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In Search of Distress Risk
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Please share how this access benefits you. Your story matters.
Campbell, John Y., Jens Hilscher, and Jan Szilagyi. 2008. In
Citation Search of Distress Risk. Journal of Finance 63, no. 6: 2899-2939.
July 11, 2016 3:00:09 PM EDT
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http://ssrn.com/abstract=770805 In Search of Distress Risk John Y. Campbell, Jens Hilscher, and Jan Szilagyi1 First draft: October 2004 This version: June 27, 2005 Corresponding author: John Y. Campbell, Department of Economics, Littauer Center 213, Harvard University, Cambridge MA 02138, USA, and NBER. Tel 617-496-6448, email firstname.lastname@example.org. This material is based upon work supported by the National Science Foundation under Grant No. 0214061 to Campbell. We would like to thank Robert Jarrow and Don van Deventer of Kamakura Risk Information Services (KRIS) for providing us with data on corporate bankruptcies and failures, and Stuart Gilson, John Griﬃn, Scott Richardson, Ulf von Kalckreuth, and seminar participants at Humboldt Universität zu Berlin, HEC Paris, the University of Texas, the Wharton School, and the Deutsche Bundesbank 2005 Spring Conference for helpful discussion.
Abstract This paper explores the determinants of corporate failure and the pricing of ﬁnancially distressed stocks using US data over the period 1963 to 2003. Firms with higher leverage, lower proﬁtability, lower market capitalization, lower past stock returns, more volatile past stock returns, lower cash holdings, higher market-book ratios, and lower prices per share are more likely to ﬁle for bankruptcy, be delisted, or receive a D rating. When predicting failure at longer horizons, the most persistent ﬁrm characteristics, market capitalization, the market-book ratio, and equity volatility become relatively more signiﬁcant. Our model captures much of the time variation in the aggregate failure rate. Since 1981, ﬁnancially distressed stocks have delivered anomalously low returns. They have lower returns but much higher standard deviations, market betas, and loadings on value and small-cap risk factors than stocks with a low risk of failure. These patterns hold in all size quintiles but are particularly strong in smaller stocks. They are inconsistent with the conjecture that the value and size eﬀects are compensation for the risk of ﬁnancial distress.
1 Introduction The concept of ﬁnancial distress is often invoked in the asset pricing literature to explain otherwise anomalous patterns in the cross-section of stock returns. The idea is that certain companies have an elevated risk that they will fail to meet their ﬁnancial obligations, and investors charge a premium for bearing this risk.2 While this idea has a certain plausibility, it leaves a number of basic questions unanswered. First, how do we measure the failure to meet ﬁnancial obligations?
Second, how do we measure the probability that a ﬁrm will fail to meet its ﬁnancial obligations? Third, even if we have answered these questions and thereby constructed an empirical measure of ﬁnancial distress, is it the case that the stock prices of ﬁnancially distressed companies move together in response to a common risk factor?
Finally, what returns have ﬁnancially distressed stocks provided historically? Is there any evidence that ﬁnancial distress risk carries a premium?
In this paper we consider two alternative ways in which a ﬁrm may fail to meet its ﬁnancial obligations. First, we look at bankruptcy ﬁlings under either Chapter 7 or Chapter 11 of the bankruptcy code. Second, we look at failures, deﬁned more broadly to include bankruptcies, delistings, or D (“default”) ratings issued by a leading credit rating agency. The broader deﬁnition of failure allows us to capture at least some cases where ﬁrms avoid bankruptcy by negotiating with creditors out of court (Gilson, John, and Lang 1990, Gilson 1997). It also captures ﬁrms that perform so poorly that their stocks are delisted from the exchange, an event which sometimes precedes bankruptcy or formal default.
