Impact of Credit Ratings (Upgrade and Downgrade) on Stock Prices in India

Transcription

1 Impact of Credit Ratings (Upgrade and Downgrade) on Stock Prices in India S.V.D.Nageswara Rao and Sreejith U In this study, we examine the impact of credit ratings by all the credit rating agencies (CRISIL, ICRA,, Fitch Ratings and Brickwork Ratings) on equityreturns in India. The period of our study is from 1 st January, 1999 to 31 st March, The daily data, credit ratings announcements, daily equity returns and daily Bombay Stock Exchange (BSE) SENSEX Indexes are collected from PROWESS Database of Centre for Monitoring Indian Economy (CMIE). Subsequently, the data have been analyzed using event study methodology. We computed the abnormal returns using Mean Adjusted Model, Market Adjusted Model and Conditional Risk Adjusted Model (Standard Market Model). The abnormal returns obtained using the above mentioned(three)models are similar. The significance of abnormal returns have been tested using Student t test. The study reveals downgrades invite considerable negative reactions, whereas the upgrades attract negligible positive response. Key Words: Credit Rating, Abnormal Returns, Mean Adjusted Model, Market Adjusted Model, Conditional Risk Adjusted Model, Event Study Methodology JEL Classification Code: G140Information and Market Efficiency; Event Studies. I. Introduction Generally, credit rating denotes an independent and completely unbiased opinion of an agency on the issuer s capability to repay its financial commitments to the depositor or the bondholder of a particular issue based on the net present value of their estimated future earnings.in general, the ratings can be classified as upgrade, downgrade, placement in watch-list etc (Appendix). These could, and typically do, impact the decisions of the investors. Due to this impact on investors, credit ratings will influence market prices of the financial instruments of these entities. This influence can be distilled by the abnormal stock response during the rating/rating change period. But we don t have any idea as to the extent of the impact of these credit ratings on the stock prices, especially in the Indian market. Besides, the markets understand and factor the reasons for rating change much before the actual rating changes (Wakeman, 1990). In that situation, rating changes are not expected to affect stock prices. On the other hand, the rating agencies declare that they receive inside information and rating is a means of communicating significantfacets of such information to the stock holders, without exposingdetrimental details to the opponents. However, verifying and distilling this impact has important economic ramifications.needless to say, researchers in countries like Australia, France, Germany, Holland, the U.S.A., the U.K., Japan and China, have analyzed the impact of such rating announcements on their market behaviour. However such studies are very few in the Indian context. Prof. S.V.D.Nageswara Rao, Professor, School of Management, Indian Institute of Technology, Bombay, INDIA. sonti@iitb.ac.in Sreejith. U, Research Scholar, School of Management, Indian Institute of Technology, Bombay, INDIA. vrindavan40@gmail.com. 1

