finance fie

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PREFACE

This report presents data issues and challenges faced by researchers in the broad area of

Indian Securities Markets research. The availability of reliable and quality database are

crucial for high quality research. Network for Securities Market Data, an initiative of

NISM, expected to address most of the data challenges as it provides a common interface

through which researchers can access different data provided by different vendors in a

uniform accessible manner. Further, the report give a brief overview of relevant research

issues in broad areas of Market Microstructure and Mutual Funds and highlights data

unavailability to pursue such important research questions in the present context. NSMD,

proposed to be launched in July 2010, is a plausible solution as it aims to uncover the data

inconsistencies and provide comprehensive researcher friendly database.

The report gives an overview on data problems faced by researchers working on Indian

Securities Market research, in particular research focusing on Market Microstructure and

Mutual Funds area. Probably, most of these data issues will be resolved with the launch of

Network of Securities Market Data, an initiative of NISM. The first phase of NSMD, to be

launched in July 2010, covers Price (Daily as well as Intraday) database and Financial

Accounting database.

The next phases of NSMD will cover databases relating to IPO, Mutual Funds, Corporate

Governance, Debt markets and others. NISM is committed to support research in securities

markets and intends to provide access to quality data to large number of researchers at a

reasonable price and in most efficient accessible manner.


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ACKNOWLEDGEMENT

I t gives me immense pleasure and sense of accomplishment in presenting

my project report on TAX EFFECT IN SECURITY MARKET as a partialfulfillment of the requirement of MBA.

No task however small can be completed with out proper guidance and

encouragement. This acknowledgement transcends the reality of formality,

hence I would l ike to express my deep grat i tude to al l those behind the

scene who have helped me to transform the idea into a working project.

I extend my heartfel t thanks to Mrs. Garima mam for his guidance and

help towards completion of this project.

And last but not the least my family and teachers who had contributed their

best efforts in sending me away from them and appreciated online for

achieving the target.

All these persons who have provided many construct ive and valuablesuggestions and all the people of the organization who have give me time to

contribute his/her views about their organizations.


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Table Of Contents


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Introduction

In Budget 2004-2005 Finance minister P. Chidambaram announced the implementation of

a transaction cost of 0.15% on all dealings carried out through Stock Exchanges acrossIndia, while abolishing the Long Term Capital Gains Tax and reducing Short term Capital

Gains tax to a flat 10% before surcharge and educational cess. The rational behind the

move as communicated by the finance ministry is two fold to remove speculation in

Stock markets thereby reducing volatility and to generate revenues to the tune of 7000

crores per year from the Transaction Tax compared to around 350 crores generated by the

Long Term Capital Gains Tax. However empirical evidence and economic analysis of the

subject suggests that the such a move can only increase the market volatility. Moreover the

tax figures could be significantly lower than expected due to reduction of tax base itself.

We examine the effect of such taxes on the Stock Market Dynamics of three particular

cases of Sweden, Japan and UK and elaborate on what could be in store for Indian Stock

markets.

Securities Transaction Tax (STT) is a tax being levied on all transactions done on the

stock exchanges at rates prescribed by the Central Government from time to time. Pursuant

to the enactment of the Finance (No.2) Act, 2004, the Government of India notified the

Securities Transaction Tax Rules, 2004 and STT came into effect from October 1, 2004

TAX EFFECTS IN SECURITY MARKETS:

Tax Effects in the Relative Pricing of Treasury Bonds

For investors with different tax rates, the relative values of treasury bonds will appear

different because the after-tax cash flows they obtain from one bond versus another will

look dramatically different. For example, where a tax exempt institution or a securities

dealer might view two bonds as perfect substitutes, an individual investor might only

purchase them at differing prices because of their different tax treatment. Which clientele

ends up determining the relative pricing of the bonds then becomes an empirical issue.

Research in this area develops econometric methods for discerning the presence of such tax


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effects in the relative pricing of default-free bonds. The findings show dramatic evidence of

a shift in the importance of tax effects in relative pricing around the time of the 1986 Tax

Reform in the United States.

Term and Tax Effects in the Pricing of Municipal Bonds

The municipal bond market has always been of special interest to scholars in financial

economics, because it affords the chance to observe the pricing of bonds that are very

similar to treasuries or corporates, except for their tax treatment. Coupon income on

municipals is tax-exempt. Traditional theories predict that the yields on par taxable and tax-

exempt bonds should be related in a simple way. The tax-exempt yield should be the

taxable yield times one minus the tax rate of a representative investor. This relationship

explains the relative pricing of the two types of bonds fairly well for short maturities, but is

grossly violated in the data for long maturity bonds. This research provides a simple theory

which predicts such behavior as a consequence of taxable investors' attempts to minimize

their tax burden in the construction of their portfolios of taxable bonds. The theory is tested

using data though the post-war period, and explains the relative yields on taxables and tax

exempts quite well.

The Effect of Capital Gains Taxation on the Optimal Trading and Equilibrium

Pricing of Financial Assets

Capital gains and losses on financial assets are not taxed until the asset is sold, giving

investors the option to time their asset sales so as to minimize the present value of the taxes

paid to the government. The first purpose of this research is to investigate the optimal tax-

trading policies of investors in the presence of capital gains taxation and transaction costs.

The optimal tax-trading policy is characterized by an optimal cutoff level above which all

capital gains are deferred and below which all capital gains and losses are realized. The

optimal cutoff level depends upon the magnitude of the long-term and short-term capital

gains tax rates, the length of the investor's holding period, and length of time before the

investor's holding period becomes long-term, and the volatility of the asset. The second

purpose of this research is to investigate the equilibrium pricing implications of investors'

optimal tax-trading strategies. If the tax-timing option is valuable to investors, then it


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In direct lending, the Badla financier lent money to the long buyer to carry the trade

forward. When the long buyer decided to settle his position, he returned this money to the

Badla financier along with the interest (Badla charge) due on it.

In lending short positions, carry forward was done as a two-way

transaction, both parts of which were executed simultaneously by the Badla financier

during the Badla session. The first leg was a stock purchase transaction at the standard rate

in the current settlement while the second leg was a stock sale transaction (to the long

buyer) at the standard rate plus the interest charge (Badla charge) in the next settlement.

This system imparted liquidity to the market but the lack of adequate

margin requirements made it very easy to indulge in speculative trading.

In 1994, the market crashed after a long bull run and the trading volumes were very low. At

this stage, the Securities and Exchange Board of India (SEBI) banned Badla financing as

well as forward trading until 1996.

Many different forms of forward trading system were considered subsequent to the era of

Badla financing and the current one consists of three groups of shares:

BSE forward shares.

These are the most frequently traded shares and trading in these shares can be

carried forward to the next settlement. There is a 10 per cent margin requirement on

all trading with the exchange reserving the right to impose furtherad hoc margins

in case of volatile trading in any particular scrip.

BSE cash B1 shares.

Trading in these shares has to be completed by the end of the settlement period.

