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

Haochen Li, Yi Cao, Maria Polukarov, Carmine Ventre
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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.
金融市场的实证分析:来自统计物理应用的启示
在本研究中,我们引入了一个受统计物理学启发的物理模型,通过利用三级订单簿数据来预测价格波动和预期回报。通过将极限订单簿中的订单与物理系统中的粒子进行类比,建立了系统动能和动量的独特度量,作为理解和评估极限订单簿状态的一种方法。我们的模型通过引入“活跃深度”的概念,超越了仅仅检查订单簿的参与者,这是一种有效的计算方法,用于识别影响价格动态的订单簿水平。我们的经验证明,我们的模型优于传统方法和机器学习算法的基准。我们的模型提供了对市场微观结构的细致理解,并对波动性和预期回报做出了更准确的预测。通过结合统计物理原理,本研究为理解市场参与者的行为和订单动态提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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