An Empirical Analysis on Financial Markets: Insights from the Application of Statistical Physics

TL;DR

This paper introduces a physics-inspired model for predicting financial market volatility and returns using detailed order book data, outperforming traditional and machine learning benchmarks.

Contribution

It presents a novel approach applying statistical physics concepts to market microstructure, including the innovative 'active depth' measure for better prediction accuracy.

Findings

Model outperforms traditional benchmarks

Incorporates 'active depth' for efficient analysis

Provides deeper insights into order book dynamics

Abstract

In this study, we introduce a physical model inspired by statistical physics for predicting price volatility and expected returns by leveraging Level 3 order book data. By drawing parallels between orders in the limit order book and particles in a physical system, we establish unique measures for the system's kinetic energy and momentum as a way to comprehend and evaluate the state of limit order book. Our model goes beyond examining merely the top layers of the order book by introducing the concept of 'active depth', a computationally-efficient approach for identifying order book levels that have impact on price dynamics. We empirically demonstrate that our model outperforms the benchmarks of traditional approaches and machine learning algorithm. Our model provides a nuanced comprehension of market microstructure and produces more accurate forecasts on volatility and expected returns. By incorporating principles of statistical physics, this research offers valuable insights on understanding the behaviours of market participants and order book dynamics.