key: cord-0739774-dswxlpb4 authors: Sahoo, Manamani title: COVID‐19 impact on stock market: Evidence from the Indian stock market date: 2021-01-28 journal: J Public Aff DOI: 10.1002/pa.2621 sha: c01b47d70efa9c18b27608e76b97b698b59b3860 doc_id: 739774 cord_uid: dswxlpb4 This paper has been empirically investigated the existence of the day‐of‐the‐week effect by using closing daily data for Nifty 50, Nifty 50 Midcap, Nifty 100, Nifty 100 Midcap, Nifty 100 Smallcap, and Nifty 200 for before and during the COVID‐19 health crisis. This study used secondary data for all indices over the period 1 April 2005–14 May 2020. The present study used both dummy variable regression and the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. The total study period is divided into two sub‐periods, that is, during and before the COVID‐19 health crisis. A negative return is found for Mondays when the during‐COVID‐19 health crisis period is examined; in contrast, it was positive for the before COVID‐19 period. Tuesday's effect on index return is found statistically significant and positive for all indices during the COVID‐19 crisis. existence; some anomalies are appearing once and then disappearing, whereas other anomalies are frequently observed. Market anomalies also include the day-of-the-week effect, also known as Monday effect, the weekend effect, or intraday effect. An extensive body of research has examined the weekend effect (Alagidede, 2008; Berument & Dogan, 2012; Brusa & Liu, 2004; Chukwuogor, 2007) . The "traditional" view of a weekend effect is that stocks tend to exhibit lower return on Mondays compared to Fridays, due to investor behavior (Du Toit et al., 2018) . The reason for large returns on Fridays compared to Mondays is that portfolios are mostly sold on Mondays, as investors re-evaluate their portfolios on a Monday after bad news released over the weekend (Lakonishok & Maberly, 1990) . However, there are some papers which reveal no significant day-of-the-week effects with regards to volatility, so this finding has not been confirmed. The existence of the "weekend effect" on the stock market has resulted in inconsistent evidence (Bhana, 1985; Chukwuogor, 2007; Coutts & Sheikh, 2002; Kalidas et al., 2013; Mbululu & Chipeta, 2012; Plimsoll et al., 2013) . The purpose of the present study is to investigate the day-of-the-week effect on Nifty 50, Nifty 50 Midcap, Nifty 100, Nifty100 midcap, Nifty 200, Nifty 100 Smallcap. The purpose is to identify calendar anomalies using day-of-the-week-effect, whether there is a significant difference among weekdays' returns. Including introduction, this paper consists of four sections. Next section provides a detailed review literature on the day-of-the-week and weekend effect on index returns. Section 3 discusses the data and methodology used for the study. This is followed by the discussion on empirical results of the study. The last section provides the conclusion. The coronavirus pandemic 2019 has created a significant turmoil in the global economic activity (Baldwin & Di Mauro, 2020) and in stock markets around the world (Fama, 1981; Huang & Kracaw, 1984; Vassalou, 2003) . The stock prices change because of supply and demand. The stock price would fall when the number of people wanting to sell a stock is more than the number of people wanted to buy it (there would be greater supply than demand for that particular stock). The stock markets will react adversely due this outbreak in the short run, but, in the long run, markets eventually automatically correct themselves and again start increasing (Gormsen & Koijen, 2020) . The continental crisis could mainly affect stockholders' wealth due to the bank-run effect (the public to lose confidence in solvent banks) and the informational effect (the information about asset quality could lead investigators to revise their valuation of other banks; Aharony & Swary, 1983) . Due to feverish stock price reactions to COVID-19, the aggregate stock market fell strongly. So, recent health crisis morphed into a financial and economic crisis (Ramelli & Wagner, 2020 (Sansa, 2020) . Behavior of stock market is an early and visible evidence of the recent COVID-19 pandemic. It has adversely impacted the stock market (Baker et al., 2020; Ichino et al., 2020) . Alexakis and Xanthakis (1995) The data for the period from January 1990 to June 1995 are used for this study. It found the presence of the day-of-the-week effect on both stock return and volatility. Although both the return and volatility are not identical in all seven cases, the effect may be due to a possible spill-over from Japanese stock. Keim and Stambaugh (1984) documented high Friday return and low Monday return have been dubbed the "day-of-the-week" effect and the "weekend (Monday) effect." Berument and Kiymaz (2001) where, R t stands for index return at time t, ln is natural logarithm, Price t-1 and Price t are two consecutive daily closing price. A dummy variable regression model is fitted to examine the days of the week and weekend effect as follows: where R t represents index return at time t. D 1t , D 2t , D 3t , and D 4t are the dummies for Tuesday, Wednesday, Thursday, and Friday, respectively, which are defined in the following (Table 1) . To Conditional Mean Equation Here, this conditional means equation is just an extension of Equation (1), the dummy variable regression equation, by including an autoregressive term of the return series. The minimum SC and AIC criteria are used for selection of the number of autoregressive terms. In Equation (2) Note: Authors calculation based on the data obtained from https://www.investing.com. Null Hypothesis: Index return has a unit root. *1% level of significance. T A B L E 4 Dummy variable regression results (3), h t is the conditional variance of ε t , Φ 1 is the constant term, ω 1 is the Auto Regressive Conditional Heteroscedasticity (ARCH) coefficient which measures the influence of past squared residuals, that is, ε 2 t −1 on recent volatility, ω 2 is the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) coefficient which measures the influence of recent past period's volatility on current volatility at time t. Here, ω i is greater than zero and sum of ω 1 + ω 2 ≤1. In the conditional variance equation, Φ 1 is the intercept coefficient, which measures direction and the degree of weekend effect that is Monday effect on index return. R t = α 1 + β 1 D 1t (Tue) + β 2 D 2t (Wed) + β 3 D 3t (Thu) + β 4 D 4t (Fri) + ε t T A B L E 5 GARCH (1, 1) test results : To avoid spurious estimation, we need to conduct some preestimation test such as unit root test. Generally, time series data exhibit nonstationary behavior, like trend effects and random walk which lead to a nonsense results while analyzing relationship between a given set variables. Therefore, to capture a stationary condition we employ Augmented Dickey-fuller (ADF, 1984) and Phillips-Perron (PP, 1988) unit root tests. The Augmented Dickey-fuller (ADF, 1984) and Phillips-Perron (PP, 1988) unit root test results are reported in The dummy variable regression model for before and during COVID-19 results are reported in Table 3 To overcome autocorrelation and heteroscedasticity issues, this study switched to Generalized Autoregressive Conditional Heteroskedasticity model, and the results of GARCH are reported in Table 5 . The lower and upper panel of the Table 5 represent This finding is in contrast with Paital & Panda, 2018 . In addition to this, we also found a positive Wednesday effect for all indices during COVID-19 crisis, and the returns on Tuesday are higher than the returns on Tuesday and Thursday. The empirical results suggest that the coefficient for Friday effect is statistically insignificant for all indices. The Monday effect coefficient (α 1 ) for before-COVID-19 crisis is found to be statistically significant and positive return for all indices except Nifty200 (no Day-of-the-Week effect) and Nifty 100 smallcap (positive Tuesday effect). The main objective of this study is to investigate the existence and possible changes of day-of-the-week effect before and during the COVID-19 health crisis. This paper has empirically investigated the existence of day-of-the-week effect by using closing daily data for All the data are obtained electronically from https://www. investing.com. 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