Bulletin of Monetary Economics and Banking, Vol. 22, No. 2 (2019), pp. 213 - 236
REVISITING CALENDAR ANOMALIES IN BRICS COUNTRIES*
Harald Kinateder1, Kimberly Weber2, Niklas F. Wagner3
1School of Business, Economics and Information Systems, University of Passau, Innstrasse 27, 94030 Passau, Germany. Email:
2School of Business, Economics and Information Systems, University of Passau, Innstrasse 27, 94030
Passau, Germany. Email: KimberlyWeber@gmx.net
3Corresponding author: School of Business, Economics and Information Systems, University of Passau, Innstrasse 27, 94030 Passau, Germany. Email: nwagner@alum.calberkeley.org
ABSTRACT
We use a generalized autoregressive conditional heteroskedasticity dummy approach to analyze the influence of calendar anomalies on conditional daily returns and risk for the stock markets of Brazil, Russia, India, China, and South Africa from 1996 to 2018.
Keywords: Abnormal returns; Efficient market hypothesis; Calendar effects; GARCH; Holiday effects.
JEL Classification: G1; G4.
Article history: |
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Received |
: March 20, 2019 |
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Revised |
: July 15, 2019 |
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Accepted |
: July 22, |
2019 |
Available online : July 30, |
2019 |
https://doi.org/10.21098/bemp.v22i2.1092
*We would like to thank two anonymous referees for many helpful comments. All omissions and errors remain those of the authors.
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I. INTRODUCTION
Calendar anomalies are a widely researched subject given its broader implications for financial market performance. Daily, weekly, and monthly effects can be referred to as seasonalities, where “seasonality is a usual and recurring variation in a time series that occurs occasionally over a span of less than a year” (Prajapati et al., 2013). Therefore, it would be possible for investors to predict stock market developments based on past information and profit from the abnormal returns resulting from these effects (e.g., Darrat et al., 2013; Sharma et al., 2014). This behavior portends market inefficiency, since it should be impossible to generate abnormal returns because of systematic price changes, and these anomalies should not exist in an efficient market (e.g., Safeer and Kevin, 2014; Patel, 2016). Since less mature stock markets gain importance as investing opportunities for international stockholders, an investigation with respect to calendar anomalies seems promising, since these markets are basically considered less efficient compared to developed ones (e.g., Fountas and Segredakis, 2002; Seif et al., 2017).
In this paper, we reconcile a comprehensive set of calendar anomalies in Brazil, Russia, India, China, and South Africa (BRICS). The anomalies examined are the
We focus on calendar anomalies in the BRICS countries for two reasons: First, the literature covering these effects in these nations is relatively sparse, since most papers analyze developed nations such as the United States and European countries. Second, within emerging markets, the BRICS countries have recently gained enormous investor attraction (Kinateder et al., 2017). To account for the stylized facts of stock market returns (i.e., leptokurtosis and heteroscedasticity), we use a generalized autoregressive conditional heteroskedasticity (GARCH) specification with dummy variables in the mean and variance equation (e.g., Auer and Rottmann, 2014). This approach offers two benefits. First, it allows us to investigate not only how calendar anomalies affect returns, but also how they
1Studies addressing the MOY effect include, for example, those of Lakonishok and Smidt (1988), Meneu and Pardo (2004), Yakob et al. (2005), Lucey and Zhao (2008), Sun and Tong (2010), Darrat et al. (2013), and Patel (2016).
2Studies addressing the TOM effect include, for example, those of Chen and Chua (2011), Prajapati et al. (2013), Auer and Rottmann (2014), Safeer and Kevin (2014), and Kayacetin and Lekpek (2016).
3Studies dealing with the DOW effect include, for example, Keim and Stambaugh (1984), Wang et al. (1997), Mehdian and Perry (2001), Draper and Paudyal (2002), and Narayan et al. (2015).
4Studies dealing with the holiday effect include, for example, McGuinness (2005), Bialkowski et al.
(2013), Gama and Vieira (2013), Yuan and Gupta (2014), and Yang (2016).
