Stock market prediction based on adaptive training algorithm in machine learning

Hongjoong Kim, Sookyung Jun and Kyoung-Sook Moon

Quantitative Finance, 2022, vol. 22, issue 6, 1133-1152

Abstract: This study deals with one of the most important issues for understanding financial markets, future asset fluctuations. Predicting the direction of asset fluctuations accurately is very difficult due to the uncertainty of the stock market, the influence of various economic indicators, and the sentiment of investors, etc. In this study, we present a new method to improve the effectiveness of machine learning by selecting appropriate training data using an adaptive method. The application to various sector data of the S&P 500 and many machine learning methods shows that the proposed adaptive data selection algorithm improves the prediction accuracy of the stock price direction. In addition, it can be seen that the adaptive data selection method increases the return on the asset investment.

Date: 2022
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DOI: 10.1080/14697688.2022.2041208

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