BSE cash B2 shares. This category comprises the remaining stocks on the market, which have to be dealt

in strictly on a cash basis.


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Until 1994, the Indian market had a 14-day settlement period but since 1996 it has been

shortened to one week. The settlement period ends on a Friday on the BSE and a Tuesday

on the NSE.

The stock market is one of the most important sources forcompanies to raise money. This

allows businesses to be publicly traded, or raise additional capital for expansion by selling

shares of ownership of the company in a public market. The liquidity that an exchange

provides affords investors the ability to quickly and easily sell securities. This is an

attractive feature of investing in stocks, compared to other less liquid investments such

as real estate.

History has shown that the price ofshares and other assets is an important part of the

dynamics of economic activity, and can influence or be an indicator of social mood. An

economy where the stock market is on the rise is considered to be an up and coming

economy. In fact, the stock market is often considered the primary indicator of a country's

economic strength and development. Rising share prices, for instance, tend to be associated

with increased business investment and vice versa. Share prices also affect the wealth of

households and their consumption. Therefore, central banks tend to keep an eye on the

control and behavior of the stock market and, in general, on the smooth operation

offinancial system functions. Financial stability is the raison d'tre of central banks.

Exchanges also act as the clearinghouse for each transaction, meaning that they collect and

deliver the shares, and guarantee payment to the seller of a security. This eliminates the

risk to an individual buyer or seller that the counterparty could default on the transaction.

The smooth functioning of all these activities facilitates economic growth in that lower

costs and enterprise risks promote the production of goods and services as well as

employment. In this way the financial system contributes to increased prosperity. An

important aspect of modern financial markets, however, including the stock markets, isabsolute discretion. For example, American stock markets see more unrestrained

acceptance of any firm than in smaller markets. For example, Chinese firms that possess

little or no perceived value to American society profit American bankers on Wall Street, as

they reap large commissions from the placement, as well as the Chinese company which

yields funds to invest in China. However, these companies accrue no intrinsic value to the
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long-term stability of the American economy, but rather only short-term profits to

American business men and the Chinese; although, when the foreign company has a

presence in the new market, this can benefit the market's citizens. Conversely, there are

very few large foreign corporations listed on the Toronto Stock Exchange TSX, Canada's

largest stock exchange. This discretion has insulated Canada to some degree to worldwide

financial conditions. In order for the stock markets to truly facilitate economic growth via

lower costs and better employment, great attention must be given to the foreign participants

being allowed in.

Relation of the stock market to the modern financial system

The financial systems in most western countries has undergone a remarkable

transformation. One feature of this development is disintermediation. A portion of the

funds involved in saving and financing flows directly to the financial markets instead of

being routed via the traditional bank lending and deposit operations. The general public's

heightened interest in investing in the stock market, either directly or through mutual funds,

has been an important component of this process. Statistics show that in recent decades

shares have made up an increasingly large proportion of households' financial assets in

many countries. In the 1970s, in Sweden, deposit accounts and other very liquid assets with

little risk made up almost 60 percent of households' financial wealth, compared to less than

20 percent in the 2000s. The major part of this adjustment in financial portfolios has gone

directly to shares but a good deal now takes the form of various kinds of institutional

investment for groups of individuals, e.g., pension funds, mutual funds, hedge funds,

insurance investment of premiums, etc. The trend towards forms of saving with a higher

risk has been accentuated by new rules for most funds and insurance, permitting a higher

proportion of shares to bonds. Similar tendencies are to be found in otherindustrialized

countries. In all developed economic systems, such as the European Union, the United

States, Japan and other developed nations, the trend has been the same: saving has movedaway from traditional (government insured) bank deposits to more risky securities of one

sort or another.
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The stock market, individual investors, and financial risk

Riskier long-term saving requires that an individual possess the ability to manage the

associated increased risks. Stock prices fluctuate widely, in marked contrast to the stability

of (government insured) bank deposits or bonds. This is something that could affect not

only the individual investor or household, but also the economy on a large scale. The

following deals with some of the risks of the financial sector in general and the stock

market in particular. This is certainly more important now that so many newcomers have

entered the stock market, or have acquired other 'risky' investments (such as 'investment'

property, i.e., real estate and collectables).

With each passing year, the noise level in the stock market rises. Television commentators,

financial writers, analysts, and market strategists are all overtaking each other to get

investors' attention. At the same time, individual investors, immersed in chat rooms and

message boards, are exchanging questionable and often misleading tips. Yet, despite all

this available information, investors find it increasingly difficult to profit. Stock prices

skyrocket with little reason, then plummet just as quickly, and people who have turned to

investing for their children's education and their own retirement become frightened.

Sometimes there appears to be no rhyme or reason to the market, only folly.

Taxation

According to much national or state legislation, a large array of fiscal obligations are taxed

forcapital gains. Taxes are charged by the state over the transactions, dividends and capital

gains on the stock market, in particular in the stock exchanges. However, these fiscal

obligations may vary from jurisdiction to jurisdiction because, among other reasons, it

could be assumed that taxation is already incorporated into the stock price through the

different taxes companies pay to the state, or that tax free stock market operations are

useful to boost economic growth.

Taxes in India are levied by the Central Government and the State Governments. Some

minor taxes are also levied by the local authorities such the Municipality or the Local

Council.
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The authority to levy a tax is derived from the Constitution of Indiawhich allocates the

power to levy various taxes between the Centre and the State. An important restriction on

this power is Article 265 of the Constitution which states that "No tax shall be levied or

collected except by the authority of law."[1] Therefore each tax levied or collected has to be

backed by an accompanying law, passed either by the Parliament or the State Legislature

Constitutionally established scheme of Taxation

Article 246[2]of the Indian Constitution, distributes legislative powers including taxation,

between theParliament and theState Legislature. Schedule VII[3] enumerates these subject

matters with the use of three lists;

List - I entailing the areas on which only the parliament is competent to makes

laws,

List - II entailing the areas on which only the state legislature can make laws, and

List - III listing the areas on which both the Parliament and the State Legislature

can make laws upon concurrently.

Separate heads of taxation are provided under lists I and II. There is no head of taxation in

the Concurrent List (Union and the States have no concurrent power of taxation). The list

of thirteen Union heads of taxation and the list of nineteen State heads are given below:

S.

No.Parliament

1 Taxes on income other than agricultural income (List I, Entry 82)

2 Duties of customs including export duties (List I, Entry 83)

3 Duties of excise on tobacco and other goods manufactured or produced in India except

(i) alcoholic liquor for human consumption, and (ii) opium, Indian hemp and other

narcotic drugs and narcotics, but including medicinal and toilet preparations containing
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alcohol or any substance included in (ii). (List I, Entry 84)

4 Corporation Tax (List I, Entry 85)

5Taxes on capital value of assets, exclusive of agricultural land, of individuals and

companies, taxes on capital of companies (List I, Entry 86)

6 Estate duty in respect of property other than agricultural land (List I, Entry 87)

7Duties in respect of succession to property other than agricultural land (List I, Entry

88)

8Terminal taxes on goods or passengers, carried by railway, sea or air; taxes on railway

fares and freight (List I, Entry 89)

9Taxes other than stamp duties on transactions in stock exchanges and futures markets

(List I, Entry 90)

10Taxes on the sale or purchase of newspapers and on advertisements published therein

(List I, Entry 92)

11Taxes on sale or purchase of goods other than newspapers, where such sale or

purchase takes place in the course of inter-State trade or commerce (List I, Entry 92A)

12 Taxes on the consignment of goods in the course of inter-State trade or commerce(List I, Entry 93A)

13 All residuary types of taxes not listed in any of the three lists (List I, Entry 97)
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S.

