Machine Learning for Market Microstructure and High-Frequency Trading

Michael Kearns and Yuriy Nevmyvaka

In this chapter, we give an overview of the uses of machine learning for high-frequency trading (HFT) and market microstructure data and problems. Machine learning is a vibrant subfield of computer science that draws on models and methods from statistics, algorithms, computational complexity, artificial intelligence, control theory and a variety of other disciplines. Its primary focus is on computationally and informationally efficient algorithms for inferring good predictive models from large data sets, and thus it is a natural candidate for application to problems arising in HFT, for both trade execution and the generation of Alpha.

The inference of predictive models from historical data is obviously not new in quantitative finance; ubiquitous examples include coefficient estimation for the capital asset pricing model (CAPM), Fama and French factors (Fama and French 1993) and related approaches. The special challenges for machine learning presented by HFT generally arise from the very fine granularity of the data – often microstructure data at the resolution of individual orders, (partial) executions, hidden liquidity and cancellations – and a lack of understanding of how such