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Stock buy recommendations and their impact: evidence from Indian capital markets.

Introduction

An explosion in the investment information is seen with the expansion in the stock market participation. Individual investors have an incredible variety of sources for investment guidance. But the investors have a hard time in scrutinizing the abundant information in a professional manner for making an investment decision. Rather, they base their decisions on one word recommendations by stock analysts. According to market efficiency hypothesis the stock prices are considered to reflect the performance of a firm in general. These prices are sensitive and responsive to various factors. Stock analysis is the examination and evaluation of the stock market. It can take the form of analysis of an individual stock, sector or broader markets. Stock analysis is also referred to as market analysis, or equity analysis. An analyst looks over data and comes to a conclusion regarding it. A stock recommendation represents an analyst's professional judgment on the expected rise or decline in price of a given stock in the near future. Based on thorough analysis of various parameters, an analyst provides a recommendation which may be to buy, sell or hold a particular stock.

Analysts' recommendations may change due to variety of causes like mergers and restructuring; unfriendly takeovers; leverage buyouts; and new strategic directions and various other corporate actions. These recommendations are followed by investors for making investment decisions. Further, these analysts are classified as sell-side analysts and the buy-side analysts. 'Sell-side analysts' are securities analysts employed by banks and brokerage firms. 'Buy-side analysts' are those employed by institutional investment firms, such as pension funds, mutual funds, and insurance companies. 'Sell-side analysts' are direct sellers of information whereas 'buy-side analysts' and asset managers sell their information indirectly. The 'buy-side' firm analysts make less optimistic stock recommendations than their 'sell-side' counterparts, consistent with their facing fewer conflicts of interest and having preference for liquid stocks. It being private rather than public information, 'buy-side' research is potentially more valuable to users than 'sell-side' research. Further the reports and recommendations from 'sell-side analyst' are a common knowledge and can be accessed by anyone, whereas the 'buy-side analyst' reports and recommendations are limited by access.

A buy recommendation implies that the investment house believes the firm to be undervalued and its price is likely to increase in near future. A buy recommendation, also, means that the investors holding the stocks of the particular firm should not sell their stocks as the price is expected to increase in near future. The strongest form of the Efficient Market Hypothesis (EMH) predicts that an analyst's recommendation would result in no adjustment at all whereas a weaker form allows the recommendation to carry information and predict that prices will adjust as soon as the analyst's clients have access to the information. Under this version, investors purchase undervalued stock in anticipation of abnormal returns. As long as a stock is undervalued, investors continue to purchase, till the information contained in the recommendation is completely reflected in the price.

One type of recommendation is a change of opinion by an analyst on a stock. New 'buy' recommendations are usually well scrutinized by a research committee of the brokerage firm before release and have been in the planning stage for several days or weeks before an announcement. Sudden changes in recommendations (especially, removals of 'buy' recommendations) may occur in response to new and significant information about the company. Womack (1996) observed that new recommendation changes, particularly 'added to the buy list' and 'removed from the buy list', create significant price and volume changes in the market. For example, on the day that a new 'buy' recommendation is issued, the target stock typically appreciates 2 percent or more, and its trading volume doubles.

The efficient market hypothesis states that investors should not be able to trade profitably based on the information available, such as the analysts' recommendations, as markets react quickly to the information before any abnormal profits can be earned out of it. The studies conducted to typically examine the market reactions to specific kind of announcements are called event studies. These basically examine how fast stock prices adjust to specific significant economic events.

A large group of analyst buy recommendations have been analysed using a statistical methodology that measures returns different from what would be expected given no new information. These returns are called the abnormal returns. The investment houses spend huge sums of money to hire the analysts and to carry out the analysis so as to be able to generate superior returns for their clients as well as for themselves. The magnitude of abnormal return at the time of event is a measure of the impact of the event on the firm and its clients. Over a short horizon, these studies show that the abnormal returns imply an inconsistency of the event with the market efficiency but over a longer horizon these show a consistency with market efficiency.

There exists an extensive literature examining the reaction of security prices to analysts' recommendations. Empirical evidence to-date strongly suggests that market prices do react albeit slowly to the information contained in buy recommendations. Recommendations themselves are function of new information as Womack (1996) found that about 12 percent of recommendation changes were within one day of a quarterly earnings report in the 1989-1991 time periods. In another study linking recommendations and volume, Frey and Herbst (2014) studied buy-side analyst recommendations at a large global asset management fund and found that changes in buy-side stock recommendations are followed by increases in the fund's trading in those stocks.

