Active Machine Learning Based Trading and Mutual Fund Performance
U.S. equity mutual funds are increasingly using machine learning to improve investment results, tracked by a new measure called Active Machine Learning Based Trading (AMLT). Funds in the top AMLT decile outperform those in the bottom by 2.4% to 3.0% per year, mainly due to better stock selection and lower costs. Combining machine learning with human expertise leads to consistent success. The study’s deep neural network analysis is thorough, but more research on risks is needed.
What it examines
This paper studies how U.S. equity mutual funds use machine learning (ML) to improve investment performance. The authors create a new measure, AMLT, to track how funds align their portfolios with ML trading signals, aiming to understand ML adoption, its impact, and the drivers of fund success.
What it concludes
The study finds that mutual funds using ML strategies outperform others, mainly due to better stock selection and cost management. These results show ML can boost investment returns. Applications include fund manager evaluation, trading strategy design, and AI talent assessment. Future research may explore ML’s role in other financial areas.
Evidence objects
U.S. equity mutual funds are increasingly leveraging machine learning, with a new measureActive Machine Learning Based Trading (AMLT)revealing that top AMLT funds outperform bottom decile peers by 2.4%--3.0% annually, risk-adjusted.
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The study credits superior stock selection, lower expenses, and smart trading cost management for these gains, and finds that blending machine learning with human expertise delivers consistent outperformance across various market conditions and investment styles.
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AMLT offers a precise way to track machine learning adoption, surpassing older methods based on fund names or staff skills, but the research notes limited exploration of ML risks and a small sample of self-identified AI funds.
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This paper introduces the novel AMLT measure, quantifying mutual funds alignment with forward-looking machine learning signals using both numerical and textual data. Its comprehensive, holdings-based approach uniquely distinguishes ML adoption in investment strategy. Large-scale empirical analysis and performance decomposition make it a compelling, original, and significant contribution to investment management literature.
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Raw abstract and provenance
Fund Performance, Managerial Skill, Machine Learning, Artificial Intelligence, Active Share Related eJournals. Mutual Funds, Hedge Funds & Investment Industry
Source row: 106 · abstract type: snippet