Company Profile Mutual Fund

7/31/2019 Company Profile Mutual Fund

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------

INTRODUCTION

Mutual fund is one of the most preferred investment route by small investor, who

allocate part of their funds to capital market. In India, the mutual funds industry has been in

existence for more than four decades. Unit Trust of India (UTI) was the first mutual fund

to be set up in India under the UTI Act 1963. In 1987, the Government allowed mutual

funds to be promoted by public sector banks and other financial institutions and in 1993 the

doors were opened for private sector mutual funds. As on June 3, 2006, Association of

Mutual Funds of India (AMFI) reported that there are 29 mutual funds in India offering

592 schemes with an investment in assets of about 60 billion US dollars.

In view of large investor interest, the performance of mutual fund managers needs

continuously evaluated.

The performance studies deal mainly with two aspects

(1) evaluating stock selection skills and

(2) examining the market timing abilities of the fund managers.

Studies of stock selection date back to Jensen (1969) who finds that managers

deliver negative abnormal returns. Using more recent data Ippolito (1989) finds evidence

of positive abnormal returns. However, Elton et al (1992) show that the benchmark chosen

by Ippolito causes this result. Using, multi-factor model, they find that abnormal fund

returns are on average negative.

CONCEPTUALIZATION OF MUTUIAL FUND

A Mutual Fund is a trust that pools the savings of a number of investors who share

a common financial goal. The money thus collected is then invested in capital market

instruments such as shares, debentures and other securities. The income earned through

these investments and the capital appreciations realized are shared by its unit holders inproportion to the number of units owned by them. Thus a Mutual Fund is the most suitable

investment for the investment-illiterate people as it offers an opportunity to invest in a

diversified, professionally managed basket of securities at a relatively low cost.

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------INDIAN MUTUAL FUINDS

The origin of Mutual Fund Industry in India is with the introduction of the concept

of mutual fund by Unit Trust of India (UTI) in the year 1963. Though the growth was slow

initially, it has been accelerated from the year 1987 when non-UTI players entered the

industry. With the boom of June 1990 and then again 1991 due to the implementation of

new economic policies leading to structural change of securities pricing in stock market,

the performance of the mutual fund industry is encouraging. Because, individual investors

have been emphasized in India in contrast to advanced countries where mutual funds

depend largely on institutional investors. In general, it appears that the mutual fund in India

have given a good account of itself so far.

With the entry of private sector funds in 1993, a new era started in the Indian mutual fund

industry, giving the Indian investors a wider choice of fund families. The industry now

functions under the SEBI (Mutual Fund) Regulations 1996. The number of mutual fund

houses goes on increasing with many foreign mutual funds setting up funds in India and

also the industry has witnessed several mergers and acquisitions. As at the end of January

2005, there were 33 mutual funds with total assets of Rs.121805 cores.

The industry has grown in size and manages total assets of more than $30351

million. Of the various sectors, the private sector accounts for nearly 91% of the resources

mobilized showing their overwhelming dominance in the market. Individuals constitute

98.04% of the total number of investors and contribute US $12062 million, which is

55.16% of the net assets under management.

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STRUCTURE OF MUTUAL FUND IN INDIA

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Sponsor

Akin to the promoter of the companyEstablishes the fundGets it registerd with SEBI

Forms the trust, and appoints board of trustees

Trustees

Hold assets on behalf of the unit holders in the trustAppoint asset management company and ensure that all the activities ofthe AMC are in accordance with the SEBI regulationsAppoint the custodian of the fund

Distributors/agentsSell units on behalf of the fund

Banker

Facilitates financial transactionsProvide remittance facilities

Custodian

Holds the funds securities in safekeeping

Settles securities transactions for the fund

Collects interests and dividends paid onsecurities, and

Records information on stock splits and othercorporate actions.

Asset Management Company

Floats schemes and manages them inaccordance with the SEBI regulations

Banker

Facilitates financial transactions Provide remittance facilities

Registrar and transfer agents Maintains records of unit

holders accounts andtransactions

Disburses and receives fundsfrom the unit holdertransactions, prepares anddistributes account statementand tax information, handlesunit holder communication,

Provides unit holder transact

services.


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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------The Models of Mutual Fund Performance

Prior studies of mutual funds performance focus on stock selection or market timing

ability. In this section, we describe the models for evaluating both the managerial skills

along with innovations applied in recent literature.