No.State Legislature

1

Land revenue, including the assessment and collection of revenue, the maintenance of

land records, survey for revenue purposes and records of rights, and alienation of

revenues (List II, Entry 45)

2 Taxes on agricultural income (List II, Entry 46)

3 Duties in respect of succession to agricultural income (List II, Entry 47)

4 Estate Duty in respect of agricultural income (List II, Entry 48)

5 Taxes on lands and buildings (List II, Entry 49)

6 Taxes on mineral rights (List II, Entry 50)

7

Duties of excise for following goods manufactured or produced within the State (i)

alcoholic liquors for human consumption, and (ii) opium, Indian hemp and other

narcotic drugs and narcotics (List II, Entry 51)

8Taxes on entry of goods into a local area for consumption, use or sale therein (List II,

Entry 52)
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9 Taxes on the consumption or sale of electricity (List II, Entry 53)

10 Taxes on the sale or purchase of goods other than newspapers (List II, Entry 54)

11Taxes on advertisements other than advertisements published in newspapers and

advertisements broadcast by radio or television (List II, Entry 55)

12Taxes on goods and passengers carried by roads or on inland waterways (List II, Entry

56)

13 Taxes on vehicles suitable for use on roads (List II, Entry 57)

14 Taxes on animals and boats (List II, Entry 58)

15 Tolls (List II, Entry 59)

16 Taxes on profession, trades, callings and employments (List II, Entry 60)

17 Capitation taxes (List II, Entry 61)

18Taxes on luxuries, including taxes on entertainments, amusements, betting and

gambling (List II, Entry 62)

19 Stamp duty (List II, Entry 63)

Any tax levied by the government which is not backed by law or is beyond the powers of

the legislating authority may be struck down as unconstitutional.

Other Major Taxation Laws
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Other major taxation laws enacted by the Parliament are;

1. Wealth Tax Act, which has a regular history of being passed and repealed;

2. Service Tax, imposed under Finance Act, 1994, which taxes the provision

of services provided by service providers within India or services imported by

Indian from outside India;

3. Central Excise Act, 1944, which imposes a duty of excise on goods

manufactured or produced in India;

4. Customs Act, 1962, which imposes duties of customs, counterveiling duties

and anti-dumping duties on goods imported in India;

5. Central Sales Tax, 1956, which imposes sales tax on goods sold in inter-

state trade or commerce in India;

6. Transaction Tax, which taxes transactions of sale of securities and other

specified transactions;

The major taxation enactments passed by the State Legislatures are in the nature of the

following;

1. Excise duties on tobacco, alcohol and narcotics;

2. Sales tax, on sale of goods within the State;

3. Stamp duties, on sale of property situated within the State;

4. Entertainment taxes

Trading on the Indian market is through brokers and sub-brokers.

The market was computerised on the NSE in 1990 and on the BSE in 1996. This made

it possible to offer direct market quotes to investors. Prior to that, investors had to give

trading limits and depend upon brokers for the actual price of trade execution. Often,

members of the exchange specialise in trading of specific stocks and offer two-way

quotes, but there are no official market makers. Another feature of the market is the 31

March financial year ending (as opposed to 31 December in many countries). There is

a capital gains tax imposed on gains from the market. One can also offset capital losses

against capital gains for tax purposes.
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The tests performed are from the parametric group and the various hypothesis

tested are listed below. Multiple Regression using dummy variables has been carried out

and theF- as well as t-tests have been done to test for significance. The daily returns are

calculated as:Equation1

whereI(t) refers to index price on day t

Data

The data for this study consist of BSE data that comprise of weekly data for the period

1979-1998 and daily data for the period 1987-1998. The NSE data are daily and weekly

from 1990 to 1998. The daily data are used for the day-of-the-week and weekend effect

while the weekly data are used for the January/April effect. All the data points where

returns are zero have been eliminated. Also those weeks where data are not available for all

days of the week have been eliminated.

Week day effects

Day-of-the-week effect.

Equation 2

whereRtis the return on day t,R(t)=Ln(I(t))/I(t1)*100 whereI(t) refers to index price on

dayt; aiis the mean return for each day-of-the-week; d1 through d5 are day-of-the-week

dummies that are either 0 or 1 (d1=1 for Monday and 0 otherwise and so on); utis the

random error term for day t.Hypothesis (Ho): a1=a2=a3=a4=a5 If this hypothesis is

rejected, it would imply that the mean daily returns aiare significantly different from each
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other, i.e. there is seasonality in returns across different days of the week.Hypothesis (Ho):

Each aiis tested for significance (difference from zero) Weekend effect. This tests the

following two hypotheses:

Trading time hypothesis.

Mean return (Monday) = Mean return (other days of the week):Equation 3

whereRtis the return on dayt; a0, expected Monday return; a2 to a5, difference between

expected Monday returns and the returns on other days of the week; d2 through d5 are day-

of-the-week dummies that are either 0 or 1 (d2=1 for Tuesday and 0 otherwise and so

on).Hypothesis (Ho): a2=a3=a4=a5=0 If this hypothesis is rejected, it implies that the

Monday returns are significantly different from other days of the weeks. Moreover, the

sign of coefficients a2 to a5 indicates whether the difference is positive or negative.

For the calendar time hypothesis.

Mean return (Monday)=3*mean return (other days of the week):

Equation 4

whereRtis the return on day t; a0, expected Monday return/3; a2 to a5, difference

between one-third of Monday returns and the returns on other days of the

week; d2 through d5 are day-of-the-week dummies that are either 0 or 1 (d2=1 for Tuesday

and 0 otherwise and so on).Hypothesis (Ho): a2=a3=a4=a5=0 If this hypothesis is

rejected, it implies that one-third of Monday returns are significantly different from other
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days of the weeks. The sign of coefficients a2 to a5 indicates whether the difference is

positive or negative.

Month of the year effect

January effectEquation 5

whereRtis the monthly return in month t; a0, expected January return; ait, difference

between the expected return for January and the other months of the year; dit, dummy

variable for months of the year and are 0 or 1 (d2t=1 for February and 0 otherwise and so

on).Hypothesis (Ho): a2 = a3= = a12=0 If this hypothesis is rejected, it implies thatJanuary returns are significantly different from other months of the year. The signs of

coefficients a2 to a12 indicate whether the difference is positive or negative.

