Can Risk Management Help Prevent Bankruptcy? Monica Marin * July, 2007 Abstract Whether companies engage in risk management activities for risk reduction or speculation purposes has been subject to continuous debate. This paper compares the use of risk management instruments by firms that eventually file for bankruptcy to matched firms that do not file for bankruptcy during the sample period. Results indicate that the odds of filing for bankruptcy are approximately 96% lower for firms that manage risk. The paper also takes an alternative approach in predicting the probability of default by deriving the distance to default from equity prices with the Black-Scholes-Merton option pricing model. The distance to default compares the market value of assets to the size of a one standard deviation move in the asset value. Results show that the number of standard deviations by which firms in the sample are away from the default point increases by 3.2 units when they engage in risk manage- ment activities. The main findings are reinforced when examining the equity-implied asset volatility as a separate measure of firm risk and its relationship with risk management. More specifically, asset volatility decreases by 64% when firms manage risk. * Moore School of Business, University of South Carolina, Columbia, SC 29208. E-mail: monica [email protected]
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Can Risk Management Help Prevent Bankruptcy?
Monica Marin∗
July, 2007
Abstract
Whether companies engage in risk management activities for risk reduction or speculation purposeshas been subject to continuous debate. This paper compares the use of risk management instrumentsby firms that eventually file for bankruptcy to matched firms that do not file for bankruptcy duringthe sample period. Results indicate that the odds of filing for bankruptcy are approximately 96% lowerfor firms that manage risk. The paper also takes an alternative approach in predicting the probabilityof default by deriving the distance to default from equity prices with the Black-Scholes-Merton optionpricing model. The distance to default compares the market value of assets to the size of a one standarddeviation move in the asset value. Results show that the number of standard deviations by which firmsin the sample are away from the default point increases by 3.2 units when they engage in risk manage-ment activities. The main findings are reinforced when examining the equity-implied asset volatilityas a separate measure of firm risk and its relationship with risk management. More specifically, assetvolatility decreases by 64% when firms manage risk.
∗Moore School of Business, University of South Carolina, Columbia, SC 29208. E-mail:monica [email protected]
1 Introduction
In the 2002 Berkshire Hathaway annual report, Warren Buffet warned against the use of
derivatives, referring to them as ”financial weapons of mass destruction, carrying dangers
that, while now latent, are potentially lethal.” His view on derivatives opposes the classical
belief that they are used to reduce risk (Smith and Stulz (1985)), being known primarily as
hedging instruments. While most of the concern over the use of derivatives has been expressed
by practitioners, a recent academic study (Faulkender (2005)) shows that firms often use in-
terest rate risk management instruments to time the market.
I provide evidence on whether risk management instruments are used for risk reduction
purposes by examining whether the use of risk management instruments reduces the probabil-
ity of financial distress. This question has been investigated by several academic studies, but
the evidence is limited. Nance, Smith, and Smithson (1993), and Mian (1996)) do not find any
relation between the use of derivatives and the probability of bankruptcy, while Fok, Carroll,
and Chiou (1997) find a weak negative relation. More recently, Judge (2003) shows a strong
negative relation in a study examining the risk management activity of U.K. firms. He also
finds a stronger relation for the U.K. firms than for the U.S. firms, and attributes this result to
differences in the bankruptcy codes. Unlike the previous studies that analyze solely the use of
derivatives, Judge (2003) uses a broader definition of risk management activity, that includes
both financial derivatives and hedging methods other than financial derivatives.
I use a pair-matched sample of U.S. bankrupt and non-bankrupt companies between 1998
and 2005, and estimate a duration analysis model for the likelihood of bankruptcy as a function
of their risk management activity. Second, I estimate the distance to default with a structural
model and then evaluate whether risk management helps to explain the distance to default.
Last, I investigate the relationship between risk management and the equity-implied asset
volatility as an alternative measure of firm risk.
A word of caution is necessary given that the sample of bankrupt and non-bankrupt firms
1
is a non-random sub-sample of public companies. If risk management is driven by reasons of
financial distress and bankruptcy costs, then this sample is biased towards firms where risk
management is most desirable. Therefore, inferences about the impact of risk management
on firms’ riskiness should not be made beyond this sample. However, if financial distress is
one of the primary determinants of risk management, then the analysis of bankrupt and non-
bankrupt firms would probably provide the most interesting insights.
