Applications of Deep Learning and Machine Learning in Trading

Abstract

This review paper investigates applications of machine learning and deep learning in trading, with a particular emphasis on recent advances in deep learning. It provides an overview of algorithms, including support vector machines (SVMs), random forests, deep neural networks (DNNs), long short-term memory networks (LSTM networks), and deep reinforcement learning (DRL). Findings show that while machine learning and deep learning models were able to surpass traditional strategies in general in terms of profitability, they were also better at risk management. However, despite their performances showing superiority, their performances varied significantly under different market conditions, including markets during periods of high and low volatility. In particular, LSTM networks and random forests can generate substantial returns, higher Sharpe ratios, and lower drawdowns compared to the benchmarks, whereas DNNs struggled during highly volatile periods, as reflected in the returns in the periods. Moreover, the improved DRL agent TradeNet-CR can manage risk significantly better than another, despite not surpassing the original TradeNet-CR model.