Deep Reinforcement Learning for Asset Allocation in US Equities

This talk demonstrates the application of reinforcement learning to create a financial model-free solution to the asset allocation problem; learning to solve the problem using time series and deep neural networks. We use a deep reinforcement model on US stocks using different deep learning architectures. We use Long Short Term Memory networks; Convolutional Neural Networks; and Recurrent Neural Networks and compare them with more traditional portfolio management approaches like mean-variance; minimum variance; risk parity; and equally weighted. The Deep Reinforcement Learning approach shows better results than traditional approaches using a simple reward function and only being given the time series of stocks.