计量经济学
波动性(金融)
随机森林
库存(枪支)
股票市场
股票市场指数
计算机科学
回归
机器学习
人工智能
经济
统计
数学
工程类
地理
机械工程
背景(考古学)
考古
作者
Giovanni Campisi,Silvia Muzzioli,Bernard De Baets
标识
DOI:10.1016/j.ijforecast.2023.07.002
摘要
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.
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