Deep Learning for Portfolio Optimization

Abstract

We adopt deep learning models to directly optimise the portfolio Sharpe ratio. The framework we present circumvents the requirements for forecasting expected returns and allows us to directly optimise portfolio weights by updating model parameters. Instead of selecting individual assets, we trade Exchange-Traded Funds (ETFs) of market indices to form a portfolio. Indices of different asset classes show robust correlations and trading them substantially reduces the spectrum of available assets to choose from. We compare our method with a wide range of algorithms with results showing that our model obtains the best performance over the testing period, from 2011 to the end of April 2020, including the financial instabilities of the first quarter of 2020. A sensitivity analysis is included to understand the relevance of input features and we further study the performance of our approach under different cost rates and different risk levels via volatility scaling.

arXiv comment12 pages, 6 figures

References in corpus (5)

• Adam: A Method for Stochastic Optimization
• Playing Atari with Deep Reinforcement Learning
• DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
• Financial Trading as a Game: A Deep Reinforcement Learning Approach
• Extending Deep Learning Models for Limit Order Books to Quantile Regression

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• A Universal End-to-End Approach to Portfolio Optimization via Deep Learning
• Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks
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