Deep Tangency Portfolios

Abstract

We propose a parametric approach to directly estimate the tangency portfolio weights on high-dimensional individual assets by combining fundamental finance theory with deep learning techniques. The deep tangency portfolio combines the market factor and a deep long-short factor constructed using a large number of firm characteristics. We apply our approach to the corporate bond market. The deep factor acts as a market hedge and achieves a sizable market price of risk with an out-of-sample annualized Sharpe ratio of 1.79. The deep tangency portfolio outperforms those constructed from commonly used observable or latent factors with an out-of-sample annualized Sharpe ratio of 2.29. We also find evidence supporting the integration between the bond and equity markets.

Keywords: Tangency Portfolios, Deep Learning, Factor Models, Portfolio Optimization, Corporate Bonds.

JEL Classification: C45,G11,G12.