Review: Application of Machine Learning to Investment Portfolios

Abstract

This review focuses on the use of three machine learning methods in portfolios using reinforcement learning, recurrent neural networks and random forests. Machine learning algorithms create a model from training or sample data, train themselves by continuously learning from the data, and then adjust their actions based on the insights found to gain the ability to make better predictions and decisions. As today's big data continues to expand and grow, the market demand for machine learning is expected to increase significantly. This is because companies can use it to understand trends in customer behavior and business operation patterns to support the development of new products. Thus, the use of machine learning methods to build an optimal portfolio can help investors minimize risk and maximize returns. Asset management firms enable investors to invest in the best investment opportunities by developing investment plans based on specific client requirements and return expectations.