1) Stock Selection

A traditional approach to measure selectivity is to regress the excess returns of a

portfolio on the market factor. Assuming that market beta (or slope coefficient) is constant,

then the unconditional alpha (or intercept) is a measure of average performance, as in

Jensen (1968), i.e.

where

RPt - RFt and RMt - RFt are excess portfolio and market returns.

And are the intercept and slope coefficients.

et is an error term.

We can also measure selectivity using the Carhart (1997) four-factor model

Where,RPt - RFt is the excess portfolio return

FK = returns on the factors including the excess market return, the Fama-French (1993)

size and book to market factors and Carhart's momentum factor.

K = sensitivity coefficient

= intercept term

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------Previous research shows that the last three factor capture most of the anomalies of Sharpe's

(1964) single-factor CAPM. We include these additional factors to avoid rewarding

managers for simply exploiting these anomalies.

2) Market Timing

Market timing refers to the dynamic allocation of capital between broad asset

classes.Treynor and Mazuy (1966) use the following regression to test for market timing:

Where Rpt - RFt is the excess return on a portfolio at time t, RMt - RFt is the excess return

on , on the market and is a measure of timing ability. If a mutual fund manager

increases (decreases) the portfolio's market exposure prior to a market increase (decrease)

then the portfolio's return will be a convex function of the market's return, and will be

positive.

Henriksson and Merton (1981) develop a different test of market timing. In their model, the

mutual fund manager allocates capital between risk free assets and equities based onforecasts of the future excess market return, as we test a model with two target betas via the

following regression:

Where r = 1 when RMt - RFt > 0, equal to 0 otherwise is used as a market timing measure

in HM framework. We use both timing models to measure timing ability in our sample of

mutual funds. Grinblatt and Titman (1994) show that the tests of market performance are

quite sensitive to choice of benchmark. For this reason, we use the four-factor versions of

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------equation (3) and (4) based on Carhart (1997) model and those that control for prominent

CAPM anomalies as stated in the previous sub-section.

The modified market timing models are used in Bollen and Busse [(2001), (2005)] and

look like

For the TM model where F2 1t is the square value of factor 1, i.e., the excess market returnwhile additional factors in the summation represent size, value and momentum factors; and

For the HM model, where Dt = 1 for F1 (RM - RF) to be positive and Dt = 0 for F1 (RM -

RF) to

be zero or negative.

However, timing measures in (5) and (6) do not provide a complete picture. In these

versions, -market timing simply means increasing (decreasing) the sensitivity (or slope

coefficient) of fund returns to market returns using market upturns (downturns). The

presence of additional factors that control for investment style characteristics may warrant

a multi-dimensional market timing ability.

For instance, besides timing the market factor, the fund managers may be timing

the size, value as well as the momentum factors. Thus, market timing also implies

increasing (decreasing) the sensitivity of fund returns to size, value and momentum factors

during the periods when small firms are expected to outperform (underperform) big firms,

high book to market equity or BE/ME firms are expected to outperform (underperform)

low BE/ME firms and stocks with high short-term past returns (past winners) are expected

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------to outperform (underperform) stocks with low short-term past returns (past osers). This

requires measuring of additional timing coefficients in the multi-factor regressions. This ill

lead to the following versions of the abovesaid models:

for TM model where rs represent timing measures for each factor, and

where DK

s are dummy variables for the slope coefficient of each factor, such that

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The RM - RF, SMB, HML and WML depict market size, value and momentum factors

respectively whose construction is explained in a later section.

We believe that our model versions given in (7) and (8) shall provide a more

comprehensiveinference about market timing abilities of fund managers.

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3) Data

The data comprises of dividend-adjusted Net Asset Values (NAVs) for 59 natural

fund schemes fromJanuary 2000 to December 2004. 57 of the sample schemes have a re-

investment option and hence weuse their re-investment based NAVs. All the sample

schemes are open-ended in nature and are predominantly equity-based with growth and

growth-income as their objectives. The NAV values are used to estimate percentage daily

and monthly returns for the sample schemes. We also collectinformation about entry load

and management expenses for the sample funds. The data source is ICRA Mutual Funds

Software. The data set is limited due to non-availability of regular NAV and expenses

information both cross-sectionally as well as over long periods of time.

