Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
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, CARE, Fitch Ratings and Brickwork
st
Ratings) on equityreturns in India. The period of our study is from 1
st
January, 1999 to 31 March, 2013. 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.
Email : sonti@iitb.ac.in
Sreejith. U, Research Scholar, School of Management, Indian Institute of Technology, Bombay, INDIA.
Email: vrindavan40@gmail.com.
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
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, CARE, 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
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
abnormal returns by collecting 162 Moody‟s credit rating changes for the period of five years
(1977-1981). 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 1982. 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, (1970-1997) 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.
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
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
2003. 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.
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
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)?
H0: The stock pricesdo not react significantly to rating announcements (revisions) and credit watch.
(H0:
)
H1: The stock prices react significantly to rating announcements (revisions). (H1:
)
(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).
H0: The reaction to announcements of upgrades and downgrades are not expected
H1: 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, Pt 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.
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
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, (PEi,t), for each sample firm „i' on each event day t during the period of
interest is estimated as:
PEi,t = Ri,t – ( αi+ βi Rm,t)
………………....................... [5]
where, Ri,t is continuous compounded rate of return on the common stock of firm i on event day t
defined as log (St/St-1). Rm,tis Continuously compounded rate of return on the S & P Index on event
day t given as log (It/It-1).αi, and βiare ordinary least squares of market model parameters. αi, and
βiRm,t will give us estimated Ri,t (‫ﮧ‬Rit). 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 ( ̂ ) )
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
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, PEit are averaged across the Nt 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
Event
Windows
T-5 to T-1
T-3 to T-1
T
T to T+1
T to T+2
T to T+5
0.0726 (estimated from T-160 to T-60)
Window Average
Prediction
Cumulative Abnormal
Error(WAPE)
T
Prediction Errors (CAPE)
1.08%
0.92*
4.58%
1.46%
1.06*
7.68%
1.24%
0.98*
7.02%
2.52%
1.72*
9.36%
2.08%
1.56*
8.58%
2.56%
0.90*
4.46%
*99% level of confidence
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Proceedings of 23rd International Business Research Conference
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Table 1B: Abnormal Returns Associated with Upgrades S&P BSE 100 (Market Adjusted
Method)
Upgrade
mal return
Event
Windows
T-5 to T-1
T-3 to T-1
T
T to T+1
T to T+2
T to T+5
0.0718 (estimated from T-160 to T-60)
Window
Average
Prediction Error
Cumulative Abnormal Prediction Errors
(WAPE)
T
(CAPE)
1.12%
0.90*
4.54%
1.58%
1.04*
7.61%
1.18%
0.97*
6.93%
2.72%
1.93*
9.74%
2.02%
1.81*
8.69%
2.64%
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)
Window
Average
Event
Prediction Error
Cumulative Abnormal Prediction Errors
Windows
(WAPE)
T
(CAPE)
0.94%
0.36*
1.42%
T-5 to T-1
1.06%
0.44*
1.18%
T-3 to T-1
T
0.84%
0.08*
0.76%
T to T+1
1.32%
0.42*
1.62%
T to T+2
1.14%
0.56*
1.32%
T to T+5
1.28%
0.62*
1.04%
*99% level of confidence
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Proceedings of 23rd International Business Research Conference
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Diagram 1a: Abnormal Returns Associated with Upgrades S&P Bse 100 (Mean Adjusted
Method)
Cumulative Abnormal Prediction Errors
(CAPE)
10.00%
CAPE
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)
Cumulative Abnormal Prediction Errors (CAPE)
12.00%
10.00%
CAPE
8.00%
6.00%
Cumulative Abnormal
Prediction Errors (CAPE)
4.00%
2.00%
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
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Proceedings of 23rd International Business Research Conference
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Diagram 1C: Abnormal Returns Associated with Upgrades S&P BSE 100 (Conditional
Risk Adjusted Method)
Cumulative Abnormal Prediction Errors
(CAPE)
2.00%
CAPE
1.50%
1.00%
Cumulative Abnormal
Prediction Errors (CAPE)
0.50%
0.00%
T-5 to T- T-3 to T1
1
T
T to T+1 T to T+2 T to T+5
Event Windows
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.
