Predicting Financial Distress And The Performance Of Distressed Stocks

Predicting Financial Distress And The Performance Of Distressed Stocks via CSInvesting

Abstract

In this paper we consider the measurement and pricing of distress risk. We present a model of corporate failure in which accounting and market-based measures forecast the likelihood of future financial distress. Our best model is more accurate than leading alternative measures of corporate failure risk. We then use our measure of financial distress to examine the performance of distressed stocks from 1981 to 2008. We find that distressed stocks have highly variable returns and high market betas and that they tend to underperform safe stocks by more at times of high market volatility and risk aversion. However, investors in distressed stocks have not been rewarded for bearing these risks. Instead, distressed stocks have had very low returns, both relative to the market and after adjusting for their high risk. The underperformance of distressed stocks is present in all size and value quintiles. It is lower for stocks with low analyst coverage and institutional holdings, which suggests that information or arbitrage-related frictions may be partly responsible for the underperformance of distressed stocks.

Predicting Financial Distress And The Performance Of Distressed Stocks – Introduction

Interest in the pricing of financially distressed firms is widespread. Chan and Chen (1991) describe marginal and distressed 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.” Asset pricing theory suggests that investors will demand a premium for holding such stocks. It is an empirical question whether or not investors are indeed rewarded for bearing such risk. We investigate the pricing of financially distressed stocks in two steps: First, we present a model predicting financial distress. Second, we consider the historical performance of investing in distressed stock portfolios.

Our proposed measure of financial distress is the probability of failure. Following Shumway (2001) we predict failure in a hazard model using explanatory variables constructed from observable accounting and market-based measures. This approach is related to an earlier literature pioneered by Beaver (1966) and Altman (1968) who introduced Z-score as a measure of bankruptcy risk, and has recently been used by Beaver, McNichols, and Rhie (2005).

We classify a firm as more distressed if it is more likely to file for bankruptcy under Chapter 7 or Chapter 11, delist for performance related reasons, or receive a D rating from a rating agency. This expanded measure of failure (relative to measuring only bankruptcy filings) allows us to capture at least some instances in which firms fail but reach an agreement with creditors before an actual bankruptcy filing (Gilson, John, and Lang 1990, Gilson 1997). Our data set is monthly and includes more than 2 million firm-months and close to 1,750 failure events.

We predict failure over the next month (similar to Chava and Jarrow (2004)). However, in addition we also consider the probability of failure for longer horizons. After all, an investor will certainly care not only about imminent failure, but rather will want to get a sense well in advance which are the firms that are most likely to fail. Although probably quite accurate, it may not be useful to predict a heart attack with a person clutching their hand to their chest.

Firms that are distressed have the characteristics we would expect: they have recently made losses, have high leverage, their stock returns have been low and volatile, and they have low levels of cash holdings. Our best model, which makes several changes relative to Shumway (2001) and Chava and Jarrow (2004), improves forecast accuracy by 16% when compared to these models. It also outperforms another leading alternative – ‘distance-to-default’ – a measure based on the famous Merton (1974) model of risky corporate debt and popularized by Moody’s KMV (see, for example, Crosbie and Bohn(2001)). Relative to distance-to-default our model almost doubles forecast accuracy.

We next investigate the performance of distressed stocks using our best model to measure financial distress. Portfolios of distressed stocks have very high levels of volatility and high market betas, which means that they are risky and should command a high risk premium. However, their returns from 1981 to 2008 have been low: distressed stocks have significantly underperformed the S&P 500. A portfolio going long safe stocks and shorting distressed stocks has been a highly profitable strategy and has a significantly higher average return and Sharpe ratio than the S&P 500.

The underperformance of distressed stocks is puzzling given that investors seem to realize that distressed stocks are risky: The high market betas of distressed stocks imply that the market perceives distressed stocks as being more sensitive to overall market conditions. Furthermore, we find that distressed stocks underperform more severely at times of increases in market volatility, as measured by the VIX, the implied volatility of S&P 500 index options. In the last four months of 2008 a strategy of long safe, short distressed stocks earned a return of 59%, while the return for all of 2008 was 145%.

Even if the average investor does not react, such high performance levels should attract significant arbitrage capital and over time we should see declining profits to this strategy. One reason why we have not observed this could be that it is difficult to obtain information about the health of distressed stocks and that they may be difficult to short sell. Due to these constraints arbitrage activity could be limited. Consistent with this hypothesis we find that the distress effect is more concentrated in stocks with low analyst coverage and in stocks with low levels of institutional holdings, which has been proposed as a proxy for the ability to short such stocks.