, namely EVA and net income both under the median, does

not significantly affect the dependent variable at level α = 10 %. The results indicate that

investors/potential acquirers do not choose a company that will be targeted without

considering its performance, because the decision will affect the incentives or bonuses to be

earned (Healy 1985; Holthausen et al. 1995; Gaver and Austin 1995). Thus, companies with

lower both EVA and net income does not affect the probability of becoming a target

company mergers and acquisitions.

The control variables in these tests namely SIZE and PBV also does not significantly

affect the dependent variable on the level of = 10 %. Firm size (SIZE) that does not

significantly affect the probability of a target company in accordance with the results of Nisa

(2007), where large companies are categorized based on total assets, as well as small, does

not affect the probability of the company being targeted mergers and acquisitions. a) Effect of low EVA but high Net Income againts Target Mergers and Acquisitions

Probability

To study the influence of EVA and net income together against the possibility of

becoming a target company mergers and acquisitions, both are transformed into a dummy

variable (DUM1), with a value of 1 if the EVA is under the median and net income above the

median and 0 if other. Based on test results, Table 8 shows that the variable DUM1 has a

positive and significant coefficient at α = 10 %. The results of this test indicate that DUM1

not affect the probability of becoming a target company mergers and acquisitions. It can be

concluded that companies with low EVA, but high net profit, has a probability of becoming a

target of mergers and acquisitions.

Based on the results of this test, it can be seen that investors/potential acquirers who

have the motivation to acquire companies that have low performance, not considering the low

net income as consideration for the targeting, but companies are likely to see the value of

EVA as performance evaluation. Investor/potential acquirer may be aware that companies

with high profit not necessarily have a good performance (Utomo 1999), because the profit

rate could be influenced by the choice of accounting methods used and business decision

management (Bernstein and Siegel 1979). Moreover, if managers attempt to profit

management companies to raise prices in order to negotiate the acquisition market (Trueman

and Titman 1988). So with the concerned management to show high profits to efforts that


performance looks good, but on the other hand it shows that the value of the company as well

as the value does not indicate an increase in net profits.

b) Effect of high EVA but low Net Income against Target Mergers and Acquisitions

Probability.

The next test for the hypothesis 4a is intended to study the effect of EVA and net

income simultaneously on the dependent variable, ie the probability of becoming a target

company mergers and acquisitions. EVA and net income is transformed into a dummy

(DUM2), with a value of 1 if the EVA is in the net income above the median and below the

median, and 0 if other. The test results indicate that the variable DUM1 has a positive and

significant coefficient at α = 5 %. The regression results indicate that DUM2 influence on the

probability of merger and acquisition targets. This means that companies with high EVA Ebei

low but can increase the probability of becoming a target company mergers and acquisitions.

These results support the hypothesis 4a, so that the hypothesis 4a is not rejected (accepted).

Based on these results, it can be seen that the investors/potential acquirers who have

the motivation to acquire companies with low performance are more likely to judge a

company based on the value of it net earnings. This is consistent with Biddle et al. (1995),

Liu et al. (2002), and Francis et al. (2003), income has an important role as the primary

source of information used by investors than the current dividends and cash. Net income was

assessed by investors/potential acquirers as an important part to reflect the company's

performance (Dechow 1994).

On the other hand, the results of this study also showed that companies with low

profits do not necessarily have a bad performance. Thus, for investors/potential acquirers, it

will tend to benefit. Due to the company's net income on the other hand has a high value of

EVA. Companies may have a particular investment or investment that results have not been

fully provide returns to the company, so the company's net profit seen lower, but it has a high

value added.

c) Effect of low EVA and Net Income against Target Mergers and Acquisitions

Probability.

