Enhancing Portfolio Performance: A Random Forest Approach to Volatility Prediction and Optimization

Machine learning has diverse applications in various domains, including disease diagnosis in healthcare, user behavior analysis, and algorithmic trading. However, machine learning’s use in portfolio volatility predictions and optimization has only been recently explored and requires further investigation to prove valuable in real-world settings. We thus propose an effective method that accomplishes both these tasks and is targeted at people who are new to the realm of finance. This paper explores (a) a novel approach of using supervised machine learning with the Random Forest algorithm to predict portfolio volatility value and categorization and (b) a flexible method taking into account users’ restrictions on stock allocations to build an optimized and customized portfolio. Our framework also allows a diversified number of assets to be included in the portfolio. We train our model using historical asset prices collected over 8 years for six mutual funds and one cryptocurrency. We validate our results by comparing the volatility predictions against recent asset prices obtained from Yahoo Finance. The research underlines the importance of harnessing the power of machine learning to improve portfolio performance.