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Value investing in credit markets

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Abstract

We outline a parsimonious empirical model to assess the relative usefulness of accounting- and equity market-based information to explain corporate credit spreads. The primary determinant of corporate credit spreads is the physical default probability. We compare existing accounting-based and market-based models to forecast default. We then assess whether the credit market completely incorporates this default information into credit spreads. We find that credit spreads reflect information about forecasted default rates with a significant lag. This unique evidence suggests a role for value investing in credit markets.

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Notes

  1. Beaver et al. (2012) combine the bankruptcy database from Beaver et al. (2005), which was derived from multiple sources including CRSP, Compustat, Bankruptcy.com, Capital Changes Reporter, and a list provided by Shumway with a list of bankruptcy firms provided by Chava and Jarrow and used in Chava and Jarrow (2004).

  2. To ensure that prediction is made out of sample and to avoid a potential bias of ex post over-fitting the data, we estimate coefficients using an expanding window approach. In particular, to estimate the probability of default for calendar year 2011 (January 2011 to December 2011), we combine all the available accounting and market data from January 1980 to December 2009, use it to predict bankruptcy outcomes for January 1981 to December 2010, retain the coefficients, and use them to estimate the probability of default for 2011. To obtain an estimate of the probability of default for the period from February 2011 to January 2012, we include one more month in the estimation. In particular, we combine all the available accounting and market data from January 1980 to January 2010, use it to predict bankruptcy outcomes for January 1981 to January 2011, and apply the estimated coefficients to accounting and market data available at January 2011.

  3. We compare the ability of the default prediction models in correctly predicting bankruptcies based on the area under the receiver operating curves (ROC). Based on this comparison, we find that the areas under the ROC for the models that we use in our main analysis are significantly larger. The finding that market-based models that we use for our main analysis perform better than pure accounting-based models is consistent with Hillegeist et al. (2004).

  4. We use Moody’s/KMV EDF score directly as a measure of probability of default. The EDF score is empirically mapped by Moody’s/KMV to their default database, using a more flexible empirical transformation.

  5. Our return calculation is imprecise as it misses some aspects of accrued interest for bonds and the impact of “roll” over the course of the month (Lok and Richardson 2011). While these are second order effects, we have re-estimated all of our returns-based tests using total returns inclusive of accrued interest and roll as per Merrill Lynch’s total return computations. Our results are virtually identical between using our pseudo return measure or total bond returns. We have chosen to present the results based on the return approximation, as this allows greater comparability with the CDS market data.

  6. Campbell et al. (2008) find that a reduced form specification that includes accounting and market variables has higher predictive power for bankruptcy than a measure of distance to default estimated using the approach outlined by Hillegeist et al. (2004). However, they do not use Moody’s/KMV measure, EDF, in their analysis, and therefore their results are not necessarily inconsistent with ours.

  7. The option adjusted spread is the difference between a bond’s yield and the yield of a duration matched treasury issue, adjusted for the portion of that difference that is due to embedded options.

  8. In untabulated analysis we find that the predictive power of the EDF measure for bankruptcy and with respect to credit market returns is higher after 2002, the year in which Moody´s acquired KMV. It is possible that EDF data became more widely ‘known’ to the market at this time. The positive relation we document between EDF based measures of credit relative value and future credit returns suggests that the market continues to fail to incorporate value relevant information in a timely manner.

  9. Our results are robust to the inclusion of bond returns for month t + k − 1 as an additional explanatory variable to control for bond market momentum.

  10. The Sharpe ratio for the Bharath and Shumway (2008) model is the highest among all four. This result is explained by the fact that this model includes equity returns and therefore captures information spillover effects from the equity market to the credit market. Note that the characteristic regressions reported in Table 6 document a lower relative predictive ability for the Bharath and Shumway measure as evidenced by the lower test statistics for its coefficient when the equity return characteristics (e.g., MOMS and MOML) are directly controlled for.

  11. The average cumulative long/short returns over the next 3 (6) months for CDS instruments are 87 (139) bps. CDS contracts are usually more liquid than bonds. Moreover, the cost of buying CDS protection is in general substantially lower than the cost of shorting bonds. For these reasons, long/short CDS returns should be sufficient to cover transaction costs.

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Acknowledgments

We are grateful to Jim Ohlson (editor), Itzhak Venezia (discussant), an anonymous referee, seminar participants at the 2011 Citi Global Quantitative Research Conference, 5th LSE/MBS Conference, Kepos Capital LP, London Business School 2011 Accounting Symposium, Moody’s Analytics, NHH, Norges Bank Investment Management, Padova University, State Street Global Markets European Quantitative Forum 2011, Mark Carhart, John Core, Doug Dwyer, Dan Galai, Peter Feldhutter, Erika Jimenez, Partha Mohanram,Tapio Pekkala, Tjomme Rusticus, Pedro Saffi, Stephen Schaefer, Kari Sigurdsson, Richard Sloan, Jing Zhang, and Julie Zhang for helpful discussion and comments. We are especially grateful to Moody’s/KMV for making available a history of point in time EDF data. Any errors are our own.

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Correspondence to Maria Correia.

Appendix: Variable definitions

Appendix: Variable definitions

Compustat mnemonics in parenthesis.

