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How well the log periodic power law works in an emerging stock market?

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  • Bikramaditya Ghosh
  • Dimitris Kenourgios
  • Antony Francis
  • Suman Bhattacharyya

Abstract

A growing body of research work on Log Periodic Power Law (LPPL) tries to predict market bubbles and crashes. Mostly, the fitment parameters remain confined within certain specific ranges. This paper examines these claims and the robustness of the reformulated LPPL model of Filimonov & Sornette (2013) for capturing large falls in the S&P BSE Sensex, an Indian heavyweight index over the period 2000–2019. Thirty-five mid to large-sized crashes are identified during this period, forming a clear LPPL signature. This confirms the possibility to predict the embedded risk of future uncertain events in the Indian stock market with the LPPL approach.

Suggested Citation

  • Bikramaditya Ghosh & Dimitris Kenourgios & Antony Francis & Suman Bhattacharyya, 2021. "How well the log periodic power law works in an emerging stock market?," Applied Economics Letters, Taylor & Francis Journals, vol. 28(14), pages 1174-1180, August.
    • Handle: RePEc:taf:apeclt:v:28:y:2021:i:14:p:1174-1180
    DOI: 10.1080/13504851.2020.1803484
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    Cited by:

    1. Min Shu & Ruiqiang Song & Wei Zhu, 2021. "The 2021 Bitcoin Bubbles and Crashes—Detection and Classification," Stats, MDPI, vol. 4(4), pages 1-21, November.
    2. Li, Jiang-Cheng & Tao, Chen & Li, Hai-Feng, 2022. "Dynamic forecasting performance and liquidity evaluation of financial market by Econophysics and Bayesian methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    3. José Parra-Moyano & Daniel Partida & Moritz Gessl & Somnath Mazumdar, 2024. "Analyzing swings in Bitcoin returns: a comparative study of the LPPL and sentiment-informed random forest models," Digital Finance, Springer, vol. 6(3), pages 427-439, September.

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