The (in)efficiency of USA Education Group stocks: before, during and after COVID-19

The (in)efficiency of USA Education Group stocks: before, during and after COVID-19 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The (in)efficiency of USA Education Group stocks: before, during and after COVID-19 Leonardo H S Fernandes, José P V Fernandes, Jose W L Silva, Ranilson O A Paiva, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3822682/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Feb, 2024 Read the published version in Fractals → Version 1 posted You are reading this latest preprint version 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 behaviour (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. Econometrics COVID-19 Education stocks Multifractal detrended fluctuation analysis Generalized Hurst exponent Multifractal spectrum Asymmetry Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 18 Feb, 2024 Read the published version in Fractals → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3822682","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264415066,"identity":"ee8b1b94-b608-45d5-9154-254206f46c69","order_by":0,"name":"Leonardo H S Fernandes","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"H S","lastName":"Fernandes","suffix":""},{"id":264415067,"identity":"d02e140c-22aa-47e7-862d-a1645d15ce87","order_by":1,"name":"José P V 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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 behaviour (𝛼0 \u0026gt; 0.5), a higher degree of multifractality and the dominance of large fluctuations. 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