Value investing in credit markets

Correia, Maria; Richardson, Scott; Tuna, İrem

doi:10.1007/s11142-012-9191-x

Cited by 120 publications

(50 citation statements)

References 30 publications

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“…Therefore we select the representative bond based on amount outstanding. Similar bond selection criteria are used by Correia et al (2012) and Cascino (2017). an estimate of the historical correlation between equity and bond returns.…”

Section: Historical Market Datamentioning

confidence: 99%

“…Following Correia et al (2012), we use quarterly financial data to compute the default barrier and update market data on a monthly basis to obtain monthly estimates of the probabilities of bankruptcy. Market variables are measured at the end of each month, and accounting variables are based on the most recent quarterly information reported before the end of the month.…”

Section: Bankruptcy Data and Distance To Defaultmentioning

confidence: 99%

“…We conduct standard cross-sectional return predictability regressions and examine whether CRV accounting can forecast returns (over and above CRV market). Specifically, we run the following cross-sectional regression model using the Fama and Macbeth (1973) approach as described by Correia et al (2012).…”

Section: Return Predictionmentioning

confidence: 99%

“…We focus on the latter group. While there is a rich literature examining how accounting data can be used to help forecast corporate bankruptcy and default (e.g., Beaver 1966;Altman 1968;Ohlson 1980;Beaver et al 2005;Bharath and Shumway 2008;Campbell et al 2008;Correia et al 2012), there is scant analysis of how fundamental measures of risk can be used to improve credit-related investment decisions. Most of these studies use a mix of fundamental and market-based variables to predict bankruptcy, but a theme in this research is the central importance of market-based measures of volatility.…”

mentioning

confidence: 99%

“…This raises the possibility that credit markets are not paying enough attention to fundamental-based measures. Using the regression framework from Correia et al (2012), we assess whether measures of credit risk mispricing (the difference between observed credit spreads and modelled credit spreads using either market-or fundamental-based measures of asset volatility) help predict credit excess returns. If the market is not paying enough attention to fundamental measures of asset volatility, we would expect to see measures of credit risk mispricing based on fundamental asset volatility better predict credit excess returns.…”

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Asset volatility

2017

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We examine whether fundamental measures of volatility are incremental to market-based measures of volatility in (i) predicting bankruptcies (out of sample), (ii) explaining cross-sectional variation in credit spreads, and (iii) explaining future credit excess returns. Our fundamental measures of volatility include (i) historical volatility in profitability, margins, turnover, operating income growth, and sales growth; (ii) dispersion in analyst forecasts of future earnings; and (iii) quantile regression forecasts of the interquartile range of the distribution of profitability. We find robust evidence that these fundamental measures of volatility improve out-of-sample forecasts of bankruptcy and help explain cross-sectional variation in credit spreads. This suggests that an analysis of credit risk can be enhanced with a detailed analysis of fundamental information. As a test case of the benefit of volatility forecasting, we document an improved ability to forecast future credit excess returns, particularly when using fundamental measures of volatility.

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Section: Historical Market Datamentioning

confidence: 99%

Section: Bankruptcy Data and Distance To Defaultmentioning

confidence: 99%

Section: Return Predictionmentioning

confidence: 99%

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Asset volatility

2017

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The low beta anomaly: A corporate bond investor's perspective

Bektić

2018

Rev Financ Econ

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The low beta anomaly is well documented for equity markets. However, the existence of such a factor in corporate bond markets is less explored. I find that European corporate bonds of firms with a low equity beta have higher risk-adjusted returns, on average, than European corporate bonds of firms with a high equity beta. The results are economically and statistically significant as low beta credit portfolios improve the Sharpe ratio up to 30%. Moreover, even after accounting for transaction costs and by considering long-only portfolios, the risk-adjusted return remains substantial indicating practical implementability of the strategy for corporate bond investors. K E Y W O R D Sanomalies, corporate bonds, factor investing, low beta, risk premium | INTRODUCTIONAlthough the capital asset pricing model (CAPM) assumes a positive risk/return relation, there are numerous empirical studies which report that the relation between risk and return is flatter than implied by CAPM or even negative. One of the first and best documented anomalies in equity markets is the low beta anomaly. 1 For instance, Haugen and Heins (1972) and Black, Jensen, and Scholes (1972) show that a portfolio that is short high-beta stocks and long low-beta stocks generates significant positive abnormal returns. Recent research confirms these puzzling findings and tries to give an explanation for the existence of this kind of anomaly. Frazzini and Pedersen (2014) provide a model which suggests that investment constraints (e.g., leverage) and human behavior are the main drivers behind the low beta anomaly.However, while empirical research on anomalies is predominantly focused on equity markets, similar studies for corporate bonds are rather new. For example, Correia, Richardson, and Tuna (2012) document that value investing is useful for corporate bond investors. Jostova, Nikolova, Philipov, and Stahel (2013) show that credit momentum is profitable in the U.S. high yield (HY) corporate bond market. Choi and Kim (2016) suggest that asset growth and investment anomalies exist in credit markets and Chordia, Goyal, Nozawa, Subrahmanyam, and Tong (2017) provide evidence that size, profitability and past equity returns can predict corporate bond returns. In addition, Bekti c, Wenzler, Wegener, Schiereck, and Spielmann (2016) state that the four Fama and French (2015) equity factors (size, value, profitability and investment) exhibit significant excess returns in the U.S. HY market but the results are mixed for U.S. and European investment grade (IG) bonds. Finally, Lin, Wu, and Zhou (2017) document that momentum is the most pronounced cross-sectional corporate bond market anomaly using trend signals.Analyzing the relation between firm's equity beta and the corresponding corporate bond excess return is promising for corporate bond investors for at least the following two reasons. On one hand, according to structural credit risk models like Merton (1974), equities and bonds should be related since they represent claims against the assets of the same firm, an...

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Global Macro

Kakushadze¹,

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2018

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Value investing in credit markets

Cited by 120 publications

References 30 publications

Asset volatility

Asset volatility

The low beta anomaly: A corporate bond investor's perspective

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