Deep Reinforcement Learning Approach to Portfolio Optimization in the Australian Stock Market

The future of portfolio management is evolving from relying on human expertise to incorporating artificial intelligence techniques. Traditional techniques such as fundamental and technical analysis will eventually be replaced by more sophisticated deep reinforcement learning (DRL) algorithms. However, it is still a long way from designing a profitable strategy in the complex and dynamic stock market. While previous studies have focused on the American stock market, this paper applies two DRL algorithms, the proximal policy optimization (PPO) and the advantage actor–critic (A2C), to trade the constituent stocks of the Australian Securities Exchange 50 (ASX50) Index. This paper also incorporates a weighted moving average into the action space and introduces a transaction threshold to help agents minimize trivial trades that lead to high transaction costs. The results are presented and benchmarked against the ASX50 Index. The A2C agent was better at following trends and had the higher upside potential but can suffer from more severe damage during bearish markets. On the other hand, the PPO agent had the lowest annual volatility and the highest maximum drawdown, which is more helpful in a bearish or volatile market.