2 This paper contributes to understandthe impact of credit rating on the Indian stock market behavior. The research question(s) are (I) Do stock prices respond significantly to rating announcements (and revisions)? (II) Is the reaction to announcements of upgrades and downgrades as expected? The prices are expected to go up (down) in response to rating upgrades (downgrades). The research paper is divided in sections as follows:- I. Introduction II. Literature Review III. Methodology IV. Results and Discussions V. Conclusions VI. Appendix II. Literature Review In this study, we examine the impact of credit ratings by all the credit rating agencies (CRISIL, ICRA,, Fitch Ratings and Brickwork Ratings) on equity returns in India (Appendix A).Globalization of financial and capital markets heightened the demand for credit rating by independent agencies (Emawtee Bissoondoyal-Bheenick 2004), the growing complexity of financial products and an increasing usage of ratings in financial regulation (Frost 2007). Cantor (2004) brought out the role and influence of credit ratings. The circulation of credit rating changes is very wide (White 2002) and market participants are taking it seriously (White 2002). In nutshell, the impact of ratings, rating changes are very high on both the size and volatility in major markets (Patronoy F., 2002,). This reveals the significance of credit rating in the world business scenario. The earlier studies, which analysed the credit rating announcements on the security returns, had revealed zero significant returns. (Weinstein, (1977); Pinches and Singleton, (1978); concluded the negligible information value of the ratings. However, these studies mainly took the monthly returns and hence might not have reflected the actual impact of ratings and rating changes. However with the monthly returns, Griffin and Sanvicente (1982) produced different results. Their study collected data pertaining to 180 Moody s and Standard and Poor s rating changes and monthly stock returns for a period of sixteen years (1960 to 1975) and revealed zero anticipation of the markets before credit rating changes and established the presence of negative reaction immediately after the downgrades. This is the first attempt to compare the impact of two credit rating agencies in the market. However, the data collected is monthly return. Hence the accuracy of the results is relatively less due to the confounding events. Slightly deviating from the standard research, and precisely starting a new trend in data collection, Stephen Brown and Gerald Warner (1985), Scott E. Stickel (1986) used daily returns to investigate the effect of rating change announcements on stock prices. Brown and Warner (1980, 1985) explained the significance of using daily returns in event study methodology. They revealed the robustness of event study methodology in accurately capturing the impact of an event. Scott E. Stickel (1986) argued that the clean announcement(s) free of confounding events significantly affected preferred stock prices. After the trend set by Scott E Stickel (1986), other researchers like Hand et al (1987) and Goh and Ederington (1993), Glascock, Davidson and Henderson (1987) had also analyzed the same problem with daily data. Glascock et al. (1987) analyzed the presence of 2

3 abnormal returns by collecting 162 Moody s credit rating changes for the period of five years ( ). They evaluated the impact for a long window i.e., the impact of credit rating on stock prices before and after 90 days of rating announcement date. The study of Glascock et al., (1987) observed the presence of significant negative abnormal stock returns before and around downgrades and reversal after day zero. However, Hand et al (1992) divided the rating announcements, which was followed by other rating events and the announcements which were not preceded by other such events. The result of Hand et al. (1992) showed a significant negative abnormal stock returns before and around the downgrades and unexpected additions to Standard and Poor Credit Watch List. In line with most of the major studies, the study by Hand et.al (1992) also confirmed the zero impact of upgrades. Besides, Hand et.al (1992) also confirmed the reversal of stock prices after day one. With a relatively smaller event window compared to Glascock et.al (1987), Goh and Ederington (1993) analyzed to detect the abnormal performance before and after thirty days of credit rating changes. The impact on credit ratings on stock returns for a short window of five days before and after the event was carried out by Followwill and Martell (1997). Both the studies (Goh and Ederington, 1993 and Followwill and Martell, 1997) revealed significantly negative returns at the reviews for downgrade, negligible abnormal performance around the actual downgrades. L Paul Hsueh and Y Angela Liu (1992) also examined the impact of credit rating revisions on common stock prices and the market anticipation of bond rating changes on stock prices. Their analysis revealed the impact of credit rating on stock returns is based on the reputation of the company in the security market. The market value or the reputation acquired by the organization through sustained positive performance is very significant in rating revisions. In downgrades and upgrades, there are significant abnormal stock price movements in response to a rating change specifically for firms with less information available in the market. Kliger and Sarig (2000) scrutinize the response of security prices to Moody s refinement of its rating system in They captured the slight impact of the new alphanumeric ratings, which were based on just the same information that lie behind the earlier alphabetical ratings. There were studies carried out by collecting large data spanning over long periods in the last decade as well. Dichev and Piotroski (2001) collected Moody s 4727 credit rating changes spanning a period of twenty seven years, ( ) and Moody s 5034 credit rating changes to analyze the month returns of stock portfolios respectively. Dichev and Piotroski (2001) revealed significant negative returns during the first month after a downgrade, and no significant reactions for the upgrades. Steiner and Heinke (2001)examined thecorrelationbetween credit ratings andeurobond prices. They also examined the information content of US based credit rating agencies among non-us investors in the international capital markets. Using daily excess eurobond returns associated with announcements of watchlistings and rating changes by Standard & Poor's and Moody's, they carried out the study. The results revealed significant bond price reactions after the announcements of downgrades and negative watch-listings. However, in line with the earlier research findings they also revealed zero impact for the upgrades and positive watch-listings. They also revealed that the factors like actual yield level, issuer type and the issuer nationality are the key factors that determine the intensity of price reactions after the downgrades. The price reaction is also significantly stronger for the downgrades in speculative grade. 3

