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Study On The Objectives Of The Bse Sensex Finance Essay

Paper Type: Free Essay Subject: Finance
Wordcount: 4754 words Published: 1st Jan 2015

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BSE – SENSEX is the short form of the BSE – Sensitive Index. The index is widely used to measure the performance of the Indian Stock Market. It is a Market Capitalization Weighted index of 30 stocks representing a sample of large, liquid, well established and financially sound companies. The index is widely reported in both, the domestic and international, print and electronic media and is widely used to measure the performance of the Indian stock markets. The BSE Sensex is the benchmark index of the Indian capital market and one which has the longest social memory. In fact the Sensex is considered to be the pulse of the Indian stock markets. It is the oldest index in India and has acquired a unique place in the collective consciousness of investors. Further, as the oldest index of the Indian Stock Market, it provides time series data over a fairly long period of time. One of the most important attributes of Sensex is to maintain continuity with the past i.e. to update the base year average. The base year value adjustment ensures that the rights issue and new capital of the index scrips do not destroy the value of the index. The day-to-day maintenance of the Sensex is done by the Bombay Stock Exchange and special care is taken to include only those scrips, which pass through several filters.

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The Stock Exchange, Mumbai popularly known as BSE was established in 1875 as The Native Share and Stock Brokers Association. It is the oldest one in Asia, even older than the Tokyo Stock Exchange, which was established in 1878. It is a voluntary non-profit making Association of Persons (AOP) and is the first Stock Exchange in the country to have obtained permanent recognition in 1956 from the Government of India under the Securities Contracts (Regulation) Act, 1956. The Exchange, while providing an efficient and transparent market for trading in securities, debt and derivatives upholds the interests of the investors and ensures redressal of their grievances whether against the companies or its own member brokers.

A Governing Board having 20 directors is the apex body, which decides the policies and regulates the affairs of the Exchange. The Governing Board consists of 9 elected directors, who are from the broking community (one-third of them retire every year by rotation), three SEBI nominees (Securities & Exchange Board of India), six public representatives an Executive Director, Chief Executive Officer and a Chief Operating Officer. The Executive Director and the Chief Executive Officer are responsible for the day-to-day administration of the Exchange and he is assisted by the Chief Operating Officer and other Heads of Departments.

OBJECTIVES The BSE Sensex is the benchmark Index of the Indian Stock Market with wide acceptance among individual investors, institutional investors and fund managers. The objectives of the index are:


Given its long history and wide acceptance, no other index matches the BSE Sensex in reflecting market movements and sentiments. Sensex is widely used to describe the mood in the Indian Stock Market.


The inclusion of the Blue chip companies and the wide and balanced industry representation in the Sensex makes it the ideal benchmark for fund managers to compare the performance of their funds.


Since Sensex comprises of leading companies in all the significant sectors in the economy, we believe that it will be the most liquid contract in the Indian market and will garner a pre dominant market share

LISTING OF SECURITIES Listing means admission of securities to dealings on a recognized stock exchange. The securities may be of any public limited company, Central or State Government, quasi-governmental and other financial institutions/corporations, municipalities etc. The objectives of listing are mainly to:

ƒ˜ Provide liquidity to securities

ƒ˜ Mobilize savings for economic development

ƒ˜ Protect interest of investors by ensuring full disclosures.

The Exchange has a separate Listing Department to grant approval for listing of securities of companies in accordance with the provisions of the Securities Contracts (Regulation) Act, 1956, Securities Contracts (Regulation) Rules, 1957, Companies Act, 1956, Guidelines issued by SEBI and Rules, Bye-laws and Regulations of the Exchange.

SELECTION CRITERIA The criteria for selection and review of scrips for the BSE Sensex can be explained in the following manner:


1. MARKET CAPITALIZATION: The Scrip should figure in the top 100 companies listed by market capitalization. Also market capitalization of each of the scrip should be at least. 0.5 % of the total market capitalization of the Index i.e. the minimum weight should be 0.5%. Since the BSE Sensex is a market capitalization weighted index, this is one of the primary criteria for scrip selection. (Market Capitalization would be averaged for last 6 months).


a. Trading Frequency: The scrip should have been traded on each and every trading day for the last six months. Exceptions can be made for extreme reasons like scrip suspension etc.

b. Number of Trades: The scrip should be among the top 150 companies listed by average number of trades per day for the last one year.

c. Value of Shares Traded: The scrip should be among the top 150 companies listed by average value of shares traded per day for the last one year.

d. Trading Activity: The average number of shares traded per day as a percentage of the total number of outstanding shares of the company should be greater than 0.05 % for the last year.

