2026
G. Ritter. Perturbation theory for nonlinear transient impact. Working paper.
Gordon Ritter
Adjunct Professor at Baruch College (CUNY), the Courant Institute of Mathematical Sciences (NYU), and at Columbia University.
About
Gordon Ritter completed a PhD in Mathematical Physics from Harvard University in 2007, under the direction of Arthur M. Jaffe, Landon T. Clay Professor of Mathematics and Theoretical Science with an MA from Harvard in 2002. His publications while at Harvard were in quantum field theory, differential geometry, quantum computation and abstract algebra, including a well-known simplicity theorem for Kac-Moody groups and a mathematically rigorous treatment of Euclidean QFT on Riemannian manifolds. Prior to Harvard he earned his Bachelor's degree with honors in Mathematics from the University of Chicago, on an invitation-only math track involving Honors Analysis (Math 207-8-9) and subsequently, all of Chicago's first-year graduate mathematics sequences. Dr. Ritter currently teaches mathematical finance in the award-winning MFE program at Baruch College (see the QuantNet rankings), and at Columbia University, and New York University. In the past he has taught at the University of Chicago and Rutgers. He was named Buy-Side Quant of the Year in 2019, and Quant Educator of the Year in 2024.
In parallel with teaching, Dr. Ritter works full time in the industry, running systematic absolute-return trading strategies across multiple asset classes and geographies, based on cutting-edge technology and rigorous applications of the scientific method to investment problems. Ritter is an advanced technical scuba diver certified to plan and execute deep technical wreck exploration on hypoxic trimix (no depth limit), using closed-circuit rebreathers, diver propulsion vehicles, etc.
Research
Ritter’s research develops the mathematics of trading. A central theme is the use of reinforcement learning and stochastic optimal control for dynamic portfolio choice, hedging, and optimal execution, where an agent acts repeatedly under transaction costs and market impact. A second theme is portfolio construction: mean–variance optimization, Bayesian estimation, and the Black–Litterman framework.
His current work studies optimal execution when market impact is transient and nonlinear. Using perturbation theory, it describes how the optimal trading schedule responds to weak departures from the linear-impact model, and establishes conditions under which the model admits no price manipulation. Earlier in his career he worked in mathematical physics, on quantum field theory on curved spacetimes and on the algebra of quantum information.
Reinforcement learning in finance · Optimal execution and market microstructure · Market impact modeling · Portfolio optimization and Bayesian methods · Statistical machine learning
Publications
Selected working papers
Quantitative finance
2020
P. N. Kolm and G. Ritter. Modern perspectives on reinforcement learning in finance. The Journal of Machine Learning in Finance 1(1).
2016
G. Ritter. Stable linear-time optimization in arbitrage pricing theory models. Risk 29(9), 82–85.
Mathematical physics
2007
G. Ritter. A Hardy–Ramanujan formula for Lie algebras. Experimental Mathematics 16(3), 375–384.
2005
W. G. Ritter. Lie algebras and suppression of decoherence in open quantum systems. Physical Review A 72, 012305.
Teaching
Ritter teaches graduate courses in quantitative and computational finance, with an emphasis on machine learning, optimal execution, and portfolio management. In the current academic year:
Baruch College, CUNY. MTH 9855 - Asset Allocation and Portfolio Management
Courant Institute, NYU. MATH-GA.2048-001 - Scientific Computing in Finance
Columbia University. MATH 5431 - Advanced Machine Learning for Finance
He has also lectured at the University of Chicago and Rutgers University on algorithmic trading, portfolio management, and continuous-time finance, and has supervised numerous master’s and PhD theses.
Contact
Email: ritter@post.harvard.edu