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:

Join the IAQF for virtual presentations and conversations with the winners of the IAQF Academic Paper Competition

Join the IAQF for virtual presentations and conversations with the winners of the IAQF Academic Case Competition

Thursday, October 19th

5:00pm - 6:15pm

Zoom Event

Registration is Complimentary for All

Moderator:

Richard Lindsey, IAQF Board Chair

Schedule of Events:

Introductions & Presentation of Problem

Questions to Each of the Team 

Following an overview of the problem that students were asked to work on, the moderator will turn it over to each of the Teams who will have 10 minutes to discuss how they came to their conclusion.

This will be followed by questions to the students 

Participating Teams Are:

Team 212 from University of California, Los Angeles, Master of Financial Engineering. The team was led by student team captain Tianchen Wang. Team members included Yahan Fu, Xiaoyue (Bruce) Li, Xiaobo Wei, Sumeng (Simon) Wu, and Kaiting (Michael) Zhao.

Team 75bps from University of Chicago, Financial Mathematics Program The team was led by student team captain Sean Lin. Team members included Younghun Lee, Ivan Petrov, Rahul Agarwal, and Tobias Rodriguez del Pozo.

Team QCF from Georgia Institute of Technology, Quantitative & Computational Finance The team was led by student team captain Eshwar Valady Hariharan. Team members included Daniel Alejandro Gordon Beltran, Kalai Karur Nagamani, and Biwei Tang.

Team Neo RenTech from New York University, Tandon School of Engineering, Masters in Financial Engineering. The team was led by student team captain Aditya Daftari. Team members included Rahul Bhagtani, Raktim Roychoudhury, Mitun Lakshminarayanan, Chengzhe Su, and Joey Yue.

Team Smile Like Volatility from New York University, Tandon School of Engineering, Masters in Financial Engineering. The team was led by student team captain Kaiyu Gu. Team members included Layla Li, Qi Peng, Ziyi Zhang, Jiaying Yang, and Xinyi Li.