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Distress risk, financial leverage, and stock returns for presentation at UESTC, June 2011. Book-to-Market Equity, Distress Risk, and Stock Returns by GRIFFIN and LEMMON (2002, JF). Dichev (1998) uses measures of bankruptcy risk proposed by Ohlson (1980) and Altman (1968)
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Distress risk, financial leverage, and stock returnsfor presentation at UESTC, June 2011
Book-to-Market Equity, Distress Risk, and Stock ReturnsbyGRIFFIN and LEMMON (2002, JF) • Dichev (1998) uses measures of bankruptcy risk • proposed by Ohlson (1980) and Altman (1968) • to identify firms with a high likelihood of financial distress and • finds that these firms tend to have low average stock returns. • This is the distress risk puzzle! • This paper attempts to find out: • Where is the source of this puzzle?
Could low B/M have high distress risk? • Using Ohlson's measure of the likelihood of bankruptcy • (0-score) as a proxy for distress risk, • we show that the group of firms with the highest risk of distress • includes many firms with high BE/ME ratios and low past stock returns, • but actually includes • more firms with low BE/ME ratios and high past stock returns.
Firms with high O-score and high B/M • Among firms in the high 0-score quintile, • those with high BE/ME ratios • exhibit characteristics traditionally associated with distress risk, • such as weak earnings, high leverage, and low sales growth. • The subsequent returns of these firms, however, • are only slightly higher than returns of other high BE/ME firms, and • intercepts from the Fama and French model are not significantly different from zero. • Thus, for these firms,0-score does not seem • to contain information about distress risk beyond that contained in their high BE/ME ratios.
Firms with high O-score and low B/M • They do not appear to have high levels of distress risk. • Although these firms have weak current earnings, • they have higher capital and R&D expenditures • than any other group of firms, and • have relatively high sales growth. • Interestingly, these firms earn subsequent returns • that are significantly lower than those of other low BE/ME firms. • In fact, the average returns for this group of firms are • roughly similar to the risk-free rate.
The source of the distress risk puzzle • Thus, the low average returns of firms with high distress risk • documented by Dichev (1998) • are driven by the poor stock price performance of those low B/M firms. • Why??
Lower risk? • One possible explanation for • the low returns earned by high 0-score firms with low B/M ratios • is that they are less risky than other firms. • However, this does not appear to be the case. • The low BE/ME firms in the highest 0-score quintile exhibit • factor loadings in the FF three-factor model that are • higher in absolute magnitude than those of other low BE/ME firms; • earnings persistently below those of other firms. • They are also the most likely to be delisted from CRSP for performance reasons.
An alternative explanation • low B/M stocks are overpriced. • (e.g., Lakonishok, Shleifer, and Vishny (1994)). • Consistent with this idea, firms in the highest 0-score quintile • tend to be small firms with low analyst coverage. • They also have weak current fundamentals, • which may make them more difficult to value. • Consistent with the mispricing argument, we find that • the difference in abnormal earnings announcement returns between • high and low BE/ME stocks • is largest for firms in the highest 0-score quintile.
low BE/ME firms with low analyst coverage • similar to our findings using 0-score, • low BE/ME firms with low analyst coverage • earn abnormally low returns.
Conclusion • High 0-score firms with low BE/ME tend to • look like other low BE/ME firms in that • they are concentrated in industries • with high sales growth and relatively high R&D and capital spending, • yet these firms have little or no current earnings. • Overall, the evidence suggests that investors may • underestimate the importance of current fundamentals and • overestimate the payoffs from future growth opportunities • for low BE/ME firms in the high 0-score group.
Assessing the Probability of Bankruptcyby Hillegeist, Keating, and Cram (2004, RAS) • We assess whether two popular accounting-based measures, • Altman’s (1968) Z-Score and Ohlson’s (1980) O-Score, • effectively summarize publicly-available information • about the probability of bankruptcy. • We compare the relative information content of these Scores to • a market-based measure of the probability of bankruptcy • based on the Black–Scholes–Merton option-pricing model, BSM-Prob. • Our tests show that • BSM-Prob provides • significantly more information than either of the two accounting-based measures.
forward-looking vs. backward looking • There are several concerns about the use of accounting models in estimating the default risk of equities. • Accounting models use information derived from financial statements. • Such information is inherently backward looking. • In contrast, Merton’s (1974) model uses • the market value of a firm’s equity in calculating its default risk. • Market prices reflect • investors’ expectations about a firm’s future performance. • As a result, they contain forward-looking information, • which is better suited for calculating the likelihood • that a firm may default in the future.
