THE (IN)EFFICIENCY OF USA EDUCATION GROUP STOCKS: BEFORE, DURING AND AFTER COVID-19

Leonardo H. S. Fernandes, Jos㉠P. V. Fernandes, Jos㉠W. L. Silva, Ranilson O. A. Paiva, Ibsen M. B. S. Pinto and Fernando H. A. DE ARAÚJO
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Leonardo H. S. Fernandes: Department of Economics and Informatics, Federal Rural University of Pernambuco, Serra Talhada, Brazil
Jos㉠P. V. Fernandes: ��Anhembi Morumbi University School of Medicine, Dr. Almeida Lima Street, 1134 — Mooca, São Paulo — SP, 03101-001, Brazil
Jos㉠W. L. Silva: ��Department of Statistics and Informatics, Federal Rural University of Pernambuco, Recife, Brazil
Ranilson O. A. Paiva: �Núcleo de Excelência em Tecnologias, Sociais vinculado ao Instituto de Computação da Universidade, Federal de Alagoas — Macéio — AL, 57072-900, Brazil
Ibsen M. B. S. Pinto: �Núcleo de Excelência em Tecnologias, Sociais vinculado ao Instituto de Computação da Universidade, Federal de Alagoas — Macéio — AL, 57072-900, Brazil
Fernando H. A. DE ARAÚJO: �Federal Institute of Education, Science and Technology of Paraíba, Campus Patos, PB. Acesso Rodovia, PB 110, S/N Alto, Tubiba — CEP

FRACTALS (fractals), 2024, vol. 32, issue 03, 1-18

Abstract: This paper represents a pioneering effort to investigate multifractal dynamics that exclusively encompass the return time series of USA Education Group Stocks concerning two non-overlapping periods (before, during, and after COVID-19). Given this, we employ the Multifractal Detrended Fluctuations Analysis (MF-DFA). In this sense, we investigate the generalized Hurst exponent h(q) and the Rényi exponent τ(q) for each asset and quantify their statistical properties, which allowed us to observe separately the contributing small scale (primarily via the negative moments q) and the large scale (via the positive moments q). We perform a fourth-degree polynomial regression fit to estimate the complexity parameters that describe the degree of multifractality of the underlying process. Also, we shall apply the inefficiency multifractal index to assess the COVID-19 shock for both periods. Our findings show that for both periods, the majority of these assets are marked by multifractal dynamics associated with persistent behavior (α0 > 0.5), a higher degree of multifractality and the dominance of large fluctuations. At the same time, most of these assets show asymmetry parameter (R > 1) for both periods, indicating that large fluctuations contributed more to multifractality in the time series of returns.

Keywords: COVID-19; Education Stocks; Multifractal Detrended Fluctuation Analysis; Generalized Hurst Exponent; Multifractal Spectrum; Asymmetry (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X24500476

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