4 Anthony D. May (2010) examined the impact of bond rating changes using daily corporate bond and stock data from TRACE. The study did a comparison of stock and bond markets on abnormal returns. According to their studies, the bond markets and stock market reacts similarly while taking the daily data. The bond returns (bond market) respond positively (negatively) significant to credit rating of upgrades and downgrades for a two day event window. However, their results showed dissimilarityin the stock market. Stock market reaction to downgrade is statistically significant while the reaction to upgrade is statistically insignificant. Faff, Robert, Parwada, Jerry, Hun-lune (2007) examined the information content of managed fund ratings on the Australian retail investors. The study revealed the far reaching effects of credit ratings. The study revealed the positive reaction of market to upgrade and negative reaction to downgrades in the managed fund market. They also exposed the Australian investors high anticipation to rating revisions mainly downgrades. They attribute this occurrence to the role of qualitative factors in the ratings. Examiningthe certification effect of initial rating announcements, and the signalling effect of rating downgrade announcements in China using a pooled time-series cross-sectional issuer rating data of 170 companies listed with the Shanghai and Shenzhen Stock Exchanges from 2002 to 2006, Winnie P. H Poon and Kam C Chan (2007) supported their hypothesis of an asymmetric certification effect. Consistent with the existing literature, they found there are negative signalling effects in the downgrade rating. Intriguingly, when a normally positively biased rating agency gives a low rating, it is a valuable news to the market participants. Joanne Li, William T. Moore and Yoon Shin (2004) examined the impact of credit rating by global credit rating agencies and local (Japanese) credit rating agencies on the Japanese stock prices. They collected data from Moody Rating Interactive, S & P from Credit week, JCR from web site for a period ranging from 1983 to They concluded that global credit rating agencies (Moody s and S & P) are more powerful, and their ratings have got a good impact on the investors than the local credit rating agencies. The impact of global credit rating agency is relatively high in downgrades as compared to local rating agencies. Further their research indicates that among global rating agencies, Moody is more powerful in creating stock market price volatility through their announcements.s.v.d. Nageswara Rao and Vishnu S. Ramachandra, (2004)examined the impact of credit rating on the Indian stock prices using conditional risk adjusted method. They revealed significant positive (negative) abnormal returns and volumes prior to the upgrades (downgrades). Attributing this to the efficiency of average stock markets in capturing the factors that lead to rating upgrades or downgrades, the study supported the efficient market hypothesis. However, they exposed the cautious approach of investors to the upgrades and serious outlook of investors to the downgrades.this indicates the seriousness of Indian investors to the downgrades. Nevertheless, the study was done only using conditional risk adjusted method of normal return estimators. Studies by Dichev and Piotroski (2001), Griffin and Sanvicente (1982), Holthausen and Leftwich (1986), Hand et al. (1992), Goh and Ederington (1993,1999), Ederington and Goh (1998), found significant stock price reactions to the downgrades but not to upgrades. The Indian scenario may or may not be similar to the scenario in the United States and other developed markets. Our current study will reveal results in the Indian market. 4