Researchers have been investigating in many studies whether stock prices are impacted by analysts' recommendation or not. Although few initial researches initially (for example see Cowles, 1933) commented on analyst performance and concluded that analysts' recommendation have no impact on stock prices but later on it has been found that the recommendations, in general, have investment value, a notion that has been empirically supported by many researchers, (for example see Barber et al, 2001; Stickel, 1995; and Womack, 1996). It is believed by analysts that value of stocks in buy list will increase to a point where then it will be considered as overvalued and thus, removed from buy list. It has been found that stock prices rise after firm's addition to buy list and prior to removal from buy list and an opposite pattern is observed for sell list. Short-term price reaction is found to be a function of the strength of the recommendation, the magnitude of the change in recommendation price, reputation of the analyst, the size of the brokerage house, the size of the recommended firm, and contemporaneous earnings forecast revisions.

The focus of the present study is to examine the market reaction to buy recommendations or the influence of investment houses on stock prices using comprehensive set of buy recommendations from major foreign investment houses. The buy recommendations along with their target prices have been considered from only the reputed foreign investment houses so as to avoid the bias in recommendations that a country specific investment house can have towards the domestic stocks. In the event study methodology, we only need to look in for returns as this study assumes that all other factors are reflected in stock price changes due to efficient market hypothesis and this is what makes event study methodology extremely popular with the researchers. The event findings are very clear and easy to interpret and share. Thus, various studies have been done to study the effect of changes in investment house recommendations on the stock prices using this methodology.

The present paper is divided into five sections namely--Section 1 which includes the introduction setting the theme of the paper and presents a brief introduction to the topic. Section 2 deals with review of literature which highlights the results of several studies performed in this direction of financial research. Section 3 presents the research methodology performed to achieve the objectives of the study. Section 4 discusses the results and Section 5 presents the conclusion of the study.

Review of Literature

The recommendations made by analysts are considered to be having great ability to influence stock prices and an important source of information in capital markets. The semi-strong market efficiency hypothesis states that the investors shouldn't be able to make any profits from the publicly available information such as analyst recommendations. But the analysts and various brokerage houses spend large sums of money on gathering information regarding securities expecting that they can create superior returns for clients through their recommendations. The search for investment value in analysts' recommendations has a history dating back to the 1930s. The history reveals that the 1930's was a particularly difficult period in the stock market that included the great crash of 1929 and also at that time there was not a good understanding or bencnmarking investments relative to risk incurred in that period of time. Cowles (1933) initially analyzed the attempt of 16 financial services, in some 7500 recommendations over a five year period to foretell which specific securities would prove profitable and concluded that analysts' recommendations have no impact on stock prices. Since then, many researchers have presented theories to rationalize the existence of analysts' recommendation, and mixed support has been found for these theories.

The target price (TP) is a stock valuation price projected by an analyst at which a trader is willing to buy or sell a stock. Under the Efficient Market Hypothesis, the information content in the stock recommendation and target price is public information; hence the market should not react when target price or stock recommendation is issued by an analyst because all the useful public information is already reflected in prices. However, over the years, several studies provide evidence of stock price reaction to TP or stock recommendation by an analyst.

Why stock prices react to analyst recommendations, has been explained by Scholes (1972) who proposed two alternatives to the perfect capital market hypothesis. One is the price pressure hypothesis and the other is the information hypothesis. The price pressure hypothesis states that an initial price reaction to buy recommendations is solely driven by temporary buying-pressure from naive investors, which should be reversed afterwards. Second, the information hypothesis, assumes that abnormal returns are caused by new, relevant information, leading to a permanent revaluation of the security.

The recent studies on the value of analysts' recommendations often include references to research from the beginning of the 1980's. Several researchers (for example see Groth et al, 1979; Dhiensiri, 2010; Barber and Loeffler, 1993) found that analysts' recommendations do create value for the investors. Such researches concluded that increase in price of shares is the result of either price pressure or information content present in the recommendations of analysts. Kerl and Walter (2007) asserted under price pressure hypothesis that heavy buying pressure by naive investors drove abnormal returns on publication or announcement day. These studies, using the price pressure hypothesis explained positive abnormal returns on the publication day and negative returns during the subsequent 20 days. However, in later studies they have also documented slow reversals in prices to pre-publication levels.

