Fund performance evaluation with explainable artificial intelligence

Veera Raghava Reddy Kovvuri, Hsuan Fu, Xiuyi Fan and Monika Seisenberger

Finance Research Letters, 2023, vol. 58, issue PB

Abstract: We apply explainable artificial intelligence (xAI) to a large dataset of global equity funds. Our approach combines the XGBoost model with Shapley values; the former is a machine learning framework that enhances model fitness while the latter is an xAI method that provides informed explanations regarding the direction and significance of predictors. Based on macro-finance and fund-level factors, our fund performance evaluation of G10 countries uncovers novel insights into the diversification of country portfolios: both over- and under-diversification are associated with poor performance. Our analysis establishes consistency through a benchmark linear regression model and robustness at country level.

Keywords: Global Open-Ended Funds; Country portfolios; Herfindahl–Hirschman Index; SHapley Additive exPlanations; Machine learning; eXtreme Gradient Boosting (search for similar items in EconPapers)
JEL-codes: C52 C55 G11 G15 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:58:y:2023:i:pb:s1544612323007912

DOI: 10.1016/j.frl.2023.104419

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