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.
Synopsis:
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.
Bio:
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".
Notes:
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.