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| dc.contributor.author | Bielinskyi, Andriy | |
| dc.contributor.author | Семеріков, Сергій Олексійович | |
| dc.contributor.author | Serdiuk, Oleksandr | |
| dc.contributor.author | Solovieva, Victoria | |
| dc.contributor.author | Соловйов, Володимир Миколайович | |
| dc.contributor.author | Pichl, Lukáš | |
| dc.date.accessioned | 2020-12-24T16:53:53Z | |
| dc.date.available | 2020-12-24T16:53:53Z | |
| dc.date.issued | 2020-10-10 | |
| dc.identifier.citation | Bielinskyi A. Econophysics of sustainability indices [Electronic resource] / Andriy Bielinskyi, Serhiy Semerikov, Oleksandr Serdiuk, Victoria Solovieva, Vladimir Soloviev, Lukáš Pichl // Machine Learning for Prediction of Emergent Economy Dynamics 2020 : Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020 / Edited by : Arnold Kiv // CEUR Workshop Proceedings. – 2020. – Vol. 2713. – Pp. 372-392. – Access mode : http://ceur-ws.org/Vol-2713/paper41.pdf | uk_UA |
| dc.identifier.issn | 1613-0073 | |
| dc.identifier.uri | http://ceur-ws.org/Vol-2713/paper41.pdf | |
| dc.identifier.uri | http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4118 | |
| dc.identifier.uri | https://doi.org/10.31812/123456789/4118 | |
| dc.description | 1. Accelerating Green Finance: A report to Government by the Green Finance Taskforce. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_d ata/file/703816/green-finance-taskforce-accelerating-green-finance-report.pdf (2018). Accessed 25 Oct 2018 2. Antonakakis, N., Babalos, V., Kyei, C.K.: Predictability of sustainable investments and the role of uncertainty: Evidence from a non-parametric causality-in-quantiles test. Applied Economics 48(48), 4655–4665 (2016). doi:10.1080/00036846.2016.1161724 3. Bandt, C., Pompe, B.: Permutation Entropy: A Natural Complexity Measure for Time Series. Phys. Rev. Lett. 88, 174102 (2002). doi:10.1103/PhysRevLett.88.174102 4. Bielinskyi, A., Soloviev, V., Semerikov, S.: Detecting Stock Crashes Using Levy Distribution. CEUR Workshop Proceedings 2422, 420–433 (2019) 5. Boltzmann, L.: Weitere Studien über das Wärmegleichgewicht unter Gasmolekülen. Sitzungsberichte Akademie der Wissenschaften 66, 275–370 (1872). doi:10.1142/9781848161337_0015 6. Castilla-Rubio, J.C., Zadek, S., Robins, N.: Fintech and sustainable development: Assessing the implications. International Environment House, Geneva. http://unepinquiry.org/wpcontent/uploads/2016/12/Fintech_and_Sustainable_Development_Assessing_the_Implicat ions.pdf (2016). Accessed 21 Mar 2017 7. Cen, T., He, R.: Fintech, Green Finance and Sustainable Development. Advances in Social Science, Education and Humanities Research 291, 222–225 (2018). doi:10.2991/meeah18.2018.40 8. Clausius, R., Hirst, T.A.: The Mechanical Theory of Heat: With Its Applications to the Steam-engine and to the Physical Properties of Bodies. John van Voorst, London (1867) 9. Delgado-Bonal, A., Marshak, A.: Approximate Entropy and Sample Entropy: A Comprehensive Tutorial. Entropy 21(6), 541 (2019). doi:10.3390/e21060541 10. Delgado-Bonal, A.: Quantifying the randomness of the stock markets. Scientific Reports 9, 12761 (2019). doi:10.1038/s41598-019-49320-9 11. Derbentsev, V., Semerikov, S., Serdyuk, O., Solovieva, V., Soloviev, V.: Recurrence based entropies for sustainability indices. E3S Web of Conferences 166, 13031 (2020). doi:10.1051/e3sconf/202016613031 12. Duan, W.