Identification of phase correlations in Financial Stock Market Turbulence

ArXiv ID: 2508.20105 “View on arXiv”

Authors: Kiran Sharma, Abhijit Dutta, Rupak Mukherjee

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.

Keywords: information asymmetry, market turbulence, spectrum analysis, price patterns, efficient market hypothesis, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 2.5/10
• Quadrant: Lab Rats
• Why: The paper proposes a framework based on spectrum analysis (Fourier/wavelet techniques) and concepts from mathematical physics (turbulence cascades, scales) to decompose price patterns, indicating moderate-to-high math complexity. However, the excerpt is purely theoretical, citing foundational literature and proposing a methodological approach without presenting backtests, code, dataset specifics, or performance metrics, placing it in the low empirical rigor category.

  flowchart TD
    A["Research Goal<br>Identify Phase Correlations in<br>Financial Stock Market Turbulence"] --> B["Data Input<br>National Stock Exchange India Data<br>High-frequency Price Series"]
    B --> C["Key Methodology<br>Spectrum Analysis via<br>Mathematical Physics Models"]
    C --> D["Computational Process<br>Decomposition of Cyclical<br>Price Fluctuations"]
    D --> E["Computational Process<br>Analysis of Decomposed<br>Phase Correlations"]
    E --> F["Key Outcome<br>Identification of Asymmetry<br>and Turbulence Patterns"]
    F --> G["Conclusion<br>Framework for Assessing<br>Market Turbulence"]