Selecting Portfolios Directly Using Recurrent Reinforcement Learning (Student Abstract)

Abstract

Portfolio selection has attracted increasing attention in machine learning and AI communities recently. Existing portfolio selection using recurrent reinforcement learning (RRL) heavily relies on single asset trading system to heuristically obtain the portfolio weights. In this paper, we propose a novel method, the direct portfolio selection using recurrent reinforcement learning (DPS-RRL), to select portfolios directly. Instead of trading single asset one by one to obtain portfolio weights, our method learns to quantify the asset allocation weight directly via optimizing the Sharpe ratio of financial portfolios. We empirically demonstrate the effectiveness of our method, which is able to outperform state-of-the-art portfolio selection methods.