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impact risk. Second, this approach is a natural choice for capturing large parts of the
We analyze calendar anomalies in the daily returns of major BRICS stock market indices from January 1996 to March 2018. Our results underline that, in Brazil, Russia, and South Africa, a weak MOY effect exists in several months, but not in January. For the Indian and Chinese indices, no MOY anomaly is detected. Moreover, the TOM effect is found in several BRICS countries. The Brazilian stock market exhibits anomalous behavior two days before the TOM, whereas the returns of the Russian stock market are anomalous one day after the TOM. The Chinese and Indian indices display a TOM effect one day before, one day after, and two days after the TOM. Moreover, the TOM effect manifests itself one and two days after the TOM in the South African equity market. The DOW anomaly is found on Fridays in the Brazilian index, on Mondays and Tuesdays in the Russian index, and on Tuesdays in the Indian index. Furthermore, the DOW anomaly exists on Tuesdays and Thursdays in China, on Tuesdays in India, and on Mondays and Tuesdays in the South African stock market. The holiday inconsistency, which is split between a pre- and a
Although the behavior of stock prices might be predictable, investors have no guarantee that they will earn abnormal returns, because equity prices could react differently than in previous years (Fountas and Segredakis, 2002). The disappearance of an inconsistency can be attributed to investors attempting to exploit it (Haugen and Jorion, 1996). For example, if the price of a certain stock rises on a particular day of the month, investors will buy shares beforehand and sell them on this day. Hence, the selling pressure increases, the stock price decreases, and the effect disappears. Additionally, the supposedly anomalous behavior of equity prices could be due only to institutional market features or an incorrectly specified market model and is therefore not an anomaly at all (Claessens et al., 1995). Moreover, it might simply not be possible to arbitrage calendar anomalies because of transaction costs, explaining the persistence of these effects and making them compatible with equilibrium prices (e.g., Haugen and Jorion, 1996; Dongcheol, 2006).
The remainder of this paper is structured as follows: Section II describes the data and methodology used to identify the calendar effects. Section III discusses the results. Section IV concludes the paper.
II.DATA AND METHODOLOGY A. Data
To study calendar effects, we use a representative stock market index for each country. For Brazil, the Índice Bolsa de Valores de São Paulo (IBOVESPA) is analyzed. Furthermore, the IBOVESPA is capitalization weighted. After the dissolution of the Soviet Union, new stock markets developed in Russia, and on
September 1, 1995, the Russian Trading System Index (RTSI), where stocks are capitalization weighted, was established (McGowan and Ibrihim, 2009). The
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Bombay Stock Exchange (BSE) in India opened in 1875. The index, which is used for the Indian stock market, is the
The daily closing prices of the indices are extracted from the Thomson Reuters Eikon and cover from January 1, 1996, to March 30, 2018, since the indices all already exist in this period. Therefore, the results are comparable, because the same time span is examined for all the indices. To guarantee further comparability of the results, the currency used for all the daily prices is the US dollar, which also helps to adopt the perspective of an international investor (Basher and Sadorsky, 2006). Furthermore, the data are not corrected for dividends, because it is not likely that these are set to specific DOWs (McGuinness, 2005).
Table 1.
Descriptive Statistics
The table shows key descriptive statistics of the returns of BRICS stock market indices: the IBOVESPA, RTSI, S&P BSE SENSEX, SSE Composite and FTSE/JSE All Share. Reported are the median, maximum and minimum of the daily returns. Additionally, the standard deviation, skewness and kurtosis of the returns distribution are presented and results of the
Index |
IBOVESPA |
RTSI |
S&P BSE |
SSE |
FTSE/JSE |
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SENSEX |
Composite |
All Share |
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Median |
0.0006 |
0.0003 |
0.0003 |
0.0000 |
0.0006 |
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Maximum |
0.1801 |
0.2020 |
0.1905 |
0.0940 |
0.1289 |
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Minimum |
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Standard Deviation |
0.0241 |
0.0249 |
0.0164 |
0.0163 |
0.0168 |
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Skewness |
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Kurtosis |
9.4676 |
11.6011 |
9.9406 |
8.7078 |
9.0704 |
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10175.80 |
18043.03 |
11652.00 |
8034.33 |
9065.01 |
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0.0000 |
0.0000 |
0.0000 |
0.0000 |
0.0000 |
Table 1 reports key descriptive statistics for each stock market index investigated. The RTSI has the highest standard deviation (0.0249), and the SSE Composite the lowest (0.0163). The return distributions are not normal, since all the indices exhibit mild negative skewness and have a kurtosis value that is significantly higher than three. This result is confirmed by the
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B. Methodology
For our analysis, we use daily continuously compounded returns, Rt, which are based on the daily closing prices, Pt, of the respective stock market indices on day t, where
B1. MOY Effect
The MOY assumes that investors can earn abnormal returns in a particular MOY. This effect is studied by an
(1)
This equation includes a constant c and a
where the term |
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(2) |
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allows for a possible asymmetric response of conditional |
volatility to negative return innovations, with the indicator function , and the GARCH term refers to
the
After the model parameters are estimated, we use the
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squared standardized residuals are no longer autocorrelated. Based on these results, we set the correct specification for the lag numbers of the AR terms in the mean equation (n) and the ARCH term (p), as well as the GARCH term (q) in the variance equation. The models for the remaining calendar effects are based on the same GARCH approach, but using other dummies, which are explained thereafter.