Variable

Description

BETA i,t

Equity market beta estimated from a rolling regression of 60 months of data requiring at least 36 months of non-missing return data

BTM i,t

Book to market ratio measured at the most recent fiscal quarter end (‘CEQQ’/‘PRRC’*‘CSHOQ’)

CRV i,t

Credit relative value and is computed as \( \ln \left( {\frac{{CS_{i,t} }}{{CS_{i,t}^{*} }}} \right) \), where \( CS_{i,t}^{*} \) is the theoretical (implied) credit spread for firm i in month t using D2D, BCM-BOTH, BS, or EDF default prediction model

CS i,t

Actual credit spread for firm i in month t

\( CS_{i,t}^{D2D} \)

Theoretical (implied) credit spread for firm i in month t using D2D default prediction

\( CS_{i,t}^{BCM - BOTH} \)

Theoretical (implied) credit spread for firm i in month t using BCM-BOTH default prediction model

\( CS_{i,t}^{BS} \)

Theoretical (implied) credit spread for firm i in month t using BS default prediction model

\( CS_{i,t}^{EDF} \)

Theoretical (implied) credit spread for firm i in month t using EDF default prediction model

dIP t

\( \ln \left( {\frac{{IP_{t} }}{{IP_{t - 1} }}} \right) \), where IP t is Industrial Production Index at the end of month t from the Board of Governors of the Federal Reserve System (INDPRO), available at the St Louis Fed web site: http://research.stlouisfed.org/fred2/

dRP t

Change in risk premium, RP t  − RP t−1, where RP t is the difference between the Moody’s Seasoned BAA Corporate Bond Yield from the Board of Governors of the Federal Reserve System (BAA) and the 10-Year Treasury constant maturity rate from the Board of Governors of the Federal Reserve System (GS10). BAA and GS10 are available at the St Louis Fed web site: http://research.stlouisfed.org/fred2/.

dTS t

Change in term structure, TS t  − TS t−1, where TS t is the difference between the 10-Year Treasury constant maturity rate (GS10) and the 2-Year Treasury constant maturity rate (GS2), both from the Board of Governors of the Federal Reserve System. Both GS10 and GS2 are available at the Louis Fed web site: http://research.stlouisfed.org/fred2/

dVIX t

Change in volatility, VIX t  − VIX t−1, where VIX t is average daily CBOE Volatility Index from the Chicago Board Options Exchange (VIX) for month t. VIX is available at the St Louis Fed web site: http://research.stlouisfed.org/fred2/

\( \frac{E}{{P_{it} }} \)

Net Income (‘NIQ’) from the most recent four quarters divided by the market capitalization at the fiscal period end date

\( \frac{EBIT}{{TL_{it} }} \)

Net income before interest, taxes, depreciation, depletion and amortization (‘OIBDPQ’) divided by total liabilities (‘LT’)

HML

Monthly mimicking factor portfolio return to the value factor, obtained from Ken French’s website.

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

LRSIZE i,t

Logarithm of the ratio of the firm’s market capitalization at the end of the month and the market capitalization of all firms

MOM

Average return on the two high prior return portfolios minus the average return on the two low prior return portfolios, obtained from Ken French’s website.

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

MOML

3 month half-life weighted average of stock return for the 11 months ending in the beginning of month t

MOMS

Stock return for month t

\( \frac{NI}{{TA_{it} }} \)

Net income (‘NIQ’) divided by average total assets (‘ATQ’)

\( NROAI_{i,t} \)

Indicator variable equal to 1 if the return on assets (ROA i,t ) is negative

\( PD_{i,t}^{D2D} \)

Physical default probability for firm i in month t using D2D default prediction model

\( PD_{i,t}^{BCM - BOTH} \)

Physical default probability for firm i in month t using BCM-BOTH default prediction model

\( PD_{i,t}^{BS} \)

Physical default probability for firm i in month t using BS default prediction model

\( PD_{i,t}^{EDF} \)

Physical default probability for firm i in month t using EDF default prediction model

R MKT

Monthly excess (to risk-free rate) market return, obtained from Ken French’s website.

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

RET i,t+k

Monthly return calculated as per Eq. (13).

ROA i,t

Return on assets, defined as earnings before interest (‘NIQ’) adjusted for interest income tax (‘XINTQ’*(1-tax rate)), scaled by average total assets (‘ATQ’)

RETURNS i,t

Prior 12 months security returns from CRSP monthly files

\( \sigma_{{E_{i,t} }} \)

Standard deviation of excess returns computed over the previous 12 months. Monthly returns are extracted from CRSP, and a one factor CAPM is used to compute excess returns

SIZE it

Logarithm of market capitalization, calculated at the end of the month as ‘PRC’*’SHROUT’ from CRSP monthly file

SMB

Monthly mimicking factor portfolio return to the size factor, obtained from Ken French’s website.

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

\( \frac{TL}{{TA_{it} }} \)

Ratio between total liabilities (‘LTQ’) and total assets (‘ATQ’)

\( V_{{A_{i,t} }} \)

Market value of equity at the end of the month plus book value of debt (X it ), calculated as described below

X it

Book value of short-term debt (‘DLCC’) + 0.5* book value of long-term debt (‘DLTTQ’)

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Correia, M., Richardson, S. & Tuna, İ. Value investing in credit markets. Rev Account Stud 17, 572–609 (2012). https://doi.org/10.1007/s11142-012-9191-x

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