The sample of 344 bankrupt and non-bankrupt firms is restricted to those non-financial
and non-utility firms that have available accounting data (from Compustat), as well as risk
management information disclosed in their annual reports for at least one year prior to filing
for bankruptcy. I define a firm as engaging in risk management in a particular year if the firm
uses any risk management instruments, including both financial derivative instruments and
methods other than financial derivatives. For example, I classify a firm as engaging in risk
management if the firm reports a fixed rate debt issue as a hedging activity under SFAS 133
(Accounting for Derivative Instruments and for Hedging Activities). As Faulkender (2005),
Kedia and Mozumdar (2003), and Judge (2003) argue, this approach provides a more accurate
picture of a firm’s risk management strategy than simply using derivative use to classify firms
as engaging in risk management. In addition, to identify whether a company engages in any
type of risk management, I also identify whether a firm engages in interest rate risk manage-
ment, foreign currency risk management, or commodity price risk management. I follow each
firm in time, starting with the first year when it discloses its position on risk management (can
be as early as 1994) and ending with the fiscal year before bankruptcy filing (can be as late as
2004).
I first estimate a discrete time complementary loglog hazard model for firms’ probability
to file for bankruptcy within one year. The duration analysis approach is more appropriate for
my sample and, according to Shumway (2001), it performs better than the conditional binary
models that are widely used. Since the likelihood of financial distress could influence the use
2
of risk management instruments, I use GMM (Generalized Method of Moments) models to
control for the endogeneity between the probability of bankruptcy and the risk management
decision.
I find that, all else equal, the odds of filing for bankruptcy are 96% lower for the firms
that manage risk as opposed to the ones that do not. This result suggests that the use of risk
management instruments helps reduce risk and extend a firm’s life.
Second, I estimate the distance to default from equity prices in a Black-Scholes-Merton
option pricing framework. The distance to default measures how far (in terms of the number
of standard deviations) the firm’s asset value is from the default point. Next, I estimate a
GMM model to study the relationship between risk management and the distance to default.
The results obtained with this approach are consistent with those from the duration analy-
sis approach. All else equal, any risk management activity is associated with a higher distance
to default. On average, the number of standard deviations by which firms are away from the
default point increases by 3.2 units when they manage risk. This evidence confirms the finding
from the cloglog model, namely that engaging in risk management activities tends to reduce
the probability of default.
Third, I investigate the relationship between risk management and the equity-implied asset
volatility1 as a distinct measure of firm risk, and estimate a GMM model that accounts for
the endogenous regressors.
The results confirm the previous findings in the paper and show that, all else equal, firms
that use risk management instruments have a significantly lower asset volatility. More specifi-
cally, on average firms’ asset volatility is reduced by 64% when they manage risk. Furthermore,
separate analysis of interest rate risk management and foreign currency risk management in-
dicate that these are also associated with lower asset volatility.
As a first robustness check, I investigate the relationship between the changes in the dis-
1Asset volatility is also used in the distance to default calculation, as shown in Section 3.2.
3
tance to default or asset volatility and the changes in the firms’ risk management activity.
I find that changes in the risk management activity in general are positively related to the
changes in the distance to default and negatively related to the changes in asset volatility,
results which are consistent with the main findings. In addition, a positive and significant
relationship is found between the changes in the distance to default and the changes in inter-
est rate risk management, as well as between the changes in the distance to default and the
changes in foreign currency risk management. On the other hand, the changes in commodity
price risk management are negatively associated with the changes in the distance to default,
which constitutes, counter-evidence to the risk-reduction hypothesis.
Next, I use sub-sample analysis to examine the relationship between risk management and
the distance to default or the asset volatility. I calculate the mean risk exposure (i.e. interest
and then manually collect data on whether the firms disclose that they manage either interest
rate, foreign currency, or commodity price risks from the footnotes of the annual reports. Only
335 of the firms report the use or non use of risk management instruments in the footnotes of
the annual report for at least one fiscal year prior to the fiscal year of the bankruptcy filing.
