Artificial Intelligence and Machine Learning in Fund Performance Evaluation — A Review

Xize Guo, Xiaolu Hu and On Kit Tam

Chapter 7 in FinTech Research and Applications:Challenges and Opportunities, 2023, pp 265-304 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Financial technology (Fintech) has become an emerging and powerful tool that contributes to the advancement of finance research. With the recent development of Fintech, machine-learning (ML) techniques have been continuously deployed for developing more effective models in financial research. This chapter aims to provide in-depth details of ML implementation in fund performance evaluation and prediction. Building on the theoretical basics of ML, we first introduce several widely applied ML algorithms, including linear regression, least absolute shrinkage and selection operator (LASSO), ridge, K nearest neighbors (KNN), decision trees (DT), random forest (RF), support vector machine (SVM), deep learning (DL), and artificial neural networks (ANN). We then focus on each method’s applicable conditions and how it contributes to forecasting and evaluating fund performance. The advantages of using ML methods over traditional methods in evaluating fund performance are also discussed.

Keywords: FinTech; FinTech Regulation; Artificial Intelligence; Machine Learning; Cryptocurrencies; Smart Contracts; Financial Fraud Detection; FinTech in Financial Services (search for similar items in EconPapers)
JEL-codes: G2 O3 O33 (search for similar items in EconPapers)
Date: 2023
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