2026/07/14 – In-Person and On-Line
Alternative data, traditional expertise and ownership
Seminar by Maxime Bonelli
The LQG is very grateful to Bloomberg our hosts of this seminar.
In this seminar Maxime Bonelli will discuss how the availability of alternative data affects the performance and investment strategies of active mutual funds. Using the staggered release of stock-specific alternative data (e.g., satellite imagery tracking retail parking lots), we find that coverage significantly reduces funds’ stock-picking ability in affected stocks. The decline is strongest among funds that rely on traditional forms of expertise, such as industry specialization and geographic proximity. In response, these funds reduce their holdings of covered stocks and reallocate capital toward uncovered peers. At the same time, quantitative hedge funds increase their ownership of covered firms, leading to changes in shareholder turnover and ownership structure. Overall, our findings suggest that alternative data can reshape the sources of investment skill, alter the composition of firms’ shareholder bases, and influence corporate outcomes.
The seminar will deliver both new results and refer to this paper: Does Big Data Devalue Traditional Expertise? Evidence from Active Funds
About the Speaker
Maxime Bonelli
Assistant Professor of Finance
London Business School
Maxime Bonelli Faculty Headshots LBS 15.5.24 Photo © Sheila Burnett
Maxime Bonelli is an Assistant Professor of Finance at London Business School. His research interests span investment and asset management, corporate finance, and household finance. His work studies how professional investors, firms, and individuals allocate capital and make investment decisions. A central theme of his research is how advances in Big Data and Artificial Intelligence are reshaping the organization of the financial sector, with implications for capital allocation, market efficiency, and real economic outcomes. Prior to joining London Business School, he earned a Ph.D. in Finance from HEC Paris (2023) and a Ph.D. in Mathematics from Inria (2016). Before pursuing his doctoral studies, he worked for more than four years as a quantitative researcher in the asset management industry.