We also use individual securities data for the construction of size, value and

momentum factorsin returns both on daily and monthly basis. The data includes daily share

prices for 452 companies thatform part of BSE-500 index from January 1999 to December

2003. The sample securities account formore than 90% of market capitalisation and market

trading activity. Hence, the sample set is fairlyrepresentative of market performance. The

share prices are adjusted for capitalisation changes, such asbonus, rights and stock splits

and are used to compute percentage returns on the sample securities. Theshare prices are

obtained from Smart Investor, a technical software.

The Bombay Stock Exchange (BSE)-500 index is used as the surrogate for

aggregate economic wealth. The BSE-500 series is available from January 2000 onwards.

We therefore splice this series with the BSE-100 series for the year 1999 as the correlation

between the two indices is 0.93 for the 2000-2004 period. BSE-500 is a broad-based and

value weighted market proxy constructed on lines of Standard & Poor, USA. The datasource is BSE website. The 91-day treasury-bills are used as a riskfree proxy and are

compiled from the Reserve Bank of India (RBI) website.

We also collect information for company characteristics such as market

capitalisation (price times number of shares outstanding) and book equity to market equity

(BE/ME) ratios for the sample companies. The annual figures for market capitalisation and

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------BE/ME are obtained for December-end and March-end respectively from CMIE Provess, a

financial software.

4) Estimation Procedure

We estimate one measure of stock selectivity, i.e., the Jensen's model and two

measures of market timing, i.e., TM and HM models. We deal with two important issues in

the estimation process: (1) model selection and rectification of inherent biases and (2)

observation frequencies and the related correction procedure.

We estimate our performance measures of selectivity and market timing abilities

using both one-factor as well as multi-factor benchmarks which are described in Section 2.

The latter shall provide a selectivity measure (or alpha) that is net of compensations to

investment style characteristics. It shall also provide a matrix of market timing measures

for TM and HM models, where in addition to the market timing coefficient, we obtain

timing measurers for characteristic-based portfolios. A comparison of results between one-

factor (traditional) and four-factor (modified) versions of performance models shall bring

to light how the style characteristics affect fund performance.

The four-factor model needs construction of three additional factors, besides the

excess market return factor provided by standard CAPM. Two of the additional factors, i.e.,

size and value factors are computed based on Fama-French (1993) methodology. Using the

market capitalisation at the end of calendar year t-1, we sort the 452 sample stocks that

form part of the BSE-500 index into two groups using median break point: small or S

(bottom 50%) and big or B (top 50%). We re-rank the stocks on the basis of book equity to

market equity (BE/ME) ratio observed in March t-1 and form three groups: low (bottom

33.3%), medium (between 33.3% - 66.6%) and high (above 66.6%). The BE/ME

information is available only in March each-year as India has a April-March financial year.Combining the two size and three BE/ME groups, we generate six portfolios, i.e., S/L,

S/M, S/H, B/L, B/M and B/H. While S/L represents small cap and low BE/ME stocks, B/H

comprises of big cap high BE/ME firms. The equally-weighted monthly (daily) returns are

calculated on each of the six portfolios for the year t. The portfolios are rebalanced at the

end of t based on fresh information on company size and BE/ME for the sample stocks.

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We use the six sample portfolios to construct the size (SMB) factor. SMB (small

minus big) is measured as a difference between the average monthly (daily) returns on the

small stock portfolios and the average returns on the big stock portfolios. The construction

procedure ensures that the size factor is neutral of value effect. Similarly, we construct

value (HML) factor in returns. The HML (High minus Low) factor is the monthly (daily)

difference in the average returns on high BE/ME portfolios and the average returns on low

BE/ME portfolios. The value effect is free of size effect by construction.

We also construct the Carhart (1997) momentum factor. We rank the sample stocks

based on their average monthly (daily) returns for one-calendar year prior to portfolio

formation, i.e., year t 1 and form five groups. The bottom 20% based on past returns are

referred to as losers portfolio, while top 20% past performers form the winners portfolio.

The momentum factor is defined as the difference between the returns on equally-weighted

winners and losers portfolios or WML (Winners minus Losers). The portfolios are re-

balanced at the end of t on one-year prior return criterion. The momentum (WML) factor

has been estimated on monthly as well as daily basis.

