Market Microstructure in the Age of Machine Learning
48 Pages Posted: 11 Jun 2018
Date Written: June 10, 2018
In this presentation, we analyze the explanatory (in-sample) and predictive (out-of-sample) importance of some of the best known market microstructural features. Our conclusions are drawn over the entire universe of the 87 most liquid futures worldwide, covering all asset classes, going back through 10 years of tick-data history.
Keywords: Market microstructure, machine learning, feature importance, prediction, out-of-sample
JEL Classification: C02, D52, D53, G14
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