ABFR Symposium: AI and Big Data in Investment Funds

Описание: AI and Big Data in Investment Funds

Presenters: Maxime Bonelli (London Business School), Jinfei Sheng (University of California, Irvine), Yiming Zhang (Nankai University)
Distinguished Keynote Speaker: Ron Kaniel (University of Rochester)
Host: Will Cong (Cornell University)
Oct 30, 2025 9:45-11am Eastern Time

00:00:00​​​ Welcome remarks
00:03:26 Presentation 1
00:25:50 Presentation 2
00:46:22 Presentation 3
00:29:40 Discussion

     Paper 1: Does Big Data Devalue Traditional Expertise? Evidence from Active Funds - Abstract:
We investigate how the availability of alternative data affects the performance of active mutual funds that rely on traditional expertise to produce information. To do so, we evaluate the impact of the release of stock-specific data, which provide new information but require data science expertise to leverage. We find that this release significantly reduces mutual funds’ stock-picking abilities in covered stocks, with a stronger effect for funds that rely on traditional expertise, like industry specialization, leading them to divest from covered stocks. Alternative data can therefore reshape the determinants of fund performance by reducing the value of traditional information sources.

     Paper 2: Generative AI and Asset Management - Abstract:
Using a novel measure of investment companies’ reliance on generative AI, we document a sharp increase in generative AI usage by hedge funds after ChatGPT’s 2022 launch. A difference-in-differences test shows that hedge funds adopting generative AI earn 2-4% higher annualized abnormal returns than non-adopters, while non-hedge funds do not benefit. The outperformance originates from funds’ AI talent and ChatGPT’s strength in analyzing firm- specific information. GenAI usage by hedge funds improves price efficiency, while initially exacerbating information asymmetry. A survey of fund managers’ GenAI usage provides direct validation of our measure and offers additional new insights.

     Paper 3: Do Mutual Funds Benefit from the Adaption of AI Technology? - Abstract:
This paper examines the impact of AI technology adoption in the mutual fund industry by developing a new measure of AI adoption based on hiring practices. We find that this measure can predict fund performance. Funds with a high AI ratio outperform non-AI funds, after controlling for relevant variables. Further empirical evidence indicates that this outperformance is driven by improved stock picking skill rather than market timing skill. Mutual funds that adopt AI technology tend to tilt their portfolios toward stocks with voluminous information, and these stocks contribute to their superior performance. These findings suggest that AI is good at processing large amounts of data and providing a more comprehensive analysis of stocks.
     
ABFR:
ABFR is an interdisciplinary community of scholars with an interest in the methodology, applications, and socioeconomic implications of AI and big data for a wide range of areas in economics and finance. The forum organizes monthly presentations and discussions of papers by the leading world experts in the area, followed by an informal general post-seminar discussion. For more information about the seminar and symposium series, including registration, please visit our website:
https://www.abfr-forum.org​​

ABFR is organized by Pietro Bini, Svetlana Bryzgalova, Lin William Cong, Maryam Farboodi, Serhiy Kozak, Markus Pelger, and Léa Stern. It is also supported by The Advisory Committee, which includes Kay Giesecke, Gerald Hoberg, Wei Jiang, Bryan Kelly, Stefan Nagel, Andrew Patton, and Laura Veldkamp. ABFR hosting institutions are the Cornell FinTech Initiative and the Stanford AFTLab."