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SPILLOVER EFFECTS OF COVID-19 ON USA EDUCATION GROUP STOCKS

Department of Economics and Informatics, Federal Rural University of Pernambuco, Serra Talhada, PE 56909-535, Brazil

E-mail Address: leonardo.henrique@ufrpe.br

Federal Institute of Education, Science and Technology of Paraíba, Campus Patos, PB. Acesso rodovia PB 110, S/N Alto Tubiba - CEP: 58700-030, PB, Patos, Brazil

E-mail Address: fhenrique14@gmail.com

Corresponding author.

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JOSÉ W. L. SILVA

https://orcid.org/0000-0002-9127-2866

Department of Statistics and Informatics, Federal Rural University of Pernambuco, Recife, PE 52171-900, Brazil

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JOSÉ P. V. FERNANDES

https://orcid.org/0000-0001-6443-3540

Department of Medical Sciences, Anhembi Morumbi University, Dr. Almeida Lima Street, 1134 - Mooca, São Paulo - SP, 03101-001, Brazil

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URBANNO P. S. LEITE

Pitágoras University Unopar Anhanguera - Unopar Edwy, Taques de Araújo street, n^∘ 900 - Jardim Burle Marx, Londrina - PR, 86047-790, Brazil

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LUCAS M. MUNIZ

Federal University of Alagoas - Arapiraca Campus, Manoel Severino Barbosa avenue - Bom Sucesso, Arapiraca - AL, 57309-005, Brazil

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RANILSON O. A. PAIVA

https://orcid.org/0000-0002-0894-751X

Núcleo de Excelência em Tecnologias Sociais vinculado ao, Instituto de Computação da Universidade Federal, de Alagoas - Macéio - AL, 57072-900, Brazil

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IBSEN M. B. S. PINTO

https://orcid.org/0000-0002-6543-143X

Núcleo de Excelência em Tecnologias Sociais vinculado ao, Instituto de Computação da Universidade Federal, de Alagoas - Macéio - AL, 57072-900, Brazil

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, and

BENJAMIN MIRANDA TABAK

https://orcid.org/0000-0002-7935-3188

School of Public Policy and Government, Getulio Vargas Foundation, SGAN 602 Módulos A, B, C, Asa Norte, Brasília, DF 70830-020, Brazil

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https://doi.org/10.1142/S0218348X24500749Cited by:0 (Source: Crossref)

Abstract

In this paper, we explore the price dynamics of 16 representative records of USA Education Group stocks, encompassing two non-overlapping periods (before, during, and after COVID-19). Based on information theory and cluster analysis techniques, our study provides insights into disorder, predictability, efficiency, similarity, and resilience/weakness, considering the most diverse financial stakeholders. Our findings contribute significantly to understanding price dynamics in the education sector, encompassing non-crisis and crisis periods, and provide valuable information for financial risk management, highlighting the importance of considering financial assets’ efficiency and resilience/weakness during economic turbulence, such as the COVID-19 crisis.

Keywords:

