Discover how the Lillo Mike Farmer model has been validated on a large financial market dataset for the first time. Gain insights into its effectiveness in a language that’s easy to understand.
Economics and physics are distinct fields of study, yet some researchers have been bridging the two together to tackle complex economics problems in innovative ways. This resulted in the establishment of an interdisciplinary research field, known as econophysics, which specializes in solving problems rooted in economics using physics theories and experimental methods.
Researchers at Kyoto University carried out an econophysics study aimed at studying financial market behavior using a statistical physics framework, known as the Lillo, Mike, and Farmer (LMF) model. Their paper, published in Physical Review Letters, outlines the first quantitative validation of a key prediction of this physics model, which the team used to analyze microscopic data containing fluctuations in the Tokyo Stock Exchange market spanning over a period of nine years.
The LMF model is a simple statistical physics model that describes so-called order-splitting behavior. A key prediction of this model is that the order of signs representing buy or sell orders in the stock market is associated with the microscopic distribution of metaorders.
Kanazawa and his colleagues were the first to perform a quantitative test of the LMF model on a large microscopic financial market dataset. Notably, the results of their analyses were aligned with this model’s predictions, thus highlighting its promise for tackling economic problems and studying the financial market’s microstructure.
The study demonstrates the potential of statistical physics in clarifying financial market behavior with large, microscopic datasets. By analyzing this microscopic dataset further, researchers aim to establish a unifying theory of financial market microstructure parallel to the statistical physics programs from microscopic dynamics.
This achievement is particularly noteworthy as it opens doors for new metrics for liquidity measurements that could potentially be used to address economic problems. The study has broad implications for financial markets and underscores the power of interdisciplinary research in finding innovative solutions to complex economic challenges.
Yuki Sato et al, Inferring Microscopic Financial Information from the Long Memory in Market-Order Flow: A Quantitative Test of the Lillo-Mike-Farmer Model, Physical Review Letters (2023). DOI: 10.1103/PhysRevLett.131.197401.
Physical Review Letters
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