A novel method for analyzing financial market efficiency through fuzzy set theory
Abolfazl Askari and
Ehsan Hajizadeh
Finance Research Letters, 2025, vol. 78, issue C
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.
Keywords: Market efficiency; Financial markets; Inefficiency measurement; Fuzzy set theory (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612325005069
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:78:y:2025:i:c:s1544612325005069
DOI: 10.1016/j.frl.2025.107243
Access Statistics for this article
Finance Research Letters is currently edited by R. Gençay
More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().