Momentum in Corporate Bond Returns (with Gergana Jostova, Stanislava (Stas) Nikolova, and Christof W. Stahel)

Review of Financial Studies, 2013, 26(7), 1649-1693.

This paper finds significant price momentum in US corporate bonds. The analysis is based on 3.2 million monthly observations from 77,150 bonds from two transaction and three dealer-quote databases over the period from 1973 to 2008. Bond momentum profits are significant in the second half of the sample period, 1991 to 2008, and amount to 64 basis points per month. Momentum strategies are only profitable among non-investment grade bonds, where they yield 190 basis points per month. Similar to recent findings in equities, profits disappear after removing the worst-rated bonds -- about 8% of all observations -- but contrary to equities, bond momentum profits derive primarily from winners. However, losers are more actively traded than winners, giving them a greater share in momentum profitability when using trade-based data. Bond momentum is equally profitable in quote- and trade-based data, hence illiquidity is unlikely to explain it. Lack of information and transparency is also an unlikely explanation since momentum profits have increased in recent years, after the introduction of the TRACE reporting system.

Anomalies and Financial Distress (with Doron Avramov, Tarun Chordia, and Gergana Jostova)

Journal of Financial Economics, 2013, 108(1), 83-101.

Winner: 2013 JFE Best Paper Award: Fama/DFA Prize for Capital Markets and Asset Pricing, Second Prize

This paper explores commonalities across asset-pricing anomalies. In particular, we assess implications of financial distress for the profitability of anomaly-based trading strategies. Strategies based on price momentum, earnings momentum, credit risk, dispersion, idiosyncratic volatility, and capital investments derive their profitability from taking short positions in high credit risk firms that experience deteriorating credit conditions. Such distressed firms are highly illiquid and hard to short sell, which could establish nontrivial hurdles for exploiting anomalies in real time. The value effect emerges from taking long positions in high credit risk firms that survive financial distress and subsequently realize high returns. The accruals anomaly is an exception - it is robust amongst high and low credit risk firms as well as during periods of deteriorating, stable, and improving credit conditions.

The World Price of Credit Risk (with Doron Avramov, Tarun Chordia, and Gergana Jostova)

Review of Asset Pricing Studies, 2012, 2(2), 112-152.
Winner: Best Paper for 2012

Global asset pricing models have failed to capture the cross section of country equity returns. Emerging markets display robust positive pricing errors and country-level characteristics play a role in pricing international equities. This paper offers a risk-based explanation for such asset pricing deviations. A world credit risk factor is significantly priced in the cross section of country equity returns. In its presence, the positive pricing errors in emerging markets disappear and country-level characteristics no longer play a role. The risk premium for exposure to the credit risk factor is 80 basis points per month and has increased in recent years.

Credit Ratings and The Cross-Section of Stock Returns (with Doron Avramov, Tarun Chordia, and Gergana Jostova)

Journal of Financial Markets, 2009, 12(3), 469 - 499.

Low-credit-risk firms realize higher returns than high-credit-risk firms. This effect is puzzling because investors seem to pay a premium for bearing credit risk. This paper shows that the credit risk effect exists only in periods around credit rating downgrades and is due to the significant negative response of the lowest-rated stocks to downgrades. Around downgrades, low-rated firms experience considerable negative returns, precipitated by substantial deterioration in their operating and financial performance, large negative analyst forecast revisions and earnings surprises, and strong institutional selling. In contrast, returns do not differ across credit risk groups in stable or improving credit conditions. Remarkably, the group of low-rated stocks driving the credit risk effect accounts for less than 4% of the total market capitalization, suggesting that there is no pervasive distress factor in the cross-section of returns.

Dispersion in Analysts' Earnings Forecasts and Credit Rating (with Doron Avramov, Tarun Chordia, and Gergana Jostova)

Journal of Financial Economics, 2009, 91(1), 83 - 101.

This paper shows that the puzzling negative cross-sectional relation between dispersion in analysts' earnings forecasts and future stock returns is a manifestation of the credit risk effect. In particular, the profitability of dispersion based trading strategies is concentrated in a small number of the worst-rated firms and is significant only during periods of deteriorating credit conditions. In such periods, the negative dispersion-return relation emerges as low-rated firms experience substantial price drop along with considerable increase in forecast dispersion. Moreover, even for this small universe of worst-rated firms, the dispersion-return relation is nonexistent when either the dispersion measure or return is adjusted by credit risk. The results are robust to previously proposed explanations for the dispersion effect such as short-sale constraints, illiquidity, and leverage.