Abnormal returns primarily emerged from the buy recommendation when there is a new target price. However, the effect is seen when this new target price generates a market surprise, which according to the Efficient Market Hypothesis produces an adjustment in the price to reflect that new information. Brav and Lehavy (2003) use the hypothesis that TPs provide useful information to the market and they studied the possibility of analysts finding abnormal returns on the announcement day. Jayadev and Chetak (2015) further asserted that buy recommendations do contain positive abnormal returns for a shorter period of not more than three days from the date of recommendation.

Using an event study framework e.g. Stickel (1995), Womack (1996) and Ryan and Taffler (2006) demonstrated that the market adjusted abnormal returns is significantly positive for analysts' upgrades and significantly negative for their downgrades. In these studies, the magnitude of the abnormal returns is the biggest in the days just surrounding the recommendation change. This suggests that analysts do generate informational value through their recommendations and thus, reduce the informational asymmetries occurring between outsider and insiders. However, Loh and Stulz (2011) argue that not all the stock recommendation revisions have informational value; they posit that only 12 per cent of stock recommendation revisions influence investors. In addition, only the stock recommendation revisions made by leader, star and influential analysts are taken into account by the market for generating an important stock price reaction. Moreover, the literature also suggests that target prices and stock recommendation provide useful information to the investors regardless the analysts target price accuracy. Gregoire and Marcet (2014) provided empirical evidence that even less accurate target prices generate abnormal returns on the announcement day. Such results suggest that target prices have an important informational power despite of the low target price accuracy.

The stock recommendations of financial analysts and their impact on capital market efficiency is a well researched topic internationally. The researches have been carried out in different countries like United States (Palmon et al (2009); Germany (Kerl and Walter, 2007); Sweden (Liden 2007) and even emerging markets like China (Jiang et al 2014). In the US context, Davies and Canes (1978) studied recommendations appearing in Wall Street Journal's 'Heard on the Street' column in 1970 and 1971. Abnormal prices were detected on day of publication and day afterwards. Authors also observed much stronger reaction for sell as compared to buy recommendations. The similar findings were later supported by Beneish (1991) and Liu et al (1990). In another study, Brody and Rees (2011) have found that investors can earn returns by following recommendations published in popular magazines only in the very short term period and the mean return was not significantly different from market return. In another study in US context, Palmon et al (2009) examined value of stock recommendations from leading business magazines found that following columnists' advice in 2000-2003 no consistent significant returns are possible. Cumulative average abnormal return in three day window period surrounding the date of recommendation is 1.41 per cent and they further observed that these returns increase gradually from 3 to 4 days prior and reach a peak return on the date of recommendation. They concluded that analysts and columnists differ in many aspects and this has a potential to influence abnormal returns also stated that recommendations on small and illiquid stocks have strong market reactions.

The empirical evidence on recommendations in Germany (Kerl and Walter 2007) depicted that 'buy' recommendations of the stocks published by five different German personal finance magazines earned 2.58 percent abnormal return in the five-day window period. In another important study in German context, according to Soucek and Wasserek (2014), the financial analysts' recommendations changes in German stock market over the last decade demonstrated that changes in recommendations yield significant positive (negative) abnormal gross returns for upgrades (downgrades), respectively. For the analysts' initial recommendation, a buy rating generates a significantly positive return, whereas a hold or sell rating leads to a significantly negative return. A bulk of market reactions appear on the recommendation event date and shortly before so that investors must trade in a timely manner to profit from analyst recommendations. Even a single day delayed reaction to the change in recommendations does not allow for significant abnormal returns for most of the recommendation shifts.

In Austrian context, Murg et al (2016) found from investigation of recommendations during 2000-2014 that analysts do provide additional information and updates in target prices do not necessarily cause higher abnormal returns than the pure recommendations groups (Buy, Hold, Sell), changes in recommendation groups are followed by larger average price impacts. Gregoire and Marcet (2014) provided empirical evidence for the Chilean stocks that the target price, primarily focused on buy recommendations, generates abnormal returns on the announcement day. In context of Korean market, the results by Choi and Lee (2015) demonstrated that abnormal buying by client institutions significantly increases beginning four days prior to the upward recommendation. Employing a sample of target price announcements classified as 'buy' (positive) recommendations for Israeli stocks, Kudaryavtsev et al (2014) documented their significantly positive effect on stock prices both on the day of the announcement and during a short period following the announcement. The effect of target price releases was also found to be significantly stronger for smaller stocks.