-Q., Stanley, H.: Volatility, irregularity, and predictable degree of accumulative return series. Phys. Rev. E 81, 066116 (2010). doi: 10.1103/PhysRevE.81.066116 13. Durand, R., Paugam, L., Stolowy, H.: Do investors actually value sustainability indices? Replication, development, and new evidence on CSR visibility. Strategic Management Journal 40(9), 1471–1490 (2019). doi:10.1002/smj.3035 14. Escrig-Olmedo, E., Fernández-Izquierdo, M.A., Ferrero-Ferrero, I., Rivera-Lirio, J.M., Muñoz-Torres, M.J.: Rating the Raters: Evaluating how ESG Rating Agencies Integrate Sustainability Principles. Sustainability 11(3), 915 (2019). doi:10.3390/su11030915 15. Fabregat-Aibar, L., Barberà-Mariné, M.G., Terceño, A., Pié, L.: A Bibliometric and Visualization Analysis of Socially Responsible Funds. Sustainability 11(9), 2526 (2019). doi:10.3390/su11092526 16. Feng, H., Chen, X., Heck, P., Miao, H.: An Entropy-Perspective Study on the Sustainable Development Potential of Tourism Destination Ecosystem in Dunhuang, China. Sustainability 6(12), 8980–9006 (2014). doi:10.3390/su6128980 17. Ganchuk, A., Derbentsev, V., Soloviev, V. N. Multifractal Properties of the Ukraine Stock Market. arXiv:physics/0608009v1 [physics.data-an] (2006). Accessed 17 Aug 2020 18. Gibbs, J.W.: Elementary Principles in Statistical Mechanics: Developed with Especial Reference to the Rational Foundation of Thermodynamics. C. Scribner's Sons, New York (1902). doi:10.5962/bhl.title.32624 19. Havrylenko, M., Shiyko, V., Horal, L., Khvostina, I., Yashcheritsyna, N.: Economic and mathematical modeling of industrial enterprise business model financial efficiency estimation. E3S Web of Conference 166, 13025 (2020). doi:10.1051/e3sconf/202016613025 20. Henri, M., Judge, G.: Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series. Econometrics 7(1), 10 (2019). doi:10.3390/econometrics7010010 21. Hurst, H.E.: A Suggested Statistical Model of some Time Series which occur in Nature. Nature 180, 494 (1957). doi:10.1038/180494a0 22. Kantelhardt, J.W., Zschiegner, S.A., Koscielny-Bunde, E., Havlin, S., Bunde, A., Stanley, H.E.: Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and its Applications 316(1–4), 87–114 (2002). doi:10.1016/S0378- 4371(02)01383-3 23. Khvostina, I., Havadzyn, N., Horal, L., Yurchenko, N.: Emergent Properties Manifestation in the Risk Assessment of Oil and Gas Companies. CEUR Workshop Proceedings 2422, 157–168 (2019) 24. Kung, O.Y.: "Green Finance for a Sustainable World" - Keynote Speech by Mr Ong Ye Kung, Minister for Education, Singapore and Board Member, Monetary Authority of Singapore, at SFF x SWITCH 2019 on 11 November 2019. https://www.mas.gov.sg/news/speeches/2019/green-finance-for-a-sustainable-world (2019). Accessed 28 Nov 2019 25. Kutner, R., Ausloos, M., Grech, D., Matteo, T.Di., Schinckus, C., Stanley, H.E.: Econophysics and sociophysics: Their milestones & challenges. Physica A: Statistical Mechanics and its Applications 516, 240–253 (2019). doi:10.1016/j.physa.2018.10.019 26. Liang, X., Si, D., Zhang, X.: Regional Sustainable Development Analysis Based on Information Entropy-Sichuan Province as an Example. International Journal of Environmental Research and Public Health 14(10), 1219 (2017). doi:10.3390/ijerph14101219 27. Lucia, U., Grisolia, G: Exergy inefficiency: An indicator for sustainable development