References

1. A. D. Gonçalves, L. H. Fernandes and A. D. Nascimento , Dynamics diagnosis of the COVID-19 deaths using the pearson diagram, Chaos Solitons Fractals 164 (2022) 112634, https://doi.org/10.1016/j.chaos.2022.112634, https://www.sciencedirect.com/science/article/pii/S0960077922008153. Crossref, Web of Science, Google Scholar
2. T. Bouazizi, K. Guesmi, E. Galariotis and S. Vigne , Crude oil prices in times of crisis: The role of COVID-19 and historical events, Int. Rev. Financ. Anal. 91 (2023) 102955, https://doi.org/10.1016/j.irfa.2023.102955, https://www.sciencedirect.com/science/article/pii/S1057521923004714. Crossref, Web of Science, Google Scholar
3. T. K. Burki , COVID-19: Consequences for higher education, Lancet Oncol. 21(6) (2020) 758. Crossref, Web of Science, Google Scholar
4. Y. B. de Bruin, A.-S. Lequarre, J. McCourt, P. Clevestig, F. Pigazzani, M. Z. Jeddi, C. Colosio and M. Goulart , Initial impacts of global risk mitigation measures taken during the combatting of the COVID-19 pandemic, Saf. Sci. 128 (2020) 104773, https://doi.org/10.1016/j.ssci.2020.104773, https://www.sciencedirect.com/science/article/pii/S0925753520301703. Crossref, Web of Science, Google Scholar
5. N. Apergis, C. K. Lau and B. Xu , The impact of COVID-19 on stock market liquidity: Fresh evidence on listed chinese firms, Int. Rev. Financ. Anal. 90 (2023) 102847, https://doi.org/10.1016/j.irfa.2023.102847, https://www.sciencedirect.com/science/article/pii/S1057521923003630. Crossref, Web of Science, Google Scholar
6. P. M. Romer , Human capital and growth: Theory and evidence, Carnegie–Rochester Conf. Ser. Public Policy 32 (1990) 251–286. Crossref, Google Scholar
7. P. M. Romer , Endogenous technological change, J. Political Econ. 98(5, Part 2) (1990) S71–S102. Crossref, Web of Science, Google Scholar
8. C. I. Jones and P. M. Romer , The new kaldor facts: Ideas, institutions, population, and human capital, Am. Econ. J.: Macroecon. 2(1) (2010) 224–245. Crossref, Web of Science, Google Scholar
9. R. Jedwab, P. Romer, A. M. Islam and R. Samaniego , Human capital accumulation at work: Estimates for the world and implications for development, Am. Econ. J.: Macroecon. 15(3) (2023) 191–223. Crossref, Web of Science, Google Scholar
10. V. J. García-Morales, A. Garrido-Moreno and R. Martín-Rojas , The transformation of higher education after the COVID disruption: Emerging challenges in an online learning scenario, Front. Psychol. 12 (2021) 616059. Crossref, Web of Science, Google Scholar
11. I. Ghosh, E. Alfaro-Cortés, M. Gámez and N. García-Rubio , Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning & XAI, Int. Rev. Financ. Anal. 87 (2023) 102558, https://doi.org/10.1016/j.irfa.2023.102558, https://www.sciencedirect.com/science/article/pii/S1057521923000741. Crossref, Web of Science, Google Scholar
12. L. H. Fernandes and F. H. Araújo , Taxonomy of commodities assets via complexity-entropy causality plane, Chaos Solitons Fractals 137 (2020) 109909, https://doi.org/10.1016/j.chaos.2020.109909, https://www.sciencedirect.com/science/article/pii/S096007792030309X. Crossref, Web of Science, Google Scholar
13. O. A. Rosso, H. Larrondo, M. T. Martin, A. Plastino and M. A. Fuentes , Distinguishing noise from chaos, Phys. Rev. Lett. 99(15) (2007) 154102. Crossref, Web of Science, Google Scholar
14. C. Vignat and J.-F. Bercher , Analysis of signals in the Fisher–Shannon information plane, Phys. Lett. A 312(1) (2003) 27–33, https://doi.org/10.1016/S0375-9601(03)00570-X, https://www.sciencedirect.com/science/article/pii/S037596010300570X. Crossref, Web of Science, Google Scholar
15. W. J. Procasky and A. Yin , The impact of COVID-19 on the relative market efficiency and forecasting ability of credit derivative and equity markets, Int. Rev. Financ. Anal. 90 (2023) 102926, https://doi.org/10.1016/j.irfa.2023.102926, https://www.sciencedirect.com/science/article/pii/S1057521923004428. Crossref, Web of Science, Google Scholar
16. J. V. Sáenz, F. M. Quiroga and A. F. Bariviera , Data vs. information: Using clustering techniques to enhance stock returns forecasting, Int. Rev. Financ. Anal. 88 (2023) 102657, https://doi.org/10.1016/j.irfa.2023.102657, https://www.sciencedirect.com/science/article/pii/S1057521923001734. Crossref, Web of Science, Google Scholar
17. F. Lúcio and J. Caiado , Covid-19 and stock market volatility: A clustering approach for S&P 500 industry indices, Fin. Res. Lett. 49 (2022) 103141, https://doi.org/10.1016/j.frl.2022.103141, https://www.sciencedirect.com/science/article/pii/S1544612322003646. Crossref, Web of Science, Google Scholar
18. C. Bandt and B. Pompe , Permutation entropy: A natural complexity measure for time series, Phys. Rev. Lett. 88(17) (2002) 174102. Crossref, Web of Science, Google Scholar
19. B. Dima, Ş. M. Dima and R. Ioan , Remarks on the behaviour of financial market efficiency during the COVID-19 pandemic. The case of VIX, Fin. Res. Lett. 43 (2021) 101967, https://doi.org/10.1016/j.frl.2021.101967, https://www.sciencedirect.com/science/article/pii/S1544612321000489. Crossref, Web of Science, Google Scholar
