Econophysics of sustainability indices

Abstract

In this paper, the possibility of using some econophysical methods for quantitative assessment of complexity measures: entropy (Shannon, Approximate and Permutation entropies), fractal (Multifractal detrended fluctuation analysis – MF-DFA), and quantum (Heisenberg uncertainty principle) is investigated. Comparing the capability of both entropies, it is obtained that both measures are presented to be computationally efficient, robust, and useful. Each of them detects patterns that are general for crisis states. The similar results are for other measures. MF-DFA approach gives evidence that Dow Jones Sustainability Index is multifractal, and the degree of it changes significantly at different periods. Moreover, we demonstrate that the quantum apparatus of econophysics has reliable models for the identification of instability periods. We conclude that these measures make it possible to establish that the socially responsive exhibits characteristic patterns of complexity, and the proposed measures of complexity allow us to build indicators-precursors of critical and crisis phenomena.

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.…”

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

“…Our empirical analysis shows further perspectives for constructing effective algorithmic strategies and forecasting models based on complex systems theory. In the future, it would be interesting to consider other methods of classical multifractal analysis or its cross-correlation modifications in combination with other methods of complex systems theory [3,4,26,29,30,[42][43][44][45]49].…”

Sustainable development of Ukraine as a combination of social, economic and environmental components: structural econometric model of three-pillar approach

“…Taghvaee et al [25] vector autoregressive model Udemba and Keleş [26] ARDL Liu et al [27] ARIMA de Armas et al [28] operations research Wu et al [29] graph neural networks Chai et al [30] fuzzy logic Moreta et al [31] text analysis Zomchak and Starchevska [32] logistic regression Holloway and Mengersen [33] machine learning Alharbi et al [34] hierarchical framework Bielinskyi et al [35] econophysics Chen et al [36] data envelopment analysis Sutthichaimethee and Ariyasajjakorn [37] structural equation model Izonin et al [38] Wiener polynomial approximation Horoshkova et al [39] Kuznets curve Matviychuk et al [40] fractal analysis Valaskova et al [41] regression analysis…”