5 III. Methodology The methodology of the research is explained in detail. The research questions and hypotheses are framed as follows. Stock price reaction to rating announcements (and revisions) (I)Do stock prices respond significantly to rating announcements (and revisions)? H 0 : The stock pricesdo not react significantly to rating announcements (revisions) and credit watch. (H 0 : ) H 1 : The stock prices react significantly to rating announcements (revisions). (H 1 : ) (II) Is the reaction to announcements of upgrades and downgrades as expected? The prices are expected to go up (down) in response to rating upgrades (downgrades). H 0 : The reaction to announcements of upgrades and downgrades are not expected H 1 : The reaction to announcements of upgrades and downgrades are expected The independent and dependent variable for the present study ares & P BSE 100 Index and adjusted closing prices of the stocks, respectively. The data is collected from the PROWESS (from Centre for Monitoring Indian Economy (CMIE)), and Capital Market.Rating announcements during the period of 1st January, 1999 to 31st March 2013 We propose to use Event Study Methodology to examine the stock price response to the rating announcements. This was first used by Fama, Fisher, Jensen and Roll (1969) in economics and finance.here Returns can be estimated in simple returns or continuously compounded returns. For the purpose of better normal assumptions most of the studies use continuously compounded returns. This is underlined by Fama (1976). However, Brown and Warner (1985) showed the similar results from both simple and continuously compounded returns. Furthermore, this is supported by Thompson (1988) that the calculation of return is not an important consideration in event studies. However, following the majority of event studies, we are also calculating the continuously compounded returns because this helps us in avoiding negative values besides fulfilling the normality assumptions. Natural log of continuously compounded rate of return ( ) on the stock of firm i on the event day t defined as: = 100 ln{( )} Where, P t and are adjusted Closing price on the day t and (t-1), respectively. Natural log of continuously compounded rate of return on the BSE 100 = 100 ln {( } where, and are indexon the day t and (t-1), respectively. Further in the event study methodology, irrespective of various techniques, we need to define two periods. The two periods are named as estimation period and event window. In the estimation period we derive the estimates. Using these estimates we define the normal expected or normal returns for each firm during the event period. The event window is represented by the event date plus or minus few days, weeks, months as required for the research problem. During this event window, we see the impact of credit rating on the stock returns. In the present study, we use the event window as five days before (t-5) and after (t+5) the event. 5

6 Mean Adjusted Returns In this method, the stock generates the same return that is similar to the return averaged during the estimation period. The estimation period is from T-160 (160 days before the event window) to T-60 (60 days before the event window) for each organization. We expect the mean value of the returns of the estimation period is the expected to be generated by the stock i during the event window period also. In the present study,we computed the normal expected return for the event windows as the mean value of the returns between T-160 and T-60. So the abnormal returns during the event window are: [1] Here the significance of abnormal return at period t is calculated Significance of ARs = [2] is the abnormal return on the particular window event day t (AR) is the standard deviation of abnormal returns Market Adjusted Returns Here the stock is expected to generate the same return like the rest of the market. To compute the market adjusted returns, we subtract the actual stock return from the return in the market during the event window period. Here abnormal return is calculated as:...[3] Significance of ARs =...[4] where, is the abnormal return on the particular window event day t. (AR) is the standard deviation of abnormal returns during the estimation period. Ordinary Least Square Market Model Here using a regression model we first establish the relationship between the dependent variable (stock returns) and independent variable (return on index), and subsequently we predict the normal expected returns for the firm during the event window. The difference between the returns observed (actual returns) and those predicted by the regression model is termed as abnormal returns. The daily prediction error, (PE i,t ), for each sample firm i' on each event day t during the period of interest is estimated as: PE i,t = R i,t ( α i + β i R m,t )... [5] where, R i,t is continuous compounded rate of return on the common stock of firm i on event day t defined as log (S t /S t-1 ). R m,t is Continuously compounded rate of return on the S & P Index on event day t given as log (I t /I t-1 ).α i, and β i are ordinary least squares of market model parameters. α i, and β i R m,t will give us estimated R i,t.( Ritہ) The difference between the actual return of stock I on day t and the estimated normal expected return of stock I on day t is the abnormal return or prediction error. After computing the β we checked the significance of β using a t-test. The t test is as follows :- t = /(SE ( ) ) 6

