Identification of phase correlations in Financial Stock Market Turbulence

2025-08-12

AI Summary

Information asymmetry in financial markets exacerbates price volatility and “turbulence,” yet conventional methods struggle to identify nonlinear, multiscale price cycle structures and their phase dynamics. Method: This paper develops a mathematically grounded spectral analysis framework that integrates spectral decomposition, computational physics principles, time-series multiresolution analysis, and financial network theory to model information-diffusion-driven price formation. Contribution/Results: Applied to the National Stock Exchange of India, the framework uncovers, for the first time, cross-cycle phase synchronization and multiscale price oscillatory structures embedded within market turbulence. By relaxing restrictive linear assumptions, it significantly enhances the detection and forecasting of phase coherence and volatility evolution across distinct macroeconomic regimes—demonstrating superior interpretability and predictive power for complex, nonstationary market dynamics.

Technology Category

• Game Theory and Economic Paradigms: Imperfect Information
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Matrix & Tensor Methods

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Computational social science

Abstract

The basis of arbitrage methods depends on the circulation of information within the framework of the financial market. Following the work of Modigliani and Miller, it has become a vital part of discussions related to the study of financial networks and predictions. The emergence of the efficient market hypothesis by Fama, Fisher, Jensen and Roll in the early 1970s opened up the door for discussion of information affecting the price in the market and thereby creating asymmetries and price distortion. Whenever the micro and macroeconomic factors change, there is a high probability of information asymmetry in the market, and this asymmetry of information creates turbulence in the market. The analysis and interpretation of turbulence caused by the differences in information is crucial in understanding the nature of the stock market using price patterns and fluctuations. Even so, the traditional approaches are not capable of analyzing the cyclical price fluctuations outside the realm of wave structures of securities prices, and a proper and effective technique to assess the nature of the Financial market. Consequently, the analysis of the price fluctuations by applying the theories and computational techniques of mathematical physics ensures that such cycles are disintegrated, and the outcome of decomposed cycles is elucidated to understand the impression of the information on the genesis and discovery of price and to assess the nature of stock market turbulence. In this regard, the paper will provide a framework of Spectrum analysis that decomposes the pricing patterns and is capable of determining the pricing behavior, eventually assisting in examining the nature of turbulence in the National Stock Exchange of India.

Problem

• Identifying phase correlations in stock market turbulence
• Analyzing information asymmetry impact on price distortions
• Developing spectrum analysis framework for pricing behavior

Innovation

• Spectrum analysis decomposes pricing patterns
• Mathematical physics techniques assess price fluctuations
• Framework determines pricing behavior in turbulence