How do retail investors use order flow data?

Study on retail traders' use of order flow data, with insights into market microstructure and regulatory developments.

Paper Metadata

• Publication Date: 2025-03-03

• Source: SSRN

• Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5160381

• Authors

  • Philipp Chapkovski, Lotharstrasse 65, Duisburg, D-47057, Germany,

  • Mariana Khapko, Toronto, Ontario M5S 3E6, Canada,

  • Marius Zoican (Contact Author), 2500 University Drive, NW, Calgary, Alberta T2N 1N4, Canada,

Keywords

• retail trading

• order flow

Notes for Review

Recommendation: 92.0%

Introduction: This paper explores how retail traders value and learn from order flow data using a randomized online experiment. The study reveals that sophisticated participants, those with formal financial education, value order flow data in line with a fully optimizing Bayesian investor, while others pay only 59% of the fair value. Overconfidence leads to a 28% higher data valuation. The authors also examine the impact of data access on trading behavior, finding that participants gain limited benefits from order flow data, capturing only 11.8% of a Bayesian trader's value and reducing trading errors by just 6.25% (11.7% for sophisticated traders). Data access mitigates the disposition effect but also fuels excessive trading. Cognitive load rises slightly with data access, particularly for unsophisticated traders. The paper provides valuable insights into the market microstructure and regulatory developments in the US market, including the Securities and Exchange Commission's efforts to increase competition in market data provision. The study's findings have significant implications for retail traders, brokers, and regulators, highlighting the need for more affordable and accessible market data. Key findings include the importance of data-driven trading and the need for regulatory oversight to ensure fair market practices. Overall, this paper is a must-read for anyone interested in market microstructure, retail trading, and regulatory developments.

Abstract

We study how retail traders value and learn from order flow data using a randomized online experiment. Sophisticated participants-those with formal financial education-value order flow data in line with a fully optimizing Bayesian investor, while others pay only 59% of the fair value. Overconfidence leads to a 28% higher data valuation. Participants gain limited benefits from order flow data, capturing only 11.8% of a Bayesian trader's value and reducing trading errors by just 6.25% (11.7% for sophisticated traders). Data access mitigates the disposition effect but also fuels excessive trading. Cognitive load rises slightly with data access, particularly for unsophisticated traders.