B2. TOM Effect
The TOM effect arises if investors earn abnormal returns on the last few trading days of the previous month and on the first trading days of the current month, which is analyzed using the following equations:
(3)
and
(4)
where PreTt,d is a dummy that takes the value of one on the last three trading days d of the previous month, and CurTt,d is a dummy that takes the value of one on the first three trading days d of the current month, and otherwise the two dummies equal zero.
B3. DOW Effect
The DOW effect assumes that particular DOWs generate abnormal returns. The mean and variance equations of the DOW effect are constructed similarly to the MOY effect:
(5)
and
(6)
where Dt,d is the dummy for day d that takes on the value of one on day d, and zero otherwise. We analyze the DOW effect separately for each trading DOW, that is, from d=1 (Monday) to d=5 (Friday). Therefore, we are able to study calendar anomalies for all five trading DOWs, since this procedure guarantees that we will not encounter econometric problems due to too many dummy variables. In this context, Kiymaz and Berument (2003) and Sharma and Narayan (2012) stress
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that, if all five DOW dummies are considered in a single equation, there could be a dummy variable trap. In this case, the authors recommend dropping the Wednesday dummy. Since we intend on studying all the trading days, including Wednesday, we investigate the DOW effect for each trading day separately.
B4. Holiday Effect
In this paper, a holiday is defined as an official public holiday that is firmly established in the respective country’s laws and when the stock markets are closed. In addition, holidays that do not take place in every year of the investigated period are omitted. We define a pre- and
(7)
and
(8)
where PreHt and PostHt are two dummies that take the value of one on the trading days before and after a holiday, respectively, and are zero otherwise.
III.RESULTS A. MOY Effect
For the Chinese stock market, Table 2 reports the estimated coefficients and corresponding
5If the holiday falls on a Friday, we choose the next trading day, usually a Monday, to measure the
Table 2.
MOY Effect in China
The table shows the findings of the MOY effect for the SSE Composite. For each month, the coefficient and the
leverage term as well as a month dummy. The measures of fit include the adjusted
Variable |
January China |
February China |
March China |
April China |
May China |
June China |
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Coefficient |
Coefficient |
Coefficient |
Coefficient |
Coefficient |
Coefficient |
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Panel A: Mean Equation from January to June |
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log(ht) |
0.0007 |
0.0050* |
0.0007 |
0.0037* |
0.0007 |
0.0030* |
0.0007 |
0.0048* |
0.0007 |
0.0034* |
0.0007 |
0.0029* |
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c |
0.0064 |
0.0029* |
0.0066 |
0.0021* |
0.0067 |
0.0016* |
0.0064 |
0.0028* |
0.0067 |
0.0018* |
0.0069 |
0.0015* |
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0.0541 |
0.0000* |
0.0540 |
0.0000* |
0.0541 |
0.0000* |
0.0545 |
0.0000* |
0.0543 |
0.0000* |
0.0545 |
0.0000* |
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Mt,m |
0.0006 |
0.2203 |
0.0004 |
0.4253 |
0.0001 |
0.8765 |
0.0006 |
0.2314 |
0.0001 |
0.9217 |
0.1798 |
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Panel B: Variance Equation from January to June |
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ω |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
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0.0929 |
0.0000* |
0.0912 |
0.0000* |
0.0917 |
0.0000* |
0.0907 |
0.0000* |
0.0910 |
0.0000* |
0.0922 |
0.0000* |
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0.0522 |
0.0025* |
0.0515 |
0.0026* |
0.0532 |
0.0020* |
0.0539 |
0.0018* |
0.0519 |
0.0024* |
0.0517 |
0.0027* |
0.8815 |
0.0000* |
0.8831 |
0.0000* |
0.8817 |
0.0000* |
0.8820 |
0.0000* |
0.8831 |
0.0000* |
0.8818 |
0.0000* |
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Mt,m |
0.3892 |
0.5436 |
0.0000 |
0.4365 |
0.0000 |
0.5784 |
0.9173 |
0.0000 |
0.4570 |
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Panel C: Diagnostics from January to June |
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Adjusted |
0.0008 |
0.0012 |
0.0011 |
0.0015 |
0.0010 |
0.0011 |
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BIC |
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LB(1) |
0.164 |
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0.985 |
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0.162 |
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0.168 |
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0.161 |
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0.171 |
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LB(5) |
0.163 |
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0.772 |
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0.156 |
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0.168 |
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0.153 |
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0.165 |
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LB2(1) |
0.974 |
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0.985 |
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0.960 |
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0.950 |
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0.941 |
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0.946 |
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LB2(5) |
0.778 |
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0.772 |
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0.781 |
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0.773 |
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0.768 |
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0.767 |
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Table 2.
