This paper investigates the role of credit risk in the relationship between short-selling activity and future stock returns. We find that the predictive power of short interest for future returns is concentrated in the worst-rated stocks. Low-grade stocks with the largest short interest decrease outperform those with the largest short interest increase by 1.09 percent in the following month. This return spread is robust to controls for cross-sectional effects and firm characteristics, and is much more pronounced during periods of high investor sentiment and low liquidity. Distressed firms with large short interest increases experience a worse performance subsequently.

This paper investigates the roles of illiquidity and credit risk in determining the relations between price volatility of a bond and its trading frequency and trade size based on a large transaction dataset from October 2004 to June 2012. We find a positive relation between volatility and trading frequency and a negative relation between volatility and trade size. Consistent with the prediction of the search-based theory, the relations are much stronger for illiquid and risky bonds. Furthermore, both liquidity and credit risk become more important in times of stress and their effects are reinforcing. Results strongly suggest that search frictions and credit risk are important factors driving the relation between volatility and trading activity in the corporate bond market.

This paper extends the macroeconomic frailty model to include sectoral frailty factors that capture default correlations among firms in a similar business. We estimate sectoral and macroeconomic frailty factors and their effects on default intensity using the data for Japanese firms from 1992 to 2010. We find strong evidence for the presence of sectoral frailty factors even after accounting for the effects of observable covariates and macroeconomic frailty on default intensity. The model with sectoral frailties performs better than that without. Results show that accounting for the sources of unobserved sectoral default risk covariations improves the accuracy of default probability estimation.

The structural approach offers an integrated framework to deal with yield spreads and default probability simultaneously. However, structural models perform poorly in predicting corporate bond spreads. It is unclear whether this poor performance is caused by characteristics of individual models, missing factors, or different calibration procedures. This study evaluates the performance of four structural models by incorporating two important factors, personal taxes and the liquidity factor, and calibrating these models to data. To ensure our results are not contingent on the calibration method, we further apply the maximum likelihood estimation method to a large sample of individual bonds. Results consistently show that the ability of structural models to predict spreads improves considerably when personal taxes and liquidity are taken into account. Our findings suggest that the poor performance of standard structural models is more likely due to missing factors than the characteristics of individual models or the calibration procedure.

Existing term structure models of defaultable bonds have often underestimated corporate bond spreads. A potential problem is that investors’ taxes are ignored in these models. We propose a pricing model that accounts for stochastic default probability and differential tax treatments for discount and premium bonds. By estimating parameters directly from bond data, we obtain significantly positive estimates for the income tax rate of a marginal corporate bond investor after 1986. This contrasts sharply with the previous finding that the implied tax rates for Treasury bonds are close to zero. Results show that taxes explain a substantial portion of corporate bond spreads.

This paper examines volume and volatility dynamics by accounting for market activity measured by the time duration between two consecutive transactions. A time-consistent vector autoregressive (VAR) model is employed to test the dynamic relationship between return volatility and trades using intraday irregularly spaced transaction data. The model is used to identify the informed and uninformed components of return volatility and to estimate the speed of price adjustment to new information. It is found that volatility and volume are persistent and highly correlated with past volatility and volume. The time duration between trades has a negative effect on the volatility response to trades and correlation between trades. Consistent with microstructure theory, shorter time duration between trades implies higher probability of news arrival and higher volatility. Furthermore, bid–ask spreads are serially dependent and strongly affected by the informed trading and inventory costs.

Cheng F. Lee, Chunchi Wu, K. C. John Wei, The Heterogeneous Investment Horizon and the Capital Asset Pricing Model: Theory and Implications, The Journal of Financial and Quantitative Analysis, Vol. 25, No. 3 (Sep., 1990), pp. 361-376

Using machine learning and many predictors, we find strong bond return predictability, with an out-of-sample R-squared of 4.48% and an annualized Sharpe ratio of 3.27. ML models identify important predictors for aggregate predictors (bond market returns, TERM and HML factors, GDP growth) and bond characteristics (downside risk, short-term reversal, return skewness, and credit spreads). Predictability varies over time, being stronger during periods of high investor risk aversion, slow economic growth, and strong cross-sectional factor explanatory power. Our results highlight the benefits of leveraging both cross-sectional and time-series predictors to forecast corporate bond returns while considering public and private bonds.