Testing the hypothesis 5a is intended to study the effect of EVA and net income

together against the probability of becoming a target company mergers and acquisitions. Low

EVA and net income was transformed as dummy variable (DUM3). Based on the regression

results of Table 8 shows that the variable has a negative coefficient DUM3 and not

significant at α = 10 %. The regression results indicate that DUM3 no effect on the

probability of merger and acquisition targets. This means that companies with low EVA and

EBEI can not increase the probability of becoming a target company mergers and

acquisitions. These results do not support the hypothesis 5a, so that the hypothesis5a is

rejected.

Based on these results, it shows that the possibility of the company chosen as the

target depends on the motivation of investors/potential acquirers. Companies with this

condition will not be selected if the investor or prospective acquirer to realize that the

company is not going to bring beneficial synergies. As the Haspeslagh and Jemison (1991)

that the decisionmaking process for mergers and acquisitions as a stage analysis process

begins with determining goals, do a search and filtering systematically, then evaluate the

strategy and finances. So that investors and potential acquirers actually consider the condition


of the target company in order to generate profitable synergies. This is done

investors/potential acquirers so that the merger and acquisition decisions are taken for profit,

as suggested by Heller (2000) that the success of mergers and acquisitions depends on a

company's ability to identify targets with a good strategy. Thus, investors/potential acquirers

do not just pick a target even possible to have a low acquisition price.

On the other hand, companies with lower both EVA and net income has the

probability to be selected by the investor/prospective acquirer if they are motivated by a

comparison between the cost of acquisition of the new project. This is because of the thought

that acquiring undervalued companies that will be cheaper compared to the cost of new

investment projects (Hasbrouck 1985). Alcalde and Espita (2003) in his research found that

companies that want to be acquired is a company that has low profitability and poor corporate

value.

5. CONCLUSION AND LIMITATIONS

This study aims to empirically examine whether EVA and net income affect the

likelihood of becoming a target company mergers and acquisitions in Indonesia. The larger

the value of EVA companies, then the company will increase the likelihood of becoming a

target for mergers and acquisitions. Conversely, the greater the net income decreases the

possibility of becoming a target company mergers and acquisitions. These results also

showed that net profit more influence on the decisions of investors/potential acquirers in

mergers and acquisitions selecting targets compared to EVA. It can be seen from the

significance of the variable net income and EVA. So from these results it appears that

although EVA conceptually better than net income, but investors remained practically using

net income as part of the decision making.

On the other hand, the combination of EVA and earnings are also taken into account

by investors/potential acquirers. Companies that have a combination of low EVA but higher

net income, and EVA high but lower net income affect the likelihood of becoming a target

company mergers and acquisitions. Conversely, when the EVA and net profit showed values

equally low, does not affect the possibility of the company chosen as a target.

The presence of a combination of EVA and net income as consideration becomes the

target company mergers and acquisitions may be influenced by the motivation of

investors/potential acquirers. First, if the investor/prospective acquirer is more likely to

choose investments in companies that have a good performance based on earnings

information, then the company will be selected as the target of companies with high profit

without considering it low EVA value. On the other hand, investors/potential acquirers who

have the motivation to choose a company with a low performance, will be seen the value of

EVA despite higher net profit.

Second, if investors/potential acquirers have the motivation to acquire a low

performing companies based on net income figures, the company with the lower net income

would have the possibility to be targeted. Along with that, if the investor/acquirer candidates

today are also considering the company's ability to create value through a number of EVA,

the company with the lower net income, but has a high value of EVA will have the possibility

to become a target. EVA here gives a signal to investors/potential acquirers that the company


has the ability to create added value. Thus, investors/potential acquirers would tend to judge

that the company may have a low performance if judged from the amount of net income, but

on the other hand, this company has the ability to create added value that may not be

measured by the amount of profit.

In addition, other factors such as the value of the company PBV and firm size (SIZE)

is not shown to have an influence on the probability of becoming a target company mergers

and acquisitions. This proves that the investors/potential acquirers in choosing targets tend to

look at the performance of the company compared to the value and size of the company.