April effect. The April effect test is similar to the January effect test. It is being done for

the Indian market to test the tax-loss selling hypothesis because the financial year ending in

India is 31 March.

Equation 6

whereRtis the return in month t; a0, expected April return; ait, difference between the

expected return for April and the other months of the year; dit, dummy variable for months

of the year and are 0 or 1 (d2t=1 for February and 0 otherwise and so on).Hypothesis

(Ho): a2=a3==a12=0 If this hypothesis is rejected, it implies that April returns are

significantly different from other months of the year. The sign of

coefficients a2 to a12 indicates whether the difference is positive or negative.

Results
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Week day effects

Day-of-the-week effect.

The results indicate that the variation in the returns across different days of the week is not

significant at the 10 per cent level, i.e. the null hypothesis cannot be rejected (Table I).

The Monday returns are however, not negative, thereby contradicting the negative Monday

effect. In fact, Monday returns are strongly positive (at the 5 per cent significance level).

The Tuesday returns are negative (though not significant) as also found by Agrawal and

Tandon (1994) and Dubois and Louvet (1996). The day-of-the-week effect being

insignificant is in contrast to the result found by Chan et al. (1996) on the Indian market.

Weekend effect.

As Monday returns are found to be significantly positive, the data has been analysed for the

weekend effect (Table II)

The Monday returns again are not significantly different from the other days of the week at

the 10 per cent level. The returns for all four days are lower than Monday (Tuesday,
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Wednesday and Thursday returns are significantly lower than the Monday returns for the

BSE data; Tuesday returns are significantly lower than Monday for the NSE data).

As Monday returns are much higher than for other days of the week, the calendar time

hypothesis has been tested (Table III)

and proves significant at 8.5 per cent (BSE) and 1.6 per cent (NSE). Tuesday returns are

still significantly lower than Monday returns for both BSE and NSE. Infact, returns for all

four days are lower than Monday (BSE) even for the calendar time hypothesis but only

Tuesday and Wednesday are significant. On the NSE, Tuesday and Thursday are lower

than Monday but only Tuesday is significant.

Month of the year effect

January effect. The returns for January prove to be different from those for the other

months of the year at the 5 per cent significance level (BSE) (Table IV).
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The January returns are not, however, higher than those for most of the other months as

noted by Lee (1992) in the Pacific basin countries. In fact, barring March, May, October

and November the returns for all other months are higher than for January. From the

average returns for each month it is observed that December is definitely not among the

lower return months as observed in many other studies.

April effect. The April effect test has been done to corroborate the tax-loss selling

hypothesis(Table V).

April returns are found to be significantly different from other days of the week at the 5

per cent (BSE) and 10 per cent (NSE) levels. The April returns are higher than those for

nine other months on the BSE but only October and November are significant. February

and June returns are higher than April on the BSE.

implications

This study indicates that though the Indian market does exhibit seasonality in returns, this

seasonality is very different from that observed commonly in other markets. The negative

Monday effect and the positive January effect has not been observed. In fact, Monday

returns are significantly higher than the other days of the week even for the calendar time

hypothesis. The Tuesday returns are negative (though not significantly so) as has been

observed by some other studies.
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The positive Monday returns could have a possible explanation in the settlement period

hypothesis because the 14-day settlement period in India used to start on a Monday and end

on a Friday. Thus, low Friday closing prices coupled with high opening Monday prices

could lead to the positive Monday returns.

The April effect held true for nine out of 11 months and seems to corroborate the tax-loss

selling hypothesis to some extent. It should however, be noted that the March returns are

not among the lowest in the year (as should be the case for the tax-loss selling).

The variance in seasonality in the Indian market as compared to the other developed

markets implies that this market is not yet integrated with the other world markets and can

provide a good portfolio diversification opportunity.

The stock market is likely to carry on with the feel-good factor

of the Budget in the coming week and open on a positive note tomorrow, say analysts,

adding however, going forward, the market will take cues from its global peers to find

direction."The Budget was an obstacle for the market. As it is over now, the market will

move freely. For sometime, the market will follow the positive cues from the Budget,"

HDFC Securities head for private broking and wealth management Vinod Sharma

said.Analysts also say as trading would be truncated this week, market will remain mostlypositive and gradually start following global cues, probably by the later part of the

week."On the back of a good Budget, the market will open on a strong note on Tuesday

and later on, it will follow global cues," CNI Research managing director Kishor Ostwal

said.The Budget gave major personal income tax sops but effected a 2 per cent hike in

excise duty across the board while increased levies on petrol and diesel-- in effect a partial

rollback of the stimulus measures.The 30-share BSE Sensex settled with 175 points gain on

Friday-- first time in four on a Budget day-- to settle at 16,429.55 points. The index had

surged 420 points intra-day on the Budget day after it announced increased expenditure for

infrastructure sector and income tax sops and efforts to bring down fiscal deficit.

Seasonal variations in production and sales are a well known fact in

business. Seasonality refers to regular and repetitive fluctuation in a time series which


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occurs periodically over a span of less than a year. The main cause of seasonal variations in

time series data is the change in climate.

For example, sales of woolen clothes generally increase in winter season. Besides this,

customs and tradition also affect economic variables for instance sales of gold increase

during marriage seasons. Similarly, stock returns exhibits systematic patterns at certain

times of the day, week or month. The most common of these are monthly patterns; certain

months provide better returns as compared to others i.e. the month of the year effect.

Similarly, some days of the week provides lower returns as compared to other trading days

i.e. days of the week effect.

The existence of seasonality in stock returns however violates an

important hypothesis in finance that is efficient market hypothesis. The efficient market

hypothesis is a central paradigm in finance. The EMH relates to how quickly and

accurately the market reacts to new information.

New data are constantly entering the market place via economic reports,

company announcements, political statements, or public surveys. If the market is

informationally efficient then security prices adjust rapidly and accurately to new

information. According to this hypothesis, security prices reflect fully all the information

that is available in the market. Since all the information is already incorporated in prices, a

trader is not able to make any excess returns. Thus, EMH proposes that it is not possible to

outperform the market through market timing or stock selection. However, in the context of

financial markets and particularly in the case of equity market seasonal component have

been recorded. They are called calendar anomalies (effects) in literature.

The presence of seasonality in stock returns violates the weak form of market efficiency

because equity prices are no longer random and can be predicted based on past pattern.

This facilitates market participants to devise trading strategy which could fetch abnormal

profits on the basis of past pattern. For instance, if there are evidences of day of the week

effect, investors may devise a trading strategy of selling securities on Fridays and buying

on Mondays in order to make excess profits. Aggarwal and Tandon (1994) and Mills and

Coutts (1995) pointed out that mean stock returns were unusually high on Fridays and low

on Mondays. One of the explanation put forward for the existence of seasonality in stock


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returns is the tax-loss-selling hypothesis. In the USA, December is the tax month. Thus,

the financial houses sell shares whose values have fallen

to book losses to reduce their taxes. As of result of this selling, stock prices decline.