Schimd and Zimmermam (2003) investigated price and volume behavior of Swiss stocks around recommendation as published in major financial newspapers in Switzerland. Authors found significant price reaction in the week of recommendation publication. Authors also observed systematic increase in trading volume in the week before the announcement, as well as significant and systematic decrease afterwards. There have been few studies reported in Spanish market also. In some studies (Gonzalo and Inurrieta, 2001; Menendez, 2005) authors investigated the performance of brokerage recommendations in Spanish market and reported positive and significant risk adjusted returns the days before the recommendation is made public. In one of the other studies, Gomez and Lopez (2006) investigated the performance of consensus recommendations in Spanish market and reported positive and significant risk adjusted returns the day before the recommendation is made public.

In context of Chinese markets, Wanli (2014) provides evidence that analysts' cumulative rating values have significant positive impact on the cumulative abnormal returns during first 31 days of the event, and a lower rating released corresponds to lower cumulative abnormal returns. Further the larger the number of rating agencies, higher is the degree of stock information is, with the wider information dissemination and the higher degree of concern to the company. In one of the multi-country studies, Dimon and Marsh (1984) extensively reviewed stock recommendations in Australia, Canada, Hongkong, United Kingdom and United States, the researchers noted that following stock recommendations only provide modest profitability. Jegadeesh and Kim (2006) analyzed the recommendations of analysts for the G7 countries and evaluate the value of those recommendations. They analyze the returns on the announcement day and afterwards and found that the accumulated return increases over time. This finding was common to all the G7 countries except Italy.

In one of the very comprehensive studies on impact of recommendations on stock prices, Blandon and Bosch (2009) analyzed five types of recommendations namely buy, hold, outperform, underperform and sell. Authors found that positive (negative) abnormal returns are associated to positive (negative and neutral) recommendations, on the day of the publication of recommendation and the day before, but not the day after the publication. Authors have also observed asymmetry in the effect of recommendation on the stock trading volume following the signs of the recommendation.

Some of the researchers also commented on magnitude of reaction as Barber and Loeffler (1993) found 4 per cent average abnormal return and doubling of average traded volume for two days following publication. Similarly in another study by Barber et al (2001) authors have found that purchases made after consensus buy recommendations yielded annual abnormal gross return of greater than 4 per cent.

On the home front, Indian stock market is one of the largest stock market in the world with presence of small number of firms with large market capitalization and large number of firms with small market capitalization. The availability and dependence on recommendations in India is a practice of recent origin. Kumar et al (2009) studied the impact of buy and sell recommendations issued by analysts on the stock prices of companies listed on the National Stock Exchange (NSE) of India. Event study methodology was used to compute the abnormal returns around the event window, which was taken as -10 to +10. The study found that buy recommendations issued by analysts on public domains help the investors generate abnormal returns on the day of the recommendation. On the other hand, sell recommendations do not show significant negative abnormal returns.

The review of literature in this direction of financial research depicts evidence of mixed results. In most of the studies, as a result of 'buy' recommendations abnormal positive returns have been found which may occur even prior to event day. Indian studies in this context have been found to be less in number and further no research was found which studied the impact of 'buy' recommendation on different market capitalization stocks. This study is an attempt in this regard.

Research Methodology

This paper aims to study the impact of buy recommendations on stock prices. The study was limited to the shares listed on National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). For the purpose, the study considered the buy recommendations that occurred prior to March 31, 2015 till a sample of 100 stock recommendations was reached. Event day corresponds to the day of announcement of recommendation. The stocks were further categorized on the basis of market capitalization into large cap (greater than 10,000 crores), mid cap (market capitalization of more than 2000 crores but less than 10000 crores) and small cap stocks (market capitalization of less than 2000 crores). The study focused only on recommendations from reputed foreign brokerage houses that have in-house research facilities and whose recommendations are easily accessible. In the list of 100 'buy' recommendation 50 stocks were of large cap, 14 of mid cap and 36 stocks were of small cap category. The stock prices of selected stocks were collected from www.bseindia.com or www.nseindia.com for the period ranging from e-100 to e+30.