To measure the probability that a ﬁrm enters either bankruptcy or failure, we adopt a relatively atheoretical econometric approach. We estimate a dynamic panel model using a logit speciﬁcation, following Shumway (2001), Chava and Jarrow (2004), and others. We extend the previous literature by considering a wide range of explanatory variables, including both accounting and equity-market variables, and by explicitly considering how the optimal speciﬁcation varies with the horizon of the Chan and Chen (1991), for example, attribute the size premium to the prevalence of “marginal ﬁrms” in small-stock portfolios, and describe marginal ﬁrms as follows: “They have lost market value because of poor performance, they are ineﬃcient producers, and they are likely to have high ﬁnancial leverage and cash ﬂow problems. They are marginal in the sense that their prices tend to be more sensitive to changes in the economy, and they are less likely to survive adverse economic conditions.” Fama and French (1996) use the term “relative distress” in a similar fashion.
forecast. Some papers on bankruptcy concentrate on predicting the event that a bankruptcy will occur during the next month. Over such a short horizon, it should not be surprising that the recent return on a ﬁrm’s equity is a powerful predictor, but this may not be very useful information if it is relevant only in the extremely short run, just as it would not be useful to predict a heart attack by observing a person dropping to the ﬂoor clutching his chest. We also explore time-series variation in the number of bankruptcies, and ask how much of this variation is explained by changes over time in the variables that predict bankruptcy at the ﬁrm level.
Our empirical work begins with monthly bankruptcy and failure indicators provided by Kamakura Risk Information Services (KRIS). The bankruptcy indicator was used by Chava and Jarrow (2004), and covers the period from January 1963 through December 1998. The failure indicator runs from January 1963 through December
2003. We merge these datasets with ﬁrm level accounting data from COMPUSTAT as well as monthly and daily equity price data from CRSP. This gives us about 800 bankruptcies, 1600 failures, and predictor variables for 1.7 million ﬁrm months.
We start by estimating a basic speciﬁcation used by Shumway (2001) and similar to that of Chava and Jarrow (2004). The model includes both equity market and accounting data. From the equity market, we measure the excess stock return of each company over the past month, the volatility of daily stock returns over the past three months, and the market capitalization of each company. From accounting data, we measure net income as a ratio to assets, and total leverage as a ratio to assets. We obtain similar coeﬃcient estimates whether we are predicting bankruptcies through 1998, failures through 1998, or failures through 2003.
From this starting point, we make a number of contributions to the prediction of corporate bankruptcies and failures. First, we explore some sensible modiﬁcations to the variables listed above. Speciﬁcally, we show that scaling net income and leverage by the market value of assets rather than the book value, and adding further lags of stock returns and net income, can improve the explanatory power of the benchmark regression.
Second, we explore some additional variables and ﬁnd that corporate cash holdings, the market-book ratio, and a ﬁrm’s price per share contribute explanatory power.
In a related exercise we construct a measure of distance to default, based on the practitioner model of KMV (Crosbie and Bohn 2001) and ultimately on the structural default model of Merton (1974). We ﬁnd that this measure adds relatively little
explanatory power to the reduced-form variables already included in our model.3
Third, we examine what happens to our speciﬁcation as we increase the horizon at which we are trying to predict failure. Consistent with our expectations, we ﬁnd that our most persistent forecasting variable, market capitalization, becomes relatively more important as we predict further into the future. Volatility and the market-book ratio also become more important at long horizons relative to net income, leverage, and recent equity returns.
Fourth, we study time-variation in the number of failures. We compare the realized frequency of failure to the predicted frequency over time. Although the model underpredicts the frequency of failure in the 1980s and overpredicts it in the 1990s, the model ﬁts the general time pattern quite well.
Finally, we use our ﬁtted probability of failure as a measure of ﬁnancial distress and calculate the risks and average returns on portfolios of stocks sorted by this ﬁtted probability. We ﬁnd that ﬁnancially distressed ﬁrms have high market betas and high loadings on the HML and SMB factors proposed by Fama and French (1993, 1996) to capture the value and size eﬀects. However they do not have high average returns, suggesting that the equity market has not properly priced distress risk.