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Proceedings of 23rd International Business Research Conference
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Table 2A: Abnormal Returns Associated with Downgrades S&P BSE 100 (Mean Adjusted
Method)
Downgrade
(estimated from T-160 to T-60)
al return
Event
Windows
T-5 to T-1
T-3 to T-1
T
T to T+1
T to T+2
T to T+5
Window
Average
Prediction Error
(WAPE)
T
Cumulative Abnormal Prediction
Errors (CAPE)
-3.74%
-1.98*
-21.64%
-5.48%
-4.64%
-6.86%
-5.08%
-2.92%
-2.78*
-2.28*
-3.26*
-2.62*
-1.48*
-27.86%
-24.72%
-29.48%
-26.92%
-20.02%
*99% level of confidence
Table 2B: Abnormal returns associated with downgrades S&P BSE 100 (Market Adjusted
Method)
Downgrade
(estimated from T-160 to T-60)
al return
Event
Windows
T-5 to T-1
T-3 to T-1
T
T to T+1
T to T+2
T to T+5
Window
Average
Prediction Error
(WAPE)
T
Cumulative Abnormal Prediction
Errors (CAPE)
-3.48%
-2.02*
-21.26%
-5.26%
-4.46%
-6.58%
-4.82%
-1.82%
-2.82*
-2.32*
-3.04*
-2.56*
-1.28*
-27.64%
-24.52%
-29.18%
-26.18%
-18.28%
*99% level of confidence
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Proceedings of 23rd International Business Research Conference
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Table 2c: Abnormal Returns Associated with Downgrades S&P Bse 100 (Conditional
Risk Adjusted Method)
Downgrade
(estimated from T-160 to T-60)
al return
Event
Windows
T-5 to T-1
T-3 to T-1
T
T to T+1
T to T+2
T to T+5
Window
Average
Prediction Error
(WAPE)
T
Cumulative Abnormal Prediction
Errors (CAPE)
-4.42%
-2.12*
-19.48%
-5.86%
-4.96%
-6.82%
-4.74%
-2.84%
-2.82*
-2.42*
-3.18*
-2.22*
-1.78*
-28.62%
-26.22%
-29.46%
-25.84%
-21.08%
*99% level of confidence
Diagram 2A: Abnormal returns associated with downgrades S&P BSE 100(Mean
Adjusted Method)
Cumulative Abnormal Predictions Errors
Event Windows
0
-0.05
T-5 to T-1 T-3 to T-1
T
T to T+1
T to T+2
T to T+5
CAPE
-0.1
-0.15
-0.2
-0.25
-0.3
-0.35
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Diagram 2B: Abnormal returns associated with downgrades S&P BSE 100 (Market
Adjusted Method)
Cumulative Abnormal Prediction Errors
CAPE
0
Event Windows
T-5 to T-1
T-3 to T-1
-0.1
T
T to T+1T to T+2T to T+5
-0.2
-0.3
-0.4
Event Windows
Diagram 2C: Abnormal returns associated with downgrades S&P BSE 100(Conditional
Risk Adjusted Method)
Cumulative Abnormal Prediction Errors
(CAPE)
Event Windows
2.00%
CAPE
1.50%
1.00%
Cumulative Abnormal
Prediction Errors (CAPE)
0.50%
0.00%
T-5 to T- T-3 to T1
1
T
T to T+1 T to T+2 T to T+5
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
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
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.
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
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Proceedings of 23rd International Business Research Conference
18 - 20 November, 2013, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-36-8
Appendix
Long Term - including Debentures, Bonds, Preference Shares
Rating
Highest
High
Adequate Inadequate Risk Substantial
Agencies
safety
safety
safety
safety
prone
risk
Default
ICRA
LAAA
LAA
CRISIL
AAA
AA
CARE
AAA
AAA
(ind)
CARE
AA
CARE
FITCH
LBBB
AA (ind)
A
BBB
CARE A
CARE
BBB
A (ind)
BBB (ind)
LBB
LB
LC
LD
BB
B
C
D
CARE BB
BB (ind)
CARE
B
CARE C
B
(ind)
C (ind)
CARE
D
D (ind)
Medium Term including Certificate of Deposit and Fixed Deposit Programmes
Rating
Highest
High
Adequate Inadequate Risk Substantial
Agencies
safety
safety
safety
safety
prone
risk
Default
ICRA
MAAA
MAA
MBBB
MBB
CRISIL
FAAA
FAA
FB
CARE
AAA
tAAA
(ind)
CARE
AA
tAA
(ind)
FA
CARE A
CARE
BBB
tA (ind)
tBBB (ind)
CARE
FITCH
Rating
Agencies
CARE BB
tBB (ind)
MB
CARE
B
tB
(ind)
MC
MD
FC
FD
CARE C
CARE
D
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
CARE
FITCH
P1
PR1
F1 (ind)
P2
PR2
F2 (ind)
P3
PR3
F3 (ind)
P4
PR4
F4 (ind)
P5
PR5
F5 (ind)
17