The method chosen to analyze the impact of recommendations on stock price is event study methodology. This method measures the stock price reaction to the announcement of the event. This methodology is based on the efficient market hypothesis (Fama, 1970), which states that if market faces an unanticipated event, here event is a financial event which is likely to have financial impact on the firm and provides new information that is unanticipated by the market, abnormal negative or positive returns may happen to stock prices, if prices are reflecting all the available information. For our purpose, event day corresponded to the day of recommendation made by foreign brokerage house. Further, if the markets were fully efficient, the impact would incur on event day (day 0 or e) or the day following the event day (e+1), but practically the study has considered event window including days -5 to +5, estimation window including days -100 to -5 and post event window including days +5 to +30. Inclusion of days before and after event day allows for the possibility that the arrival of information regarding the 'buy' recommendation has been leaked before the event day (so days before the event day have been included) and also allows the possibilities of rigidities and lagged response behavior by the investors (so days after the event day have been included). The impact of the event was appraised by measuring the abnormal rate of return during the event window or afterwards. The abnormal return was obtained by deducting the normal rate of return from actual rate of return in the same period. The normal return is defined as the expected return in the absence of the event. For firm i and time t, abnormal rate of return is defined as (MacKinlay, 1997).

[AR.sub.it] = [R.sub.it] - E ([R.sub.it] | [X.sub.t])

Where [AR.sub.it], [R.sub.it], and E ([R.sub.it] | [X.sub.t]) are the abnormal, actual and normal (or expected) returns respectively for the firm i in time period t. Normal (or expected) returns were estimated using market model as

E([R.sub.it]/[X.sub.it]) = [[alpha].sub.i] + [[beta].sub.i][R.sub.mt] + [[epsilon].sub.it]

where [X.sub.it] is commonly defined as market rate of return; [R.sub.it] is the return on share price of firm i on day t, [R.sub.mt] is market rate of return i.e. return on stock market index on day t, [[alpha].sub.i] is intercept term, [[beta].sub.i] is systematic risk of stock i and [[epsilon].sub.it] is error term such that E([[epsilon].sub.it]) = 0. So abnormal returns have been estimated as

[AR.sub.it] = [R.sub.it] - ([[alpha].sub.i] + [[beta].sub.i][R.sub.mt])

Where [[alpha].sub.i] and [[beta].sub.i] are the ordinary least square (OLS) parameter estimates obtained from regression of [R.sub.it] on [R.sub.mt] over estimation period preceding event day including returns from estimation window (e-5 to e-100 days).

Suitable market indices for assessment of Rmt were considered for stocks under various categories of market capitalization. Both pre-event and post-event data of share prices were adjusted with respect to corporate action of bonus, share splits and rights. Once the returns were estimated using current rate of return on market, [alpha] and [beta] coefficients with respect to individual stocks, it was deducted from actual rate of return on stocks ([R.sub.it]) to arrive at abnormal rate of return on each stock for each day. The abnormal returns thus represent returns earned by the firms after adjustments for the normal expected return, which is determined by market model. Null hypothesis ([H.sub.0]) states that 'buy recommendation' has no impact on return behavior during event window (e-5 to e+5 days). The distribution of the sample abnormal return of a given observation in the event window is

[AR.sub.it] ~ N (0, [[sigma].sup.2] ([AR.sub.it])

For the purpose of drawing overall inferences, average daily abnormal returns were calculated for all the firms for each day of the event window (i.e., 11 days starting from e-5, e to e+5 days). These average daily abnormal returns were tested with the help of 't' statistics for each of the market capitalization stocks. Further aggregation of abnormal returns was done through time (days in event window) for each firm and across firms. Cumulative abnormal returns for the firm i is defined and computed as

[CAR.sub.i] (e-5 to e+5 days) = 0t = -5 to +5 [AR.sub.it]

For aggregation across firms, CAR was computed using

CARifirm (-5 to +5 days) = 1/N (c)

Student 't' test was applied on both abnormal returns and cumulative abnormal returns with null hypothesis that 'buy recommendation' has no impact on rate of return (abnormal or cumulative abnormal return) during the stated event window.