There is a large related literature that studies the prediction of corporate bankruptcy. The literature varies in choice of variables to predict bankruptcy and the methodology used to estimate the likelihood of bankruptcy. Altman (1968), Ohlson (1980), and Zmijewski (1984) use accounting variables to estimate the probability of bankruptcy in a static model. Altman’s Z-score and Ohlson’s O-score have become popular and widely accepted measures of ﬁnancial distress. They are used, for example, by Dichev (1998), Griﬃn and Lemmon (2002), and Ferguson and Shockley (2003) to explore the risks and average returns for distressed ﬁrms.
Shumway (2001) estimates a hazard model at annual frequency and adds equity market variables to the set of scaled accounting measures used in the earlier literature.
He points out that estimating the probability of bankruptcy in a static setting introduces biases and overestimates the impact of the predictor variables. This is because the static model does not take into account that a ﬁrm could have had unfavorable indicators several periods before going into bankruptcy. Hillegeist, Cram, Keating and This ﬁnding is consistent with recent results of Bharath and Shumway (2004), circulated after the ﬁrst version of this paper.
Lunstedt (2004) summarize equity market information by calculating the probability of bankruptcy implied by the structural Merton model. Adding this to accounting data increases the accuracy of bankruptcy prediction within the framework of a hazard model. Chava and Jarrow (2004) estimate hazard models at both annual and monthly frequencies and ﬁnd that the accuracy of bankruptcy prediction is greater at a monthly frequency. They also compare the eﬀects of accounting information across industries.
Duﬃe and Wang (2003) emphasize that the probability of bankruptcy depends on the horizon one is considering. They estimate mean-reverting time series processes for a macroeconomic state variable–personal income growth–and a ﬁrm-speciﬁc variable–distance to default. They combine these with a short-horizon bankruptcy model to ﬁnd the marginal probabilities of default at diﬀerent horizons. Using data from the US industrial machinery and instruments sector, they calculate term structures of default probabilities. We conduct a similar exercise using a reducedform econometric approach; we do not model the time-series evolution of the predictor variables but instead directly estimate longer-term default probabilities.
The remainder of the paper is organized as follows. Section 2 describes the construction of the data set, outlier analysis and summary statistics. Section 3 discusses our basic dynamic panel model, extensions to it, and the results from estimating the model at one-month and longer horizons. We ﬁnd that market capitalization, the market-book ratio, and equity volatility become relatively more signiﬁcant as the horizon increases. This section also considers the ability of the model to ﬁt the aggregate time-series of failures. Section 4 studies the return properties of equity portfolios formed on the ﬁtted value from our bankruptcy prediction model. We ask whether stocks with high bankruptcy probability have unusually high or low returns relative to the predictions of standard cross-sectional asset pricing models such as the CAPM or the three-factor Fama-French model. Section 5 concludes.
2 Data description
In order to estimate a dynamic logit model we need an indicator of ﬁnancial distress and a set of explanatory variables. The bankruptcy indicator we use is taken from Chava and Jarrow (2004); it includes all bankruptcy ﬁlings in the Wall Street Journal Index, the SDC database, SEC ﬁlings and the CCH Capital Changes Reporter. The indicator equals one in a month in which a ﬁrm ﬁled for bankruptcy under Chapter 7 or Chapter 11, and zero otherwise; in particular, the indicator is zero if the ﬁrm disappears from the dataset for some reason other than bankruptcy such as acquisition or delisting. The data span the months from January 1963 through December
1998. We also consider a broader failure indicator, which equals one if a ﬁrm ﬁles for bankruptcy, delists, or receives a D rating, over the period January 1963 through December 2003.
Table 1 summarizes the properties of our bankruptcy and failure indicators. The ﬁrst column shows the number of active ﬁrms for which we have data in each year.