Research
Published Papers
joint with Philippe Mueller (Warwick Business School) and Cesare Robotti (Warwick Business School), Journal of Financial Economics, Volume 150, Issue 2, November 2023 (Selected as the Editor's Choice for the November 2023 volume of the Journal of Financial Economics)
Abstract:
Recent studies document strong empirical support for factor models that aim at explaining the cross-sectional variation in corporate bond returns. We revisit these results and provide evidence that common factor pricing in corporate bonds is exceedingly difficult to establish. Based on returns in excess of the one-month Treasury bill rate, we demonstrate that previously proposed corporate bond risk factors do not provide any incremental pricing information to the corporate bond market factor. In addition, when considering duration-adjusted corporate bond returns, the market factors in the equity and bond CAPM display nontrivial pricing ability for several cross-sections of corporate bond returns. Finally, pricing industry sorted corporate bond portfolios appears to be quite demanding for all of the considered factor models. Our results challenge the status quo with respect to priced risk in the cross-section of corporate bond returns.
joint with Daniele Bianchi (Queen Mary) and Mykola Babiak (Lancaster), Journal of Banking & Finance, Volume 142, May 2022
Abstract:
We provide empirical evidence within the context of cryptocurrency markets that the returns from liquidity provision, proxied by the returns of a short-term reversal strategy, are primarily concentrated in trading pairs with lower levels of market activity. Empirically, we focus on a moderately large cross section of cryptocurrency pairs traded against the U.S. Dollar from March 1, 2017 to March 1, 2022 on multiple centralised exchanges. Our findings suggest that expected returns from liquidity provision are amplified in smaller, more volatile, and less liquid cryptocurrency pairs where fear of adverse selection might be higher. A panel regression analysis confirms that the interaction between lagged returns and trading volume contains significant predictive information for the dynamics of cryptocurrency returns. This is consistent with theories that highlight the role of inventory risk and adverse selection for liquidity provision.
Working Papers
The Low Frequency Trading Arms Race: Machines Versus Delays
Abstract
We propose a novel framework to compute transaction costs of trading strategies using infrequently traded assets. The method explicitly accounts for the trade-off between bid-ask spreads and execution delays. The benefit of waiting for a better trading opportunity with lower bid-ask spreads is partly offset by the opportunity cost of delayed or missed execution. Applying this method to corporate bonds that trade infrequently, we show that even the latest machine-learning-based trading strategies earn zero or negative bond CAPM alphas after transaction costs. Consequently, our results raise doubts about the realistic outperformance capabilities of active bond trading strategies relative to the bond market factor.
Data and Code
Back-testing software, data and code coming soon.
joint with Christian Julliard (LSE and CEPR) and Philippe Mueller (Warwick Business School)
Abstract
Analyzing over 562 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 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 factors being salient for pricing corporate bonds, and 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.
Data and Code
See The Bond Factor Zoo.
joint with Cesare Robotti (Warwick Business School) and Giulio Rossetti (Warwick Business School)
Abstract
We argue that the documented large abnormal returns to investors from corporate bond anomalies such as return reversals and momentum mainly stem from ignoring market microstructure noise in transaction-based bond prices and relying on ad hoc return winsorization. To address these issues, we construct bond data that is largely free of microstructure noise and closely mimics industry-grade quote data. We revisit prior findings in the literature and provide conclusive evidence that return-based anomalies, once properly constructed, generate negligible average returns and alphas. Finally, we show that the considered return-based factors (and their underlying signals) are not related to average bond returns.
Data and Code
See the Data downloads section on the companion website here.
A Credit Risk Explanation of the Correlation between Corporate Bonds and Stocks
joint with Mathieu Fournier (UNSW Business School), Alexandre Jeanneret (UNSW Business School) and Philippe Mueller (Warwick Business School)
Abstract
We develop a model to study the correlation between corporate bonds and stocks. Firms are exposed to a common asset factor with stochastic volatility, and interest rate fluctuations. The model predicts opposite variance and interest rate exposures of stocks and bonds, breaking the perfect comovement implied by mainstream theories. Quantitatively, asset and interest rate risks drive the tight link between stock-bond correlation and default risk. The model also suggests that the Sharpe ratio of portfolios combining stocks and bonds increases with firms' creditworthiness. We provide empirical support for the model predictions and show that they have important implications for asset allocation.
Work in Progress
A Unified Model for the Cross-section of Stock and Corporate Bond Returns
joint with Jan Ericsson (McGill), Mathieu Fournier (HEC Montreal) and Piotr Orlowski (HEC Montreal)
We develop a structural model of the firm and derive predictions about the factor structure of stocks and corporate bonds and their exposures to systematic risks. The model predicts that equity and debt are positively exposed to aggregate asset risk but differ in their exposures to aggregate variance and interest rates. It also illustrates the highly nonlinear pattern in exposures. We analyze the factor structure in the two markets using a regression version of the model with nonparametric exposures which account for nonlinearities. In the joint cross-section, the regression-model outperforms its benchmarks and helps reconcile the two markets’ return dynamics.
Online version to be made available soon.
A Cross-dissection of Equity Option Returns
joint with Mathieu Fournier (UNSW Business School), Kris Jacobs (University of Houston) and Piotr Orlowski (HEC Montreal)
Online version to be made available soon.
Decomposing Bond Value
Online version to be made available soon.