Identifying stock market crashes by fuzzy measures of complexity
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Date
2021
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ДВНЗ «Київський національний економічний університет імені Вадима Гетьмана»
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.
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Keywords
crash, critical event, stock market, entropy, recurrence plot, fuzzy set theory, indicator-precursor of crisis phenomena, fuzzy measure of complexity
Citation
Identifying stock market crashes by fuzzy measures of complexity / Andrii Bielinskyi, Vladimir Soloviev, Serhiy Semerikov, Viktoria Solovieva // Нейро-нечіткі технології моделювання в економіці : наук.-анал. журн. / М-во освіти і науки України, ДВНЗ «Київ. нац. екон. ун-т ім. Вадима Гетьмана» ; [редкол.: А. В. Матвійчук (голов. ред.) та ін.]. – Київ : КНЕУ, 2021. – № 10. – С. 3–45.