In addition, and most importantly • accounting models do not take into account • the volatility of a firm’s assets in estimating its risk of default. • Accounting models imply that • firms with similar financial ratios will have similar likelihoods of default. • This is not the case in Merton’s model, • where firms may have similar levels of equity and debt, • but very different likelihoods to default, • if the volatilities of their assets differ. • Clearly, the volatility of a firm’s assets provides • crucial information about the firm’s probability to default.
Conclusion • We recommend that researchers use BSM-Prob, • instead of Z-Score and O-Score, • in their studies. • This paper provides the SAS code to calculate BSM-Prob.
Credit Ratings and Stock LiquidityOdders-White and Ready (2006, RFS) • credit ratings capture • uncertainty about the value of a firm’s debt. • equity-market adverse selection risk reflect • the uncertainty about equity value that is revealed through trades. • In general, increased uncertainty about total firm value means • increased uncertainty about the value of both debt and equity; • yet the literature • on credit ratings and the literature on equity-market adverse selection • have developed completely independently of one another.
this article link these two distinct lines of research • by examining the relation between credit ratings and • market microstructure measures of equity adverse selection. • We begin by developing a simple model that decomposes • the uncertainty about future asset value into shocks • observed by all market participants simultaneously, and • shocks that are initially observed by one or more ‘‘insiders.’’ • While both types of asset-value uncertainty are relevant • for credit ratings, • it is the latter type that causes a linkage between • a firm’s credit rating and • the level of adverse selection in the trading of the firm’s equity.
Empirical results • We demonstrate in panel data regressions that • credit ratings are poorer when adverse selection— • as captured by • quoted and effective spreads, • Glosten and Harris’ (1988) adverse selection component of the spread, • Hasbrouck’s (1991) information-based price impact measure, and • Easley et al.’s (1996) probability of informed trading— • is higher.
predict future rating changes • Our estimation of an ordered probit model reveals that • future rating changes can be predicted using • recent changes in the levels of adverse selection. • This provides additional evidence that • the agencies are sometimes slow to react and • also suggests that • adverse selection measures may be a useful tool • for assessing credit risk on a more timely basis.
Conclusion • Collectively, our results provide • independent validation of the adverse selection measures, • which are used extensively in the microstructure literature and elsewhere, • by showing that they behave as would be expected from microstructure theory. • Our results also offer new insights into • the value of credit ratings, • their relationship to firm-value uncertainty, and • the speed with which they reflect changes in uncertainty.
Inter-Firm Linkages and the Wealth Effects of Financial Distress Along the Supply Chain byHertzel, Officer, Li, and Cornaggia (2008, JFE) • Financial distress at one firm can have valuation implications • for firms that are linked in the product market (industry rivals) and • for firms that are connected along the supply chain (customers and suppliers). • This paper • examines the wealth effects of distress on customers and suppliers, as well as • considers how these effects interact with the wealth effects for industry rivals.
industry rivals • Lang and Stulz (1992) finds that, on average, • industry rivals suffer negative stock price effects (contagion effects) • around the time that a competitor files for bankruptcy. • but, for a subset of filings, rivals experience positive stock price effects (competitive effects) • that could be driven by shifts in market share and, • possibly, increased market power of the remaining firms. • Our analysis of filing firm suppliers and customers provides • insights into the nature of intra-industry effects as well as • new evidence on the extent to which contagion effects • extend beyond industry competitors along the supply chain.
Suppliers can impose costs on distressed firms • In discussions of the trade-off theory, • the actions of suppliers and customers of firms in distress are often • cited as a source of indirect costs • that can arise with impending bankruptcy. • Suppliers can impose costs on distressed firms by • failing to supply trade credit, • backing away from entering into long-term contracts, or • delaying shipments.
Customers can also impose costs on distressed firms • Customers, wary of • product quality, • reduced value of warranties, • continuity of supply, and serviceability, • impose costs by shifting purchases to existing and/or new suppliers. • Although potentially important, • the magnitude of these indirect costs of distress is • difficult to estimate practically and • evidence of their existence is thus far mostly anecdotal.
implication • While we do not directly measure the magnitude of these costs • to the distressed firm, • our analysis of customers and suppliers provides • a better understanding of how impairment (both economic and financial) at one firm can ripple through other layers of the supply chain. • This, in turn, provides perspective on • how expectations of distress at one level in the supply chain • could influence corporate policy (e.g., capital structure, product-market behavior) at another.
Main findings on industry rivals and suppliers • The central finding of our study is that • significant pre-filing and filing-date contagion effects affect industry rivals and • extend beyond industry competitors • along the supply chain to suppliers of the filing firms. • This finding suggests that financial distress • has greater economy-wide effects than previously documented.
