Identifying stock market crashes by fuzzy measures of complexity

- Bielinskyi, Andrii (orcid.org/0000-0002-2821-2895), Soloviev, V.N. (orcid.org/0000-0002-4945-202X), Semerikov, Serhiy O. (orcid.org/0000-0003-0789-0272) and Solovieva, Victoria (orcid.org/0000-0002-8090-9569) (2021) Identifying stock market crashes by fuzzy measures of complexity Neuro-Fuzzy Modeling Techniques in Economics (10). pp. 3-45. ISSN 2415-3516

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Abstract

This study, for the first time, presents the possibility of using fuzzy set theory in combination with information theory and recurrent analysis to construct indicators (indicators-precursors) of crisis phenomena in complex nonlinear systems. In our study, we analyze the 4 most important crisis periods in the history of the stock market – 1929, 1987, 2008 and the COVID-19 pandemic in 2020. In particular, using the sliding window procedure, we analyze how the complexity of the studied crashes changes over time, and how it depends on events such as the global stock market crises. For comparative analysis, we take classical Shannon entropy, approximation and permutation entropy, recurrent diagrams, and their fuzzy alternatives. Each of the fuzzy modifications uses three membership functions: exponential, sigmoidal, and simple linear functions. Empirical results demonstrate the fact that the fuzzification of classical entropy and recurrence approaches opens up prospects for constructing effective and reliable indicators-precursors of critical events in the studied complex systems.

Item Type:	Article
Keywords:	crash, critical event, stock market, entropy, recurrence plot, fuzzy set theory, indicator-precursor of crisis phenomena, fuzzy measure of complexity
Subjects:	Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 00 Prolegomena. Fundamentals of knowledge and culture. Propaedeutics > 004 Computer science and technology. Computing. Data processing > 004.9 ІКТ ( Application-oriented computer-based techniques ) > 004.94 Simulation Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 3 Social Sciences > 33 Economics. Economic science
Divisions:	Institute for Digitalisation of Education > Department of Cloud-Oriented Systems and Artificial Intelligence in Education
Depositing User:	Сергій Олексійович Семеріков
Date Deposited:	03 Jan 2023 10:58
Last Modified:	03 Jan 2023 10:58
URI:	https://lib.iitta.gov.ua/id/eprint/733696

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