Irreversibility of financial time series: a case of crisis

Abstract

The focus of this study to measure the varying irreversibility of stock markets. A fundamental idea of this study is that financial systems are complex and nonlinear systems that are presented to be non-Gaussian fractal and chaotic. Their complexity and different aspects of nonlinear properties, such as time irreversibility, vary over time and for a long-range of scales. Therefore, our work presents approaches to measure the complexity and irreversibility of the time series. To the presented methods we include Guzik’s index, Porta’s index, Costa’s index, based on complex networks measures, Multiscale time irreversibility index and based on permutation patterns measures. Our study presents that the corresponding measures can be used as indicators or indicator-precursors of crisis states in stock markets.

The Analysis of Multifractal Cross-Correlation Connectedness Between Bitcoin and the Stock Market

“…Further, for measuring the degree of multifractal cross-correlations between S&P 500, HSI, and BTC, we present the comparative dynamics of the described indicators calculated with the usage of the sliding window approach [5,6] along with the studied series. The presented measures are calculated for the standardized returns of S&P 500, HSI, and BTC, where returns are calculated as…”

Proceedings of the Selected and Revised Papers of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2022). Virtual Event, Kryvyi Rih, Ukraine, November 17-18, 2022

“…The data were extracted using Yahoo! Finance API based on Python programming language [52]; • the indicators described in the previous sections were calculated using the sliding window procedure [12,53,54,55,56,57,58]. The essence of this procedure is that: (1) a fragment (window) of a series of a certain length 𝑤 was selected; (2) a network measure was calculated for it; (3) the measure values were stored in a pre-declared array; (4) the window was shifted by a predefined time step ℎ, and the procedure was repeated until the series was completely exhausted; (5) further, the calculated values of the network measure were compared with the dynamics of the stock index.…”

“…Based on the recommendations by Korenyuk and Kopil [18], Kyshakevych et al [19], Godlewska-Majkowska [20], Jac and Vondrackova [21], Bushynskyi [22], Leshchuk [23], Lagler We use data for 2019 and 2020 from the materials of the State Statistics Service of Ukraine [55] and the Ministry of Development of Communities and Territories of Ukraine [15] for calculations. Obtained results will also be compared to assess changes in regions' position in the groups in which the regions are located.…”

Machine learning for prediction of emergent economy dynamics

“…xvii This article highlights further research by the authors, begun in [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33]. The focus of the article "Irreversibility of financial time series: a case of crisis" [34] by Andrii O. Bielinskyi, Serhii V. Hushko (figure 5), Andriy V. Matviychuk, Oleksandr A. Serdyuk, Serhiy O. Semerikov and Vladimir N. Soloviev to measure the varying irreversibility of stock markets. A fundamental idea of this study is that financial systems are complex and nonlinear systems that are presented to be non-Gaussian fractal and chaotic.…”