RESEARCH ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN FUND MANAGER IDENTIFICATION

Yuping Song, Zhenwei Li, Jing Han and Xiaochen Wang
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Yuping Song: School of Finance and Business, Shanghai Normal University, Shanghai, P. R. China
Zhenwei Li: School of Finance and Business, Shanghai Normal University, Shanghai, P. R. China
Jing Han: ��School of Finance and Management, Shanghai University of International Business and Economics, Shanghai, P. R. China
Xiaochen Wang: ��College of Mathematics and Sciences, Shanghai Normal University, Shanghai, P. R. China

The Singapore Economic Review (SER), 2025, vol. 70, issue 04, 973-1007

Abstract: For the selection of fund managers in fund investment, traditional measurement methods were mainly based on descriptive analysis and regression modeling for a small sample of numerical data. They did not make full use of the relevant big data information of fund managers and ignored the nonlinearity between data. As a result, the prediction error was large. In this paper, we use the heterogeneous data of fund managers, such as numerical data and textual data, to fully explore the characteristic factors of fund performance, and further employ the artificial intelligence algorithms including the traditional machine learning models and deep learning model to consider the nonlinear characteristics between the data to identify the style of fund managers. It is found that there is a nonlinear characteristic between the characteristic factor and the performance of the fund. The artificial intelligence algorithm, especially, the deep learning method has a better performance in the classification and prediction of fund managers, especially, after adding text information. The research in this paper has enriched the application of intelligent investment advisor in fund investment to a certain extent, and provided a scientific basis for investors’ choice.

Keywords: Artificial intelligence; fund investment; nonlinear characteristics; text message (search for similar items in EconPapers)
JEL-codes: C88 G11 G17 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0217590820480033

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