Overall, this study found that the performance of the company is the foundation

consideration of investors/potential acquirers in decision making, especially in mergers and

acquisitions. EVA and net income is a performance measurement tool that can be used to

look at the possibility of becoming a target company mergers and acquisitions. However,

when compared with EVA, net profit is still the most dominant information used by investors

in considering the merger and acquisition targets.

This study has limitations by using the figures obtained from published financial

statements to calculate the value of EVA. The study also does not take into account the

company's stock price targets and non targets as a proxy. As for the sample, this study is only

limited to domestic companies listed, and do not distinguish sample target company merger

or acquisition targets.

The next study may compare the effect of EVA that can be measured using the figures

of financial statements with information that EVA is calculated by using the

Stewardadjustment. Entering the stock price as a proxy to determine the company became the

target of mergers and acquisitions. Distinguishing influence consideration of EVA and net

income to determine the target of mergers and acquisitions on foreign and domestic

companies are listed and non listed, also separating the target company merger and

acquisition targets


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APPENDIX

Table 1: Matrix Dummy EVA and EBEI

EVA

EBEI

High Low

High Base H3a

Low H4a H5a

Source: The research hypothesis

Description :

EVA : Economic Value Added

EBEI : Earnings Before Extraordinary Items

Table 2: Descriptive Statistics

ALL SAMPLE (n=176)

Mean Median Maximum Minimum Std. Dev.

TargetMA 0.50 0.50 1.00 0.00 0.50

AvgEVA -0.54 -0.31 17.41 -41.51 3.45

AvgEBEI 0.02 0.02 0.73 -0.75 0.13

DUM1 0.36 0.00 1.00 0.00 0.48

DUM2 0.28 0.00 1.00 0.00 0.45

DUM3 0.10 0.00 1.00 0.00 0.30

PBV 2.54 1.43 43.03 -17.94 5.13

SIZE (Rp Billion) 10,103.74 1,982.92 313,000.00 13.17 28,157.17

TARGET SAMPLE (n=88)

Mean Median Maximum Minimum Std. Dev.

TargetMA 1.00 1.00 1.00 1.00 1.00

AvgEVA -0.19 -0.23 17.41 -4.70 2.04

AvgEBEI 0.00 0.02 0.73 -0.75 0.16

DUM1 0.43 0.00 1.00 0.00 0.50

DUM2 0.32 0.00 1.00 0.00 0.47

DUM3 0.06 0.00 1.00 0.00 0.23

PBV 2.14 1.42 35.45 -17.94 4.82

SIZE (Rp Billion) 11,391.48 2,029.70 313,000.00 13.17 35,858.57

PAIR SAMPLE (n=88)

Mean Median Maximum Minimum Std. Dev

TargetMA 0.00 0.00 0.00 0.00 0.00

AvgEVA -0.89 -0.40 2.29 -41.51 4.41

AvgEBEI 0.05 0.03 0.39 -0.39 0.09

DUM1 0.30 0.00 1.00 0.00 0.46

DUM2 0.25 0.00 1.00 0.00 0.44

DUM3 0.14 0.00 1.00 0.00 0.35

PBV 2.95 1.45 43.03 -1.62 5.43

SIZE (Rp Billion) 8,816.00 1,981.34 99,758.45 17.12 17,480.65

TargetMA = the target company mergers and acquisitions, AvgEVA = Average Economic Value Added firm i

before it becomes a target, ie in year t - 3 to t - 1, AvgEBEI = Average Profit before extraordinary accounts in

t - 3 to t - 1, DUM1 = dummy variable (1.0) with a value of 1 if the EVA is below the median and above the


median Ebei, and 0 if other, DUM2 = dummy variable (1.0) with a value of 1 if the EVA is above the median

and below the median Ebei, and 0 if other, DUM3 = dummy variable (1.0) with a value of 1 if the EVA and

