Recurrence entropy and financial crashes

Soloviev, Vladimir; Serdiuk, Olexandr; Семеріков, Сергій Олексійович; Kohut-Ferens, Oksana

doi:10.2991/mdsmes-19.2019.73

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…It is necessary to allocate several scientific articles focusing the modeling of financial markets and identification of crisis periods with the use of economic and other interdisciplinary methods. These articles prove the effectiveness of entropy methods, network models, the Heisenberg uncertainty principle, scale-dependent Lyapunov indicators and other approaches for detecting and predicting crisis phenomena (See, e.g., Soloviev and Saptsin, 2011;Soloviev and Belinskij, 2018;Soloviev et al, 2019;Danylchuk et al, 2016;Danylchuk et al, 2019;Danylchuk et al, 2020).…”

Section: Introductionmentioning

confidence: 86%

Modeling of Crisis Phenomena in Regional Stock Markets by Wavelet-Entropy Method

Danylchuk¹,

Kibalnyk²

2021

MDES

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The article reflects the results of a study on modelling regional stock markets using wavelet entropy. Particular attention is paid to periods of crises as special states of markets. Countries with the developed economies were selected for the study, namely, the United States, United Kingdom, Germany, France, China, Japan and Hong Kong. The values of stock indices of regional stock markets were used as a statistical base. The research period is from 2015 to 2021. The aim of the article is to monitor the current state of markets and to demonstrate the possibilities of using the wavelet entropy index as a precursor of crisis phenomena. The wavelet entropy method was used. All calculations were performed in the Matlab environment - a system for modeling nonlinear dynamical systems. The results of the study demonstrate the absence of crisis phenomena in these markets as of April 2021. It is shown that the use of wavelet entropy as a precursor indicator is reasonable. In particular, the reaction of stock markets to such crisis situations as the COVID-19 pandemic as well as other socio-political events is shown. Based on the results of calculations, conclusions are made about the current state of stock markets of the studied countries. All stock market participants, investors, and relevant ministries to develop effective strategies can use the results obtained, as well as the method in general.

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Section: Introductionmentioning

confidence: 86%

Modeling of Crisis Phenomena in Regional Stock Markets by Wavelet-Entropy Method

Danylchuk¹,

Kibalnyk²

2021

MDES

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“…At the moment, there are various research papers on what crises and crashes are and how to classify such interruptions in the market of cryptocurrencies. Taking into account the experience of previous researchers and our own [25,26,27,28,29,30,31,32,33,34,35,36], we present our classification of such leaps and falls from the previous articles [28,29,30,33] on the cryptocurrency topic, relying on Bitcoin time series during the period (01.01.2013 -09.04.2020) of verifiable fixed daily values of the Bitcoin price (BTC) (https://finance.yahoo. com/cryptocurrencies).…”

Section: Data Preparation and Classificationmentioning

confidence: 99%

Coverage of the Coronavirus Pandemic through Entropy Measures

Soloviev¹,

Bielinskyi²,

Kharadzjan³

2021

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The rapidly evolving coronavirus pandemic brings a devastating effect on the entire world and its economy as awhole. Further instability related to COVID-19will negatively affect not only on companies and financial markets, but also on traders and investors that have been interested in saving their investment, minimizing risks, and making decisions such as how to manage their resources, how much to consume and save, when to buy or sell stocks, etc., and these decisions depend on the expectation of when to expect next critical change. Trying to help people in their subsequent decisions, we demonstrate the possibility of constructing indicators of critical and crash phenomena on the example of Bitcoin market crashes for further demonstration of their efficiency on the crash that is related to the coronavirus pandemic. For this purpose, the methods of the theory of complex systems have been used. Since the theory of complex systems has quite an extensive toolkit for exploring the nonlinear complex system, we take a look at the application of the concept of entropy in finance and use this concept to construct 6 effective entropy measures: Shannon entropy, Approximate entropy, Permutation entropy, and 3 Recurrence based entropies. We provide computational results that prove that these indicators could have been used to identify the beginning of the crash and predict the future course of events associated with the current pandemic.

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“…These studies [74,75] consider that measures of financial and macroeconomic activity can be drivers of Bitcoin returns. Reviewing papers of the researches above, the experience of others and our own [76][77][78][79][80][81][82][83][84][85], we have revised our classification of such leaps and falls, relying on Bitcoin time series during the entire period (01.01.2011-21.01.2021) of verifiable fixed daily values of the Bitcoin price (BTC) (https://finance.yahoo.com/cryptocurrencies). We emphasize that • crashes are short and time-localized drops that last approximately two weeks, with the weighty losing of price each day.…”

Section: Data and Classificationmentioning

confidence: 99%

“…The corresponding measure of entropy is related to the recurrence properties that may be peculiar for the nonlinear complex system and important class of recurrence quantifiers are those that try to capture the level of complexity of a signal [78,84,297]. In accordance with this study, the entropy diagonal line histogram (DLEn) is of the greatest interest which uses the Shannon entropy of the distribution of diagonal lines P(l) to determine the complexity of the diagonal structures within the recurrence plot.…”

Section: Chaos Recurrent Measuresmentioning

confidence: 99%

Econophysics of cryptocurrency crashes: an overview

et al. 2021

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Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

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Recurrence entropy and financial crashes

Cited by 10 publications

References 11 publications

Modeling of Crisis Phenomena in Regional Stock Markets by Wavelet-Entropy Method

Modeling of Crisis Phenomena in Regional Stock Markets by Wavelet-Entropy Method

Coverage of the Coronavirus Pandemic through Entropy Measures

Econophysics of cryptocurrency crashes: an overview

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