analysis. Energy Reports 5, 62–69 (2019). doi:10.1016/j.egyr.2018.12.001 28. Mandelbrot, B.B.: The Fractal Geometry of Nature. W. H. Freeman and Company, New York (1982) 29. Mayer, A.L., Donovan, R.P., Pawlowski, C.W.: Information and entropy theory for the sustainability of coupled human and natural systems. Ecology and Society 19(3), 11 (2014). doi:10.5751/ES-06626-190311 30. Mills, M., Wardle, M.: The Global Green Finance Index 4 (2019). doi:10.13140/RG.2.2.28337.33124 31. Miralles-Quiros, M.M., Miralles-Quiros, J.L., Arraiano, I.G.: Sustainable Development, Sustainability Leadership and Firm Valuation: Differences across Europe. Business Strategy and the Environment 26(7), 1014–1028 (2017). doi:10.1002/bse.1964 32. Mynhardt, H., Makarenko, I., Plastun, A.: Market efficiency of traditional stock market indices and social responsible indices: the role of sustainability reporting. Investment Management and Financial Innovations 14(2), 94-106 (2017). doi:10.21511/imfi.14(2).2017.09 33. Nassiry, D.: The Role of Fintech in Unlocking Green Finance: Policy Insights for Developing Countries. ADBI Working Paper Series 883. https://www.adb.org/sites/default/files/publication/464821/adbi-wp883.pdf (2018). Accessed 25 Oct 2019 34. Ohlson, J.A.: Earnings, Book Values, and Dividends in Equity Valuation: An Empirical Perspective. Contemporary Accounting Research 18(1), 107–120 (2001). doi:10.1506/7TPJ-RXQN-TQC7-FFAE 35. Peng, C.-K., Buldyrev, S.V., Havlin, S., Simons, M., Stanley, H.E., Goldberger, A.L.: Mosaic organization of DNA nucleotides. Physical Review E 49, 1685 (1993). doi:10.1103/PhysRevE.49.1685 36. Pincus, S., Kalman, R.E.: Irregularity, volatility, risk, and financial market time series. PNAS 101(38), 13709–13714 (2004). doi: 10.1073/pnas.0405168101 37. Pincus, S.M.: Approximate entropy as a measure of system complexity. PNAS 88(6), 2297– 2301 (1991). doi:10.1073/pnas.88.6.2297 38. RobecoSAM: About RobecoSAM. https://www.robecosam.com/en/about-us/aboutrobecosam.html (2020). Accessed 17 Aug 2020 39. S&P Dow Jones Indices: S&P Global BMI. https://www.spglobal.com/spdji/en/indices/equity/sp-global-bmi (2020). Accessed 17 Aug 2020 40. S&P Global Switzerland: The Sustainability Yearbook 2020. https://www.spglobal.com/esg/csa/yearbook/ (2020). Accessed 17 Aug 2020 41. Saptsin, V., Soloviev, V.: Relativistic quantum econophysics – new paradigms in complex systems modelling. arXiv:0907.1142v1 [physics.soc-ph] (2009). Accessed 17 Aug 2020 42. Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27(3), 379–423 (1948). doi:10.1002/j.1538-7305.1948.tb01338.x 43. Śliwiński, P., Łobza, M.: Financial Performance of Socially Responsible Indices. International Journal of Management and Economics 53(1), 25–46 (2017) 44. Soloviev, V., Belinskij, A.: Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors. CEUR Workshop Proceedings 2104, 116–127 (2018) 45. Soloviev, V., Bielinskyi, A., Solovieva, V.: Entropy Analysis of Crisis Phenomena for DJIA Index. CEUR Workshop Proceedings 2393, 434–449 (2019) 46. Soloviev, V., Saptsin, V.: Heisenberg uncertainty principle and economic analogues of basic physical quantities. arXiv:1111.5289v1 [physics.gen-ph] (2011). Accessed 17 Aug 2020 47. Soloviev, V., Semerikov, S., Solovieva, V.: Lempel-Ziv Complexity and Crises of Cryptocurrency Market. Advances in Economics, Business and Management Research 