Rating changes and the impact on stock prices.
| Autor | Baraccat, Bruno Borges |
1 Introduction
The informational content of corporate rating changes is a topic that has long been debated in the literature. Pinches and Singleton (1978) and Glascock, Davidson, and Henderson (1987) find no significant unanticipated effects on stock prices of companies having their risk ratings downgraded. Papers published afterwards that study larger and more frequent (monthly and daily) databases find strong evidence that downgrades have an impact on short- and long-term stock returns (Griffin & Sanvicente (1982), Followill & Martell (1997), Dichev & Piotroski (2001), Norden & Weber (2004), and Linciano (2004)). Other authors, such as Holthausen and Leftwich (1986), Hand, Holthausen, and Leftwich (1992), and Dichev and Piotroski (2001), also verify that rating upgrades do not have significant impacts. Goh and Ederington (1993) find interesting results in the form of negative impacts on prices after downgrades due to falling profits, and positive impacts in terms of price increases after downgrades due to increased leverage.
This empirical evidence on the impact of increased bankruptcy risk on stock prices is controversial as to the direction in which it operates. It is known as the "distress puzzle." Some studies show a positive relationship between increased default risk and the rate of return on the stock, while others find the opposite result.
For example, Vassalou and Xing (2004), Chava and Purnanandam (2010), and Friewald, Wagner, and Zechner (2014) find evidence of increased profitability for higher credit risk securities. In these cases, the risk of default is measured by using the methodology proposed in Merton (1974). The empirical results in Friewald et al. (2014) document that measuring credit risk by credit default swap (CDS) spreads and building portfolios by buying high-credit risk companies and selling low-credit risk companies yields a positive alpha after controlling for standard risk factors. They use data from between 2001 and 2010, including the 2008 crisis.
On the other hand, there are several articles documenting a negative relationship between stock returns and increased default risk, such as those of Hand, Holthausen, and Leftwich (1992), Dichev (1998), Dichev and Piotroski (2001), Griffin and Lemmon (2002), and most recently by Campbell, Hilscher, and Szilagyi (2008), Avramov, Chordia, Jostova, and Philipov (2009), and Vassalou and Xing (2013). In these articles, however, the increase in risk is measured by rating changes published by rating agencies (Moody's and S&P) and by credit-risk metrics based on historical data such as Z-Score and O-Score, as suggested by Altman (1968) and Ohlson (1980), respectively.
Positive results appear to be in line with the efficient market theory, as investors demand higher returns on riskier assets, except if we consider default risk as a systematic risk and therefore not diversifiable. In this sense, Vassalou and Xing (2014) demonstrate that the risk of default is systematic and even partially captured by the factors that generate the price formation anomalies pointed out in Fama and French (1992). Articles that find a negative relationship between profitability and increased risk of default appear to be in conflict with the efficient market hypothesis: after all, rating agencies publish their results after analyzing public data and meetings held at the companies. Even if the company could pass on private information at these meetings, these effects would be short-term, which contradicts several long-term results previously mentioned, for example by Dichev and Piotroski (2001), who find abnormal returns of 10% to 14% one year after the rating downgrade. Vassalou and Xing (2013) explain this apparent contradiction by the fact that when the rating change announcement occurs, the affected company changes its risk behavior, thus creating an inverted V effect on default risk metrics by using the default model in Merton (1974). For example, downgraded companies seek to reduce risk and thus expected returns will be lower than for comparable companies.
Another academic argument to justify the apparent contradiction claims that the controversial results of the distress puzzle can be explained by the methodology chosen. Thus, traditional event study methodologies such as the one proposed by Campbell, Lo, and Mackinlay (1997) use CAR (cumulative abnormal return) and fail to capture part of the variations in return explained by other factors such as company size, rating, and book-to-market ratio. To correct this bias, Dichev and Piotroski (2001) propose the use of the buy and hold returns (BHAR) methodology, following Barber and Lyon (1996). This methodology compares the return on securities with changes in their credit risk with comparable corporate portfolios by using metrics for size, book-to-market ratio, and credit risk. Even so, the long-term results are preserved. Subsequently, Jorion and Zhang (2006) find a moderating effect of the previous credit rating, which shows that this metric should be included in the models to avoid omitted variable bias. Therefore, with the inclusion of previously omitted variables, we expect more consistent results.
