Microstructure in the Machine Age

• 成果类型: Article
• 署名作者: Easley, David; de Prado, Marcos Lopez; O'Hara, Maureen; Zhang, Zhibai
• 署名单位: Cornell University; Cornell University; Cornell University; Cornell University; New York University; New York University Tandon School of Engineering
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhaa078
• 发表日期: 2021
• 页码: 3316
• 关键词: cross-section; time; INFORMATION; returns; prices

摘要

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.