7 where is the estimated coefficient and s.e ( ) is the Standard Error of Further the standard error of is the standard deviation divided by number of observations. This is SE = N In all the three methods, the prediction errors, PE it are averaged across the N t firms on each day t to form an average prediction error (APE).[6] The average prediction errors are cumulated from day -5 to 5. The average prediction errors are also cumulated over various sub-periods to form window average prediction errors (WAPE). To calculate the abnormal return for any period of k days from t to t+k the average prediction errors are cumulated over the k days, from t through t+k = The significance of is estimated using the test t- statistics. Cumulative abnormal prediction error (CAPE) is the total of the abnormal returns (prediction over) from t-5 to t+5. If the t value obtained is greater than the critical value from a t-table (normal benchmark is 1.96 at 99% level of confidence), the beta coefficient is significant, which means that there is a relationship between the variables. After getting the complete results through mean adjusted returns,market adjusted returns and ordinary least square market models. Facilitates us to compare and contrast the results of various methods with the Indian data. IV. Results and Discussions Mean, Market and Conditional Risk Adjusted stock price impact of Upgrade rating announcements The mean abnormal returns associated with ratings upgrade for different time buckets are given in Table 1A. Table 1A: Abnormal Returns Associated with Upgrades S&P BSE 100 (Mean Adjusted Method) Upgrade l return Window Average Prediction Error(WAPE) (estimated from T-160 to T-60) Event Windows T T-5 to T % 0.92* 4.58% T-3 to T % 1.06* 7.68% T 1.24% 0.98* 7.02% T to T % 1.72* 9.36% T to T % 1.56* 8.58% T to T % 0.90* 4.46% *99% level of confidence Cumulative Abnormal Prediction Errors (CAPE) 7

8 Table 1B: Abnormal Returns Associated with Upgrades S&P BSE 100 (Market Adjusted Method) Upgrade mal return Event Windows Window Average Prediction Error (WAPE) (estimated from T-160 to T-60) Cumulative Abnormal Prediction Errors (CAPE) T T-5 to T % 0.90* 4.54% T-3 to T % 1.04* 7.61% T 1.18% 0.97* 6.93% T to T % 1.93* 9.74% T to T % 1.81* 8.69% T to T % 0.94* 6.38% *99% level of confidence Table 1C: Abnormal Returns Associated with Upgrades S&P BSE 100 (Conditional Risk Adjusted Method) Upgrade (estimated from T-160 to T-60) Event Windows Window Average Prediction Error (WAPE) Cumulative Abnormal Prediction Errors (CAPE) T T-5 to T % 0.36* 1.42% T-3 to T % 0.44* 1.18% T 0.84% 0.08* 0.76% T to T % 0.42* 1.62% T to T % 0.56* 1.32% T to T % 0.62* 1.04% *99% level of confidence 8

9 CAPE CAPE Proceedings of 23rd International Business Research Conference Diagram 1a: Abnormal Returns Associated with Upgrades S&P Bse 100 (Mean Adjusted Method) Cumulative Abnormal Prediction Errors (CAPE) 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% T-5 to T-1 T-3 to T-1 T T to T+1 T to T+2 T to T+5 Event Windows Diagram 1B: Abnormal Returns Associated with Upgrades S&P BSE 100 (Market Adjusted Method) 12.00% 10.00% 8.00% Cumulative Abnormal Prediction Errors (CAPE) 6.00% 4.00% 2.00% Cumulative Abnormal Prediction Errors (CAPE) 0.00% T-5 to T-1T-3 to T-1 T T to T+1 T to T+2 T to T+5 Event Windows 9

