A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices
研究利用波动率指数预测美国股市方向，比较多种机器学习方法，发现随机森林和装袋法优于传统线性回归，在S&P 500回报方向预测上表现最佳。
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.