Momentum and Credit Rating (with Doron Avramov, Tarun Chordia, and Gergana Jostova)

Journal of Finance, 2007, 62(5), 2503 - 2520.

This paper establishes a robust link between momentum and credit rating. Momentum profitability is large and significant among low-grade firms, but it is nonexistent among high-grade firms. The momentum payoffs documented in the literature are generated by low-grade firms that account for less than 4% of the overall market capitalization of rated firms. The momentum payoff differential across credit rating groups is unexplained by firm size, firm age, analyst forecast dispersion, leverage, return volatility, and cash flow volatility.

Understanding Changes in Corporate Credit Spreads (with Doron Avramov and Gergana Jostova)

Financial Analysts Journal, 2007, 63(2), 90-105.

The findings in Collin-Dufresne, Goldstein, and Martin (2001) pose a serious challenge to the ability of structural models to explain corporate bond pricing: structural model variables explain only a small portion of credit spread changes and the unexplained portion has one dominant latent factor related to supply/demand shocks. Studying 2,375 corporate bonds over 1990 through 2003, we provide new evidence supporting the use of structural models in explaining credit spread changes. We show that the poor performance of structural models is mainly a facet of high-grade bonds. For this segment, the explanatory power of structural model variables is 35%, firm-level variables play no role, and a single latent factor dominates the unexplained variation. For low-grade bonds, however, the explanatory power of structural model variables rises to 67%, firm-level fundamentals are important, and no dominant latent factor is apparent. Building on recent innovations in asset pricing, we suggest a more comprehensive set of structural model variables. Our set captures the systematic variation in credit spread changes and subsumes the explanatory power of the Fama and French (1993) factors.

Multivariate Stochastic Volatility Via Wishart Processes (with Mark E. Glickman)

Journal of Business and Economic Statistics, 2006, 24(3), 313-328.

Financial models for asset and derivatives pricing, risk management, portfolio optimization, and asset allocation rely on volatility forecasts. Time-varying volatility models, such as GARCH and Stochastic Volatility (SVOL), have been successful in improving forecasts over constant volatility models. We develop a new multivariate SVOL framework for modeling financial data that assumes covariance matrices stochastically varying through a Wishart process. In our formulation, scalar variances naturally extend to covariance matrices rather than vectors of variances as in traditional SVOL models. Model fitting is performed using Markov chain Monte Carlo simulation from the posterior distribution. Due to the complexity of the model, an efficiently designed Gibbs sampler is described that produces inferences with a manageable amount of computation. Our approach is illustrated on a multivariate time series of monthly industry portfolio returns. In a test of the economic value of our model, minimum-variance portfolios based on our SVOL covariance forecasts outperform out-of-sample portfolios based on alternative covariance models such as Dynamic Conditional Correlations and factor-based covariances.

Factor Multivariate Stochastic Volatility Via Wishart Processes (with Mark E. Glickman)

Econometric Reviews, 2006, 25(2-3), 311-334.

This paper proposes a high dimensional factor multivariate stochastic volatility (MSV) model in which factor covariance matrices are driven by Wishart random processes. The framework allows for unrestricted specification of intertemporal sensitivities, which can capture the persistence in volatilities, kurtosis in returns, as well as correlation breakdowns and contagion effects in volatilities. The factor structure allows addressing high dimensional setups used in portfolio analysis and risk management, as well as modeling conditional means and conditional variances within the model framework. Due to the complexity of the model, we perform inference using Markov chain Monte Carlo simulation from the posterior distribution. A simulation study is carried out to demonstrate the efficiency of the estimation algorithm. We illustrate our model on a data set that includes 88 individual equity returns and the two Fama-French size and value factors. With this application, we demonstrate the ability of the model to address high dimensional applications suitable for asset allocation, risk management and asset pricing.

Bayesian Analysis of Stochastic Betas (with Gergana Jostova)

Journal of Financial and Quantitative Analysis, 2005, 40(4), 747-778.

This paper proposes a mean-reverting stochastic process for the market beta. In a simulation study, the proposed model generates significantly more precise beta estimates relative to competing GARCH betas, betas scaled by aggregate or firm-level variables, and betas based on rolling regressions, even when the true betas are generated based on these competing specifications. Applying our model to US industry portfolios, we document significant improvement in out-of-sample hedging effectiveness relative to the traditional OLS beta estimate. In asset-pricing tests, our model provides substantially stronger support for the conditional CAPM relative to competing beta models. It also helps resolve asset-pricing anomalies such as the size, book-to-market, and idiosyncratic volatility effects in the cross-section of stock returns.

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George Mason University School of Management

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