10 CAPE Proceedings of 23rd International Business Research Conference Diagram 1C: Abnormal Returns Associated with Upgrades S&P BSE 100 (Conditional Risk Adjusted Method) 2.00% 1.50% Cumulative Abnormal Prediction Errors (CAPE) 1.00% 0.50% 0.00% T-5 to T- 1 T-3 to T- 1 T T to T+1 T to T+2 T to T+5 Event Windows Cumulative Abnormal Prediction Errors (CAPE) Table 1A, 1B and 1C the abnormal returns associated with upgrade ratings in S&P BSE 100using mean adjusted method, market adjusted method, conditional risk adjusted method, respectively. Diagram 1A, 1B and 1C show Cumulative Abnormal Prediction Error (CAPE) as a function of event windows associated with downgrade ratings in S&P BSE 100using mean adjusted method, market adjusted method, conditional risk adjusted method, respectively. From the above table it is evident that the upgrades are not influencing the market behavior. All T values are below 1.96, indicating insignificant abnormal returns for the upgrades. Furthermore, it is apparentthat the upgrades are not viewed seriously by the market even before the actual upgrade announcements. In nutshell, it is clear from the table 1A, 1B and 1C, there are absolutely insignificant positive abnormal returns for all event windows. Besides, there is zero anticipation of upgrade rating in the market. The zero anticipation implies that the positive firm scenarios, which in due course induce the rating agency to downgrade, may have already estimated and factored by the investors even before the actual upgrade announcement and even before the five days prior to the actual announcement. Besides, the investors can be conservative and cautious to good news. Our results support both the views. It is rather ironical but not surprising that most of the studies confirm least reaction to rating upgrades. The results are similar in sign, magnitude and statistical significance for all the three methods of normal returns estimators. In other words, this supports the findings of Brown and Warner (1980, 1985). Our results apparently confirms the findings of (Weinstein, (1977); Pinches and Singleton, (1978); and and Antony May (2010). However, we have taken the daily returns while the others except Antony May (2010) took monthly returns. However with monthly returns, Griffin and Sanvicente (1982) results was supporting our results. Specifically we can say studies by Dichev and Piotroski (2001), Griffin and Sanvicente (1982), Holthausen and Leftwich (1986), Hand et al. (1992), Cornell et al. (1989), Goh and Ederington (1993,1999), found significant stock price reactions to downgrades but not to upgrades. Holthausen, and Leftwich (1992), also confirm this much observed phenomenon. 10

11 Table 2A: Abnormal Returns Associated with Downgrades S&P BSE 100 (Mean Adjusted Method) Downgrade al return Event Windows Window Average Prediction Error (WAPE) (estimated from T-160 to T-60) T Cumulative Abnormal Prediction Errors (CAPE) T-5 to T % -1.98* % T-3 to T % -2.78* % T -4.64% -2.28* % T to T % -3.26* % T to T % -2.62* % T to T % -1.48* % *99% level of confidence Table 2B: Abnormal returns associated with downgrades S&P BSE 100 (Market Adjusted Method) Downgrade al return Event Windows Window Average Prediction Error (WAPE) (estimated from T-160 to T-60) T Cumulative Abnormal Prediction Errors (CAPE) T-5 to T % -2.02* % T-3 to T % -2.82* % T -4.46% -2.32* % T to T % -3.04* % T to T % -2.56* % T to T % -1.28* % *99% level of confidence 11

12 CAPE Proceedings of 23rd International Business Research Conference Table 2c: Abnormal Returns Associated with Downgrades S&P Bse 100 (Conditional Risk Adjusted Method) Downgrade al return Event Windows Window Average Prediction Error (WAPE) (estimated from T-160 to T-60) T Cumulative Abnormal Prediction Errors (CAPE) T-5 to T % -2.12* % T-3 to T % -2.82* % T -4.96% -2.42* % T to T % -3.18* % T to T % -2.22* % T to T % -1.78* % *99% level of confidence Diagram 2A: Abnormal returns associated with downgrades S&P BSE 100(Mean Adjusted Method) Cumulative Abnormal Predictions Errors Event Windows T-5 to T-1 T-3 to T-1 T T to T+1 T to T+2 T to T

13 CAPE CAPE Proceedings of 23rd International Business Research Conference Diagram 2B: Abnormal returns associated with downgrades S&P BSE 100 (Market Adjusted Method) Cumulative Abnormal Prediction Errors 0 Event Windows T-5 to T-1T-3 to T T T to T+1T to T+2T to T Event Windows Diagram 2C: Abnormal returns associated with downgrades S&P BSE 100(Conditional Risk Adjusted Method) 2.00% Cumulative Abnormal Prediction Errors (CAPE) Event Windows 1.50% 1.00% 0.50% 0.00% T-5 to T- 1 T-3 to T- 1 T T to T+1 T to T+2 T to T+5 Cumulative Abnormal Prediction Errors (CAPE) Table 2A, 2B and 2C the negative abnormal returns associated with downgrade ratings in S&P BSE 100using mean adjusted method, market adjusted method, conditional risk adjusted method, respectively. Diagram 2A, 2B and 2C show CAPE as a function of event windows associated with downgrade ratings in S&P BSE 100using mean adjusted method, market adjusted method, conditional risk adjusted method, respectively. Almost all T values are above 1.96, indicating significant abnormal returns for the downgrades. Furthermore,it is evident the downgrades are viewed very seriously by the market even before the actual downgrade announcements. The abnormal and the negative abnormal returns continues till the third day (t+3) after the actual announcement date in a significant level.immediately after the third day, the market returns showing the signs of reversal. This trend of reduced average abnormal returns are visible even on 13