We identify Jagannathan and Korajczyk (1986) bias in our mutual funds data that

may distort the results of our market timing measures. We adopt the bias correction

procedure suggested by Bollen and Busse (2001). We specifically construct synthetic

funds. Synthetic funds are boggy portfolios that mimic the style characteristics of actual

mutual funds but are expected to possess no market timing ability by construction. Since

we don't have daily portfolio compositions, we follow a construction methodology different

from Bollen and Busse. We regress the excess returns on a sample fund on the six size-

BE/ME portfolios and the momentum (WML) portfolio.

The regression is estimated through the origin and is constrained to have non-

negative betas (slope) which add to one. In this way, we can interpret these betas as

weights or relative exposures to the style characteristics. We then estimate weightedcharacteristics return on period to period basis, i.e, where is the

characteristic and weights and Rkt is the characteristic return in period t. We refer to this

weighted characteristic portfolio as synthetic fund, as its mimics the characteristic

properties of the actual mutual fund but without any active market timing ability. We

construct a synthetic fund as a shadow for every sample fund. We estimate the timing

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------coefficients for the synthetic funds using one-factor and four-factor versions of TM and

HM models. The acid test is to demonstrate that the timing coefficients for the sample

funds significantly exceed those of synthetic funds.

In most of the studies conducted so far, observations of mutual fund returns are

recorded monthly or annually. As discussed by Goetzmann, Ingersoll, and Ivkovic (2000),

hereafter referred to as (GII), a monthly frequency might fail to capture the contribution of

a manager's timing activities to fund returns, because decisions regarding market exposure

are made more frequently than monthly for most of the funds. We have daily observations

of mutual fund returns. This allows us to directly overcome the problem investigated by

Goetzmann et al. To determine whether observation frequency matters, we use both daily

and monthly data to test the selectivity and timing skills of fund managers. We shall use in

the next section that the tests using daily data are more powerful than those based on

monthly data.

Scholes and Williams (1977) point out that when estimating the parameters of a

factor model of daily stock returns, infrequent trading can result in biased estimates of

variance, serial correlation, and contemporaneous correlation between assets. This holds

for portfolios of infrequently traded assets as well because variance of a portfolio is largely

determined by the average covariance of the individual assets in the portfolio. While using

daily data, we employ Dimson's (1979) correction and include lagged values of the factors

as additional independent variables in the regression to accommodate infrequent trading.

5) Empirical Results-Tests of Stock Selection

Table 1 provides the results for one-factor and four-factor Jensen's selectivity

measure, using both monthly and daily data. We estimate mean alpha values for the sample

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------funds and provide the number of significantly positive alpha values at 5% level (on one-tail

basis). 25% of the sample schemes (15 out of 60) exhibit statistically significant selectivity

skills on the basis of one-factor benchamrk, when we use monthly data.

The mean alpha decline marginally from .0040 to .0036 on monthly basis, once

one accounts for investment style characteristics by shifting from one-factor to four-factor

model. The number of significantly positive alpha values increase from 15 to 17 based on

four factor benchmark as one employs daily instead of monthly data. Further, the mean

alpha improves from 4.32% to 7.5% on annualised basis when we use daily instead of

monthly return data. The mean alpha values have been annualised on the assumption of

250 trading days in a year.

Thus, we observe that after controlling for style characteristics (by employing four-

factor benchmark), the evidence on stock selectivity improves, though not substantially,

with higher observation frequency. This probably reflects that weak results on stock

selectivity reported by most previous studies, may partially be an outcome of large data

interval say use of monthly or annual data.

Table 1: Tests of selectivity based on Jensen Model

Panel -A-Monthly Data

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------One factor Four factor

Mean +ve significant

values

Mean +ve significant

values

0.0040712 15 0.0036 15

+ve- positive alpha values

Panel -B-Daily Data

One factor Four factor

Mean +ve values Mean +ve significant

values0. 0.00036 23 0.0003 17

+ve- positive alpha values

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Measures of Mutual Fund Holdings and Trades

The holdings and trades of mutual funds to evaluate the stockselection

abilities of fund managers. To examine which stocks are most widely held by

mutual funds at the end of a given quarter, we compute a measure of aggregate

stockholdings,

where Number of Shares Heldi,t is the aggregate number of shares of stock i held at the

end of quartertby all mutual funds, and Total Shares Outstandingi,t is the total number

of stocki shares outstanding as of that date.

If all mutual funds hold the market portfolio, then all stocks will have the same

FracHoldings measure, which would be roughly 12.5 percent at the beginning of 1995.