MOY Effect in China (Continued)
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July China |
August China |
September China |
October China |
November China |
December China |
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Coefficient |
Coefficient |
Coefficient |
Coefficient |
Coefficient |
Coefficient |
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Panel D: Mean Equation from July to December |
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log(ht) |
0.0007 |
0.0039* |
0.0007 |
0.0035* |
0.0007 |
0.0039* |
0.0007 |
0.0039* |
0.0007 |
0.0045* |
0.0007 |
0.0027* |
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c |
0.0066 |
0.0022* |
0.0067 |
0.0018* |
0.0065 |
0.0018* |
0.0066 |
0.0020* |
0.0064 |
0.0025* |
0.0068 |
0.0013* |
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0.0544 |
0.0000* |
0.0543 |
0.0000* |
0.0532 |
0.0000* |
0.0543 |
0.0000* |
0.0540 |
0.0000* |
0.0539 |
0.0000* |
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Mt,m |
0.0002 |
0.7078 |
0.8836 |
0.1181 |
0.6599 |
0.0005 |
0.3514 |
0.2363 |
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Panel E: Variance Equation from July to December |
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ω |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
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0.0907 |
0.0000* |
0.0905 |
0.0000* |
0.0866 |
0.0000* |
0.0906 |
0.0000* |
0.0896 |
0.0000* |
0.0904 |
0.0000* |
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0.0518 |
0.0023* |
0.0518 |
0.0023* |
0.0532 |
0.0015* |
0.0513 |
0.0025* |
0.0514 |
0.0023* |
0.0503 |
0.0028* |
0.8833 |
0.0000* |
0.8835 |
0.0000* |
0.8866 |
0.0000* |
0.8839 |
0.0000* |
0.8853 |
0.0000* |
0.8844 |
0.0000* |
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Mt,m |
0.5755 |
0.7783 |
0.0010* |
0.0000 |
0.7333 |
0.0000 |
0.4043 |
0.0000 |
0.2629 |
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Panel F: Diagnostics from July to December |
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Adjusted |
0.0010 |
0.0011 |
0.0011 |
0.0010 |
0.0010 |
0.0008 |
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BIC |
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LB(1) |
0.159 |
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0.160 |
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0.170 |
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0.160 |
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0.158 |
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0.164 |
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LB(5) |
0.151 |
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0.154 |
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0.186 |
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0.148 |
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0.159 |
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0.155 |
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LB2(1) |
0.941 |
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0.950 |
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0.977 |
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0.954 |
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0.975 |
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0.968 |
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LB2(5) |
0.776 |
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0.769 |
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0.742 |
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0.767 |
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0.778 |
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0.753 |
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Countries BRICS in Anomalies Calendar Revisiting
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Next, we summarize the findings for the other BRICS countries in Table 3. In the mean equation, we find evidence for the MOY effect in a few months of three countries (Brazil, Russia, and South Africa), mostly at the 10% significance level. China and India show no anomaly in any of the analyzed months, which indicates that investors do not earn a higher return due to the MOY effect. Moreover, our results show no evidence of the January effect. In the variance equation, we also detect no clear pattern among the various markets. In contrast to the mean equation, four months show a significant risk dummy at the 1% level (August in Brazil, December in Russia, September in China, and June in South Africa).
Table 3.
Summary of the MOY Effect
The table summarizes the results of the MOY effect for all BRICS countries. Reported are results for the significance of the dummies Mt,m in the mean equation (1) and the variance equation (2). The percentage values indicate the significance level. The sample period is from January 1, 1996 to March 30, 2018.
