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

ArXiv ID: 2308.14235 “View on arXiv”

Authors: Unknown

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.

Keywords: Statistical Physics, Limit Order Book, Market Microstructure, Order Book Dynamics, Volatility Prediction

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts from statistical physics (e.g., kinetic energy, momentum analogies) to model order book dynamics, indicating high math complexity. It is heavily empirical, utilizing high-frequency Level 3 order book data, benchmarking against traditional and machine learning methods, and demonstrating predictive accuracy with metrics like R-squared, showing high backtest-ready rigor.

  flowchart TD
    A["Research Goal<br>Predict Volatility & Returns<br>using Order Book Data"] --> B["Core Methodology<br>Physics-Inspired Model"]
    B --> C["Key Innovation<br>Active Depth Concept"]
    B --> D["Computational Process<br>Calculate Kinetic Energy & Momentum"]
    C --> D
    D --> E{"Benchmarking"}
    E --> F["Traditional Methods"]
    E --> G["Machine Learning Algorithms"]
    E --> H["Proposed Physics Model"]
    F --> I["Key Finding 1<br>Outperforms Benchmarks"]
    G --> I
    H --> I
    H --> J["Key Finding 2<br>Nuanced Microstructure Insight"]
    I --> K["Final Outcome<br>Accurate Forecasts &<br>Valuable Market Insights"]
    J --> K