Main findings on customers • customers of filing firms generally • do not experience contagion effects. • Evidence that • customers do not suffer contagion effects suggests that • customers anticipate and/or • cause the financial distress of a supplier.
when the filing firm industry also suffers contagion • A second key finding is that • supplier (and, to a lesser extent, customer) contagion effects • are more severe when the filing firm industry also suffers contagion. • We attribute this • to fewer opportunities for suppliers to switch to different customers • when the entire industry is impaired and • to the likelihood that filing firm suppliers also have • economic relations with rivals of the filing firm that also suffer • when industry contagion exists. • In contrast, • suppliers and customers do not exhibit contagion effects • when industry rivals have positive stock price reactions • to the filing firm’s distress.
Conclusion • this to be the first paper to provide direct evidence on • how distress (leading to bankruptcy) affects • distressed firms’ customers and suppliers, • how these effects are evident at different points • in the progression from distress to bankruptcy, and • how they interact with the effects on the filing firms’ horizontal rivals. • Overall, our findings provide • insight into the nature and extent of contagion and • a more complete picture of the overall wealth effects • associated with financial distress and bankruptcy.
Default Risk in Equity ReturnsVASSALOU and XING (2004, JF) • first study uses Merton’s (1974) option pricing model to • compute default measures for individual firms and • assess the effect of default risk on equity returns. • we still know very little about • how default risk affects equity returns.
default risk and size and B/M effects • default risk is intimately related to • the size and book-to-market (BM) characteristics of a firm. • Our results point to the conclusion that • both the size and BM effects can be viewed as default effects. • This is particularly the case for the size effect. • Both exist only in segments of the market with high default risk.
High default risk, high returns • high-default-risk firms earn higher returns • than low default risk firms, • only to the extent that they are small in size and high BM. • If these firm characteristics are not met, • they do not earn higher returns than low default risk firms, • even if their risk of default is actually very high.
Is default risk a systematic risk? • We finally examine whether default risk is systematic. • it is indeed systematic and therefore priced • in the cross section of equity returns. • Denis and Denis (1995), for example, show that • default risk is related to macroeconomic factors • and that it varies with the business cycle. • This result is consistent with ours since our measure of default risk also varies with the business cycle.
Conclusion • Default risk is priced. • SMB and HML contain some default-related information. • SMB and HML appear to contain other significant price information, unrelated to default risk, as well. • Risk-based explanations for this information are provided in Vassalou (2003) and Li, Vassalou, and Xing (2000). • Our results show that • default is a variable worth considering in asset-pricing tests, • above and beyond size and BM.
Clientele Change, Liquidity Shock, and the Return on Financially Distressed StocksDa and Gao (2010, JFQA) • The abnormal returns on high default risk stocks • documented by Vassalou and Xing (2004) • are driven by short-term return reversals • rather than systematic default risk. • These abnormal returns occur • only during the month after portfolio formation and • are concentrated in a small subset of stocks • that had recently experienced large negative returns. • Empirical evidence supports the view that • the short-term return reversal • arises from a liquidity shock triggered by a clientele change.
Default risk premium is not robust. • Our investigation first reveals that • stocks in the highest DLI decile earn abnormal returns • only in the first month after portfolio formation. • The returns on these stocks immediately decline • by more than one-quarter, • from 2.10% in the first month to 1.52% in the second month, and stabilize afterward. • If we skip a month and use the second-month returns • in various asset pricing tests, • we find that the returns of high default risk stocks • can be fully explained by the Fama-French (1993) three-factor model, and the additional default risk factor is no longer needed.
Furthermore, • we show that abnormal returns on the highest DLI decile • are confined to a small subset of stocks with similar DLIs that • recently experienced large negative returns and • sharp increases in their DLI measure (the high-DLI losers). • Thus, the abnormal return on high default risk stocks • documented in Vassalou and Xing (2004) • is temporary and clearly does not represent compensation for • bearing systematic default risk.
Link short-term return reversal to returns on high default risk stocks • In a cross-sectional regression framework, we confirm that • the past 1-month returns drive out DLI • in predicting the next-month stock returns.
possible causes of short-term return reversal on default risk stocks • What could be the possible causes • of such short-term return reversal on default risk stocks? • The evidence suggests that it is likely • the result of price pressure • caused by a liquidity shock around portfolio formation. • Unlike the existing literature, however, • we can identify at least one plausible economic reason • behind such demands for immediacy on high-DLI stocks: • a financial distress-induced clientele change.