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)
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


we show that the group of firms with the highest risk of distress


(0-score) as a proxy for distress risk,
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.
Ohlson's 0-score
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,


The subsequent returns of these firms, however,



such as weak earnings, high leverage, and low sales growth.
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 Bankruptcy
by Hillegeist, Keating, and Cram (2004, RAS)

We assess whether two popular accounting-based measures,


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


Altman’s (1968) Z-Score and Ohlson’s (1980) O-Score,
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.
BSM and the Probability of Bankruptcy
BSM-Prob.
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


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,


firms with similar financial ratios will have similar likelihoods of 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 Liquidity
Odders-White and Ready (2006, RFS)

credit ratings capture


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


uncertainty about the value of a firm’s debt.
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
by Hertzel, 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


our analysis of customers and suppliers provides


to the distressed firm,
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


how these effects are evident at different points



distressed firms’ customers and suppliers,
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 Returns
VASSALOU 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.
Default risk factor
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 Stocks
Da and Gao (2010, JFQA)

The abnormal returns on high default risk stocks


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


documented by Vassalou and Xing (2004)
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.
Institutional selling

We find that mutual funds and other institutions

significantly reduce their stock holdings


in firms experiencing a sharp rise in their default likelihood measures.
Examination of a proprietary institutional trading data set

confirms significant institutional selling of such stocks.
Institutional investors

Institutional investors are often confined to investment in stocks


that are liquid, with large market capitalization and stable dividends.
A sudden change in the clientele for a stock



with no offsetting increase in the demand from other investors.
This imbalance represents a liquidity shock, and

market makers will have to step in and provide liquidity,


triggers selling by one group of investors
earning substantial price concessions for providing immediacy.
Prices will bounce back once outside investors recognize

the opportunity and redeploy capital.
Contributions

Our findings contribute to a growing literature on the relation
between default risk and stock returns


by reconciling Vassalou and Xing (2004) with other findings.
As financially distressed stocks are usually


our findings highlight the importance of accounting for


small stocks and prone to liquidity shock,
liquidity shocks in the empirical examination of default or financial
distress risk.
This is especially true when the default risk measure is computed

directly using the market price of a stock.
Contributions_2

Why do agents decide to trade a large amount of particular assets
at the same time?

We contribute in this regard by providing one plausible explanation:


a sharp increase in default risk.
When a stock experiences a sharp increase in default risk,

financial institutions with binding investment restrictions

have to sell the stock immediately, creating a liquidity shock.
A resolution of the distress risk and leverage
puzzles in the cross section of stock returns
George and Hwang (2010, JFE)

Two puzzles:

Distress risk measures predict defaults for individual firms and are,
on average, larger during recessions.

However, most of the evidence indicates that



Returns are negatively related to leverage, which is another puzzle.


returns are lower for firms with greater distress intensities,
the so-called ‘‘distress risk puzzle.’’
Penman, Richardson, and Tuna (2007).
These relations seem so obviously backward that

most of the studies conclude

the puzzles are evidence of market mispricing.
A resolution of the puzzles

Market frictions could lead low-leverage firms

to have greater exposures to systematic risk,


Using a very simple model, we show that


if financial distress is costly and
firms make optimal capital structure decisions,


which dominates the amplification effect of leverage on equity risk.
then low-leverage firms are exposed to greater systematic risk than highleverage firms.
This suggests that the puzzles can be explained by

a rational model, albeit one with market frictions.
A resolution of the puzzles_2

Since firms with high costs choose low leverage,


The cross section of expected returns is therefore


low-leverage firms have the greatest exposure to systematic risk
relating to distress costs.
negatively related to leverage.
Moreover, by choosing low leverage,

high-cost firms achieve low probabilities of financial distress,


so expected returns are negatively related to distress measures as well.
This pair of negative relations constitutes the ‘‘puzzles’’
described above.
low-leverage firms suffer more in distress

since we hypothesize that firms choose low leverage



to avoid high distress costs,
we examine whether greater hardship is associated with distress for
low- leverage firms than high-leverage firms.
We find that accounting return on assets




falls more,
stays lower in subsequent years, and
becomes less predictable in distress
for low- than high-leverage firms.
they appear to avoid leverage with good reason

We also find


Not only does the earnings performance of assets suffer,


the return premium is greatest among low-leverage firms in distress.
but also the forward-looking exposure of low-leverage firms to
systematic risk increases.
The consequences of distress are severe for these firms, and

they appear to avoid leverage with good reason.
After 1980

Opler and Titman (1994) find that



events of industry distress are rare between1974and 1980, but
they increase substantially thereafter.
After 1980, the deterioration in operating performance




associated with distress for low- versus high-leverage firms
is much larger than before 1980,
as is the equity return premium associated with low versus high
leverage.
In fact, the return premium is insignificant in the pre-1980 period
in both raw and risk-adjusted returns.
Conclusion_1

Though we focus on


how determinants of capital structure choices affect equity pricing,
our results are consistent with studies

that offer direct support for trade- off theories.
Conclusion_2

firms with low (high) leverage suffer more (less)


equity markets price differences in leverage as though


such differences capture exposure to financial distress costs.
firms manage their capital structures


in financial distress, and that
to avoid financial distress costs, and
participants in equity markets are aware that

differences in capital structures

reflect differences in exposures to such costs.