AI & Big Data in Finance Research Forum (ABFR) Symposium

The Stanford AFTLab invites you to the AI & Big Data in Finance Research Forum (ABFR) symposium:

The webinar is on October 30, 6:45am-8am Pacific Time (9:45am-11am ET)

Presenters: Maxime Bonelli (London Business School), Jinfei Sheng (University of California, Irvine), Yiming Zhang (Nankai University) 

Distinguished Panelist: Ron Kaniel (University of Rochester) 

Zoom webinar link:https://cornell.zoom.us/j/98275911795?pwd=6SaHmsng1LGVImktY4DbyzxNHZgMYa.1&jst=2

Meeting ID: 982 7591 1795

Passcode: 255155

For more information, please visit our website: https://www.abfr-forum.org

To stay up to date please join our mailing list: https://groups.google.com/u/0/g/abfr-forum

Title: AI and Big Data in Investment Funds

Paper 1 

Title: Does Big Data Devalue Traditional Expertise? Evidence from Active Funds

Authors: Maxime Bonelli (London Business School), Thierry Foucault (HEC Paris)

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

Title: Generative AI and Asset Management

Authors: Jinfei Sheng (University of California, Irvine), Zheng Sun (University of California, Irvine), Baozhong Yang (Georgia State University), Alan Zhang (Iowa State University)

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 

Title: Do Mutual Funds Benefit from the Adoption of AI Technology?

Authors: Jian Yuan (Chinese University of Hong Kong), Yiming Zhang (Nankai University) 

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.

Bio of speaker (Maxime Bonelli): Maxime Bonelli is an Assistant Professor of Finance at the London Business School, which he joined in 2023. His research spans asset pricing, corporate finance, and financial intermediation, with a focus on how technology and data reshape investment decisions and markets. His work has been published or accepted in leading journals such as the Journal of Financial Economics and the Review of Financial Studies. Bonelli’s research has received multiple awards, including the AMF Best Young Researcher Prize (2024), the Best PhD Thesis in Corporate Finance Award from the French Finance Association, and the Geneva Institute for Wealth Management Research Award. He has presented at major international conferences such as the AFA, WFA, EFA, FIRS, and several NBER meetings, and his work has been recognized with best paper prizes from the EFA Doctoral Tutorial and the SUERF/UniCredit Foundation. He earned his Ph.D. in Finance from HEC Paris, where he also received the HEC Foundation Best PhD Thesis Prize, and previously held a Ph.D. in Mathematics from Inria and the University of Nice – Sophia Antipolis.

Bio of speaker (Jinfei Sheng): Jinfei Sheng is an Assistant Professor of Finance at the University of California, Irvine’s Paul Merage School of Business. His research spans empirical asset pricing, investments, FinTech, behavioral finance, and textual analysis, focusing on how technology, information, and human behavior shape financial markets. His work has been published in leading journals including the Journal of Finance, Review of Financial Studies, Journal of Financial Economics, Management Science, and Review of Asset Pricing Studies.Professor Sheng’s research has been recognized with numerous honors, including the Berkeley RDI AI & Decentralization Innovation Award, the XiYue Best Paper Award at the China International Conference in Finance, and the AMTD FinTech Centre Prize at the Asian Finance Association Conference. He is an Associate Editor of the Annual Review of FinTech, serves on multiple program committees, and has presented at major conferences such as the AFA, WFA, EFA, FIRS, and ABFER. He earned his Ph.D. in Finance from the Sauder School of Business, University of British Columbia, and holds a M.S. in Statistics from Texas A&M University and a B.A./M.A. in Economics from Nankai University.

Bio of speaker (Yiming Zhang): Yiming Zhang is an Assistant Professor of Finance at Nankai University. His research interests lie in mutual funds, empirical asset pricing, the Chinese economy, and applications of artificial intelligence in finance. His work examines how technology adoption and institutional structures influence fund performance and market efficiency. Yiming’s research has been presented at leading international conferences, including the NBER Big Data, Artificial Intelligence, and Financial Economics Conference, the ABFR Forum, the China International Conference in Finance (CICF), and the Financial Management Association (FMA) Annual Meeting. His paper “Compensation for Illiquidity in China” was published in the North American Journal of Economics and Finance. He has received several awards, including the Research Postgraduate Excellence Award and the RedBird Academic Excellence Award from HKUST, and is a recipient of the Hong Kong Ph.D. Fellowship. Yiming holds a Ph.D. in Finance from the Hong Kong University of Science and Technology, an M.Sc. in Finance and Economics from the London School of Economics and a B.S. in Finance from Tianjin University.

Bio of distinguished panelist (Ron Kaniel): Ron Kaniel is the Jay S. and Jeanne P. Benet Professor of Finance at the Simon Business School, University of Rochester, where he also serves as Chair of the Finance Area. He joined the Simon School in 2011 and has held previous appointments at Duke University’s Fuqua School of Business and the University of Texas at Austin. He is also a Senior Research Fellow at the Asian Bureau of Finance and Economic Research (ABFER), a Research Fellow at the Centre for Economic Policy Research (CEPR), and serves as Senior Finance Research Consultant at the Bank of Israel. Professor Kaniel’s research spans asset pricing, capital markets, portfolio delegation, and relative wealth considerations, with recent work exploring topics such as managerial incentives, mutual fund performance, and the impact of information frictions in financial markets. His research has appeared in leading journals including the American Economic Review, Journal of Finance, Review of Financial Studies, Journal of Financial Economics, Management Science, and Journal of Economic Theory. He has received numerous best paper awards from premier finance conferences, including the Utah Winter Finance Conference, China Financial Research Conference, and the Annual Conference on Asia-Pacific Markets, as well as the ERC Starting Grant for his project “Dynamic Delegation.” His work has been covered by major media outlets such as The Wall Street Journal, The New York Times, Bloomberg Markets, and Reuters. Kaniel is currently an Advisory Editor of the Journal of Financial Economics and serves on the editorial board of Finance Theory Insights. He previously co-edited the Journal of Financial Economics and has been actively involved with the Finance Theory Group as its former President. He frequently presents his work at leading conferences such as the American Finance Association, Western Finance Association, European Finance Association, and the NBER Summer Institute. He earned his Ph.D. in Finance from the Wharton School, University of Pennsylvania, and M.Sc. and B.Sc. degrees in Computer Science and Mathematics (both Summa Cum Laude) from the Hebrew University of Jerusalem.