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A Comparative Study of Financial Crises: Fractal Dissection of Investor Rationality

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  • Sonali Agarwal
  • Anshul Vats

Abstract

Any non-linear dynamic system can be checked for structural properties only at the time of extremes/crises. Hence, in this research article we tried to investigate stock markets for visible patterns or structures in the vicinity of crashes. We used fractal dimension analysis for studying the volatility of prices and presence of noise and patterns in the time series data of NIFTY, SENSEX and gold. We found change in market predictability of the various time series in the surrounding of crash points. There was measurable change in persistence levels around rupture points. It can be concluded that excessive order in stock markets can choke the markets which then witness crashes to relieve this symmetry and resume randomness for normal functioning. We supported the results with behavioural biases and patterns of investors. The repetitive trading psychology, different intensity of emotions of investors towards their gains and losses, and onset of irrationality and fear leads to worsening of any financial crisis. The crashes can have devastating effects on the economy and the investors. We there have tried to find visible patterns that can serve as warning signals of an approaching crisis. This can be of special assistance to the investors, traders and speculators who enjoy playing in the stock market.

Suggested Citation

  • Sonali Agarwal & Anshul Vats, 2024. "A Comparative Study of Financial Crises: Fractal Dissection of Investor Rationality," Vision, , vol. 28(2), pages 193-209, April.
    • Handle: RePEc:sae:vision:v:28:y:2024:i:2:p:193-209
    DOI: 10.1177/09722629211022518
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    References listed on IDEAS

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    1. Matteo, T. Di & Aste, T. & Dacorogna, Michel M., 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 827-851, April.
    2. Cornelis A. Los & Rossitsa M. Yalamova, 2004. "Multi-Fractal Spectral Analysis of the 1987 Stock Market Crash," Finance 0409050, University Library of Munich, Germany.
    3. B. B. Mandelbrot, 2001. "Scaling in financial prices: IV. Multifractal concentration," Quantitative Finance, Taylor & Francis Journals, vol. 1(6), pages 641-649.
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