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Portfolio Management Systems in Predicting the Performance of Mutual Funds Using Machine Learning
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Author(s): Hynul Jenofer P. (Bharath Institute of Higher Education and Research, India)and T. S. Aarathy (Bharath Institute of Higher Education and Research, India)
Copyright: 2025
Pages: 20
Source title:
Multidisciplinary Approaches to AI, Data, and Innovation for a Smarter World
Source Author(s)/Editor(s): Sonia Singh (Toss Global Management, UK), Slim Hadoussa (Brest Business School, France), Thangaraja Arumugam (Vellore Institute of Technology, Chennai, India)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3693-9375-8.ch009
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Abstract
Portfolio management systems (PMS) in mutual funds are a vital process that involve selecting, allocating, and monitoring investments within the mutual fund. They balance risk and returns consistent with the fund's objectives while ensuring it functions effectively and meets regulatory requirements. Proper PM ensures that mutual funds provide the best returns to their clients while managing risks effectively. Artificial intelligence (AI) in mutual fund management information systems (MFMIS) refers to integrating artificial intelligence technology into MFMIS. These developments help to automate tasks, optimize portfolio administration, increase risk analysis, and boost customer service. The use of machine learning (ML) to forecast mutual fund (MF) performance is becoming more essential in portfolio management systems. These systems can handle massive amounts of data, use sophisticated algorithms, and deliver data-driven insights to fund managers and investors, allowing them to make better-educated decisions.
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