Who we are

Our group conducts comprehensive research on data driven methods in market microstructure and high frequency data. Recent emergence of high frequency market data thanks to automated and algorithmic trading has necessitated new methodologies to analyze and interpret such data. Classical stochastic models often struggle to incorporate all new necessary features of such data while also maintaining mathematical tractability. For instance, traditional stochastic models of financial markets often become too complicated to contain and explain market frictions, price impacts, information effects, microstructure noises, bid and ask spreads, etc., notions which can no longer be ignored in a high frequency market. Factoring these previously ignored features inside a good model is difficult, and the most likely candidate models pose challenges in analysis due to the inherent complexity of financial markets.

As alternatives to stochastic models of the usual type, we develop data driven methods to solve practical problems that traders face in today’s financial markets. Our major tools include Reinforcement Learning, Deep Q learning, Double Q Learning, GAN, Transformer, and much more.

As an academia-industry collaboration, we work with Vivity AI on challenging problems in manufacturing industry. We utilize cutting-edge machine learning techniques to tackle industrial challenges, including smart yard and smart factories. 

 Our Topics

Machine Learning in Finance/Industry

• Deep Learning Time Series Generation

• Time Series Clustering/Classification

• Signature Methods in Finance

• Systemic Risk Measure

• Causal Inference in Finance

• Model Free Dynamic Portfolio

• Diffusion Generative Models 

• Transformer Limit Order Book

• Local Times in Algorithmic Trading

• Reinforcement Learning in Statistical Arbitrage

Mathematical/Quantitative Finance

• Open Market and Stochastic Portfolio 

• Generalized Regime Switching Dynamics 

• Lead-Lag Analysis of Intraday and Overnight Returns 

• Hawkes Processes and Applications 

• Path-dependent SDE 

• Stochastic Delay Equations and Portfolio Optimization