However, mutual fund managers actively managing their portfolios will have different

levels of investments in different stocks and, hence, FracHoldings measures will vary

substantially across stocks. If these managers have stock-selection talents, then we would

expect that stocks with largerFracHoldings measures would have higher future returns

than stocks with smallerFracHoldings measures.

We measure aggregate trades of a stock by mutual funds as the quarterly change in

the FracHoldings measure for that stock. Specifically, we define the aggregate trades of

stocki during quartertas

During quarters with net inflows into (outflows from) the mutual fund industry,

Trades will generally be positive (negative), with some dampening due to any changes in

the cash holdings of the funds. If managers actively pick stocks rather than passively

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------holding the market portfolio, then Trades will vary across stocks and will reflect the

consensus opinion about the value of those stocks.5

OurTrades measure is, in some ways, similar to the portfolio change measure

used by Grinblatt and Titman (1993; GT), but there are important differences. The GT

measure computes the change in portfolio weight of each stock for each fund, then

averages this measure across funds. Therefore, if a small fund buys a stock, while a large

fund sells the same number of shares of that stock, the GT portfolio change measure will be

positive. In contrast, our Trades measure will be zero, since we measure the net share

trades across all funds. Also, the GT measure captures active fund trading as well as

passive changes in portfolio weights that occur because of stock price changes during a

quarter. Thus, stocks increasing significantly in price receive a larger portfolio-weight

change than other stocks and, hence, the GT measure is tilted toward past winners. Our

Trades measure, however, is designed to track only active trades by funds, and will not

change when there are no net buys or sells by funds, in aggregate.

In a later section of this paper, we examine the performance of stocks held and

traded by funds with varying levels of portfolio turnover in order to determine whether

funds trading more frequently outperform other funds. Data on portfolio turnover are

obtained from the CRSP Mutual Fund files. CRSP defines the turnover of fund kduring

yeartas

whereBuysk,t(Sellsk,t) is the total value of stock purchases (sales) during year tby

fund k, and TNAk,t is the average total net assets of fund kduring year t. Note that the

CRSP definition of mutual fund turnover uses the minimum of buys and sells, since the

dollar value of buys minus sells is equal to the net inflow (or outflow) of money

(controlling for changes in cash holdings). This definition of turnover, therefore, captures

fund trading that is unrelated to investor inflows or redemptions.

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ASSET CLASS FACTOR MODELS

Factor models are common in investment analysis. Equation (1) is a generic representation:

Ri represents the return on asset i, Fi1represents the value of factor 1, Fi2 the value

of factor 2, Fin the value of the n'th (last) factor and ei the "non-factor" component of the

return on i. All these values are (potentially) unknown before-the-fact, as indicated by the

tildes. The remaining values (bi1 through bin) represent the sensitivities of Ri to factors Fi1

through Fin .

A key assumption makes a model of this sort more than simply an exercise in data

description: The nonfactor return for one asset (ei) is assumed to be uncorrelated with that

of every other (e.g. ej). In effect, the factors are the only sources of correlation among

returns.

An asset class factor model can be considered a special case of the generic type. In

such a model each factor represents the return on an asset class and the sensitivities (bij

values) are required to sum to 1 (100%). In effect, the return on an asset i is represented as

the return on a portfolio (shown by the sum of the terms in the bracketed expression)

invested in the n asset classes plus a residual component (e i). For expository convenience,

the sum of the terms in the brackets can be termed the return attributable to style and the

residual component (ei) the return due to selection. Indeed, a key contribution of this

approach is the separation of return into these two main components.

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------EVALUATING ASSET CLASS FACTOR MODELS

The usefulness of an asset class factor model depends on the asset classes chosen

for its implementation. While not strictly necessary, it is desirable that such asset classes be

1) mutually exclusive,

2) exhaustive and

3) have returns that "differ".

Pragmatically, each should represent a market-capitalization weighted portfolio of

securities; no security should be included in more than one asset class; as many securities

as possible should be included in the chosen asset classe; and the asset class returns should

either have low correlations with one another or, in cases in which correlations are high,

different standard deviations. While the appropriate measure of the efficacy of any specific

implementation depends on the uses to which the model is to be put, factor models are

typically evaluated on the basis of their ability to explain the returns of the assets in

question (i.e. the Ris). A useful metric is the proportion of variance "explained" by the

selected asset classes. Using the traditional definition, for asset i:

The right-hand side of equation (2) equals 1 minus the proportion of variance

"unexplained". The resulting R-squared value thus indicates the proportion of the variance

of Ri "explained" by the n asset classes1.

