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Dr. Kevin Webster: "Getting More for Less - Better A/B Testing via Causal Regularization"

URL
https://www.youtube.com/watch?v=yicMqrhymcA
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2
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Abstract: Causal regularization solves several practical problems in live trading applications: estimating price impact when alpha is unknown and estimating alpha when price impact is unknown. In addition, causal regularization increases the value of small A/B tests: one draws more robust conclusions from smaller live trading experiments than traditional econometric methods. Requiring less A/B test data, trading teams can run more live trading experiments and improve the performance of more trading algorithms. Using a realistic order simulator, we quantify these benefits for a canonical A/B trading experiment. Speaker Bio: Dr. Kevin Webster graduated with a Ph.D. from Princeton University Operations Research and Financial Engineering Department (ORFE). At ORFE, he studied mathematical models applied to high-frequency trading, with a significant emphasis on price impact and market making. He previously worked at Deutsche Bank and Citadel and is currently a visiting assistant professor at Imperial College, London. Dr. Kevin Webster created and taught a course, ORF 474 High-Frequency Markets: Models and Data Analysis, as a visiting lecturer at Princeton in the 2015 school year. His publications include "The self-financing equation in high frequency markets," "Information and inventories in high frequency trading," "A portfolio manager's guidebook to trade execution," and "High frequency market making."
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Name
Cornell Financial Engineering Manhattan CFEM
Date
2022/11/09
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