Information leakage is a major concern for traders who want to execute large orders without affecting the market price. In this paper, we explore the sources and effects of information leakage in historical stock market data using various methods and metrics. We first define information leakage as a pattern caused by a trader that would otherwise not occur without the traderโs activity. Using historical data, the direct impact of a potential large trade cannot be measured, but we consider a minimal impact large trade to be one that minimizes changes to the established trading data. We then analyze how information leakage varies across different stocks, and the degree of influence a potential large trade may have on a stock. We also create a method for setting a policy driven bound using ๐^๐ in the spirit of differential privacy and investigate how an ๐-bound would affect trade-offs in information leakage and speed. Finally, we propose some strategies to reduce information leakage and improve trading efficiency of large trades.
","abstract_html":"<p>Information leakage is a major concern for traders who want to execute large orders without affecting the market price. In this paper, we explore the sources and effects of information leakage in historical stock market data using various methods and metrics. We first define information leakage as a pattern caused by a trader that would otherwise not occur without the traderโs activity. Using historical data, the direct impact of a potential large trade cannot be measured, but we consider a minimal impact large trade to be one that minimizes changes to the established trading data. We then analyze how information leakage varies across different stocks, and the degree of influence a potential large trade may have on a stock. We also create a method for setting a policy driven bound using ๐^๐ in the spirit of differential privacy and investigate how an ๐-bound would affect trade-offs in information leakage and speed. Finally, we propose some strategies to reduce information leakage and improve trading efficiency of large trades.</p>","abstract_has_math":false,"creators":["Hua, Edison"],"institution":null,"degree_name":"Master of Science (M.S.)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Allison Bishop","Akira Kawaguchi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-01-01T08:00:00Z","date_published":"2023-01-01T08:00:00Z","updated_at":"2026-07-24T01:58:07Z","subjects":["Differential Privacy","Modeling","Stock Data","Distributions","Trading","Data Science","Finance","Risk Analysis","Statistical Models"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/cc_etds_theses/1147","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Allison Bishop","Akira Kawaguchi"]},{"key":"dc:creator","label":"Author","values":["Hua, Edison"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-05-26T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Differential Privacy","Modeling","Stock Data","Distributions","Trading","Data Science","Finance","Risk Analysis","Statistical Models"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/cc_etds_theses/1147"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Information leakage is a major concern for traders who want to execute large orders without affecting the market price. In this paper, we explore the sources and effects of information leakage in historical stock market data using various methods and metrics. We first define information leakage as a pattern caused by a trader that would otherwise not occur without the traderโs activity. Using historical data, the direct impact of a potential large trade cannot be measured, but we consider a minimal impact large trade to be one that minimizes changes to the established trading data. We then analyze how information leakage varies across different stocks, and the degree of influence a potential large trade may have on a stock. We also create a method for setting a policy driven bound using ๐^๐ in the spirit of differential privacy and investigate how an ๐-bound would affect trade-offs in information leakage and speed. Finally, we propose some strategies to reduce information leakage and improve trading efficiency of large trades.
"]},{"key":"dc:title","label":"Title","values":["Exploring Information Leakage in Historical Stock Market Data"]}]}],"canonical_facts":{"dc:contributor":["Allison Bishop","Akira Kawaguchi"],"dc:creator":["Hua, Edison"],"dc:date.available":["2023-05-26T07:00:00Z"],"dc:description.abstract":["Information leakage is a major concern for traders who want to execute large orders without affecting the market price. In this paper, we explore the sources and effects of information leakage in historical stock market data using various methods and metrics. We first define information leakage as a pattern caused by a trader that would otherwise not occur without the traderโs activity. Using historical data, the direct impact of a potential large trade cannot be measured, but we consider a minimal impact large trade to be one that minimizes changes to the established trading data. We then analyze how information leakage varies across different stocks, and the degree of influence a potential large trade may have on a stock. We also create a method for setting a policy driven bound using ๐^๐ in the spirit of differential privacy and investigate how an ๐-bound would affect trade-offs in information leakage and speed. Finally, we propose some strategies to reduce information leakage and improve trading efficiency of large trades.
"],"dc:identifier":["https://academicworks.cuny.edu/cc_etds_theses/1147"],"dc:subject":["Differential Privacy","Modeling","Stock Data","Distributions","Trading","Data Science","Finance","Risk Analysis","Statistical Models"],"dc:title":["Exploring Information Leakage in Historical Stock Market Data"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (M.S.)"]},"updated_at":"2026-07-24T01:58:07Z"}