It is important to recognize that this measure indicates only the extent to

which a specific model fits the data at hand. A better test of the usefulness of any

implementation is its ability to explain performance out-of-sample. For this reason it is

important to consider not only the ability of a model to explain a given set of data but also

its parsimony. Other things equal (e.g. R-squared values), the fewer the asset classes, the

more likely is the model to represent continuing fundamental relationships with predictive

content2.

To evaluate the exposures of funds to changes in the returns of key asset classes,

the appropriate measure is the collective ability of a set of such classes to explain the time-

series variability in the returns on a typical fund (e.g. mutual fund or separately-managed

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------institutional account). Note that this criterion differs from that often applied in evaluating

factor models designed to describe specific portions of the overall capital market.

For example, when constructing an equity factor model, one might consider the

ability of the selected factors to explain the time-series variation in the returns of a typical

stock. Most stock market models include factors representing returns on industry groups

and/or economic sectors -- factors that account for much of the typical security's return. If

most managers diversify across industries and economic sectors,however, inclusion of

factors related to differences in industry and sector returns will add little if any explanatory

power to a model designed to explain fund returns.

RELIANCE GROWTH FUND

INVESTMENT OBJECTIVE

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------The primary investment objective of the scheme is to achieve long-term growth of capitalby investing in equity and equity-related securities through a research-based investmentapproach.

FUND DATA

Structure . . . . . Open-ended Equity Growth SchemeDate of allotment . . . . . . . . . . . . . October 8, 1995Inception Date . . . . . . . . . . . . . . . . October 8, 1995Corpus . . . Rs 4337.02 crore (September 30, 2008)Minimum Investment . . . .Retail Plan- Rs 5,000 andin multiples of Re 1 thereafter. . . . . . . . . . . . . Institutional Plan (IP)- Rs 5 cr andin multiples of Re 1 thereafterFund Manager . . . . . . . . . . . . . . . . . .Sunil SinghaniaEntry Load . . . . . . . . . . . . Retail Plan _2cr_5cr - Nil

. . . . . . . . . . . . . . . . . . . . . . . . Institutional Plan: NilExit Load ......Retail Plan - For subscription of less thanRs 5 crs per transaction - 1% if redeemed/switchedon or before completion of 1 year from the date ofallotment, NIL if redeemed/switched after completionof 1 year from the date of allotmentFor subscription of Rs 5 crs and above per purchasetransaction, no exit load shall be charged,. . . . . . . . . . . . . . . . . . . . . . . , Institutional Plan: NilNo Entry Load for Direct Investments w.e.fJanuary 4, 2008

Benchmark. . . . . . . . . . . . . . . . . . . . BSE 100 Index

ASSET ALLOCATION

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PORTFOLIO OF RELIANCE GROWTH FUNDas on September 30, 2008

Holdings Weightage (%)

Equities 73.31

Divis Laboratories Ltd. 4.75

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Reliance Industries Ltd. 3.31

Jindal Saw Ltd. 3.30

Bank of Baroda 2.94

Lupin Ltd. 2.89

Jindal Steel & Power Ltd. 2.66

Sintex Industries Ltd. 2.15

United Phosphorus Ltd. 1.95Reliance Communications Ltd 1.95

Shiv-Vani Oil & Gas Exploration

Services Limited 1.93

Jaiprakash Associates Ltd. 1.81

Adani Enterprises Limited 1.78

Jain Irrigation Systems Ltd. 1.76

BEML Limited 1.68

ICICI Bank Ltd. 1.63

Kotak Mahindra Bank Ltd. 1.59

Maruti Suzuki India Ltd. 1.53HCL Technologies Ltd. 1.45

Gujarat Mineral Development Corp Ltd. 1.44

State Bank of India 1.41

Bharti Airtel Ltd. 1.39

Reliance Infrastructure Limited 1.37

Britannia Industries Ltd. 1.28

Crompton Greaves Ltd. 1.27

Bombay Dyeing & Manufacturing Co. Ltd. 1.20

Orient Paper & Industries Ltd. 1.13

Aia Engineering Ltd. 1.10

Gujarat State Fertilizers & Chemicals Ltd. 1.09Radico Khaitan Ltd 1.06

Equity Less Than 1% of Corpus 18.49

derivatives,debt,Cash and

Other Receivables 26.69

Grand Total 100.00

VOLATILITY MEASURES

Beta

0.9036Standard Deviation

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3.9664R Squared

0.824Sharpe Ratio

0.068Portfolio Turnover Ratio

0.86

Note: The above measures have been calculated by taking rolling return fora 3 year period from 29/09/2005 with 8.65% Risk Free returns (taken as91days T-bill yield as on 29/09/2008)