129, 299–306 (2020). doi:10.2991/aebmr.k.200318.037 48. Soloviev, V., Serdiuk, O., Semerikov, S., Kohut-Ferens, O.: Recurrence entropy and financial crashes. Advances in Economics, Business and Management Research 99, 385– 388 (2019). doi:10.2991/mdsmes-19.2019.73 49. Soloviev, V., Solovieva, V., Tuliakova, A., Ivanova, M.: Construction of crisis precursors in multiplex networks. Advances in Economics, Business and Management Research 99, 361–366 (2019). doi:10.2991/mdsmes-19.2019.68 50. Soloviev, V.N., Belinskiy, A..: Complex Systems Theory and Crashes of Cryptocurrency Market. In: Ermolayev V., Suárez-Figueroa M., Yakovyna V., Mayr H., Nikitchenko M., Spivakovsky A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2018. Communications in Computer and Information Science, vol 1007, pp. 276–297. Springer, Cham (2019) 51. Soloviev, V.N., Bielinskyi, A., Serdyuk, O., Solovieva, V., Semerikov, S.: Lyapunov Exponents as Indicators of the Stock Market Crashes. CEUR Workshop Proceedings (2020, in press) 52. Soloviev, V.N., Yevtushenko, S.P., Batareyev, V.V.: Comparative analysis of the cryptocurrency and the stock markets using the Random Matrix Theory. CEUR Workshop Proceedings 2546, 87–100 (2019) 53. Sustainalytics: Index Research Services. https://www.sustainalytics.com/index-researchservices/ (2020). Accessed 10 Apr 2020 54. Thurner, S., Hanel, R., Klimek, P.: Introduction to the Theory of Complex Systems. Oxford University Press, Oxford (2018) 55. Tillmann, J.: The link between sustainability performance and financial performance – An event study on the impact of sustainability index changes on the market value of a company. Master Thesis, University of Tilburg (2012) 56. Tiwari, A.K., Albulescu, C.T., Yoon, S.-M.: A multifractal detrended fluctuation analysis of financial market efficiency: Comparison using Dow Jones sector ETF indices. Physica A: Statistical Mechanics and its Applications 483, 182–192 (2017) 57. Tsallis, C.: Introduction to Nonextensive Statistical Mechanics: Approaching a Complex World. Springer, New York (2009) 58. Wang, W., Zhao, X., Gong, Q., Ji, Z.: Measurement of Regional Green Economy sustainable development ability based on Entropy Weight-Topsis-Coupling Coordination Degree – A case study in Shandong Province, China. Sustainability 11(1), 280 (2019) 59. Wikipedia: Dow Jones Sustainability Indices. https://en.wikipedia.org/w/index.php?title=Dow_Jones_Sustainability_Indices&oldid=97 4037263 (2020). Accessed 20 Aug 2020 60. Wikipedia: List of stock market crashes and bear markets. https://en.wikipedia.org/w/index.php?title=List_of_stock_market_crashes_and_bear_mark ets&oldid=984366080 (2020). Accessed 19 Oct 2020 61. Yahoo Finance: Dow Jones Industrial Average (^DJI). https://finance.yahoo.com/quote/%5EDJI?p=^DJI (2020). Accessed 17 Aug 2020 62. Yahoo Finance: Dow Jones Sustainability World (^W1SGI) Charts, Data & News. https://finance.yahoo.com/quote/%5EW1SGI/ (2020). Accessed 17 Aug 2020/ | |
| dc.description.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. | uk_UA |
| dc.language.iso | en | uk_UA |
| dc.publisher | Arnold Kiv | uk_UA |
| dc.subject | Dow Jones Sustainability Index | uk_UA |
| dc.subject | measures of complexity | uk_UA |
| dc.subject | precursors of stock market crashes | uk_UA |
| dc.title | Econophysics of sustainability indices | uk_UA |
| dc.type | Article | uk_UA |