20. A. M. Martins, L. H. Fernandes and A. D. Nascimento , Scientific progress in information theory quantifiers, Chaos Solitons Fractals 170 (2023) 113260, https://doi.org/10.1016/j.chaos.2023.113260, https://www.sciencedirect.com/science/article/pii/S0960077923001613. Crossref, Web of Science, Google Scholar
21. L. H. Fernandes, F. H. Araujo, M. A. Silva and B. Acioli-Santos , Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers, Results Phys. 26 (2021) 104306, https://doi.org/10.1016/j.rinp.2021.104306, https://www.sciencedirect.com/science/article/pii/S221137972100437X. Crossref, Web of Science, Google Scholar
22. L. H. Fernandes, F. H. de Araújo, I. E. Silva and J. S. Neto , Macroeconophysics indicator of economic efficiency, Physica A 573 (2021) 125946, https://doi.org/10.1016/j.physa.2021.125946, https://www.sciencedirect.com/science/article/pii/S0378437121002181. Crossref, Web of Science, Google Scholar
23. L. Zunino, M. Zanin, B. M. Tabak, D. G. Pérez and O. A. Rosso , Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency, Physica A 389(9) (2010) 1891–1901, https://doi.org/10.1016/j.physa.2010.01.007, https://www.sciencedirect.com/science/article/pii/S0378437110000397. Crossref, Web of Science, Google Scholar
24. Y. Wang, P. Shang and Z. Liu , Analysis of time series through complexity–entropy curves based on generalized fractional entropy, Nonlinear Dyn. 96(1) (2019) 585–599. Crossref, Web of Science, Google Scholar
25. P. Lamberti, M. Martin, A. Plastino and O. Rosso , Intensive entropic non-triviality measure, Physica A 334(1) (2004) 119–131, https://doi.org/10.1016/j.physa.2003.11.005, https://www.sciencedirect.com/science/article/pii/S0378437103010963. Crossref, Web of Science, Google Scholar
26. G. S. Argyroudis and F. M. Siokis , Spillover effects of great recession on Hong-Kong’s real estate market: An analysis based on causality plane and Tsallis curves of complexity–entropy, Physica A 524 (2019) 576–586, https://doi.org/10.1016/j.physa.2019.04.052, https://www.sciencedirect.com/science/article/pii/S0378437119304194. Crossref, Web of Science, Google Scholar
27. H. Xiong, P. Shang, J. He and Y. Zhang , Complexity and information measures in planar characterization of chaos and noise, Nonlinear Dyn. 100 (2020) 1673–1687. Crossref, Web of Science, Google Scholar
28. L. H. Fernandes, F. H. De Araujo, J. W. Silva, M. C. Filho and B. M. Tabak , Assessment of sector bond, equity indices and green bond index using information theory quantifiers and clusters techniques, Fractals 31(1) (2023) 2350017. Link, Web of Science, Google Scholar
29. L. H. Fernandes, J. W. Silva, F. H. Araujo and A. F. Bariviera , Quantifying the COVID-19 shock in cryptocurrencies, Fractals 32 (2024) 2450019. Link, Web of Science, Google Scholar
30. E. Brigatti, V. Netto, F. de Sousa Filho and C. Cacholas , Entropy and hierarchical clustering: Characterizing the morphology of the urban fabric in different spatial cultures, Chaos 31(11) (2021) 113138. Crossref, Web of Science, Google Scholar
31. L. H. Fernandes, F. H. De Araujo, J. W. Silva, M. C. Filho and B. M. Tabak , The synergic interplay between entropy, predictability, and informational efficiency of the shanghai sectoral index, Fractals 31(9) (2023) 2350121. Link, Web of Science, Google Scholar
32. M. Martin, A. Plastino and O. Rosso , Generalized statistical complexity measures: Geometrical and analytical properties, Physica A 369(2) (2006) 439–462, https://doi.org/10.1016/j.physa.2005.11.053, https://www.sciencedirect.com/science/article/pii/S0378437106001324. Crossref, Web of Science, Google Scholar
33. S. Sippel, H. Lange, M. D. Mahecha, M. Hauhs, P. Bodesheim, T. Kaminski, F. Gans and O. A. Rosso , Diagnosing the dynamics of observed and simulated ecosystem gross primary productivity with time causal information theory quantifiers, PLoS One 11(10) (2016) e0164960. Crossref, Web of Science, Google Scholar
34. L. H. Fernandes, F. H. de Araujo and B. M. Tabak , Insights from the (in)efficiency of chinese sectoral indices during COVID-19, Physica A 578 (2021) 126063, https://doi.org/10.1016/j.physa.2021.126063, https://www.sciencedirect.com/science/article/pii/S0378437121003368. Crossref, Web of Science, Google Scholar
35. L. H. Fernandes, E. Bouri, J. W. Silva, L. Bejan and F. H. de Araujo , The resilience of cryptocurrency market efficiency to COVID-19 shock, Physica A 607 (2022) 128218, https://doi.org/10.1016/j.physa.2022.128218, https://www.sciencedirect.com/science/article/pii/S0378437122007762. Crossref, Web of Science, Google Scholar
36. D. W. Fung, W. Y. Lee, C. C. Yang and J. J. Yeh , Risk taking, performance, and resilience to the COVID-19 pandemic: Evidence from public property-casualty insurers, Int. Rev. Financ. Anal. (2023) 102942. https://doi.org/10.1016/j.irfa.2023.102942, https://www.sciencedirect.com/science/article/pii/S1057521923004581. Web of Science, Google Scholar
37. T. Kiss, K. Kladívko, O. Silfverberg and P. Österholm , Market participants or the random walk — who forecasts better? Evidence from micro-level survey data, Fin. Res. Lett. 54 (2023) 103752, https://doi.org/10.1016/j.frl.2023.103752, https://www.sciencedirect.com/science/article/pii/S1544612323001253. Crossref, Web of Science, Google Scholar