Country |
Jan |
Feb |
Mar |
Apr |
May |
Jun |
Jul |
Aug |
Sep |
Oct |
Nov |
Dec |
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Panel A: Mean Equation |
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Brazil |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
Yes, 10% |
No |
Russia |
No |
No |
No |
No |
Yes, 10% |
No |
No |
No |
No |
No |
No |
Yes, 10% |
India |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
China |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
South Africa |
No |
No |
No |
Yes, 10% Yes, 10% |
No |
No |
No |
No |
No |
No |
Yes, 5% |
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Panel B: Variance Equation |
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Brazil |
No |
No |
Yes, 10% |
No |
No |
No |
Yes, 10% |
Yes, 1% |
No |
No |
No |
No |
Russia |
No |
No |
No |
Yes, 5% |
No |
No |
No |
No |
No |
No |
No |
Yes, 1% |
India |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
No |
China |
No |
No |
No |
No |
No |
No |
No |
No |
Yes, 1% |
No |
No |
No |
South Africa |
Yes, 10% |
No |
No |
Yes, 10% |
No |
Yes, 1% |
Yes, 5% |
No |
No |
No |
No |
No |
B. TOM Effect
After the interpretation of the results of the MOY effect, we discuss the results of the TOM anomaly for all five BRICS countries, illustrated in Table 4.
Table 4.
TOM Effect in all BRICS Countries
The table shows the findings of the TOM effect for all BRICS countries. The columns illustrate the TOM anomaly in Brazil, Russia, India, China and South Africa. For each country, the coefficient and the
to the variance equation to ensure that there is no autocorrelation left in the residuals. Moreover, the variance equation contains a constant ω and a leverage term as well as TOM dummies. The measures of fit include the adjusted
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Variable |
TOM Brazil |
TOM Russia |
TOM India |
TOM China |
TOM South Africa |
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Coefficient |
Coefficient |
Coefficient |
Coefficient |
Coefficient |
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Panel A: Mean Equation |
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log(ht) |
0.0002 |
0.6889 |
0.8199 |
0.7811 |
0.0005 |
0.0241** |
0.0003 |
0.3015 |
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c |
0.0025 |
0.5238 |
0.0002 |
0.9392 |
0.8465 |
0.0046 |
0.0181** |
0.0031 |
0.2559 |
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0.0701 |
0.0000* |
0.0890 |
0.0000* |
0.0773 |
0.0000* |
0.0038 |
0.7629 |
0.0468 |
0.0005* |
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0.0279 |
0.0330** |
0.0547 |
0.0000* |
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PreTt,3 |
0.1305 |
0.4268 |
0.0000 |
0.9924 |
0.0002 |
0.7875 |
0.0012 |
0.1182 |
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PreTt,2 |
0.0482** |
0.6220 |
0.0004 |
0.5612 |
0.1632 |
0.8001 |
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PreTt,1 |
0.5766 |
0.0010 |
0.2198 |
0.0028 |
0.0002* |
0.0022 |
0.0002* |
0.0003 |
0.6724 |
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CurTt,1 |
0.4203 |
0.0016 |
0.0965*** |
0.0024 |
0.0014* |
0.0015 |
0.0139** |
0.0021 |
0.0032* |
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CurTt,2 |
0.4544 |
0.0006 |
0.5071 |
0.0015 |
0.0417** |
0.0013 |
0.0464** |
0.0015 |
0.0638*** |
|||||
CurTt,3 |
0.0010 |
0.3786 |
0.0009 |
0.3889 |
0.0001 |
0.9342 |
0.0007 |
0.2726 |
0.6889 |
|||||
|
|
|
|
|
|
|
|
Panel B: Variance Equation |
|
|
|
|
||
ω |
0.0000 |
0.0000* |
0.0000 |
0.0178** |
0.0000 |
0.0001* |
0.0000 |
0.0000* |
0.0000 |
0.0908*** |
||||
0.0224 |
0.0063* |
0.0825 |
0.0000* |
0.0420 |
0.0000* |
0.0917 |
0.0000* |
0.6848 |
||||||
|
|
|
|
|
0.0990 |
0.0000* |
0.0395 |
0.0023* |
0.1168 |
0.0000* |
0.0517 |
0.0021* |
0.1330 |
0.0000* |
|
|
|
|
|
|
|
|
0.0573 |
0.0019* |
|||||
1.3755 |
0.0000* |
0.8949 |
0.0000* |
0.8814 |
0.0000* |
0.8828 |
0.0000* |
0.6217 |
0.0001* |
|||||
0.0040* |
|
|
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|
|
0.2320 |
0.2198 |
||||||
0.3371 |
0.0047* |
|
|
|
|
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|
0.0634*** |
||||||
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Countries BRICS in Anomalies Calendar Revisiting
223
Table 4.