3. CONTINUITY: Whenever the composition of the Index is changed, the continuity of historical series of index values is re-established by correlating the value of the revised index to the old index (index before revision). The back calculation over the last one-year period is carried out and correlation of the revised index to the old index should not be less than 0.98. This ensures that the historical continuity of the index is maintained.

4. INDUSTRY REPRESENTATION: Scrip selection would take into account a balanced representation of the listed companies in the universe of BSE. The index companies should be leaders in their industry group.

5. LISTED HISTORY: The scrip should have a listing history of at least 6 months on BSE. However, the Committee may relax the criteria under exceptional circumstances.


1. SCRIP GROUP: The Scrip should preferably be from „A‟ group.

2. TRACK RECORD: The company should preferably have continuous dividend paying record or / and promoted by management having proven record.


The NSE -50 Index was launched by the National Stock Exchange of India Limited, taking as base the closing prices of November 3, 1995 when one year of its Capital Market segment was completed. It was subsequently renamed S & P CNX Nifty- with S & P indicating endorsement of the Index by Standard and Poor‟s and CNX standing for CRISIL NSE Index. The S & P CNX NIFTY, also popularly known as the Nifty 50, is one of the most scientific indices in India that reflects the price movement of 50 blue- chips, large cap, liquid and highly traded stocks of 23 sectors. The Nifty is managed by India Index Services & Products Ltd. (IISL). The total value of all Nifty stocks is approximately 70% of the traded value of all stocks on the NSE. Nifty stocks represent about 59% of the total market capitalization.

OBJECTIVES The basic idea of this index is to ascertain the movements of the stock market as a whole by tapping the news which can affect the stock. The index also averages out the good stock – specific news for a few companies and bad stock – specific news for others and left with the news that is common to all stocks. The news that is common to all stocks is news about India, which is the sole purpose of NSE Nifty. According to NSE, the Index was introduced with the objectives of:

1. Reflecting market movement more accurately,

2. Providing Fund Managers with a tool for measuring portfolio returns vis-a-vis market returns, and

3. Providing a basis for introducing Index based derivatives.

This paper discusses Efficient Market Hypothesis (thereby referred to as EMH), seasonalities and its implications in both advanced and emerging securities markets. EMH suggests that investors cannot expect to out perform the market consistently on a risk adjusted basis (Mayo, 2003). According to Fama (1965) who developed the Efficient Market Hypothesis, “an efficient market is a market where there are a large number of rational profit-maximizers actively competing, with each trying to predict future market values of individual securities, and where important current information is almost freely available to all participants. In an efficient market, competition among the many intelligent participants leads to a situation where at any point in time, actual prices of individual securities already reflect the effects of information both on events that have already occurred and on events which, as of now, the market expects to take place in the future. In other words, in an efficient market at any point in time, the actual price of a security will be a good estimate of its intrinsic value.” On the other hand, in an inefficient market, EMH would not hold. This suggests that existence of loop holes which could be exploited to make abnormal returns by predicting market price patterns, using past price information and insider information. These market inefficiencies, also called market anomalies have received as much research work as EMH.


There are three forms of market efficiency in an informationally efficient market, where prices adjust quickly and accurately to new information (Emery et al, 2007). These forms show the degree of efficiency of security markets and attempt to answer the question of how efficient a market is. (Mayo, 2003 and Keane, 1983)

2.1 Weak Form Efficiency

The weak form of EMH asserts that the current price fully reflects information contained in the past history of prices only. Stock market price information is available via most means of mass communication. Thus, investors should be unable to make superior profit from use of public information i.e. daily stock market prices or company results available to all. Again, many investment bankers and financial analysts devise investment strategies using technical analysis of past data to outperform the market and their competitors, in satisfying their clients demand for superior returns. Transaction costs of trading, investment advice, analysis and commissions when considered, affects the investors return, especially for investors who continue to use traditional full service brokers (Mayo,2003)

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2.2 Semi Strong Form Efficiency

The semi strong form of EMH, according to Brealey et al (2006), prices reflect not just past prices but all other published information, such as you might get from reading the financial press. Similarly, Fama (1969) defined it as publicly available information with examples of announcements of annual earnings and stock splits. Semi-strong form of EMH asserts that current prices fully reflects public knowledge about the underlying companies and that efforts to acquire and analyze this knowledge cannot be expected to produce superior investment results (Lorie & Hamilton 1973).