Companies that report any type of risk management activity (including but not restricted
to the use of derivatives) are assigned a value of one, while the ones reporting no risk man-
agement activity, ”limited,” ”minimal,” or ”immaterial” use of risk management instruments
are assigned a value of zero. Companies that ignore the disclosure requirement, or state that
3Initially, companies were required by the SEC to adopt SFAS133 (Accounting for Derivative Instruments and forHedging Activities) in the years beginning after June 1998. However, in June 1999, the FASB delayed the effectivedate of this statement for one year, to fiscal years beginning June 15, 2000 due to concerns about companies’ abilityto modify their information systems and educate their managers.
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the new disclosure regulation did not affect their financial statements are not included in the
sample, since no conclusion can be drawn about their risk management involvement. Although
notional amounts of financial instruments used are desirable for this analysis, these are not
available in most cases for this particular sample. Bankruptcy filings in the sample occur
between 1998 and 2005, but data (both accounting and risk management) are not available
for the fiscal year of the bankruptcy filing. The earliest year in the sample with reported use
of hedging instruments is 1994 and the latest is 2004.
Secondly, I match each company in the sample with a company in the same industry and
of similar size (within 10% of asset size) for the fiscal year before the bankruptcy filing, which
did not file for bankruptcy. In order to construct the control sample, I use the two-digit Stan-
dard Industrial Classification code for the initial matching with similar Compustat firms. If
multiple matches exist, I manually select the best match based on the four-digit or three-digit
SIC code, on the closest asset size, and ultimately on the risk management data availability
as reported in the annual report filed with the Securities and Exchange Commission. All
matches are required to have risk management information reported in their 10-K. Based on
these criteria, I find 172 matches. The missing 163 matches occur because of the restrictions
on the asset size, as well as because of lack of risk management data. I stop following the 172
matches identified in the same year that their counterparts file for bankruptcy. This creates a
censorship issue, which I address below.
Finally, the full sample is comprised of 344 firms (172 bankrupt, and 172 non-bankrupt),
and 926 firm-year observations. Some years of data that are not common to the paired compa-
nies are systematically excluded, thus truncating the sample. The managerial ownership and
compensation data are obtained from proxy filings (DEF-14A) with the Securities and Ex-
change Commission for each of the firms and years in the sample. The accounting data come
from Compustat, while the equity data are extracted from CRSP. The return on the S&P 500
index is used as a proxy for the market return. The Reuters-Jeffries commodity index and
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currency index are used to estimate the commodity price exposure and the foreign currency
exposure respectively. More specifically, the exposure variables are estimated separately for
each firm, using monthly data, for a period starting two years before the fiscal year required
and ending two years after, with the following regression:
agement instruments. Opposite to the regression results from the sub-samples of firms with
high/low interest rate exposure and foreign currency exposure, in these sub-samples evidence
indicates that the use of risk management instruments is mainly associated with an increase
in the distance to default and a decrease in asset volatility for the firms with low commodity
price exposure. Therefore, the uses of any type risk management instruments, interest rate
risk management, and foreign currency risk management, which are related to an increase in
the distance to default and to a decrease in asset volatility, are not likely to be driven by firms’
commodity price hedging needs.
Distance to Default
For the two sub-samples of firms with commodity price exposure lower and higher than the
mean, the results of the GMM estimation for the relationship between risk management and
the distance to default are presented in Table 13. While for the firms with high commodity
price exposure, no relationship can be found between the risk management activity and the
distance to default, in the case of firms with low commodity price exposure there is a positive
and significant relationship for the Any RM, IR RM, and FX RM specifications. For example,
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a 1% increase in any risk management activity augments the distance to default by approxi-
mately 3 units.
Asset Volatility
Table 14 shows the results of the GMM estimation between risk management and asset
volatility, separately for the two sub-samples of firms with commodity price exposure lower
and higher than the mean. Any RM and FX RM are negatively related to asset volatility
for the firms with low commodity price exposure. IR RM is also negatively related to asset
volatility for both firms with high and low commodity price exposure.
5.3 Sub-sample Analysis: Bankrupt and Non-Bankrupt
Lastly, another robustness check consists of separating the sample into sub-samples of bankrupt
and non-bankrupt firms, and estimating GMM in order to examine the relationship between
risk management and the distance to default or asset volatility. This analysis examines whether
the bankrupt and/or the non-bankrupt firms show specific patterns with respect to their risk
management activities.
