Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization

One-line Summary

This study compares Deep Reinforcement Learning (DRL) and Mean-Variance Optimization (MVO) for portfolio allocation, showing DRL's strong performance across various financial metrics.

Plain-language Overview

Investing wisely involves managing a collection of investments, or a portfolio, to meet financial goals. Traditionally, financial experts use methods like Mean-Variance Optimization (MVO) to balance risk and return. This study explores a modern approach using Deep Reinforcement Learning (DRL), a type of artificial intelligence, to optimize portfolios. The research compares DRL's performance with MVO and finds that DRL can achieve impressive results in terms of returns, risk management, and other financial measures.

Technical Details