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In this study, we introduce a physical model inspired by statistical physicsfor predicting price volatility and expected returns by leveraging Level 3order book data. By drawing parallels between orders in the limit order bookand particles in a physical system, we establish unique measures for thesystem's kinetic energy and momentum as a way to comprehend and evaluate thestate of limit order book. Our model goes beyond examining merely the toplayers of the order book by introducing the concept of 'active depth', acomputationally-efficient approach for identifying order book levels that haveimpact on price dynamics. We empirically demonstrate that our model outperformsthe benchmarks of traditional approaches and machine learning algorithm. Ourmodel provides a nuanced comprehension of market microstructure and producesmore accurate forecasts on volatility and expected returns. By incorporatingprinciples of statistical physics, this research offers valuable insights onunderstanding the behaviours of market participants and order book dynamics.

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