However, as soon as the December ends, people start acquiring shares and as a result stock

prices bounce back. This lead to higher returns in the beginning of the year, that is, January

month. This is called January effect. In India, March is the tax month, it would be

interesting to find April Effect.

Theoretical Background

The term efficient market refers to a market that adjusts rapidly to new information.

Fama (1970) stated , A market in which prices always fully reflect available information

is called efficient. If capital markets are efficient, investors cannot expect to achieve

superior profits by adopting a certain trading strategy. This is popularly called as the

efficient market hypothesis.

The origins of the EMH can be traced back to Bacheliers doctoral thesis Theory of

Speculation in 1900 and seminal paper titled Proof That Properly Anticipated Prices

Fluctuate Randomly by Nobel Laureate Paul Samuelson in 1965. But it was Eugane

Famas work (1970) Efficient Capital Markets who coined the term EMH and advocated

that in efficient market securities prices fully reflect all the information.

It is important to note that efficiency here does not refer to the organisational or operational

efficiency but informational efficiency of the market. Informational efficiency of the

market takes three forms depending upon the information reflected by securities prices.

First, EMH in its weak form states that all information impounded in the past price of a

stock is fully reflected in current price of the stock. Therefore, information about recent or

past trend in stock prices is of no use in forecasting future price. Clearly, it rules out the use

of technical analysis in predicting future prices of securities. The semi-strong form takes

the information set one step further and includes all publically available information. There

is plethora of information of potential interest to investors. Besides past stock prices, such

things as economic reports, brokerage firm recommendations, and investment advisory

letters.


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However, the semi-strong form of the EMH states that current market prices reflect all

publically available information. So, analysing annual reports or other published data with

a view to make profit in excess is not possible because market prices had already adjusted

to any good or bad news contained in such reports as soon as they were revealed. The EMH

in its strong form states that current market price reflect all both public and private

information and even insiders would find it impossible

to earn abnormal returns in the stock market. However, there is the notion that some stocks

are priced more efficiently than others which is enshrined in the concept ofsemi-efficient

markethypothesis. Thus, practitioners support the thesis that the market has several tiers or

that a pecking order exist. The first tier contains well-known stocks such as Reliance

Industries and Sail which are priced more efficiently than other lesser-known stocks such

as UCO Bank.

However, instead of considering stocks, we analyzed this phenomenon using Nifty

Junior index which is an index of next most liquid stocks after S&P Nifty.

Review of Literature

Seasonality or calendar anomalies such as month of the year and day of the week effects

has remained a topic of interest for research since long time in developed as well as

developing countries. Watchel (1942) reported seasonality in stock returns for the first

time. Rozeff and Kinney (1976) documented the January effect in New York Exchange

stocks for the period 1904 to 1974. They found that average return for the month of

January was higher than other months implying pattern in stock returns. Keim (1983) along

with seasonality also studied size effects in stock returns. He found that returns of small

firms were significantly higher than large firms in January month and attributed this

finding to tax-loss-selling and information hypothesis. A similar conclusion was found by

Reinganum (1983), however, he was of the view that the entire seasonality in stock returns

cannot be explained by tax-loss-selling hypothesis. Gultekin and Gultekin (1983) examined

the presence of stock market seasonality in sixteen industrial countries. Their evidence

shows strong seasonalities in the stock market due to January returns, which is

exceptionally large in fifteen of sixteen countries. Brown et al. (1985) studied the

Australian stock market seasonality and found the evidence of December-January and July-


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August seasonal effects, with the latter due to a June-July tax year. However, Raj and

Thurston (1994) found that the January and April effects are not statistically significant in

the NZ stock market. Mill and Coutts (1995) studied calendar effect in FTSE 100, Mid 250

and 350 indices for the period 1986 and 1992. They found calendar effect in FTSE 100.

Ramcharan (1997), however, didnt find seasonal effect in stock retruns of Jamaica.

Choudhary (2001) reported January effect on the UK and US returns but not in German

returns. Fountas and Segredakis (2002) studied 18 markets and reported seasonal patterns

in returns. The reasons for the January effect in stock returns in most of the developed

countries such as US, and UK attributed to the tax loss selling hypothesis, settlement

procedures, insider trading information. Another effect is window dressing which is related

to institutional trading. To avoid reporting to many losers in their portfolios at the end of

year, institutional investors tend to sell losers in Decembers. They buy these stocks after

the reporting date in January to hold their desired portfolio structure again.

Researchers have also reported half- month effect in literature. Various

studies have reported that daily stock returns in first half of month are relatively higher

than last half of the month. Ariel(1987) conducted a study using US market indices from

1963 to 1981 to show this effect. Aggarwal and Tandon (1994) found in their study such

effect in other international markets. Ziemba (1991) found that returns were consistently

higher on first and last four days of the month.

The holiday effect refers to higher returns around holidays, mainly in the pre-holiday

period as compared to returns of the normal trading days. Lakonishok and Smidt (1988)

studied Dow Jones Industrial Average and reported that half of the positive returns occur

during the 10 preholiday trading days in each year. Ariel (1990) showed using US stock

market that more than one-third positive returns each year registered in the 8 trading days

prior to a market-closed holiday. Similar conclusion were brought by Cadsby and Ratner

(1992) which documented significant pre-holiday effects for a number of stock markets.

However, he didnt find such effect in the European stock markets. Husain (1998) studied

Ramadhan effect in Pakistan stock market. He found significant decline in stock returns

volatility in this month although the mean return indicates no significant change.

There are also evidences of day of the week effect in stock market returns. The Monday

effect was identified as early as the 1920s. Kelly (1930) based on three years data of the US


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market found Monday to be the worse day to buy stocks. Hirsch (1968) reported negative

returns in his study. Cross (1973) found the mean returns of the S&P 500 for the period

1953 and 1970 on Friday was higher than mean return on Monday. Gibbons and Hess

(1981) also studied the day of the week effect in US stock returns of S&P 500 and CRSP

indices using a sample from 1962 to 1978. Gibbons and Hess reported negative returns on

Monday and higher returns on Friday. Smirlock and Starks (1986) reported similar results.

Jaffe and Westerfield (1989) studied day of the week effect on four international stock

markets viz. U.K., Japan, Canada and Australia. They found that lowest returns occurred

on Monday in the UK and Canada. However, in Japanese and

Australian market, they found lowest return occurred on Tuesday. Brooks and Persand

(2001)studied the five southeast Asian stock markets namely Taiwan, South Korea, The

Philippines,Malaysia and Thailand. The sample period was from 1989 to 1996. They found

that neither South Korea nor the Philippines has significant calendar effects. However,

Malaysia and Thailand showed significant positive return on Monday and significant

negative return on Tuesday. Ajayi& al. (2004) examined eleven major stock market indices

on Eastern Europe using data from 1990 to 2002. They found negative return on Monday in

six stock markets and positive return on Monday in rest of them. Pandey (2002) reported

the existence of seasonal effect in monthly stock returns of BSE Sensex in India and

confirmed the January effect. Bodla and Jindal (2006) studied Indian and US market and

found evidence of seasonality. Kumari and Mahendra (2006) studied the day of the week

effect using data from 1979 to 1998 on BSE and NSE. They reported negative returns on

Tuesday in the Indian stock market. Moreover, they found returns on Monday were higher

compared to the returns of other days in BSE and NSE. Choudhary and Choudhary (2008)

studied 20 stock markets of the world using parametric as well as non-parametric tests. He

reported that out of twenty, eighteen markets showed significant positive return on various

day other than Monday. The scope of the study is restricted to days of-the week effect,

weekend effect and monthly effect in stock returns of S&P CNX Nifty and select firms.

