A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices

This paper investigates the information content of volatility indices for the purpose of predicting the future direction of the stock market. To this end, different machine learning methods are applied. The dataset used consists of stock index returns and volatility indices of the US stock market from January 2011 until July 2022. The predictive performance of the resulting models is evaluated on the basis of three evaluation metrics: accuracy, the area under the ROC curve, and the F-measure. The results indicate that machine learning models outperform the classical least squares linear regression model in predicting the direction of S&P 500 returns. Among the models examined, random forests and bagging attain the highest predictive performance based on all the evaluation metrics adopted.