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

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 $\tau (q)$ 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 $({\alpha }_0>0.5)$, 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.