The half month effect and holiday effect are not studied here.

The Indian securities markets have grown in size as well as in depth over the

years and stood out in world ranking. India has the distinction of having the second largest


30/67

number of listed companies after USA; 8th in terms of market capitalization and 15th in

terms of turnover ratio. However, compared to its size, it is a heavily under researched

market and relatively very few (countable) research papers working on Indian securities

market have featured in top financial journals. High quality research demands access to

high quality data. NISM took the initiative of providing clean and validated data to carry

out high quality academic research by establishing Network for Securities Market Data.

This paper first highlights the issues and concerns of existing data bases the next section

documents the formation of NSMD and describes specific unique features of NSMD

interface. Subsequent sections document two important research areas in securities market

where no complete data base exists and how far NSMD is planning to cover those data

gaps.

Data base issues and concerns

Unlike in developed nations, none of the data vendors in India build and maintain a data

base keeping academic researcher as the target customer. Some of the prominent issues or

concerns of academic researchers to do high quality research are listed below:

The data vendors maintain / sell data base of a particular segment of securities

market, for e.g. the vendor selling primary data will not have secondary data of

securities market. There is no single vendor who maintains all data bases at one

place. Most of the data vendors who accumulate data relating to the financial information

use their own channels and follow their own methods to acquire data.

This practice has two major implications:

They may not cover all companies which in turn leads to the missing values

problem and data inconsistencies.

They use their own definitions for various fields which in turn obstructs smooth

integration of various datasets provided by different data vendors.

Most of the data vendors replace the old data with the new data (that belongs to the

same field) for some of the fields that dont have commercial value. This has

resulted in the death of historical information for some of the major researcher

value fields.


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None of the data vendors have PhDs to design their data structures and validate

their data. They follow industry experts advice in the design and validation stage.

This has resulted in the above mentioned gaps in data acquisition.

The pricing of databases in India is quite prohibitive for researchers to engage in

any research project.

Availability of reliable data on Indian securities market has been one of stumbling

blocks for doing research.

Formation of Network for Securities Market Data

The effort to restore and build an integrated database and also to establish researcher

friendly interface was initially formalized through a concept paper of Anshuman and

Badrinath (2007). Their paper highlights the importance of having an integrated interface

with a common standards approach as followed by WRDS. National Institute of Securities

Markets (NISM), established by SEBI, recognized the need and importance to have a data

base interface that integrates, validates and disseminates comprehensive data on Indian

companies for the financial economics and accounting research community across the

world. NISM took this initiative under its arm of School for Securities Information and

Research by establishing, jointly with Indian School of Business, Network for Securities

Market Data (NSMD).Introduction to NSMD Interface

The main purpose of NSMD is to lead the research effort by providing clean, accessible,

and comprehensive data on Indian securities market and thereby enhance our

understanding of the Indian securities markets. It is expected that the NSMD will be a

researcher friendly interface as it provides a common interface through which researchers

can access different data provided by different vendors in a uniform accessible manner.

Further, the awareness of common researcher friendly interface will enhance research

culture in the Indian universities and research led teaching initiatives may evolve. In

addition, easy access to clean and reliable data will strengthen the PhD programs in India

and will produce better quality PhDs.

Relevance of Market Microstructure research


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The underlying belief in market microstructure research approach relies on the idea that the

specific trading mechanism features used in markets play an important role in influencing

theasset price behavior. The microstructure research, as it affects asset values and

efficiency of prices and provides extensive characterizations of short-term price behavior,

has important implications for other areas of research in finance. The interface of

microstructure research with other areas of finance is fast growing in the world literature.

However, very little work has been done in the Indian context. Here, we briefly outline the

relevant research questions / avenues /areas which interact with other major areas of

finance research which would help us in to understand the evolution of trading and

information absorption in to prices (so called, Market Efficiency).

Asset Pricing

The asset pricing literature focuses on linking asset price dynamics to underlying

economic fundamentals without looking at the underlying security trading specific features

such as the presence of private information or trading practices. A research agenda that

joins the asset pricing literature with microstructure specifics would fetch a good value

addition. It is a well noted fact that the expected returns must reflect a compensation for

illiquidity. A more complete understanding of time-varying nature of liquidity (and its

various forms) and its relation to risk premiums, stock returns is a much needed area of

study. Also from cross sectional view point, variations in expected returns across stocks

arise because of variation in liquidity of stocks. There is growing evidence that signifies

commonality in liquidity and little is known on the sources of this commonality in

liquidity. In these research issues, how we measure / proxy liquidity matters and a correct

proxy of liquidity (such as Impact Cost or price impact of trade for trading costs) warrants

the use of microstructure level data.

Behavioral Finance

Another important avenue is to understand the return anomalies, at least from behavioral

financeperspective, by incorporating the aspects of trader behavior as well as by studying

the trading motives of investors. The microstructure literature relies heavily on the

presence of uninformed or noise or liquidity motivated traders. A market cant exist

without these noise traders as then every trade is initiated by a party with (private)

information and results slowly in wider spreads to a point of no trade. It is important to


33/67

understand exactly who are these noise / uninformed / liquidity traders and why do they

trade? To undertake such research questions, a microstructure level data is a must and

hence microstructure research matters.

Corporate Finance

Most of the present event studies (corporate financeperspective) in Indian context use

daily returns as unit of observation. Such studies do not recognize the fact that the security

prices react to new information in a matter of minutes rather than hours / days. The price

sensitive corporate announcements made before, during and after trading hours in a day

will have different impacts on the announcement day. Significant evidence on the nature of

corporate events might be gleaned from intra-day data analysis. A researcher might be able

to make a more precise determination of the market perceptions regarding insider trading

and asymmetric information over time by using microstructure techniques such as

decomposition of spreads in to transitory and permanent (information) based components.

Use of such analysis might be very useful in testing hypotheses about the reaction of

security prices to earnings and dividend announcements.

The existing research data bases do not provide in a systematic way the date and time of

corporate announcements and often the researcher tries to collect such information

manually and tries to incorporate time of the announcement in his/her event study.

.

Coverage of NSMD for Market Microstructure Research

The major difficulty or rather challenge in undertaking these kind of research studies is that

we do not really have a well-structured and well-maintained researcher friendly data base.

The National Stock Exchange of India provides microstructure (transaction level historical

trade data from year 2000 onwards) for academic researchers at a very reasonable price.

