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

TL;DR

This paper applies spectrum analysis and integral transforms to decompose stock market cycles, aiming to better understand information effects on price formation and market efficiency in NSE.

Contribution

It introduces a novel mathematical approach using spectrum analysis to interpret stock market cycles beyond traditional technical analysis methods.

Findings

Decomposition of stock market cycles reveals underlying information effects.

Enhanced understanding of price discovery mechanisms.

Potential for improved market efficiency analysis.

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).