Can machines learn Chinese mutual funds?
Year: 2025
Type: article
Abstract: This study applies machine learning techniques to predict mutual fund performance and construct high-performing portfolios in the Chinese mutual fund market. Using Random Forest, XGBoost, and LightGBM models, we frame fund selection as a classification problem, estimating each fund's probability of outperforming the market benchmark. Filtered fund characteristics serve as predictors, allowing us to build investable portfolios ranked by predicted outperformance likelihood. Empirical results reveal that machine learning-based portfolios consistently outperform the benchmark, with LightGBM delivering the strongest performance across key metrics. Furthermore, a flexible machine learning combination framework is introduced to improve predictive robustness and portfolio stability. These findings highlight the practical value of machine learning for fund selection and strategy design in complex financial environments.
Source: Pacific-Basin Finance Journal
Authors: Haoran Wu, Zhiwei Gao, Boyang Nie, Binru Zhao
Institutions: University College Dublin, Bangor University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,090
OpenAlex ID: W4414229956