Mathematical Finance & Financial Data Science Seminar
Getting more for less - better A/B testing via causal regularization
Speaker: Kevin Webster, Citadel
Location: Online Zoom access provided to registrants
Date: Tuesday, October 4, 2022, 5:30 p.m.
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, allowing one to draw more robust conclusions from smaller live trading experiments than traditional econometric methods.
Using a realistic order simulator we quantify these benefits explicitly for a prototypical example of an A/B trading experiment.
Dr Kevin Webster graduated with a PhD from Princeton University Operations Research and Financial Engineering Department (ORFE). At ORFE, he studied mathematical models applied to high frequency trading, with a large emphasis on price impact and market making.
Upon graduation in 2014, he worked initially as a researcher at Deutsche Bank and then joined Citadel in 2016. He is currently on garden leave from Citadel.
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".
This event is free but requires registration. Please click this link to register. You will then receive the Zoom link by email about a day or so before the event.