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Some previous authors have reported evidence that distressed stocks underperform the market, but results have varied with the measure of financial distress that is used. Our results are consistent with the findings of Dichev (1998), who uses Altman’s Z-score and Ohlson’s O-score to measure financial distress, and Garlappi, Shu, and Yan (2005), who obtain default risk measures from Moody’s KMV. Vassalou and Xing (2004) calculate distance to default; they find some evidence that distressed stocks with a low distance to default have higher returns, but this evidence comes entirely from small value stocks. Da and Gao (2004) argue that Vassalou and Xing’s distressed-stock returns are biased upwards by one-month reversal and bid-ask bounce. Griffin and Lemmon (2002), using O-score to measure distress, find that distressed growth stocks have particularly low returns. Our measure of financial distress generates underperformance among distressed stocks in all quintiles of the size and value distributions, but the underperformance is more dramatic among small stocks.

What can explain the anomalous underperformance of distressed stocks? Perhaps the most obvious explanation is that stock market investors underreact to negative information about company prospects. Hong, Lim, and Stein (2000) have argued that corporate managers have incentives to withhold bad news, which therefore reaches the market only gradually. Equity analysts can speed up the flow of information, but do so only for larger companies with better analyst coverage. To test whether this hypothesis explains the distress anomaly, one could ask whether the underperformance of distressed stocks is more extreme for companies with low analyst coverage.

According to this view, the distress anomaly is related to the momentum effect and to the underperformance of companies with underfunded pension plans (Franzoni and Marin 2005).

Some investors may understand the poor average returns offered by distressed stocks, but hold them anyway. von Kalckreuth (2005) argues that majority owners of distressed companies can extract private benefits, for example by buying the company’s output or assets at bargain prices. The incentive to extract such benefits is greater when the company is unlikely to survive and generate future profits for its shareholders. Thus majority owners may hold distressed stock, rather than selling it, because they earn a greater return than the return we measure to outside shareholders.

Barberis and Huang (2004) model the behavior of investors whose preferences satisfy the cumulative prospect theory of Tversky and Kahneman (1992). Such investors have a strong desire to hold positively skewed portfolios, and may even hold undiversified positions in positively skewed assets. Barberis and Huang argue that this effect can explain the high prices and low average returns on IPO’s, whose returns are positively skewed. It is striking that both individual distressed stocks and our portfolios of distressed stocks also offer returns with strong positive skewness.

These hypotheses have the potential to explain why some investors hold distressed stocks despite their low average returns, but they do not explain why other rational investors fail to arbitrage the distress anomaly. Some distressed stocks may be unusually expensive or difficult to short, but more important limits to arbitrage are likely to be the reluctance of some investors to short stocks and the limited capital that arbitrageurs have available.

Finally, the distress anomaly may result from the preferences of institutional investors, together with a shift of assets from individuals to institutions during our sample period. Kovtunenko and Sosner (2003) have documented that institutions prefer to hold profitable stocks, and that this preference helped institutional performance during the 1980’s and 1990’s because profitable stocks outperformed the market. It is possible that the strong performance of profitable stocks in this period was endogenous, the result of increasing demand for these stocks by institutions. If institutions more generally prefer stocks with low failure risk, and tend to sell stocks that enter financial distress, then a similar mechanism could drive our results. This hypothesis implies that the underperformance of distressed stocks is a transitional and temporary phenomenon. It can be tested by relating the performance of distressed stocks over time to the changing institutional share of equity ownership and the characteristics of institutional portfolios.

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The COMPUSTAT quarterly data items used are Data44 for total assets, Data69 for net income, and Data54 for total liabilities.

To deal with outliers in the data, we correct both NITA and TLTA using the difference between book equity (BE) and market equity (ME) to adjust the value of

total assets:

–  –  –

Book equity is as defined in Davis, Fama and French (2000) and outlined in detail in Cohen, Polk and Vuolteenaho (2003). This transformation helps with the values of total assets that are very small, probably mismeasured and lead to very large values of NITA. After total assets are adjusted, each of the seven explanatory variables is winsorized using a 5/95 percentile interval in order to eliminate outliers.

To measure the volatility of a firm’s stock returns, we use a proxy, centered around zero rather than the rolling three-month mean, for daily variation of returns computed

–  –  –

To eliminate cases where few observations are available, SIGMA is coded as missing if there are fewer than five non-zero observations over the three months used in the rolling-window computation. In calculating summary statistics and estimating regressions, we replace missing SIGMA observations with the cross-sectional mean of SIGMA; this helps us avoid losing some failure observations for infrequently traded companies. A dummy for missing SIGMA does not enter our regressions significantly.

We use a similar procedure for missing lags of NIMTA and EXRET in constructing the moving average variables NIMTAAVG and EXRETAVG.

In order to calculate distance to default we need to estimate asset value and asset volatility, neither of which are directly observable. We construct measures of these variables by solving two equations simultaneously.

–  –  –

If BD is missing, we use BD = median(BD/T L) ∗ T L, where the median is calculated for the entire data set. This captures the fact that empirically, BD tends to be much smaller than T L. If BD = 0, we use BD = median(BD/T L) ∗ T L, where now we calculate the median only for small but nonzero values of BD (0 BD 0.01). If SIGM A is missing, we replace it with its cross sectional mean. Before calculating asset value and volatility, we adjust BD so that BD/(M E +BD) is winsorized at the 0.5% level. We also winsorize SIGM A at the 0.5% level. This significantly reduces instances in which the search algorithm does not converge.

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Table 1: Number of bankruptcies and failures per year The table lists the total number of active firms (Column 1), total number of bankruptcies (Column 2) and failures (Column 4) for every year of our sample period.

The number of active firms is computed by averaging over the numbers of active firms across all months of the year.

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$ 8 1m 82m 83m 84m 85m 86m 87m 88m 89m 90m 91m 92m 93m 94m 95m 96m 97m 98m 99m 00m 01m 02m 03m

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