Oliver Giesecke (Stanford Hoover): Deep Learning for Corporate Bonds

Oliver Giesecke (Stanford Hoover): Deep Learning for Corporate Bonds

Authors: Jetlir Duraj, Oliver Giesecke

Abstract: We estimate an asset pricing model for the U.S. corporate bonds market using bond portfolios, as well as a large longitudinal dataset of individual bonds that we augment with fundamental characteristics of the issuer. We further enrich the information set with a large set of macroeconomic time series. We estimate diverse model architectures with two approaches: (1) minimizing the mispricing loss, and (2) maximizing the Sharpe ratio. We find that, contrary to the equivalence of these two approaches in the sense of financial theory, maximizing the Sharpe ratio performs better for individual bonds, whereas the difference is smaller for bond portfolios. The out-of-sample annual SDF portfolio Sharpe ratios are in the range of .59 to 1.00, and show statistically significant excess returns (alphas) relative to conventional risk factors. Our results are robust to the exclusion of financials and REITs.

Link to paper: here