Results

The abnormal returns before the event day were analyzed for event period for each stock with 'buy' recommendation. Abnormal returns and cumulative average abnormal returns were analyzed for each stock with a buy recommendation using the difference between actual return and expected return. The average abnormal returns were calculated by aggregating the abnormal returns for each stock on a particular day and further divided by number of stocks.

Table 1 presents the result of our parametric tests on the abnormal returns in each of 11 days (e-5 through e+5 days) under the study and also cumulative average abnormal returns in event window (-5, +5) with respect to large cap stocks having buy recommendation. The results show that cumulative average abnormal returns in the event window [-5, +5] was negative, -0.1615 percent. The strongest positive abnormal average return was found on t = -1, and is statistically significant at 5 percent level.

On the day of the event, the average abnormal return was -0.0846 percent and is found to be statistically insignificant at 5 percent level, implying that the effect of recommendation on return was insignificant on event day. However, in case of a day earlier to the event day i.e. on t = -1 and two days after the event day i.e. on t = +2, the average abnormal return was found to be 0.9565 percent and 0.8410 percent respectively with significant p-values of 0.000 and 0.0025 respectively. Also, the cumulative average abnormal return was positive only on t=+4 but is statistically insignificant at 5 percent level. Overall results do not present the evidence of rejecting the null hypothesis of no cumulative abnormal returns on all days and of no abnormal returns for days except for t=-1 and t=+2 in the event window. Therefore, there is practically no evidence that a buy recommendation has a positive impact on large cap returns with respect to cumulative average abnormal returns. But, the evidence shows a significant positive impact can be seen of buy recommendation with respect to average abnormal return on a day prior to and two days after the event day on large cap stock returns.

Table II presents the result of our parametric tests on the abnormal returns in each of 11 days (e-5 through e+5 days) under the study and also cumulative average abnormal returns in event window (-5, +5) with respect to mid cap stocks having buy recommendation.

The results depict that cumulative average abnormal returns in the event window [-5, +5] was negative, -0.9984 percent. The strongest positive abnormal average return was found on the day of the event i.e. on t = 0, but is statistically insignificant at 5 per cent level. On the day of the event, the average abnormal return is 0.4947 per cent and is found to be statistically insignificant at 5 per cent level, implying that the effect of recommendation on return was insignificant on event day. However, in case of a day after the event day i.e. on t = +1 the average abnormal return was found to be -0.6935 per cent with significant p-values of 0.035. Also, the cumulative average abnormal return was positive only on the day of the event i.e. on t=0 but is statistically insignificant at 5per cent level. Overall results do not present the evidence of rejecting the null hypothesis of no abnormal returns or cumulative average abnormal returns in the event window. Therefore, there is particularly no evidence that a buy recommendation has a positive impact on mid cap's stock price.

Table III presents the result of our parametric tests on the abnormal returns in each of 11 days (e-5 through e+5 days) under the study and also cumulative average abnormal returns in event window (-5, +5) with respect to small cap stocks having buy recommendation.

The results show that cumulative average abnormal returns in the event window [-5, +5] is positive, 1.7766 per cent. The strongest positive abnormal average return was found on t = -5, but is statistically insignificant at 5 per cent level. On the day of the event, the average abnormal return was -0.0245 per cent and is found to be statistically insignificant at 5 per cent level, implying that the effect of recommendation on return was insignificant on event day. The average abnormal returns was found to be positive and significant at e+3 day. The cumulative abnormal returns were found to be positive on all days and the average abnormal returns positive for all days except on t=0, t=+l and t=+2 but statistically insignificant at 5 per cent level. Overall results depict that positive returns can be generated by following buying recommendations especially in small cap stocks as is evident from positive and significant cumulative average abnormal returns in the event window.

