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In Search of Distress Risk

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Campbell, John Y., Jens Hilscher, and Jan Szilagyi. 2008. In

Citation Search of Distress Risk. Journal of Finance 63, no. 6: 2899-2939.

doi:10.1111/j.1540-6261.2008.01416.x

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July 11, 2016 3:00:09 PM EDT

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http://nrs.harvard.edu/urn-3:HUL.InstRepos:3199070

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This article was downloaded from Harvard University's DASH Terms of Use repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-ofuse#LAA (Article begins on next page) HIER Harvard Institute of Economic Research Discussion Paper Number 2081 In Search of Distress Risk by John Y. Campbell, Jens Hilscher and Jan Szilagyi July 2005 Harvard University Cambridge, Massachusetts

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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 john_campbell@harvard.edu. 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.



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