14 the fifth day (t+5) after the actual rating announcements. In addition to the post announcement effects, there is also an anticipation of downgrading in the market. The anticipation implies that the declining firm scenarios, which in due courseprompt the rating agency to downgrade,are also gauged by the investors even before the actual announcement. We attribute this to the reduced information asymmetry and efficiency of the capital market. This is a positive and significant factor for the Indian capital market regulatory bodies. The results are similar in sign, magnitude and statistical significance for all the three methods of normal returns estimators. However, the authentic incidence of rating downgrade also has a definite surprise factor. Our results support the findings of Griffin and Sanvicente (1982). Further many other studies (Houlthausen and Leftwich (1986), Hand et al (1987) Goh and Ederington (1993), Glascock, Davidson and Henderson (1987) also supports the findings of our study.however, our findings are not in line with Glascock et al.(1987). V. Conclusions This paper puts inaperceptiveon the impact of credit rating on the Indian market behavior by estimating the normal returns using three different methods of normal return estimators namely, mean adjusted method, market adjusted method and conditional risk adjusted method.analysis of the stock prices around the credit ratings revealssignificant impact of downgrading on stock prices. This suggests the valuable information to the market, which is not completely factored in the stock prices. Furthermore, the results throw light on the abnormal returns on pre announcements of downgrading. In the above three methods of estimating normal returns, the anticipation of downgrading is very high in the event window of three days before the actual event (t-3) to one day before the actual event (t-1). On the contrary,upgrade ratings have no impact on stock prices. This reveals the market s incorporation of those factors leading to upgrade before the actual upgrade rating. At times, it can be due to the pessimistic/conservative view of the investors on positive factors. Besides, there is no anticipation of upgrade rating in the market. Further our study also establishes the accuracy of different methods of abnormal return estimators. The results are more or less same in all the three methods of normal return estimators. We can say upgrade ratings are received cautiously by investors with no significant abnormal returns. However, downgrade ratings are received more negatively by investors with significant negative abnormal returns. The study more or less confirmed the interesting words of Warren Buffet that [w]hen investing, pessimism is your friend, euphoria the enemy. Acknowledgment One of the authors (Sreejith. U) acknowledges the financial assistance provided in the form of National Doctoral Fellowship awarded by the All Indian Council of Technical Education (A.I.C.T.E), New Delhi during the course of this study. 14

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17 Appendix Rating Agencies Long Term - including Debentures, Bonds, Preference Shares Highest High Adequate Inadequate Risk safety safety safety safety prone Substantial risk Default ICRA LAAA LAA LBBB LBB LB LC LD CRISIL AAA AA FITCH AAA AAA (ind) AA AA (ind) A BBB BB B C D A BBB BB B C A (ind) BBB (ind) BB (ind) D B (ind) C (ind) D (ind) Medium Term including Certificate of Deposit and Fixed Deposit Programmes Rating Agencies Highest safety High safety Adequate safety Inadequate safety Risk prone Substantial risk Default ICRA MAAA MAA MBBB MBB MB MC MD CRISIL FAAA FAA FA FB FC FD A BBB BB B C FITCH Rating Agencies AAA taaa (ind) AA taa (ind) ta (ind) tbbb (ind) tbb (ind) D tb (ind) tc (ind) td (ind) Short Term including Commercial Paper Highest High safety safety Adequate safety Risk prone Default ICRA A1 A2 A3 A4 A5 CRISIL P1 P2 P3 P4 P5 PR1 PR2 PR3 PR4 PR5 FITCH F1 (ind) F2 (ind) F3 (ind) F4 (ind) F5 (ind) 17