2.3 Strong Form Efficiency

The strong form of EMH suggests that share prices fully reflect not only published information but all relevant information including data not yet publicly available. It also asserts that not even those with privileged information (insiders) can often make use of it to secure superior investment results (Lorie & Hamilton 1973).

These three forms of EMH are not independent of one another. For the market to be efficient in the semi-strong form, it must also be efficient in the weak form, because if price movements follow a predictable path which the perceptive observer can exploit profitably, the implication is that the price has reacted slowly or capriciously to published information. Likewise, for the market to be efficient in the strong form it must also be efficient at the other two levels, otherwise, the price would not capture all relevant information (Keane, 1983). He went on to state that for an inefficiency (seasonality) to be operationally significant it must be exploitable. Keane (1983) analyses four criteria an exploitable inefficiency should satisfy, these are: (a) it should be “authentic” – supportable by properly conducted statistical research. (b) It should be “identifiable”-not just strategies or people that beat the market but concrete and verifiable evidence. (c) It should be “material”- inefficiencies are not exploitable unless they are sufficient to compensate for the costs and risks of pursuing them. (d) It should be “persistent”-the value of inefficiency is not just a record of its existence in the past but that it will continue to exist in future.

These criteria are very important in understanding the different types of market seasonality or anomaly, their existence, prevalence and their implications for the EMH.


Seasonalities, as the name suggests are time regularities, patterns or predictable trends. In the financial securities market, seasonalities would suggest predictable time patterns in the behaviour of the stock market-volume of stock trades, stock returns etc. If it does exist, then investors can exploit the market for superior returns in all financial securities markets. Seasonalities as defined by Alagidede (2008) are evidences of market efficiency anomalies. These are also known as seasonal anomalies (calendar effects) which may be loosely referred to as the tendency for financial returns to display systematic patterns at certain times of the day, week, month or year. Calendar effects include: January effect, the month of the year effect, monthly effect, holiday effect, Monday effect / day of the week effect, weekend effect, turn of the year effect etc. (Guo and Wang, 2007). Discussing a few of them will be worthwhile.

3.1 The January Effect

The January effect is where returns are much higher during the month of January than any other month, i.e. “where investors can earn a disproportionately high amount of the total annual return available from both fixed income assets and equity in January” Clare et al (1995). Most research conducted in developed economies confirm the presence of the January effect, although, in more recent times they seem to be disappearing. Keim (1983) and Reinganum (1983) show that the January effect and the size effect are highly interrelated. Blume and Stambaugh (1983) discovered, after controlling for upward biases in small stock returns, the size effect was only significant in January.

An extensive amount of studies has gone into the month of the year effect. Mills and Coutts (1995) concluded that stock returns are much higher in the month of January in the UK using FTSE indices between January 1986 and October 1992(FTSE 100,Mid 250 and 350 indices). Gultekin and Gultekin (1983) using 17 countries also found evidence that the January return is much higher than other months returns,

Alagidede (2008) tested for month of the year effect in emerging African markets and concluded that the January effect is positive and significant for Nigeria, Egypt and Zimbabwe. However Guo and Wang’s (2007) study on the emerging Chinese stock market shows that there is no significant January effect in Chinese stock market.

Many researchers have sought the cause of the January effect and arrived at a number of causes which include: tax-loss selling hypothesis, provision of new information at the end of a fiscal year, firm size had the significant higher risk in the beginning of the year than the rest of the year and the systematic tendencies for closing prices to be recorded at the bid in the last traded in December and at the ask in early January (Guo and Wang’s, 2007)

3.2 The Holiday Effect

The definition of a holiday is relative, subjective and would vary for different countries and their capital markets e.g. Christian, Muslim, public holidays etc. One definition of a holiday looks at days, other than Saturday or Sunday, upon which the market is closed (Alagidede, 2008). Ariel (1990) used US data reports to show that the trading day prior to holidays on average displays high positive returns, this result was supported by Kim and Park (1994) for US, Japan and UK .However, Cadsby and Ratner (1992) using UK data concluded that the holiday effect was insignificant This conclusion was challenged by Mills and Coutts (1995) in their study of calendar effects using London stock FTSE indices. Coutts et al (2000) showed that the holiday effect is present in their study of the Athens Stock Exchange (ASE), although, no similar study has been undertaken on the ASE which would have been used as a basis of comparison. Their results were consistent with international evidences.

