Stock index direction forecasting using an explainable eXtreme Gradient Boosting and investor sentiments

Shangkun Deng, Xiaoru Huang, Yingke Zhu, Zhihao Su, Zhe Fu and Tatsuro Shimada

The North American Journal of Economics and Finance, 2023, vol. 64, issue C

Abstract: In this article, an explainable eXtreme Gradient Boosting (XGBoost) method is proposed for stock index prediction and trading simulation in the Chinese security market. Sentiment features of three types of investors, including institutional, individual, and foreign investors, are utilized as explanatory variables, and a binary classification model based on XGBoost is constructed to predict the direction of the Shanghai composite index and Shenzhen composite index movements. Additionally, the Gain function of XGBoost and SHapley Additive exPlanations (SHAP) are employed to estimate the importance of sentimental factors affecting index direction forecasting. Experimental results demonstrate that the XGBoost-based approach using multiple investor sentiments achieved the best forecasting accuracy, and sentiment features of the institutional investor were relatively more essential than the individual investor and foreign investor sentiments for index direction prediction in most out-of-sample periods. It demonstrates that the method proposed in this research can provide useful references for market participants to support their investment in the Chinese security market.

Keywords: Investor sentiment; Stock index; Direction prediction; Essential features; Explainable XGBoost (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:64:y:2023:i:c:s1062940822001838

DOI: 10.1016/j.najef.2022.101848

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