Almost all these articles analyze results in the US market, especially because the coverage of emerging countries by rating agencies is more recent. In this sense, Benjamin EE (2008) publishes one of the pioneering studies on emerging countries. He finds a significant negative abnormal return in the long run for downgrades, but which is smaller in the case of emerging countries. Freitas and Minardi (2013) find similar results for Latin American countries using the methodology of Campbell, Lo, and Mackinlay (1997). They also find results which are not significant for rating upgrades.
For this article, we studied a database covering 161 rating changes in Brazil, issued by the Moody's and S&P agencies until 2018. Following a methodology compatible with that used by Dichev and Piotroski (2001), we generated 27 control portfolios and found significant negative results for downgrades. The ratings had impacts ranging from 4.35% in 6 months to up to 24.95% in 12 months. We also detected larger impacts for lower rated companies, compatible with the moderating effect in Jorion and Zhang (2006). Not only do we innovate in terms of the methodology used, but also in the use of more data on rating fluctuations, which have increased in frequency due to the recent economic crisis the country has faced.
Our data also demonstrate that the moderating effect of ratings is not linear as it shows quadratic behavior. Therefore, the results of rating changes for companies in the middle of the risk spectrum are less economically significant than for companies with low or high credit risk. Friewald et al. (2014) had already documented this inverted U pattern, but for bankruptcy probabilities measured by the CDS (credit default swap) spread of corporate long-term securities. Our work is the first that we are aware of that detects this nonlinear effect for rating changes, which could be explained by the investor's concern about lowering the prices of companies in the middle of the risk spectrum more intensely than those of lower or higher credit risk companies. In the former this is possibly because the downgrade is still far from being a problem of increased default risk; and in the case of companies with high-credit risk it is because their prices have already been fully reduced due to the high speculative level they represent. The disciplining effect of rating changes pointed out by Vassalou and Xing (2003) also appears to be more effective for companies with ratings at the end of the risk spectrum, thus generating greater adjustments and therefore lower expected returns over the medium and long term. The fact that long-term setbacks are greater than short-term ones is also compatible with the time it takes for the company to generate effective risk mitigation measures.
In addition to their academic significance, the findings of this paper have an impact on the market as they may suggest an easy-to-implement investment strategy for stock managers, or investors who buy or sell companies that have their ratings downgraded or upgraded (not necessarily in that order). For example, Avramov, Chordia, Jostova, and Philipov (2013) show that it is possible to make abnormal gains by short selling portfolios composed of companies with downgraded ratings in the US market and our article documents similar effects for Brazilian companies.
The remainder of this paper is organized as follows. The second section discusses the literature review. The third section describes the methodology. The fourth section discusses the results obtained. The fifth section presents the conclusion.
2 Literature Review
The usual empirical tests to measure the impact of rating shifts analyze changes in stock prices before, around, and after the announcements of these rating shifts. They apply the event study methodology. If rating changes bring relevant new information to the market, a price reaction is expected after the announcement. Similarly, considering that agencies primarily formulate ratings using available public information, changes in ratings should not have an impact on stock prices because they already reflect all informational content in accordance with the efficient market hypothesis.
Dichev and Piotroski (2001) point out that one motivation for studying the effect of rating changes stems from "the fact that existing research offers only sporadic and somewhat contradictory evidence on this issue." In fact, arbitrariness in choosing how to model and to conduct statistical tests has not enabled the literature to create a standard--there is no single ideal methodology for event studies. There has been a great deal of disagreement among the authors of academic studies on the subject so far, as they have used different timeframes, criteria to...
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