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Events / Upcoming

  • IAQF & Thalesians Seminar Series: Prediction Market Accuracy: Crowd Wisdom or Informed Minority? A Seminar by Theis Ingerslev Jensen.

    • 08 Sep 2026
    • 6:00 PM
    • Fordham University McNally Amphitheater 140 West 62nd Street New York, NY 10023
    Register

    Prediction Market Accuracy:

    Crowd Wisdom or Informed Minority?

    A Seminar by Theis Ingerslev Jensen

    Tuesday September 8th, 2026

    6:00 PM Seminar Begins

    7:30 PM Reception


    Hybrid Event

    Fordham University

    McNally Amphitheater

    140 West 62nd Street

    New York, NY 10023


    Free Registration!


    For Virtual Attendees: Please select virtual instead of member type upon registration.

    Abstract:

    Prediction markets produce remarkably accurate forecasts, but the source of this accuracy is poorly understood. Two explanations dominate: crowd wisdom and insider trading. Using the universe of Polymarket transactions, we show it is neither. Instead, accuracy comes from a minority of persistently skilled traders, around 3% of accounts. Unlike insiders, whose private edge is localized, these traders exhibit depth and breadth. They react to public news when it arrives, eliminate law-of-one-price violations, and trade against the crowd’s behavioral mistakes. The crowd, in turn, generates most of the volume but little of the information, and its losses fund the minority’s profits.


    Bio:

    Theis Ingerslev Jensen is an Assistant Professor of Finance at the Yale School of Management. Prior to joining Yale, he obtained his Ph.D. in financial economics from Copenhagen Business School. He conducts research in empirical asset pricing. His projects are often data-intensive, and he is especially interested in studying expectations. Much of Theis Ingerslev Jensen's recent work focuses on prediction markets. 

    Abstract:

    We derive the optimal long-term growth rate for an agent investing in a market composed of a numéraire asset, a risky asset subject to transaction costs, and a liquidity pool within an Automated Market Maker (AMM). We first establish the necessary conditions to ensure a no-arbitrage environment within this market structure. Under these conditions, we determine the asymptotically optimal trading strategy for liquidity providers. Finally, we provide economic intuition for the strategy’s sensitivity to various market parameters, supported by numerical illustrations of our theoretical results.


    Bio:

    Maxim Bichuch holds a M.S. from NYU and a Ph.D. from Carnegie Mellon University both in Financial Mathematics. He was a Postdoctoral Research Associate & Lecturer in the ORFE department in Princeton, and an Assistant Professor at Worcester Polytechnic Institute and Johns Hopkins University, before joining the department of Mathematics at The University at Buffalo. Prior to obtaining his Ph.D. He has also gained corporate experience working for Citigroup and Bear Stearns. His research interests include optimal investment, optimal control, stochastic volatility, credit, funding and counterparty risks, and most recently electricity markets, machine learning and AI, decentralized finance and fintech.

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  • How I Became a Quant - Boston

    • 16 Sep 2026
    • 5:00 PM
    • Boston University Questrom School of Business 595 Commonwealth Ave Boston, MA 02215
    Register

    How I Became a Quant: Boston

    This annual event is a Panel Discussion on Careers in Quantitative Finance, as industry veterans will discuss their experiences, the current economic climate, and career paths in finance and data science.

    Wednesday September 16, 2026

    5:00 PM Program Begins: Panel Discussion

    6:30 PM Reception


    Location:

    Boston University Questrom School of Business

    595 Commonwealth Ave

    Boston, MA 02215


    Moderator: 

    Dan diBartolomeo, President and founder of Northfield Information Services, Inc


    Panelists:

    Bin Shi, Senior Vice President and Portfolio Manager at Acadian Asset Management

    Carlos Morales, Mass Mutual

    Oriol Ripalta I Maso, LSEG, Quantitative Analyst

    More Panelists TBA!


    Panelist Biographies

    Moderator - Dan diBartolomeo is President and founder of Northfield Information Services, Inc. Based in Boston since 1986, Northfield develops quantitative models of financial markets. He sits on boards of numerous industry organizations include IAQF and CQA. He is a director and past president of the Boston Economic Club. His publication record includes more than sixty books, book chapters, and research journal articles. In addition, Dan spent several years as a Visiting Professor at Brunel University and has been admitted as an expert witness in litigation matters regarding investment management and derivatives in both US federal and state courts. He became editor in chief of the Journal of Asset Management at the start of 2019. 

    Bin Shi, PhD, CFA, is a Senior Vice President and Portfolio Manager at Acadian Asset Management, specializing in systematic equity research and portfolio management across developed and emerging markets. With more than 20 years of experience, he combines quantitative analysis, machine learning, and artificial intelligence to develop data-driven investment strategies and research processes.

    Before joining Acadian in 2006, Bin worked at MathWorks, where he contributed to the development of machine learning algorithms for MATLAB’s financial and statistical toolboxes. He holds a PhD in Industrial and Systems Engineering from the Georgia Institute of Technology and a bachelor’s degree from Southeast University in China. Bin also teaches at Boston University’s Questrom School of Business.

    Oriol Ripalta graduated with an MSc in Mathematical Finance from Boston University and holds a BSc in Computer Science and Business Administration. He has previously worked as a software engineer in a couple of roles, using a range of programming languages. He is currently a Quantitative Analyst at LSEG, where I work on the development of ORE.


    Sponsored By:


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