As mentioned before, the characteristics of each group are shown in Table 3. The regres-
sion results support the main finding on the relationship between risk management and asset
volatility in both sub-samples, with not much evidence on the relationship between risk man-
agement and the distance to default.
Distance to Default
The results of the GMM estimation between risk management and the distance to default,
separately for the two sub-samples of bankrupt and nonbankrupt firms, are presented in Table
15. The variable IR RM is the only one that has a negative coefficient which is only significant
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at the 10% level in the sub-sample of nonbankrupt firms. It suggests that, for companies
that use interest rate risk management instruments, the number of standard deviations the
asset value is away from the default point decreases by 1 unit, all else equal. This result
does not supports the hypothesis that interest rate risk management reduces the probability
of bankruptcy in this sub-sample. All other risk management variables are not statistically
significant. No inference can be made about the relationship between the risk management
activity and the distance to default in the sub-sample of bankrupt firms.
Asset Volatility
Unlike the distance to default, asset volatility has a significant relationship with the risk
management variables for the two sub-samples of bankrupt and nonbankrupt firms, as shown
in Table 16. Any RM is negatively and significantly related to asset volatility for the sub-
sample of bankrupt firms. IR RM and FX RM are also negatively and significantly related
to asset volatility for both sub-samples. For example, a 1% increase in the interest rate risk
management reduces asset volatility by 68%.
6 Conclusion
This paper examines whether the use of risk management instruments for a sample of bankrupt
and non-bankrupt firms impacts firms’ riskiness in terms of their probability of bankruptcy,
distance to default, and asset volatility. To my knowledge, the latter two measures of firm risk
have not been used in the prior literature on risk management, while the former has not been
used in the context of duration analysis.
In a framework that controls for the endogeneity between risk management and the prob-
ability of default, this paper provides evidence that the risk management activity contributes
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to a lower probability of bankruptcy, higher distance to default, and lower asset volatility, all
of which suggest risk reduction and consequently a longer life for the firm. Both approaches
presented in this paper (duration analysis approach and option approach) show strong support
with respect to the role of risk management in general and provide some evidence with respect
to individual types of risk management: interest rate, foreign currency, and commodity price
risk management. Sub-sample analysis generally confirms the main results and brings some
insights with respect to the use of different types of risk management instruments depending
on the firms’ risk exposure (or hedging needs). For example, evidence indicates that the benefit
of using risk management instruments is greater when firms’ interest rate and foreign currency
hedging needs are high, and commodity price hedging needs are low. Overall, for firms with
high interest rate exposure and foreign currency exposure, as well as with low commodity
price exposure, the use of risk management instruments is associated with a higher distance to
default and a lower asset volatility. However, for companies with low interest rate and foreign
currency exposure, the use of commodity price risk management is negatively related to the
distance default. Similarly, the changes in commodity price risk management are negatively
related to the changes in the distance to default, suggesting that the initiations of commodity
price risk management actually reduce firms’ distance to default. The answer to the question
on whether firms with lower overall risk exposure are less likely to use commodity price risk
management instruments for risk-reduction purposes is left for future research.
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References
Adam, Tim R., 2002, Do Firms Use Derivatives to Reduce their Dependence on External CapitalMarkets?, European Finance Review pp. 163–187.
Altman, E., R. Haldeman, and P. Naraynan, 1977, ZETA Analysis: A New Model to IdentifyBankruptcy Prediction Risk of Corporations, .
Altman, Edward I., 1968, Financial ratios, discriminant analysis and the prediction of corporatebankruptcy, Journal of Finance 23, 589–609.
, 1973, Predicting Railroad Bankruptcies in America, Bell Journal of Economics and Manage-ment Science 3, 184–211.
Beaver, W. H., 1966, Financial Ratios as Predictors of Failure, Journal of Accounting Research (Sup-plement) pp. 71–102.
Black, Fischer, and Myron Scholes, 1973, The pricing of options and corporate liabilities, Journal ofPolitical Economy 81, 637–659.
Charitou, A., N. Lambertides, and L. Trigeorgis, 2004, Is the Impact of Default Risk Systematic? AnOption-Pricing Explanation, Working Paper, University of Cyprus.