3.3 The Weekend Effect

One of the most prevalent anomalies appears to be a weekend effect where stocks display significantly lower returns over the period between Friday’s close and Monday’s close (Arsad and Coutts, 1995). Jaffe and Westerfield (1985) examined the daily stock market returns in 4 international stock markets including, the London stock Exchange’s FT30 over the period 1950 – 1982 and found a significant weekend effect. Consistent with Jaffe and Westerfield (1985) findings, Condoyanni et al (1987) also found the existence of the weekend effect in the UK when examining the FT30 over the period 1979 – 1994. Arsad and Coutts (1996, 1997) also found the weekend effect in the FT30 from the period 1935 – 1994, although according to their research the effect was found not to be persistent. Board and Sutcliffe (1988) examined the weekend effect in the Financial Times all share index over the period 1962 – 1986 and found clear evidence of a weekend effect over the sample period, with the significance of the effect diminishing over time. This is consistent with later research done by Dubois and Louvet (1996) on the same index for the period 1969 – 1992, in which negative returns was found on Monday, which are compensated by abnormal positive returns on Wednesday. Agrawal and Tandon (1994) examined the weekend effect in 18 countries including the UK and found a negative Monday return when the market rises in the previous week. Furthermore, they found the effect disappearing in 1980. Mills and Coutts (1995) found evidence of the existence of the weekend effect in the UK when the FTSE 100, Mid 250, 350 and certain of the accompanying industry baskets was examined for the period from 1986 to 1992. Ajayi et al (2004) investigated day of the week stock return anomaly, using major market stock indices in eleven eastern European emerging markets for the period 1994 – 2002. The results show negative and positive Monday returns in six and five emerging markets respectively, of which only two of the six show negative Monday returns and one of the five show positive Monday returns and were statistically significant. Choudhry (2000) investigated the day of the week effect in seven emerging Asian stock markets from 1990 – 1995 and found significant weekend effect in some of the markets considered.

3.4 The Day of the Week Effect:

The day of the week effect refers to existence of a pattern on the part of stock returns, whereby these returns are linked to the particular day of the week (Poshakwale 1996). The last trading days of the week, particularly Friday, are characterised by substantially positive returns while Monday, the first trading day of the week, differs from other days, even producing negative returns (Cross 1973, Lakonishok & Levi (1982), Rogalski (1984), Keim & Stambaugh( 1984) and Harris (1986). In other words, this effect relates to the difference in returns across different days of the week with the variance in stock returns found to be largest on Mondays and lowest on Fridays (Raj & Kumari 2006). It should be noted that the day of the week effect in emerging capital markets has not been extensively researched and the presence of such an effect would mean that equity returns are not independent of the day of the week effect against random walk theory (Poshakwale 1996). On the other hand, the international evidence of the report has been somewhat mixed. Dubois and Louvert (1996) find returns to be lower for the beginning of the week (but not necessarily Monday) for European countries, Hong Kong and Canada. However, it was observed that the anomaly disappeared in the USA for the most recent periods. Agrawal and Tandon (1994), find negative Monday returns in nine countries and negative Tuesday returns in eight countries (out of a total of nineteen countries).

Several theories have been put forward regarding specific time periods anomalies in the capital market. The day of the week effect has been explained by examining various kinds of measurement errors such as: – settlement period hypothesis; which attributes the day of the week effect to the settlement dates with prices higher on the pay-in days as compared to the pay-out days. Calendar time(trading time) hypothesis; implies that since Monday returns are spread across three days (Saturday, Sunday & Monday), the returns should be three times as high as other days. The negative Monday returns go against this reasoning, which lead to the proposed theory that returns should be proportional to trading time as opposed to calendar time (Raj & Kumari 2006). Information flow hypothesis postulates that the difference in information flow over the weekend compared to other days of the week causes the Monday effect (Dyl & Maberly 1988). Often companies hold back negative information till the weekend, giving the investors two non-trading days to absorb the information before reacting with trading activity. Consequently, all sell orders get pushed to Monday, thereby giving negative returns (Raj & Kumari 2006). Retail investor trading hypothesis, suggests that negative Monday returns could be the result of individual investor trading activity (Brooks & Kim 1997). It was found that trading activity is significantly lower on Monday for large size trades, while small size trades have a higher percentage of sell orders on Monday as compared to other days of the week.

