Deep Tangency Portfolio
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We develop a deep learning framework to directly estimate tangency portfolio weights by augmenting benchmark factors with a long-short deep factor derived from high-dimensional characteristics. Guided by a Sharpe ratio maximization objective, the deep factor plays two key roles: (i) providing a natural hedge against benchmark risks, and (ii) capturing multi-market signals to enhance the performance of the benchmark. In an empirical application to U.S. corporate bonds using 132 firm-level bond, equity, and option characteristics, the deep factor achieves an out-of-sample Sharpe ratio exceeding 1, and the deep tangency portfolio benchmarked against the market factor yields a Sharpe ratio over 2, outperforming portfolios spanned by common observable or latent factors. These results demonstrate that bond risk signals are fundamentally dense, requiring the nonlinear integration of multi-market information to span the efficient frontier.
Recommended citation: Guanhao Feng, Liang Jiang, Junye Li, Yizhi Song, and Yuanzhi Wang (2015). "Deep Tangency Portfolio." Working Paper.
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