Charitou, Andreas, and Leon Trigeorgis, 2002, Option-based Bankruptcy Prediction, Working Paper,University of Cyprus.
Delianedis, R., and R. Geske, 1999, Credit Risk and Risk Neutral Probabilities: Information aboutRating Migrations and Defaults, UCLA Working Paper.
Faulkender, Michael, 2005, Hedging or market timing? Selecting the interest rate exposure of corporatedebt, Journal of Finance 60, 931–962.
Fehle, Frank, and Sergey Tsyplakov, 2005, Dynamic Risk Management: Theory and Evidence, Journalof Financial Economics.
Fok, R. C. W., C. Carroll, and M. C. Chiou, 1997, Determinants of corporate hedging and derivatives:a revisit, Journal of Economics and Business 49, 569–585.
Froot, Kenneth A., David S. Scharfstein, and Jeremy C. Stein, 1993, Risk Management: CoordinatingCorporate Investment and Financing Policies, Journal of Finance 48, 1629–1658.
Graham, John R., and Clifford Jr. Smith, 1999, Tax Incentives to Hedge, The Journal of Finance 54,2241–2262.
Hillegeist, Stephen A., Elizabeth K. Keating, Donald P. Cram, and Kyle G. Lundstedt, 2004, Assessingthe Probability of Bankruptcy, Review of Accounting Studies 9, 5–34.
Judge, Amrit, 2003, Why and How UK Firms Hedge, European Financial Management Journal 12.
Kedia, S., and A. Mozumdar, 2003, Foreign currency denominated debt: an empirical investigation,Journal of Business 76, 521–546.
Merton, C. R., 1974, On the Pricing of Corporate Debt: The Risk Structure of Interest Rates, Journalof Finance 29, 449–470.
Mian, Shehzad, 1996, Evidence on Corporate Hedging Policy, The Journal of Financial and QuantitativeAnalysis 11, 419–439.
Nance, Deana R., Clifford W. Smith, and Charles W. Smithson, 1993, On the Determinants of CorporateHedging, The Journal of Finance 48, 267–284.
31
Ohlson, James A., 1980, Financial Ratios and the Probabilistic Prediction of Bankruptcy, Journal ofAccounting Research 18, 109–131.
Papanastasopoulos, George, 2006, Using Option Theory and Fundamentals to Assessing Default Riskof Listed Firms, Working Paper, University of Peloponnese.
Purnanandam, Amiyatosh, 2007, Financial Distress and Corporate Risk Management: Theory & Evi-dence, Journal of Financial Economics, forthcoming.
Rogers, Daniel A., 2002, Does Executive Portfolio Structure Affect Risk Management? CEO Risk-taking Incentives and Corporate Derivative Usage, Journal of Banking and Finance 26, 271–296.
Shumway, Tyler, 2001, Forecasting Bankruptcy More Accurately: A Simple Hazard Model, The Journalof Business 74, 101–124.
Smith, Clifford W., and Rene M. Stulz, 1985, The Determinants of Firms’ Hedging Policies, The Journalof Financial and Quantitative Analysis 20, 391–405.
Tufano, Peter, 1996, Who Manages Risk? An Empirical Examination of Risk Management Practicesin the Gold Mining Industry, Journal of Finance 51, 1097–1137.
Vassalou, Maria, and Yuhang Xing, 2004, Default Risk in Equity Returns, The Journal of Finance 49,831–868.
Zmijewski, Mark E., 1984, Methodological Issues Related to the Estimation of Financial Distress Pre-diction Models, Studies on Current Econometric Issues in Accounting Research.
Appendix. Variables Definition
Any RM. Binary variable: 1 if the firm uses any risk management instruments and 0 otherwise.
IR RM. Binary variable: 1 if the firm manages interest rate risk and 0 otherwise.
FX RM. Binary variable: 1 if the firm manages foreign currency risk and 0 otherwise.
CP RM. Binary variable: 1 if the firm manages commodity price risk and 0 otherwise.
RMYears. Number of years of risk management activity up to that point.
Size. Natural logarithm of Total Assets.
Leverage. Total debt divided by Total Assets.
