Christian Julliard, London School of Economics and Political Science
Philippe Mueller, University of Warwick
Abstract: Analyzing 563 trillion possible models, we find that the majority of tradable factors designed to price bond markets are unlikely sources of priced risk, and only one novel tradable bond factor, capturing the bond post-earnings announcement drift, should be included in the stochastic discount factor (SDF) with very high probability. Nevertheless, the SDF is dense in the space of observable factors, with both nontradable and equity-based ones being salient for pricing corporate bonds. A Bayesian model averaging–SDF explains corporate risk premia better than all existing models, both in- and out-of-sample, and captures business cycle and market crash risks.
Abstract: We demonstrate that the literature on corporate bond factors suffers from replication failures, inconsistent methodological choices, and the lack of a common error-free dataset. Going beyond identifying this replication crisis, we create a clean database of corporate bond returns where outliers are analyzed individually and propose a robust factor construction. Using this framework, we show that most, but not all, factors fail to replicate. Further, while traditional factors are constructed from individual bonds, we create representative firm-level bonds, showing which bond signals work at the firm-level. Lastly, we show that a number of equity signals work for corporate bonds. In summary, most factors fail, but so does the CAPM for corporate bonds.
Discussant: Jaewon Choi, University of Illinois-Urbana-Champaign
Christopher Jones, University of Southern California
Mehdi Khorram, Louisiana State University
Haitao Mo, University of Kansas
Junbo Wang, Louisiana State University
Abstract: Numerous trading strategies examined in options research exhibit remarkably high mean returns and Sharpe ratios. We show some of these seemingly ``good deals'' are due to look-ahead biases. These biases stem from using information unavailable at the portfolio formation time to filter out observations suspected of being noisy or erroneous. Our results suggest that elevated Sharpe ratios may serve as potential indicators of such look-ahead biases. Furthermore, deviating from previous literature findings, we show that illiquidity is not strongly priced in stock options and that only a small set of stock characteristics are in fact associated with option expected returns.
Discussant: Aurelio Vasquez, Instituto Tecnológico Autónomo de México
Abstract: I develop simple and intuitive bounds for the false discovery rate (FDR) in cross-sectional return predictability publications. The bounds can be calculated by plugging in summary statistics from previous papers and reliably bound the FDR in simulations that closely mimic cross-predictor correlations. Most bounds find that at least 75% of findings are true. The tightest bound finds at least 91% of findings are true. Surprisingly, the estimates in Harvey, Liu, and Zhu (2016) imply a similar FDR. I explain how Harvey et al.'s conclusion that most findings are false stems from equating "false" and "insignificant."
