CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy

TL;DR

This paper introduces a novel multi-period portfolio management strategy combining clustering and deep reinforcement learning, demonstrating superior performance over traditional methods through extensive back-testing on real stock data.

Contribution

It is the first to integrate clustering with reinforcement learning for multi-period portfolio management, enhancing decision-making and returns.

Findings

Achieved an average return of 151% over 360 periods.

Outperformed traditional strategies like Robust Median Reversion.

Validated effectiveness across multiple datasets and metrics.

Abstract

In this paper, we present a novel trading strategy that integrates reinforcement learning methods with clustering techniques for portfolio management in multi-period trading. Specifically, we leverage the clustering method to categorize stocks into various clusters based on their financial indices. Subsequently, we utilize the algorithm Asynchronous Advantage Actor-Critic to determine the trading actions for stocks within each cluster. Finally, we employ the algorithm DDPG to generate the portfolio weight vector, which decides the amount of stocks to buy, sell, or hold according to the trading actions of different clusters. To the best of our knowledge, our approach is the first to combine clustering methods and reinforcement learning methods for portfolio management in the context of multi-period trading. Our proposed strategy is evaluated using a series of back-tests on four datasets, comprising a of 800 stocks, obtained from the Shanghai Stock Exchange and National Association of Securities Deal Automated Quotations sources. Our results demonstrate that our approach outperforms conventional portfolio management techniques, such as the Robust Median Reversion strategy, Passive Aggressive Median Reversion Strategy, and several machine learning methods, across various metrics. In our back-test experiments, our proposed strategy yields an average return of 151% over 360 trading periods with 800 stocks, compared to the highest return of 124% achieved by other techniques over identical trading periods and stocks.