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  • Oct 2020
  • Conference Presentation

Optimal, Truthful, and Private Securities Lending

By: Emily Diana, Michael J. Kearns, Seth Neel and Aaron Leon Roth
  • Format:Print
  • | Language:English
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Abstract

We consider a fundamental dynamic allocation problem motivated by the problem of securities lending in financial markets, the mechanism underlying the short selling of stocks. A lender would like to distribute a finite number of identical copies of some scarce resource to n clients, each of whom has a private demand that is unknown to the lender. The lender would like to maximize the usage of the resource—avoiding allocating more to a client than her true demand—but is constrained to sell the resource at a pre-specified price per unit, and thus cannot use prices to incentivize truthful reporting. We first show that the Bayesian optimal algorithm for the one-shot problem—which maximizes the resource's expected usage according to the posterior expectation of demand, given reports—actually incentivizes truthful reporting as a dominant strategy. Because true demands in the securities lending problem are often sensitive information that the client would like to hide from competitors, we then consider the problem under the additional desideratum of (joint) differential privacy. We give an algorithm, based on simple dynamics for computing market equilibria, that is simultaneously private, approximately optimal, and approximately dominant-strategy truthful. Finally, we leverage this private algorithm to construct an approximately truthful, optimal mechanism for the extensive form multi-round auction where the lender does not have access to the true joint distributions between clients' requests and demands.

Keywords

Differential Privacy; Mechanism Design; Finance; Mathematical Methods

Citation

Diana, Emily, Michael J. Kearns, Seth Neel, and Aaron Leon Roth. "Optimal, Truthful, and Private Securities Lending." Paper presented at the 1st Association for Computing Machinery (ACM) International Conference on AI in Finance (ICAIF), October 2020.
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About The Author

Seth Neel

Technology and Operations Management
→More Publications

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More from the Authors
  • On the Privacy Risks of Algorithmic Recourse By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
  • Data Privacy in Practice at LinkedIn By: Iavor Bojinov, Marco Iansiti and Seth Neel
  • Adaptive Machine Unlearning By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites
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