«The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. Campbell, John Y., Jens ...»
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.
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
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.
References Altman, Edward I., 1968, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance 23, 589—609.
Ang, Andrew, Robert J. Hodrick, Yuhang Xing, and Xiaoyan Zhang, 2005, The cross-section of volatility and expected returns, forthcoming Journal of Finance.
Asquith, Paul, Robert Gertner, and David Scharfstein, 1994, Anatomy of ﬁnancial distress: An examination of junk-bond issuers, Quarterly Journal of Economics 109, 625—658.
Barberis, Nicholas and Ming Huang, 2004, Stocks as lotteries: The implications of probability weighting for security prices, unpublished paper, Yale University and Stanford University.
Bernanke, Ben S. and John Y. Campbell, 1988, Is there a corporate debt crisis?, Brookings Papers on Economic Activity 1, 83—139.
Bharath, Sreedhar and Tyler Shumway, 2004, Forecasting default with the KMVMerton model, unpublished paper, University of Michigan.
Burgstahler, D.C. and I.D. Dichev, 1997, Earnings management to avoid earnings decreases and losses, Journal of Accounting and Economics 24, 99—126.
Campbell, John Y., Martin Lettau, Burton Malkiel, and Yexiao Xu, 2001, Have individual stocks become more volatile? An empirical exploration of idiosyncratic risk, Journal of Finance 56, 1—43.
Carhart, Mark, 1997, On persistence in mutual fund performance, Journal of Finance 52, 57—82.
Chacko, George, Peter Hecht, and Jens Hilscher, 2004, Time varying expected returns, stochastic dividend yields, and default probabilities, unpublished paper, Harvard Business School.
Chan, K.C. and Nai-fu Chen, 1991, Structural and return characteristics of small and large ﬁrms, Journal of Finance 46, 1467—1484.
Chava, Sudheer and Robert A. Jarrow, 2004, Bankruptcy prediction with industry eﬀects, Review of Finance 8, 537—569.
Cohen, Randolph B., Christopher Polk and Tuomo Vuolteenaho, 2003, The value spread, Journal of Finance 58, 609—641.
Crosbie, Peter J. and Jeﬀrey R. Bohn, 2001, Modeling Default Risk, KMV, LLC, San Francisco, CA.
Da, Zhi and Pengjie Gao, 2004, Default risk and equity return: macro eﬀect or micro noise?, unpublished paper, Northwestern University.
Davis, James L., Eugene F. Fama and Kenneth R. French, 2000, Characteristics, covariances, and average returns: 1929 to 1997, Journal of Finance 55, 389— 406.
Dechow, Patricia M., Scott A. Richardson, and Irem Tuna, 2003, Why are earnings kinky? An examination of the earnings management explanation, Review of Accounting Studies 8, 355—384.
Dichev, Ilia, 1998, Is the risk of bankruptcy a systematic risk?, Journal of Finance 53, 1141—1148.
Duﬃe, Darrell, and Ke Wang, 2004, Multi-period corporate failure prediction with stochastic covariates, NBER Working Paper No. 10743.
Fama, Eugene F. and Kenneth R. French, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33, 3—56.
Fama, Eugene F. and Kenneth R. French, 1996, Multifactor explanations of asset pricing anomalies, Journal of Finance 51, 55—84.
Ferguson, Michael F. and Richard L. Shockley, 2003, Equilibrium “anomalies”, Journal of Finance 58, 2549—2580.
Franzoni, Francesco and Jose M. Marin, 2005, Pension plan funding and stock market eﬃciency, forthcoming Journal of Finance.
Garlappi, Lorenzo, Tao Shu, and Hong Yan, 2005, Default risk and stock returns, unpublished paper, University of Texas at Austin.
Gilson, Stuart C., Kose John, and Larry Lang, 1990, Troubled debt restructurings:
An empirical study of private reorganization of ﬁrms in default, Journal of Financial Economics 27, 315—353.
Gilson, Stuart C., 1997, Transactions costs and capital structure choice: Evidence from ﬁnancially distressed ﬁrms, Journal of Finance 52, 161—196.
Griﬃn, John M. and Michael L. Lemmon, 2002, Book-to-market equity, distress risk, and stock returns, Journal of Finance 57, 2317—2336.
Hayn, C., 1995, The information content of losses, Journal of Accounting and Economics 20, 125—153.
Hwang, C.Y., 1995, Microstructure and reverse splits, Review of Quantitative Finance and Accounting 5, 169—177.
Hillegeist, Stephen A., Elizabeth Keating, Donald P. Cram and Kyle G. Lunstedt, 2004, Assessing the probability of bankruptcy, Review of Accounting Studies 9, 5—34.
Hong, Harrison, Terence Lim, and Jeremy C. Stein, 2000, Bad news travels slowly:
Size, analyst coverage, and the proﬁtability of momentum strategies, Journal of Finance 55, 265—295.
Kovtunenko, Boris and Nathan Sosner, 2003, Sources of institutional performance, unpublished paper, Harvard University.
Macey, Jonathan, Maureen O’Hara, and David Pompilio, 2004, Down and out in the stock market: the law and ﬁnance of the delisting process, unpublished paper, Yale University and Cornell University.
Merton, Robert C., 1974, On the pricing of corporate debt: the risk structure of interest rates, Journal of Finance 29, 449—470.
Mossman, Charles E., Geoﬀrey G. Bell, L. Mick Swartz, and Harry Turtle, 1998, An empirical comparison of bankruptcy models, Financial Review 33, 35—54.
Ohlson, James A., 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research 18, 109—131.
Opler, Tim and Sheridan Titman, 1994, Financial distress and corporate performance, Journal of Finance 49, 1015—1040.
Shumway, Tyler, 1997, The delisting bias in CRSP data, Journal of Finance 52, 327—340.
Shumway, Tyler, 2001, Forecasting bankruptcy more accurately: a simple hazard model, Journal of Business 74, 101—124.
Shumway, Tyler and Vincent A. Warther, 1999, The delisting bias in CRSP’s Nasdaq data and its implications for the size eﬀect, Journal of Finance 54, 2361—2379.
Tashjian, Elizabeth, Ronald Lease, and John McConnell, 1996, Prepacks: An empirical analysis of prepackaged bankruptcies, Journal of Financial Economics 40, 135—162.
Tversky, Amos and Daniel Kahneman, 1992, Advances in prospect theory: Cumulative representation of uncertainty, Journal of Risk and Uncertainty 5, 297—323.
Vassalou, Maria and Yuhang Xing, 2004, Default risk in equity returns, Journal of Finance 59, 831—868.
von Kalckreuth, Ulf, 2005, A ‘wreckers theory’ of ﬁnancial distress, Deutsche Bundesbank discussion paper.
Woolridge, J.R. and D.R. Chambers, 1983, Reverse splits and shareholder wealth, Financial Management 5—15.
Zmijewski, Mark E., 1984, Methodological issues related to the estimation of ﬁnancial distress prediction models, Journal of Accounting Research 22, 59—82.
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.
$ 8 1m 82m 83m 84m 85m 86m 87m 88m 89m 90m 91m 92m 93m 94m 95m 96m 97m 98m 99m 00m 01m 02m 03m