An analysis of capital market through the lens of integral transforms: exploring efficient markets and information asymmetry

2025-06-02

AI Summary

This study investigates how information asymmetry influences price formation and discovery in capital markets through multiscale periodic structures. Addressing the limitation of conventional technical analysis—which relies solely on single-scale cycle representation—we pioneer the systematic integration of physics-inspired spectral decomposition techniques, including Fourier transform, Hilbert–Huang transform (HHT), power spectral density estimation, and rolling-window spectral analysis, to decompose stock price fluctuations on India’s National Stock Exchange (NSE). We identify three dominant cyclical components: short-term noise, medium-term information absorption, and long-term fundamentals. Building upon this, we establish a quantitative mapping between information asymmetry intensity and energy distribution across these cycles, thereby reconstructing the empirical testing framework for the Efficient Market Hypothesis (EMH). Empirical results confirm that in weak-form efficient markets, high-frequency information lags induce persistent asymmetric price responses, yielding a 37% reduction in prediction error.

Technology Category

• Game Theory and Economic Paradigms: Imperfect Information
• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Post Modigliani and Miller (1958), the concept of usage of arbitrage created a permanent mark on the discourses of financial framework. The arbitrage process is largely based on information dissemination amongst the stakeholders operating in the financial market. The advent of the efficient market Hypothesis draws close to the M&M hypothesis. Giving importance to the arbitrage process, which effects the price discovery in the stock market. This divided the market as random and efficient cohort system. The focus was on which information forms a key factor in deciding the price formation in the market. However, the conventional techniques of analysis do not permit the price cycles to be interpreted beyond its singular wave-like cyclical movement. The apparent cyclic measurement is not coherent as the technical analysis does not give sustained result. Hence adaption of theories and computation from mathematical methods of physics ensures that these cycles are decomposed and the effect of the broken-down cycles is interpreted to understand the overall effect of information on price formation and discovery. In order to break the cycle this paper uses spectrum analysis to decompose and understand the above-said phenomenon in determining the price behavior in National Stock Exchange of India (NSE).

Problem

• Analyzing capital markets using integral transforms to study efficiency and information asymmetry
• Decomposing price cycles with spectrum analysis to understand information impact
• Exploring price behavior in NSE India through mathematical physics methods

Innovation

• Uses spectrum analysis for price behavior
• Decomposes cycles with physics methods
• Analyzes information impact on prices