Arthur Américo, Allison Bishop, Paul Cesaretti, Garrison Grogan, Adam McKoy, Robert Moss, Lisa Oakley, Marcel Ribeiro, Mohammad Shokri
{"title":"Defining and Controlling Information Leakage in US Equities Trading","authors":"Arthur Américo, Allison Bishop, Paul Cesaretti, Garrison Grogan, Adam McKoy, Robert Moss, Lisa Oakley, Marcel Ribeiro, Mohammad Shokri","doi":"10.56553/popets-2024-0054","DOIUrl":null,"url":null,"abstract":"We present a new framework for defining information leakage in the setting of US equities trading, and construct methods for deriving trading schedules that stay within specified information leakage bounds. Our approach treats the stock market as an interactive protocol performed in the presence of an adversary, and draws inspiration from the related disciplines of differential privacy as well as quantitative information flow. We apply a linear programming solver using examples from historical trade and quote (TAQ) data for US equities and describe how this framework can inform actual algorithmic trading strategies.","PeriodicalId":508905,"journal":{"name":"IACR Cryptol. ePrint Arch.","volume":"285 ","pages":"971"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IACR Cryptol. ePrint Arch.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.56553/popets-2024-0054","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
We present a new framework for defining information leakage in the setting of US equities trading, and construct methods for deriving trading schedules that stay within specified information leakage bounds. Our approach treats the stock market as an interactive protocol performed in the presence of an adversary, and draws inspiration from the related disciplines of differential privacy as well as quantitative information flow. We apply a linear programming solver using examples from historical trade and quote (TAQ) data for US equities and describe how this framework can inform actual algorithmic trading strategies.
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