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Events / Thalesians Series

About The Series

The IAQF's Thalesians Seminar Series is a joint effort on the part of the IAQF (www.iaqf.org) and the Thalesians (www.thalesians.com).  The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance.  This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion.

Call For Speakers

If you are interested in speaking at one of the upcoming seminars, please email info@iaqf.org

Past Seminars

About The Organizer

Harvey Stein is a senior VP in the Labs group at Two Sigma. From 1993 to 2022, Dr. Stein was at Bloomberg, where he served as the head of several departments including Quantitative Risk Analytics, Counterparty and Credit Risk, Interest Rates Derivatives, and Quantitative Finance R&D. Harvey is well known in the industry, having published and lectured on credit risk modeling, financial regulation, interest rate and FX modeling, CVA calculations, mortgage backed security valuation, COVID-19 data analysis, and other subjects.

Dr. Stein is on the board of directors of the IAQF, a board member of the Rutgers University Mathematical Finance program, an adjunct professor at Columbia University, and organizer of the IAQF/Thalesians financial seminar series. He's also worked as a quant researcher on the Bloomberg for President campaign.

Dr. Stein holds a Ph.D. in Mathematics from the University of California, Berkeley (1991) and a B.S. in Mathematics from Worcester Polytechnic Institute (1982).

 



Upcoming Seminars

    • 03 Oct 2023
    • 6:00 PM (EDT)
    • Fordham University: McNally Amphitheater 140 West 62nd Street New York, NY 10023
    Register


    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:

    Trading on decentralized exchanges has been one of the primary use cases for permissionless blockchains with daily trading volume exceeding billions of U.S. dollars. In the status quo, users broadcast transactions they wish to execute in the exchange and miners are responsible for composing a block of transactions and picking an execution ordering—the order in which transactions execute in the exchange. Due to the lack of a regulatory framework, it is common to observe miners exploiting their privileged position by front-running transactions and obtaining risk-free profits. Indeed, the Flashbots service institutionalizes this exploit, with miners auctioning the right to front-run transactions. In this work, we propose to modify the interaction between miners and users and initiate the study of verifiable sequencing rules. As in the status quo, miners can determine the content of a block; however, they commit to respecting a sequencing rule that constrains the execution ordering and is verifiable (there is a polynomial time algorithm that can verify if the execution ordering satisfies such constraints). Thus in the event a miner deviates from the sequencing rule, anyone can generate a proof of non-compliance. We ask if there are sequencing rules that limit price manipulation from miners in a two-token liquidity pool exchange. Our first result is an impossibility theorem: for any sequencing rule, there is an instance of user transactions where the miner can obtain non-zero risk-free profits. In light of this impossibility result, our main result is a verifiable sequencing rule that provides execution price guarantees for users. In particular, for any user transaction A, it ensures that either (1) the execution price of A is at least as good as if A was the only transaction in the block, or (2) the execution price of A is worse than this "standalone" price and the miner does not gain when including A in the block. Our framework does not require users to use countermeasures against predatory trading strategies, for example, set limit prices or split large transactions into smaller ones. This is likely to improve user experience relative to the status quo. Joint work with David C. Parkes. To appear at STOC 2023.

    Bio:

    Matheus Venturyne Xavier Ferreira is a Postdoctoral Fellow in Computer Science at Harvard John A. Paulson School of Engineering and Applied Sciences and starting in Summer 2024, he will be an Assistant Professor of Computer Science at the University of Virginia. He earned his Ph.D. (2022) and MA (2018) in Computer Science from Princeton University and his BS in Computer Engineering (2016) from the Federal University of Itajubá. His research interests include AI, Algorithmic Economics and Security. He applies artificial intelligence, optimization and theoretical computer science tools to create secure, transparent, and auditable platforms. For instance, he designs auctions that prevent auctioneers from profiting from manipulations.

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