Deep Learning for Portfolio Optimization

Abstract

In this article, the authors adopt deep learning models to directly optimize the portfolio Sharpe ratio. The framework they present circumvents the requirements for forecasting expected returns and allows them to directly optimize portfolio weights by updating model parameters. Instead of selecting individual assets, they trade exchange-traded funds of market indexes to form a portfolio. Indexes of different asset classes show robust correlations, and trading them substantially reduces the spectrum of available assets from which to choose. The authors compare their method with a wide range of algorithms, with results showing that the model obtains the best performance over the testing period of 2011 to the end of April 2020, including the financial instabilities of the first quarter of 2020. A sensitivity analysis is included to clarify the relevance of input features, and the authors further study the performance of their approach under different cost rates and different risk levels via volatility scaling. TOPICS: Exchange-traded funds and applications, mutual fund performance, portfolio construction Key Findings • In this article, the authors utilize deep learning models to directly optimize the portfolio Sharpe ratio. They present a framework that bypasses traditional forecasting steps and allows portfolio weights to be optimized by updating model parameters. • The authors trade exchange-traded funds of market indexes to form a portfolio. Doing this substantially reduces the scope of possible assets to choose from, and these indexes have shown robust correlations. • The authors backtest their methods from 2011 to the end of April 2020, including the financial instabilities due to COVID-19. Their model delivers good performance under transaction costs, and a detailed study shows the rationality of their approach during the crisis.