Microstructure in the Machine Age

The risk of machine learning

David Easley, Marcos López de Prado, Maureen O’Hara, Zhibai Zhang and Wei Jiang

The Review of Financial Studies, 2021, vol. 34, issue 7, 3316-3363

Abstract: Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. We demonstrate how machine learning can be applied to microstructural research. We find that microstructure measures continue to provide insights into the price process in current complex markets. Some microstructure features with high explanatory power exhibit low predictive power, while others with less explanatory power have more predictive power. We find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about market efficiency. We also show how microstructure measures can have important cross-asset effects. Our results are derived using 87 liquid futures contracts across all asset classes.

JEL-codes: C02 C58 E44 G14 G19 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (14)

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