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A novel method for analyzing financial market efficiency through fuzzy set theory

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  • Askari, Abolfazl
  • Hajizadeh, Ehsan

Abstract

This paper introduces the Fuzzy Market Inefficiency Measure (FMIM), a novel approach for evaluating financial market efficiency by leveraging fuzzy set theory. FMIM addresses limitations in traditional metrics by modeling inefficiency as a triangular fuzzy number, capturing the inherent uncertainties and non-linear dynamics of financial markets. The methodology incorporates fuzzy regression with triangular membership functions and employs a straightforward optimization framework for parameter estimation. Empirical analysis across diverse asset classes—including equities, commodities, and cryptocurrencies—demonstrates FMIM's robustness, particularly during periods of heightened market uncertainty, such as the 2008 financial crisis and the 2020 COVID-19 pandemic. FMIM not only detects pronounced inefficiencies during turbulence but also provides nuanced insights into subtle variations under stable conditions. By introducing a flexible and adaptive framework, FMIM offers researchers, analysts, and policymakers a powerful tool for advancing the understanding of inefficiency dynamics in complex financial environments.

Suggested Citation

  • Askari, Abolfazl & Hajizadeh, Ehsan, 2025. "A novel method for analyzing financial market efficiency through fuzzy set theory," Finance Research Letters, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:finlet:v:78:y:2025:i:c:s1544612325005069
    DOI: 10.1016/j.frl.2025.107243
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    References listed on IDEAS

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    1. Puertas, Antonio M. & Clara-Rahola, Joaquim & Sánchez-Granero, Miguel A. & de las Nieves, F. Javier & Trinidad-Segovia, Juan E., 2023. "A new look at financial markets efficiency from linear response theory," Finance Research Letters, Elsevier, vol. 51(C).
    2. Askari, Abolfazl & Hajizadeh, Ehsan, 2024. "Exploring market efficiency levels: A powerful approach based on a gamma distribution," Finance Research Letters, Elsevier, vol. 66(C).
    3. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Alvarez, Jesus, 2012. "A multiscale entropy approach for market efficiency," International Review of Financial Analysis, Elsevier, vol. 21(C), pages 64-69.
    4. Wang, Jingjing & Wang, Xiaoyang, 2021. "COVID-19 and financial market efficiency: Evidence from an entropy-based analysis," Finance Research Letters, Elsevier, vol. 42(C).
    5. Ammy-Driss, Ayoub & Garcin, Matthieu, 2023. "Efficiency of the financial markets during the COVID-19 crisis: Time-varying parameters of fractional stable dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    6. Urquhart, Andrew & McGroarty, Frank, 2014. "Calendar effects, market conditions and the Adaptive Market Hypothesis: Evidence from long-run U.S. data," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 154-166.
    7. Noda, Akihiko, 2016. "A test of the adaptive market hypothesis using a time-varying AR model in Japan," Finance Research Letters, Elsevier, vol. 17(C), pages 66-71.
    8. Mikio Ito & Akihiko Noda & Tatsuma Wada, 2014. "International stock market efficiency: a non-Bayesian time-varying model approach," Applied Economics, Taylor & Francis Journals, vol. 46(23), pages 2744-2754, August.
    9. Tran, Vu Le & Leirvik, Thomas, 2019. "A simple but powerful measure of market efficiency," Finance Research Letters, Elsevier, vol. 29(C), pages 141-151.
    10. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Yoon, Seong-Min, 2018. "Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets," Finance Research Letters, Elsevier, vol. 27(C), pages 228-234.
    11. Urquhart, Andrew & Hudson, Robert, 2013. "Efficient or adaptive markets? Evidence from major stock markets using very long run historic data," International Review of Financial Analysis, Elsevier, vol. 28(C), pages 130-142.
    12. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    13. Kian‐Ping Lim & Robert Brooks, 2011. "The Evolution Of Stock Market Efficiency Over Time: A Survey Of The Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 69-108, February.
    14. Mikio Ito & Akihiko Noda & Tatsuma Wada, 2016. "The evolution of stock market efficiency in the US: a non-Bayesian time-varying model approach," Applied Economics, Taylor & Francis Journals, vol. 48(7), pages 621-635, February.
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