TOM Effect in all BRICS Countries (Continued)
Variable |
TOM Brazil |
TOM Russia |
TOM India |
TOM China |
TOM South Africa |
|||||
|
Coefficient |
Coefficient |
Coefficient |
Coefficient |
Coefficient |
|||||
|
|
|
|
|
|
|
|
0.3475 |
0.0009* |
|
PreTt,3 |
0.8838 |
0.9620 |
0.8343 |
0.0003* |
0.0000 |
0.2279 |
||||
PreTt,2 |
0.8740 |
0.0000 |
0.3582 |
0.0000 |
0.4361 |
0.0000 |
0.2687 |
0.4319 |
||
PreTt,1 |
0.1355 |
0.0758*** |
0.0000 |
0.8477 |
0.1176 |
0.9517 |
||||
CurTt,1 |
0.0001 |
0.1356 |
0.0000 |
0.5730 |
0.5013 |
0.0592*** |
0.0000 |
0.7538 |
||
CurTt,2 |
0.6131 |
0.6127 |
0.8194 |
0.0000 |
0.1113 |
0.0000 |
0.1124 |
|||
CurTt,3 |
0.0000 |
0.2400 |
0.0001 |
0.0154** |
0.0000 |
0.2389 |
0.7764 |
0.0000 |
0.0308** |
Panel C: Diagnostics
Adjusted |
0.0031 |
0.0135 |
0.0058 |
0.0016 |
0.0019 |
|
|
|
|
|
|
||
BIC |
||||||
LB(1) |
0.201 |
0.004* |
0.240 |
0.304 |
0.244 |
|
LB(5) |
0.738 |
0.011** |
0.321 |
0.239 |
0.765 |
|
LB2(1) |
0.305 |
0.461 |
0.643 |
0.798 |
0.257 |
|
LB2(5) |
0.862 |
0.849 |
0.950 |
0.742 |
0.367 |
|
|
|
|
|
|
|
224
2019 2, Number 22, Volume Banking, and Economics Monetary of Bulletin
Revisiting Calendar Anomalies in BRICS Countries |
225 |
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In the Brazilian stock market, anomalous behavior at the TOM can be observed within the
For the RTSI, a TOM effect is identified on the first day after the TOM in an
The Indian S&P BSE SENSEX reveals a TOM effect on the day before the TOM, the day after the TOM, and two days after the TOM. The two first effects in the
The SSE Composite reacts to the TOM similarly to the Indian index. By employing an
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Bulletin of Monetary Economics and Banking, Volume 22, Number 2, 2019 |
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TOM on the risk of the investment. The
For the South African FTSE/JSE All Share index, a TOM effect is measured in an
C. DOW Effect
The results for the DOW anomaly in the SSE Composite are documented in Table 5.
Table 5.
DOW Effect in China
The table shows the findings of the DOW effect for the SSE Composite. The columns depict the single days of the week from Monday to Friday when trade on the stock exchange is possible. For each DOW, the coefficient and the and a DOW dummy. The measures of fit include the adjusted
|
|
|
|
Variable |
Monday China |
Tuesday China |
Wednesday China |
Thursday China |
Friday China |
|||||
|
|
|
|
|
Coefficient |
Coefficient |
Coefficient |
Coefficient |
Coefficient |
|||||
|
|
|
|
|
|
|
Panel A: Mean Equation |
|
|
|
|
|
||
log(ht) |
0.0009 |
0.0001* |
0.0005 |
0.0169** |
0.0008 |
0.0005* |
0.0007 |
0.0041* |
0.0007 |
0.0022* |
||||
c |
0.0082 |
0.0001* |
0.0051 |
0.0124** |
0.0077 |
0.0003* |
0.0068 |
0.0014* |
0.0069 |
0.0012* |
||||
0.0039 |
0.7577 |
0.0023 |
0.8540 |
0.0050 |
0.6900 |
0.0065 |
0.6019 |
0.0055 |
0.6615 |
|||||
0.0552 |
0.0000* |
0.0538 |
0.0000* |
0.0548 |
0.0000* |
0.0540 |
0.0000* |
0.0545 |
0.0000* |
|||||
Dt,d |
0.9045 |
0.0011 |
0.0009* |
0.0005 |
0.1334 |
0.0000* |
0.0001 |
0.8126 |
||||||
|
|
|
|
|
|
|
Panel B: Variance Equation |
|
|
|
|
|
||
ω |
0.0071* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0452** |
0.0000 |
0.6238 |
|||||
0.1008 |
0.0000* |
0.0858 |
0.0000* |
0.0920 |
0.0000* |
0.0924 |
0.0000* |
0.0905 |
0.0000* |
|||||
|
|
|
|
|
0.0612 |
0.0012* |
0.0505 |
0.0020* |
0.0553 |
0.0015* |
0.0541 |
0.0019* |
0.0548 |
0.0015* |
0.8643 |
0.0000* |
0.8874 |
0.0000* |
0.8789 |
0.0000* |
0.8813 |
0.0000* |
0.8814 |
0.0000* |
|||||
Dt,d |
0.0001 |
0.0000* |
0.0000* |
0.0062* |
0.9645 |
0.0000 |
0.0973*** |
|||||||
|
|
|
|
|
|
|
Panel C: Diagnostics |
|
|
|
|
|
||
Adjusted |
0.0006 |
0.0013 |
0.0028 |
0.0010 |
||||||||||
BIC |
||||||||||||||
LB(1) |
0.211 |
|
0.380 |
0.259 |
|
0.297 |
0.337 |
|||||||
LB(5) |
0.216 |
|
0.356 |
0.201 |
|
0.220 |
0.227 |
|||||||
LB2(1) |
0.720 |
|
0.869 |
0.807 |
|
0.941 |
0.903 |
|||||||
LB2(5) |
0.676 |
|
0.743 |
0.704 |
|
0.798 |
0.738 |
|||||||
|
|
|
|