Ebei under the median, PBV = ratio of stock price to book value of the company in year t - 1, SIZE = natural

logarithm of the book value of total assets at year t – 1

Table 3: Different Test

Variable Significance

AvgEVA 0.030 **

AvgEBEI 0.022 **

PBV 0.336

SIZE 0.488

** Significant at α = 5 %

Sources: Data processed with SPSS 17.0

Table 4: Pearson Correlation Matrix Model 1

TargetMA EVATA EBEITA PBV SIZE

TargetMA 1

AvgEVA .102

(.179) 1

AvgEBEI -.171

* (.023)

.176*

(.019) 1

PBV -.079 (.296)

-.045 (.554)

.186*

(.014) 1

SIZE -.002 (.977)

.138 (.067)

.180*

(.017) .014

(.854) 1

* Significant at α = 5 % level ( two-tailed )

Numbers in parentheses indicate the p - value

Sources: Data processed with SPSS 17.0

Table 5: Goodness of Fit Test Model 1

-2 Log Likelihood Step 0 Step 1

243,988 231,398

Predicted Percentage Correct Step 0 Step 1

50.0 60.0

Omnibus Test Chi-Square Sig.

12.590 0.013

Model Summary Cox & Snell R

2 Nagelkerke R2

0.069 0.092

Hosmer and Lemeshow Test Chi-square Sig.

7.407 0.493

Source: Data processed with SPSS 17.0


Table 6: Regression Model 1 Test Results for Test H1a and H2a

Variable Expected Sign Coefficient S.E. Sig.

Constant -0.245 1.1150 0.413

AvgEVA (-) 0.294 0.151 0.036**

AvgEBEI (-) -4.640 1.764 0.004***

PBV (-) -0.007 0.033 0.414

SIZE (-) 0.034 0.077 0.330

Predicted Percentage Correct 60.0

Hosmer and Lemeshow Test 0.493

Cox & Snell R2 0.069

Nagelkerke R2 0.092

*** Significant at α = 1 % level ( one-tailed )

** Significant at α = 5 % level ( one-tailed )

Number of observations : 176, TargetMA = the target company mergers and acquisitions,

AvgEVA = Average Economic Value Added firm i before it becomes a target, ie in year t - 3

to t - 1, AvgEBEI = Average Profit before account - outstanding accounts at year t - 3 to t - 1,

PBV = ratio of stock price to book value of the company in year t - 1, SIZE = natural

logarithm of the book value of total assets at year t – 1. Source: Data processed with SPSS 17.0

Table 7: Goodness of Fit Test Model 2

-2 Log Likelihood Step 0 Step 1

243,988 235,609

Predicted Percentage Correct Step 0 Step 1

50.0 60.2

Omnibus Test Chi-Square Sig.

8.379 0.137

Model Summary Cox & Snell R

2 Nagelkerke R2

0.046 0.062

Hosmer and Lemeshow Test Chi-square Sig.

11.509 0.174



Table 8: Regression Model 2 Test Results for Test H3a , H4a and H5a

Variable Expected Sign Coefficient S.E. Sig.

Constant -1.440 1.541 0.175

DUM1 (+) 0.711 0.489 0.073*

DUM2 (+) 0.976 0.536 0.034**

DUM3 (+) -0.213 0.634 0.368

PBV (-) -0.037 0.034 0.137

SIZE (-) 0.064 0.099 0.258

Predicted Percentage Correct 60.2

Hosmer and Lemeshow Test 0.174

Cox & Snell R2 0.046

Nagelkerke R2 0.062

** Significant at α = 5 % level (one-tailed)

* Significant at α = 10 % level (one-tailed)

Number of observations : 176, TargetMA = the target company mergers and acquisitions,

DUM1 = dummy variable (1.0) with a value of 1 if the EVA is below the median and above the

median Ebei, and 0 if other, DUM2 = dummy variable (1.0) with a value of 1 if the EVA is

above the median and below the median Ebei, and 0 if other, DUM3 = dummy variable (1.0)

with a value of 1 if the EVA and Ebei is below the median, PBV = ratio of stock price to book

value of the company in t - 1, SIZE = natural logarithm of the book value of total assets at year t

– 1


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