[3N1-GS-11] AI and Society

[3N1-GS-11-02] Microscopic origin of the long-range correlation of the market order flow: a big-data analysis of the microscopic data in the Tokyo Stock Exchange market

Yuki Sato¹, 〇Kiyoshi Kanazawa¹ (1. Kyoto University)

Keywords:Agent-based model, Financial data analysis, Socioeconophysics

Recently, various high-quality datasets have helped researchers study the microstructure of financial markets as data science. In financial markets, the market order flow is empirically known as persistent: once a buy (sell) order is observed, more buy (sell) orders are likely to appear even in future. This phenomenon is called the long-range correlation (LRC), and its microscopic theoretical model was proposed by Lillo, Mike, and Farmer (LMF) in 2005 based on the order-splitting hypothesis. However, the prediction by the LMF model has not been verified yet because large microscopic financial datasets were unavailable. In this talk (see our latest preprint arXiv:2301.13505), we show that the LMF prediction is empirically verified by data analysis of the Tokyo stock exchange market. Our dataset includes the virtual server identifiers, effectively allowing us to study the microscopic decision-making process of traders. Using this dataset, we tested the LMF prediction and found it holds.