Liquidity. Cash and Short-term Investments divided by Total Assets.
MtoB. Market to Book ratio as reported in Compustat.
Leverage∗i1 to Leverage∗i9. Interaction terms leverage - industry, where i1 to i9 are industry dummies.
LAGEAR. Lag Net Income.
DivPay. Dividend Payout as reported in Compustat; (dividend per share divided by earnings per share) missingitems are set equal to zero.
DivYield. Dividend Yield as reported in Compustat; missing items are set equal to zero.
Bonus. Natural logarithm of the dollar amount the CEO received as Bonus and other annual compensation.
StockOpt. Natural logarithm of the number of shares underlying any option-based compensation awarded tothe CEO.
Ownership. Number of shares of common stock held by the CEO as beneficial ownership.
ITRExposure. Interest Rate Exposure Variable equal to the value of β1i from Equation 1.
FXExposure. Foreign Currency Exposure Variable equal to the value of β2i from Equation 1.
CPExposure. Commodity Price Exposure Variable equal to the value of β3i from Equation 1.
Change Any RM, Change IR RM, Change FX RM, Change CP RM. Variables indicating an initiation(takes the value of 1), a halt (takes the value of -1) or no change (takes the value of 0) in a firm’s risk managementactivity. Calculated as the difference in between the current value of Any RM, IR RM, FX RM, and CP RM andprevious year value.
Change Size, Change Leverage, Change Liquidity, Change MtoB, Change LAGEAR, Change DivPay,Change DivYield, Change Bonus, Change StockOpt, Change Ownership, Change ITRExposure,Change FXExposure, Change CPExposure. Variables calculated as the difference between the currentvalue of Size, Leverage, Liquidity, MtoB, LAGEAR, DivPay, DivYield, Bonus, StockOpt, Ownership, ITRExposure,FXExposure, and CPExposure compared to the previous year value.
Table 1:
This table presents a description of the data by industry sector. The first column shows the first two digits of the SIC Code, the secondcolumn presents the number of firms for that sector (half of each filed for bankruptcy), and the third column displays the complete namefor each sector.
Data Description by Industry - 344 observations, 172 pairsSic2 NoObs Industry
10 2 Metal Mining13 12 Oil and Gas Extraction20 2 Food and Kindred Products22 6 Textile Mill Products23 4 Apparel and Other Finished Products Made from Fabrics and Similar Materials26 2 Paper and Allied Products27 2 Printing, Publishing, and Allied Industries28 24 Chemicals and Allied Products30 8 Rubber and Miscellaneous Plastic Products32 2 Stone, Clay, Glass, and Concrete Products33 18 Primary Metal Industries34 4 Fabricated Metal Products, Except Machinery and Transportation Equipment35 46 Industrial and Commercial Machinery and Computer Equipment36 30 Electronic and Other Electrical Equipment and Components,
and Watches and Clocks39 2 Miscellaneous Manufacturing Industries42 8 Motor Freight Transportation and Warehousing44 2 Water Transportation45 4 Transportation By Air48 50 Communications50 6 Wholesale Trade-durable Goods51 6 Wholesale Trade-non-durable Goods54 2 Food Stores56 2 Apparel and Accessory Stores58 8 Eating and Drinking Places59 8 Miscellaneous Retail70 2 Hotels, Rooming Houses, Camps, and Other Lodging Places73 64 Business Services79 2 Amusement and Recreation Services80 2 Health Services87 6 Engineering, Accounting, Research, Management, and Related Services
Table 2:
This table presents a year-by-year description of the data. The second column shows the number of bankruptcies that were filed eachfiscal year, while the third column identifies the number of observations in the sample for each fiscal year.
Data Description by Year:Year # Bankruptcy Filings # Observations in the Sample
This table presents summary statistics (mean and standard deviation) of firm characteristics for companies that filed for bankruptcyand the control group, for the fiscal year before bankruptcy filing. The variables shown are: any risk management (Any RM), interestrate risk management (IR RM), foreign exchange risk management (FX RM), commodity price risk management (CP RM), size (Size),leverage (Leverage), and liquidity (Liquidity). These are defined in Appendix.
Characteristic Bankrupt Firms (172) Control Group (172)Mean Std.Dev. Mean Std. Dev