High Frequency Market Making with Machine Learning

Abstract: High frequency trading has been characterized as an arms race with 'Red Queen' characteristics [Farmer,2012]. It is improbable, even impossible, that many market participants can sustain a competitive advantage through the sole reliance on low latency trade execution systems. The growth in volume of market data, advances in computer hardware and commensurate prominence of machine learning in other disciplines, have spurred the exploration of machine learning for price discovery. Even though the application of machine learning to price prediction has been extensively researched, the merit of this approach for high frequency market making has received little attention. 
This paper introduces a trade execution model to evaluate the economic impact of classifiers  through backtesting. Extending the concept of a confusion matrix, we present a 'trade information matrix' to attribute the expected profit and loss of tick level predictive classifiers under execution constraints, such as fill probabilities and position dependent trade rules, to correct and incorrect predictions. We apply the execution model and trade information matrix to Level II E-mini S&P 500 futures history and demonstrate an estimation approach for measuring the sensitivity of the P&L to classification error. Our approach directly evaluates the performance sensitivity of a market making strategy to classifier error and augments traditional market simulation based testing. 
Bio: Matthew Dixon is an Assistant Professor of Finance and Statistics at the Illinois Institute of Technology. His research in computational methods for finance is funded by Intel. Matthew began his career in structured credit trading at Lehman Brothers in London before pursuing academics and consulting for financial institutions in quantitative trading and risk modeling. He holds a Ph.D. in Applied Mathematics from Imperial College (2007) and has held postdoctoral and visiting professor appointments at Stanford University and UC Davis respectively. He has published over 20 peer reviewed academic publications on machine learning and financial modeling, has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert, and is a frequently invited speaker in Silicon Valley and on Wall Street. He has published R packages, served as a Google Summer of Code mentor and is the founder of an analytics consulting firm, Quiota LLC.

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