AI based Mutual Fund Performance Analysis: Risk, Return and Risk Adjusted, Performance Persistence and Forecasting Ability

Mutual fund performance analysis is a crucial aspect of investment decision-making, helping investors assess risk-return trade-offs and forecast future performance. This study explores the role of Artificial Intelligence (AI) in evaluating mutual fund performance across multiple dimensions, including risk, return, risk-adjusted performance, persistence, and forecasting ability. By leveraging machine learning models and deep learning techniques, we analyze historical mutual fund data to identify patterns and predict future fund returns. The study compares AI-driven models with traditional statistical methods, demonstrating that AI offers superior accuracy in performance evaluation and risk assessment. Additionally, this paper investigates the persistence of mutual fund performance over time and the effectiveness of AI-based forecasting in financial markets. The findings provide valuable insights for investors, fund managers, and financial analysts, promoting AI-driven approaches for optimizing investment strategies.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.52783/eel.v15i1.2776
Primary Topic
Stock Market Forecasting Methods
Type
article
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article

AI based Mutual Fund Performance Analysis: Risk, Return and Risk Adjusted, Performance Persistence and Forecasting Ability

Stock Market Forecasting Methods
article

AI based Mutual Fund Performance Analysis: Risk, Return and Risk Adjusted, Performance Persistence and Forecasting Ability

article en

Abstract

Mutual fund performance analysis is a crucial aspect of investment decision-making, helping investors assess risk-return trade-offs and forecast future performance. This study explores the role of Artificial Intelligence (AI) in evaluating mutual fund performance across multiple dimensions, including risk, return, risk-adjusted performance, persistence, and forecasting ability. By leveraging machine learning models and deep learning techniques, we analyze historical mutual fund data to identify patterns and predict future fund returns. The study compares AI-driven models with traditional statistical methods, demonstrating that AI offers superior accuracy in performance evaluation and risk assessment. Additionally, this paper investigates the persistence of mutual fund performance over time and the effectiveness of AI-based forecasting in financial markets. The findings provide valuable insights for investors, fund managers, and financial analysts, promoting AI-driven approaches for optimizing investment strategies.

Government of Tamil Nadu (IN)
Openalex Percentile: Top 94%
Stock Market Forecasting Methods
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