The transaction level microstructure data is huge to process and take huge amount of data

learning time (rather than on research led thinking time) even for a very short period data.

Lack of infrastructural (servers, sophisticated softwares etc.) facilities at Indian universities

and institutions resulted in dearth of research studies in microstructure area. The Network

of Securities Market Data, an initiative of NISM, attempts to provide world-class

microstructure data in a researcher friendly (ready to use) format and which in turn allows

the researchers to spend more time on research led activities instead of data cleaning and


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processing activities. The Price database of NSMD interface provides daily data as well as

intra-day data and the detailed list of variables and its coverage is listed below:

NSMD Interface coverage: Relevant to Market Microstructure Research

Price Database : Daily frequencyVariables Data Source n Period Coverage of Firms

Price Open

The raw data source is NSE. The data is available from Jan 2000 to Feb 2008. Covers all

top 500 firms of NSE (constituents of NSE S& P 500)

High

Low

Close

Adjusted

Liquidity

Volume traded (in shares)

Number of Trades

Volume traded(in value)

Impact cost Volatility Intra-day volatility CorporateAnnouncements Dividend

The raw data source is NSE. The data is

available from Jan 2007 to Dec 2009 Covers all top 500 firms of NSE (constituents of NSE

S& P 500)

Bonus

Split

Rights

Others (eg: Earnings;

M&A)

Corporate Actions: Ex-dates Dividend

The raw data source is NSE. The data is available from Jan 2007 to Dec 2010 Covers all

top 500 firms of NSE (constituents of NSE S& P 500)

Bonus

Split

Rights

Others


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Price Database : Intra-day Trades data PriceFirst

The raw data source is NSE. The data is available from Jan 2000 to Feb 2008. Covers all

top 500 firms of NSE Spot and Derivative segments.

Last

Max

Min

Average

Volume

Number of Trades Trading Volume in shares

Trading volume in value Granularity at 1,5,15,30 & 60 mins

Price Database : Intra-day Orders data Quotes Best buy quote

The raw data source is NSE order book snapshots available 4 times a day. The data is

available from Jan 2007 to Feb 2008. Covers all top 100 firms of NSE Spot and Derivative

segments. Best sell quote

Depth (for buy and sell separately)

Buy Volume offered (in shares and value) at the best (first, upto 5 and full) quote and # of

orders supporting the quote

Granularity at 11, 12, 13 and14hrs

Relevance of Research Issues in Mutual Funds

Indian financial markets are getting more and more institutionalized. Foreign investors,

local institutions and mutual funds are now playing a bigger role. Mutual Fund is a capital

market instrument for investing money. With the interest rates on banks falling and the

complexities of share price movement in the stock market, mutual fund turns out to be an

alternative source of investment, apart from other non-bank financial institutions. Mutual

Funds are essentially investment vehicles where people with similar investment objective

come together to pool their money and then invest accordingly. Mutual fund schemes are

managed by respective Asset Management Companies (AMC). Different business groups /

financial institutions / banks have sponsored these AMCs, either alone or in collaboration

with reputed international firms. With so many national and international funds operating

in India, the performance of the mutual funds and within the mutual funds the performance


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of the schemes becomes a crucial area for enhancing research for the purpose of selecting

the schemes and/or mutual funds, which draws

interests of investors, practitioners and researchers alike. Along with performance, the

other areas of interest pertaining to mutual funds include issues related to the incentive

mechanisms that align the interests of fund managers with that of the investors, the

regulation affecting mutual funds and the analysis of investors. There is a huge

proliferation of research in each of these areas. Most of this research although involving the

US mutual fund industry has relevance to India.

In this Section of the paper the next three sub sections would discuss the empirical

literature associated with the above mentioned research issues and would draw implication

about the nature of data appropriate to carry out the empirical analysis. In section 5.5, the

paper discusses the kind of data that are available from different sources in the India, their

quality and the gap in the data.

Performance Measurement

The first and foremost important issue concerning mutual fund is its performance. Mutual

fund, being an instrument for investment, needs to meet high standards of performance.

There are many techniques employed over the years in research to analyze and predict

performance of mutual funds. Performance evaluation in the finance literature dates back to

Treynor (1961, 1962), Lintner (1965) and Sharpe (1964). The method generally adopted to

estimate returns is to employ the Capital Asset Pricing Model which estimates return on a

portfolio as a function of excess market return over the risk free return. Jensen (1968) has

derived a risk-adjusted measure of portfolio performance, termed as Jensens alpha that

estimates the contribution of the fund managers forecasting ability to its returns. This has

been followed by a number of modifications suggested by Eugene, French and Fama

(1993) which is referred to as the three factor model that includes factors like market

capitalization and value. Carhart (1997) further modifies the model by periodic momentum

to market equity. These measures have been widely used in the empirical analyses of

performance evaluation. It involves calculation of the annual

returns usually proxied by annual NAV, income and capital gains reported annually. These

are further refined by including reinvestment NAVs for capital gains and income

distributions in order to account for dividend reinvestments.


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Related to the question of performance measurement is the issue of the relationship

between fund size, fund investment scale, economies of scale in the industry and

performance. To address these issues researchers have employed factors like lagged fund

size adjusting for fund heterogeneity by utilizing various performance benchmarks that

account for different loadings on small cap stock, value stock and price momentum

strategies and fund characteristics like fund age or turnover, expense ratio, total load, past-

year fund inflows, and past-year returns.

While there is a huge literature focusing on performance measurement in mutual fund, a

common criticism levied against them is that these analyses use data plagued with the

problem of survivorship bias. A database may suffer from the problem of survivorship

bias if does not account for the funds which have disappeared. Funds or schemes can

disappear either through merger with other funds or schemes or liquidation. If however,

one does not account for the disappearance the consequent performance measure turns out

to be inflated leading to predictability when there is none (Brown, Goetzman, Ibbotson and

Ross (1992). Elton, Gruber and Blake (2001) analyze the accuracy of the survivor problem

free CRSP database and make a comparison with the data available from Morningstar,

which does not account for the deceased firms. Comparing the results from the survivor

bias free data with the data from Morningstar, the paper finds that neglecting this bias may

cause overall performance measures to be inflated upto

40 basis points or more.

Incentive Problem

The purpose of mutual fund is to provide professional management and the opportunity for

investors to diversify. Each mutual fund is overseen by a board of directors, responsible for

carrying out the activities of the fund. The board of directors appoints a management

company that chooses a portfolio manager to determine the composition of the investment

portfolio within the bounds set by the funds objective. Many of these companies are

publicly traded and have a separation between ownership and control. The research in this

area include exploiting the agency costs, designing of the compensation structures, the

impact of the compensation structure on the behavior of agents. Further the impact of the


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decisions of the agents on the survival probability of the funds is also analyzed. Research

on these issues is based on data on the compensation contracts, information regarding

individual funds, their quarterly performance, and information regarding individual

shareholders. Other factors generally accounted for are investor

characteristics like account size and fund characteristics like family size, fund size, age of

fund, turnover of the fund and volatility.