|
|
|
|
|
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Countries BRICS in Anomalies Calendar Revisiting
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Bulletin of Monetary Economics and Banking, Volume 22, Number 2, 2019 |
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In the Chinese stock market, Tuesday and Thursday effects are identified at the
1% level of significance. The Tuesday inconsistency influences the returns of the SSE Composite in a positive way, whereas the Thursday effect leads to declining returns as it increases. The DOW anomaly has a strong influence on the risk of shareholders, since there are Monday, Tuesday, Wednesday, and Friday effects in the variance equation. Only the last anomaly is significant at the 10% level, whereas the others have a 1% level of significance. The Tuesday and Wednesday effects lower investor risk as they grow stronger, but the Monday and Friday inconsistencies both increase risk. Furthermore, risk is an explanation for the current returns of the SSE Composite, because the log(ht) term is significant at the 1% or 5% level for each DOW. Due to the positive sign, there is a positive
Table 6.
Summary of the DOW Effect
The table summarizes the findings of the DOW effect for all BRICS countries. Reported are results for the significance of the dummies Dt,d in the mean equation (5) and the variance equation (6). The percentage values indicate the significance level. The sample period is from January 1, 1996 to March 30, 2018.
Country |
|
Panel A: Mean Equation |
|
|
Panel B: Variance equation |
|
||||
|
|
|
|
|
|
|
|
|
|
|
|
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
|
|
|
|
|
|
|
|
|
|
|
Brazil |
No |
No |
No |
No |
Yes, 10% |
Yes, 10% |
No |
No |
Yes, 10% |
No |
Russia |
Yes, 10% |
Yes, 10% |
No |
No |
No |
No |
Yes, 1% |
No |
No |
Yes, 1% |
India |
No |
Yes, 5% |
No |
No |
No |
No |
Yes, 1% |
No |
Yes, 10% |
Yes, 5% |
China |
No |
Yes, 1% |
No |
Yes, 1% |
No |
Yes, 1% |
Yes, 1% |
Yes, 1% |
No |
Yes, 10% |
South Africa |
Yes, 1% |
Yes, 10% |
No |
No |
No |
No |
No |
No |
No |
No |
|
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Countries BRICS in Anomalies Calendar Revisiting
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Bulletin of Monetary Economics and Banking, Volume 22, Number 2, 2019 |
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The findings for the other BRICS countries are summarized in Table 6. We start with an interpretation of the results in the mean equation. In all the BRICS countries, there is no DOW effect on Wednesdays. On Tuesdays, a DOW anomaly is documented in all countries except for Brazil. Moreover, unreported results reveal that, on Tuesdays, significant negative abnormal returns are documented for Russia, India, and South Africa, whereas, in China, investors obtain significant positive returns. For the remaining days, we document no consistent results. However, on Mondays, Russian and South African investors obtain significant positive abnormal results. The findings for the variance equation mostly confirm the former results in the mean equation for Tuesdays and Wednesdays. Furthermore, we document significant differences in risk on Fridays for the Russian, Indian, and Chinese stock markets.
Next, we study whether the DOW effect is robust to the January effect, as well as the holiday effect. For this purpose, we set up the following model:
(9)
and
(10)
where Mt,1 denotes the January dummy. For the sake of brevity, we do not report these results, but they are available upon request. In the mean equation, the results do not change for Brazil and India. For the remaining markets, there are slight but not severe changes. Therefore, we conclude that the DOW effect is robust in the mean equation. In the variance equation, which accounts for risk, there is no change for Brazil. However, there are changes for the other markets. Therefore, the effects of the DOW anomaly on risk are less robust to the January and holiday effects.