3.5 Trading Month Effect

The trading month effect also called the ‘turn-of -the-month’ effect which was first documented by Ariel(1987) using US data shows that returns are only positive around the beginning and during the first half of trading months, whereas during the second half they are on average zero. This study was replicated by Jaffe and Westerfield (1989), for the UK, Japan, Canada and Australia, in their study. However, only Australia shows a significant monthly effect. A conflicting evidence for the UK in a report from Cadsby and Ratner (1992) shows a significant trading month effect in the FT 500. Ariel (1988) offered three explanations for the trading month effect which include: new information concerning corporate cash flows, changes in risk free rate and changes in the preferences of market participants leading to variation in demand for securities which cannot be offset by supply. Mills and Coutts (1996) investigated the this effect using a large sample of daily returns from the Financial Times Industrial Ordinary Share Index and found that a trading month effect is present but exists for a much shorter period than has been documented by previous studies for both the US and the UK. The information release hypothesis of French(1980) was accepted as an explanation of the trading month effect, only if the unexpected release of ‘good’ and ‘bad’ news has a tendency to fall in the final and first days of trading months, securities would be riskier during these periods , thus justifying the higher first half returns.

Context of India:

Published studies that have examined calendar effects in the Indian stock market appear to be limited. Kaur (2004) reports that few studies have examined the day-of-the-week effect in the Indian stock market, and further notes the absence of studies that examine monthly seasonality in the Indian stock market. Kaur utilized two Indian stock indexes, the Bombay Stock Exchange (BSE) 30 index and the National Stock Exchange (NSE) S&P CNX Nifty stock index, to examine the day-of-the-week effect and the monthly effect. Kaur did not find a January effect in the Indian stock market, but did find that March and September generated substantially lower returns, whereas February and December generated substantial positive returns.

Sarma (2004) adds that very few studies have examined calendar effects during the post reform era in the Indian stock market. Sarma investigated the BSE 30, the BSE 100, and the BSE 200 stock indexes to detect the day-of-the-week effect. Utilizing Kruskal-Wallis test statistics, Sarma concluded that the Indian stock market exhibited some seasonality in daily returns over the period January 1, 1996 to August 10, 2002. Bodla and Jindal (2006) examined several seasonal anomalies in the Indian stock market utilizing the S&P CNX Nifty Index for the period January 1998 to August 2005. For the monthly effect, they did find some significant differences for their sub-period, January 2002 to August 2005. However, they were unable to find any significant differences among individual months. In an earlier study, Ignatius (1998) examined seasonality in a BSE index and in the Standard and Poor’s 500 stock index for the period 1979-1990. Ignatius found that December generated the highest mean returns, and that April and June generated high returns in the Indian stock index.

Some studies examine seasonality in the Indian stock market as part of a broader analysis of seasonality in several major emerging stock markets. For example, Fountas and Segredakis (2002) investigated monthly seasonal anomalies in eighteen major emerging equity markets, including the Indian stock market. They examined the monthly effect for the period January 1987 to December 1995. For the Indian stock market, they found August returns were significantly greater than April, May, October and November returns. However, they did not find evidence consistent with hypothesized tax-loss selling in the Indian stock market, as the tax-year in India commences in April.

Yakob, Beal and Delpachitra (2005) examined seasonal effects in ten Asian Pacific stock markets, including the Indian stock market, for the period January 2000 to March 2005. They state that this is a period of stability and is therefore ideal for examining seasonality as it was not influenced by the Asian financial crisis of the late nineties. Yakob, et al., concluded that the Indian stock market exhibited a month-of-the-year effect in that statistically significant negative returns were found in March and April whereas statistically significant positive returns were found in May, November and December. Of these five statistically significant monthly returns, November generated the highest positive returns whereas April generated the lowest negative returns.

Evidence of monthly seasonality in the Indian stock market is somewhat mixed. This may be, in part, a consequence of the fact that the Indian economy is in transition and is therefore constantly evolving, supporting the notion that further research into these calendar effects in the Indian stock market is warranted.


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