BIRLA SUN LIFE MIDCAP FUND

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Investment Objective

An open-ended growth scheme with the objective to achieve long-term growth ofcapital at controlled level of risk by primarily investing in midcap stocks.

Fund Manager : Mr. A. Balasubramaniam & Mr. Sanjay Chawla

Date of inception : October 3, 2002Load Structure (Incl. for SIP):Entry Load* :< Rs. 5 crores - 2.25% Rs. 5 crores - NilExit Load** :< Rs. 5 crores - 1% if redeemed/ switched out within 12 months Rs. 5 crores - NilBenchmark : CNX MidcapAverage AUM: Rs. 337.05 Crore

Investment Performance

Past performance may or may not be sustained in future. Returns are in % and absolute

returns for period less than 1 year & CAGR for period 1 year or more.

ASSET ALLOCATION

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PORTFOLIO SELECTION

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RELIANCE TAX SAVER (ELSS) FUND

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INVESTMENT OBJECTIVEThe primary objective of the scheme is to generate long-term capital appreciation

from a portfolio that is invested predominantly in equity and equity related instruments.

FUND DATA

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Structure . . .Open-ended Equity Linked Savings SchemeDate of allotment . . . . . . . . . .September 21, 2005Inception Date . . . . . . . . . . . . .September 22, 2005Corpus . . Rs 1621.85 crore (September 30, 2008)Minimum Investment . . . . . . . Rs 500 & in multiples

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . of Rs 500Fund Manager . . . . . . . . . . . . . . . . . Ashwani KumarEntry Load . . . . . _2cr _5cr - NilExit Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NilNo Entry Load for Direct Investments w.e.fJanuary 4, 2008Benchmark. . . . . . . . . . . . . . . . . . . . BSE 100 Index

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PORTFOLIO OF RELIANCE TAX SAVER (ELSS) FUNDas on September 30, 2008

Holdings Weightage (%)Equities 66.19

Areva T & D India Ltd. 6.34ICICI Bank Ltd. 3.92Hindustan Unilever Ltd . 3.89Cipla Ltd. 3.58State Bank of India 3.23ABB Ltd. 3.06Maruti Suzuki India Ltd. 2.98Tata Consultancy Services Ltd. 2.64KSB Pumps Ltd. 2.55Triveni Engineering And Industries Ltd. 2.42Reliance Industries Ltd. 2.40

Cummins India Ltd. 2.28Eicher Motors Ltd. 2.27Pfizer Ltd. 2.11Divis Laboratories Ltd. 2.06Kingfisher Airlines Ltd 1.80Swaraj Mazda Ltd. 1.45Wipro Ltd . 1.40Tata Steel Ltd. 1.32Asahi India Glass Limited 1.28Swaraj Engines Ltd 1.18Infotech Enterprises Ltd. 1.16

Hindalco Industries Ltd . 1.15Century Plyboard India Ltd. 1.02Bharat Heavy Electricals Ltd. 1.01Equity Less Than 1% of Corpus 7.68Preference Shares, derivatives, debt,Cash and Other Receivables 33.81Grand Total 100.00

BIRLA SUN LIFE TAX RELIEF 96

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Portfolio selection of the mutual fund company E-MBA/SEM-2-----------------------------------------------------------------------------------------------------------An open-ended Equity Linked Savings Scheme (ELSS) with a lock-in of 3 years

Investment Objective

An open-ended equity linked savings scheme (ELSS) with the objective of long termgrowth

of capital through a portfolio with a target allocation of 80% equity, 20% debt and moneymarket securities

Fund Manager : Mr. Ajay GargDate of inception : March 29, 1996

Load Structure (Incl. for SIP):Entry Load* : Rs. 5 crores - 2.25%Rs . 5 crores - NilExit Load : NIL

Benchmark : BSE 200

Average AUM: Rs. 395.50 Crores

Investment Performance

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Company Profile Mutual Fund

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