An Intelligent Financial Investment Decision Model Based on Multi-Agent Reinforcement Learning

Abstract

Financial investigation decision is a complex affair that highly relies on expert knowledge. In the era of big data, it remains practical to make intelligent financial investigation decisions via computing technology. As a consequence, this work introduces reinforcement learning to search for effective decision schemes that are adaptive to the diverse financial environment. Therefore, an intelligent financial investment decision model based on multi-agent reinforcement learning is proposed in this paper. First, the long short-term memory (LSTM) model is utilized to strengthen financial investment data. Then, the hidden Markov model (HMM) is utilized to make a preliminary judgment on financial investment strategy. Next, a multi-agent deep deterministic policy gradient (MADDPG) is employed to set the goal of agent reinforcement and determine decision indicators based on the financial market environment. Multiple agents (investors) form a cooperative strategy through learning and interaction to achieve the optimal investment decision in the financial market. The experimental results on realistic data indicate that the proposal can play an important role in investment decision-making.