Conclusion

Event study which is considered as reasonable methodology to assess the impact of isolated event on stock's return was used in the present study to find the impact of 'buy' recommendation by an foreign brokerage house on stock's return. The stocks have been divided into large cap, mid cap and small cap stocks. For large cap and mid cap stocks, overall cumulative average abnormal returns have been found to be negative but not statistically significant at 5percent level of significance. For small cap stocks overall cumulative average abnormal returns have been found to be positive and significantly different from zero at 5 per cent level of significance. In terms of average abnormal returns for individual days with in the event window, null hypothesis was rejected for e-1 and e+2 days for large cap stocks; null hypothesis was rejected for e+1 day in mid cap stocks and for e+3 days for small cap stocks and in all these cases significant abnormal positive returns were found on individual days. The results are consistent with the previous studies which provide mixed evidence of a significant positive impact on firm value, resulting from 'buy' recommendations. The study does find a positive impact in terms of cumulative average abnormal returns but the impact is found only in case of small cap stocks. However these results are not necessarily conclusive as one impact may occur either before e-5 day (information may be leaked to the market before the official announcement) or impact may be seen after e+5 day depending upon the information processing capabilities of the participants involved. If markets are assumed to be efficient and do not respond to simply 'buy' recommendation exercises then such efficiency is missing in small cap stocks. This finding is an addition in the existing Indian capital market literature.

Paper received on June 23, 2016

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Namrita Singh Ahluwalia

Assistant Professor, G.H.G Khalsa College, Ludhiana, Punjab.

Mohit Gupta

Assistant Professor, School of Business Studies, Punjab Agricultural University, Ludhiana, Punjab.

Navdeep Aggarwal

Associate Professor, School of Business Studies, Punjab Agricultural University, Ludhiana, Punjab.
Table--I

Parametric Tests for AAR and CAAR (Large Cap Stocks)

t (day)         AAR * (%)   p value   CAAR ($)   p value

-5               -0.0165     0.965    -1.9188     0.584
-4               0.2499      0.491    -1.6690     0.635
-3               0.0385      0.920    -1.6307     0.646
-2               -0.4420     0.127    -2.0724     0.541
-1               0.9565      0.000    -1.1159     0.738
0 (event day)    -0.0846     0.811    -1.2006     0.722
+ 1              -0.5419     0.197     1.7425     0.605
+2               0.8410      0.025    -0.9015     0.789
+3               0.5311      0.134    -0.3703     0.913
+4               0.5912      0.148     0.2209     0.949
+5               -0.3824     0.381    -0.1615     0.963
[-5,+5]                               -0.1615

(Source: Authors' own calculations) * AAR = Average abnormal
return ($) CAAR = Cumulative average abnormal returns

Table--II

Parametric Tests for AAR and CAAR (Mid Cap Stocks)

t (day)         AAR * (%)   p value   CAAR ($)   p value

-5               0.2735      0.530    -0.8777     0.633
-4               0.1442      0.710    -0.7336     0.680
-3               0.2974      0.650    -0.4361     0.841
-2               -0.0904     0.864    -0.5265     0.831
-1               0.0909      0.795    -0.4357     0.863
0 (event day)    0.4947      0.298     0.0590     0.983
+ 1              -0.6935     0.035    -0.6343     0.825
+2               -0.8542     0.091    -1.4886     0.634
+3               0.0769      0.871    -1.4119     0.668
+4               0.2746      0.716    -1.1371     0.705
+5               0.1386      0.774    -0.9984     0.752
[-5,+5]                               -0.9984

(Source: Authors' own calculations)

* AAR = Average abnormal return

($) CAAR = Cumulative average abnormal returns

Table--III

Parametric Tests for AAR and CAAR (Small Cap Stocks)

t (day)         AAR * (%)   p value   CAAR ($)   p value

-5               1.5082      0.232     0.2003     0.907
-4               0.0734      0.847     0.2736     0.871
-3               0.1857      0.466     0.4593     0.773
-2               0.0926      0.748     0.5519     0.732
-1               0.2377      0.489     0.7895     0.639
0 (event day)    -0.0245     0.931     0.7650     0.660
+ 1              -0.1613     0.656     0.6037     0.738
+2               -0.3850     0.190     0.2186     0.905
+3               0.6572      0.041     0.8759     0.432
+4               0.4872      0.264     1.3631     0.112
+5               0.4135      0.222     1.7766     0.046
[-5,+5]                                1.7766

(Source: Authors' own calculations)

* AAR = Average abnormal return

($) CAAR = Cumulative average abnormal returns
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Author:Ahluwalia, Namrita Singh; Gupta, Mohit; Aggarwal, Navdeep
Publication:Abhigyan
Article Type:Report
Geographic Code:9INDI
Date:Jul 1, 2017
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