Regulation

Mutual fund as discussed earlier is an investment vehicle targeting to mobilize the savings

from retail investors who are incapable of investing in the stock market on their own owing

to lack of information and inability to handle the complexities and risk ensued in the stock

market. Hence in order to ensure that mutual fund serves its defined purpose it needs to be

one of the highly regulated industries in the capital market. The relationship between the

mutual fund and the regulatory environments becomes another important area of research.

Another issue related to the relationship between mutual fund and public policy is the

interaction between tax policy and mutual fund. From its very inception, mutual funds had

some equity linked saving schemes (ELSS) which include schemes of investment on

equities having tax benefits. Tax exemptions are also allowed on capital gains accruing

from transactions involving mutual funds and dividend distributions. Hence fiscal policy of

the government and its impact on the mutual fund industries also is an important research

agenda. Such a research issue would involve analysis of realized and unrealized capital

gains, net asset values of the mutual funds, the amount of dividend announced and their

interaction with tax rates.

Investor level analysis

Mutual Funds invest according to the underlying investment objective as specified at the

time of launching a scheme. So, we have equity funds, debt funds, gilt funds and many

others that cater to the different needs of the investor. One pertinent factor in this context is

that the fund has to be selected keeping the risk profile of the investor in mind because the


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products listed above have different risks associated with them. For instance, equity funds

are a good bet for long term investment, they may not be preferred by corporate or High

Net-worth Individuals (HNIs) who have short-term needs.

Research on mutual fund is based on modern finance theory which is based on the

assumption that decision to purchase individual financial asset should be based on the

investors belief regarding the future risk and return of the assets and covariance of the

return from these assets with returns from other financial assets in the portfolio of the

investor. Hence most research in this area has been restricted to the measurement of risk

and return .Very few researches have considered drawing descriptive inference of the

consumer behavior vis a vis the selection of mutual fund scheme. In this context, Capon et

al. (1996) in an exploratory study based on 3000 mutual fund investors in the US consider

the relationship between four sets of variables, namely, information sources used for

mutual fund purchases, criteria used to select between alternative mutual funds, mutual

fund purchase behavior and consumer demographic

data. Information comprises of both internal and external source. Internal source would

include previous experience of the customer, while external source consists of

advertisements, brochures, articles etc. These sources provide information on price, past

performance and level of service, which help investors in assessing the alternative

offerings. This information forms the criteria for selection. In general the selection criteria

would include three sets of variables, i.e. individual factors, like demographic and

psychological characteristics of the decision maker, brand and product characteristics, like

the price and performance level, usually measured by risk and return, as discussed above

and finally the purchase context, which again depends on the internal and

external frame of the purchasing decision of the decision maker.

An alternative source to avail the investor level data is through primary survey. In the

Indian context, there are few studies that analyze different aspects of mutual fund through

primary survey. However, the scale of such surveys has been rather limited.

Empirical Data in India


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In this section, we discuss another capital market instrument, namely mutual fund. Mutual

fund has turned out to be one of the very important sources of investment in securities

market. Although in India the share of investment in mutual fund accounts for only 6-7%

of total investment, the sector is growing over the years. In the area of financial research

too, research with respect to mutual funds is crucial. Although one can find a large

literature on mutual fund in the Indian context, there is dearth of formal or technical

research in this area. One factor contributing to it is the lack of mutual fund databases

available in the research domain. In India, to the best of our knowledge, reliable data on

mutual fund is available from three sources: CMIE, NAV India and AMFI. Apart from that

data is available with the individual websites of mutual fund houses. Recently, Morningstar

also has started providing data for the Indian mutual fund industry through the morning star

India segment. Some aggregate data on mutual funds are also posted with the SEBI

website. A very important source of information on mutual fund is the individual websites

of the AMCs. We now discuss in details the data that is available, and the problems

associated with it. AMFI provides information on Net Asset value and Asset under

Management. The NET Asset value is available for both open and closed ended funds at

monthly, quarterly and annual frequencies. However, the monthly details for all funds at a

point of time would be available only for the latest month or quarter or year at a time. It

also includes information on scheme details, dividend distribution, annual and semi-annual

accounts. Besides this, AMFI also provides

information on the distributor agents, additional information about the funds, and the new

schemes of the fund. CMIE through its portal alpha provides detailed data on schemes

under the following broad categories: Asset Management Company managing the scheme,

the nature, status and type of scheme, options allowed, period of inception, and net assets

value since inception, beta of schemes and scheme managers. It also provides information

on the dividend history of the schemes. Alpha provides financial details of the schemes,

including various performance ratios and portfolio of individual schemes. This information

is available 2001 onwards.Information similar to that provided by CMIE is available

through Capitaline NAV. This provides information on the asset Management Companies,

details about the schemes, portfolio of the schemes, ranking of the schemes and/or funds

based on performance. It also provides details about the dividend history, which includes


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the dividend announced in percentage terms and the nature of the dividend for each scheme

for a given period. The period for which they

provide the data is restricted to the latest year in question. The data covers a period from

1995 onwards, depending on the period of inception of the scheme. The database includes

data for asset and sectoral allocation. ICRA through its portal MF Explorer provides data

on mutual funds under the categories of fund and scheme names, dates of issue,

redemption, listing, their performance which includes data on net asset value, rolling

return, multiple returns, minimum and incremental investment, analyzed

SIP investments, features on NRI purchase, investments, details of fund managers, which

include their dates of appointment, performance of schemes under their management,

details about the AMC, portfolio details which include rating, corpus, number of shares,

instrument, nature, portfolio date, percentage exposure, aggregated value of PE, PB, market

capitalization and dividend yield, names of companies, average maturity and modified

duration. They also provide information on dividend frequency and turnover ratio. A

special feature of ICRA is that it provides customized analytical solutions along with raw

data which acts as a research support. These solutions take the form of statistical

comprehensives, the risk-return matrices, comparison of performances, and creation of

indices and ratings of the schemes. Information on resource mobilization at the aggregate

level is available on SEBI and RBI websites. SEBI gives information on the monthly

deployment of funds by all mutual funds to the different sectors. At present this is available

for 2009 at the website. Moreover, SEBI also gives information on the unit holding pattern

across public sector, private sector mutual funds at an aggregate level. However, historical

data on individual unit holding pattern of the mutual funds is not available on the SEBI

public platform. Yearly fund mobilization information is also made

available by SEBI. Apart from these data, SEBI provides information on the various

documents associated with mutual fund. This includes Scheme Information Document, Key

Information Memorandum, Scheme Information Document, Statement of Additional

Information. They also publish all circulars, press release related to mutual fund.

Data Gap


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The problem with the available data sources lies in its comparability. We compare the

NAV for few mutual funds from the following databases AMFI, CMIE Alpha, Capitaline

and ICRAs Mutual fund database. We make a comparison between the databases. We

arbitrarily choose Reliance banking Fund with the Institutional Growth option and query

for a period of October 27, 2009 to January 27, 2010 for daily Net Asset Value. Data is

finance fie

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