D. Holiday Effect
The next calendar anomaly that we analyze is the holiday effect. This inconsistency is divided into two effects, a pre- and a
Table 7.
Holiday Effect
The table shows the findings of the holiday effect for all BRICS countries. The columns depict the pre- and
the variance equation contains a constant ω and a leverage term as well as a pre- and
|
|
|
Variable |
Holiday Brazil |
Holiday Russia |
Holiday India |
Holiday China |
Holiday South Africa |
||||||
|
|
|
|
|
Coefficient |
Coefficient |
Coefficient |
Coefficient |
Coefficient |
|||||
|
|
|
|
|
|
|
|
Panel A: Mean Equation |
|
|
|
|
||
log(ht) |
0.0001 |
0.8025 |
0.0001 |
0.8358 |
0.0000 |
0.9753 |
0.0006 |
0.0112** |
0.0002 |
0.3476 |
||||
c |
0.0017 |
0.6741 |
0.0015 |
0.6527 |
0.0005 |
0.8387 |
0.0055 |
0.0065* |
0.0018 |
0.2223 |
||||
R |
0.0718 |
0.0000* |
0.0977 |
0.0000* |
0.0828 |
0.0000* |
0.0076 |
0.5483 |
0.0492 |
0.0002* |
||||
|
|
|
|
0.0284 |
0.0292** |
0.0534 |
0.0000* |
|
|
|||||
PreHt |
0.0006 |
0.7064 |
0.9482 |
0.0022 |
0.0119** |
0.0030 |
0.0006* |
0.7046 |
||||||
PostHt |
0.4612 |
0.8280 |
0.0037 |
0.0036* |
0.0010 |
0.4614 |
0.0022 |
0.0035* |
||||||
|
|
|
|
|
|
|
Panel B: Variance Equation |
|
|
|
|
|||
ω |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
0.0000 |
0.0000* |
||||
0.0180 |
0.0045* |
0.0985 |
0.0000* |
0.0386 |
0.0000* |
0.1079 |
0.0000* |
0.6297 |
||||||
|
|
|
|
|
0.0798 |
0.0000* |
0.0644 |
0.0000* |
0.1231 |
0.0000* |
0.0586 |
0.0028* |
0.1209 |
0.0000* |
|
|
|
|
|
|
|
|
0.0504 |
0.0010* |
|||||
1.6607 |
0.0000* |
0.7455 |
0.0000* |
0.8758 |
0.0000* |
0.8641 |
0.0000* |
1.0479 |
0.0000* |
|||||
0.0000* |
0.0819 |
0.2846 |
|
|
|
|
0.0000* |
|||||||
0.4565 |
0.0000* |
|
|
|
|
|
|
0.4331 |
0.0000* |
|||||
PreHt |
0.6690 |
0.0000* |
0.0000* |
0.0000* |
0.0000* |
|||||||||
PostHt |
0.0000 |
0.8278 |
0.0002 |
0.0000* |
0.0001 |
0.0000* |
0.9597 |
0.0000 |
0.0000* |
|||||
|
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|
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|
|
Panel C: Diagnostics |
|
|
|
|
|
|
Adjusted |
0.0034 |
0.0114 |
0.0029 |
0.0016 |
0.0024 |
|||||||||
BIC |
||||||||||||||
LB(1) |
0.237 |
|
0.008* |
|
0.238 |
|
0.369 |
|
0.448 |
|
||||
LB(5) |
0.761 |
|
0.032** |
0.335 |
|
0.226 |
|
0.893 |
|
|||||
LB2(1) |
0.477 |
|
0.883 |
|
0.560 |
|
0.804 |
|
0.334 |
|
||||
LB2(5) |
0.928 |
|
0.975 |
|
0.917 |
|
0.806 |
|
0.784 |
|
||||
|
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Countries BRICS in Anomalies Calendar Revisiting
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Bulletin of Monetary Economics and Banking, Volume 22, Number 2, 2019 |
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For the IBOVESPA, neither a pre- nor a
For the RTSI, no holiday effects are documented by the
The S&P BSE SENSEX reacts differently to public holidays than the Brazilian and Russian indices. Pre- and
Revisiting Calendar Anomalies in BRICS Countries |
233 |
|
|
The Chinese SSE Composite shows a strong
The last index investigated is the South African FTSE/JSE All Share, which shows a
IV. CONCLUSION
This paper reconsiders four calendar anomalies in BRICS countries, namely, the MOY, TOM, DOW, and holiday effects. Weak evidence for a MOY anomaly is documented in three countries: a November anomaly in Brazil, May and December effects in the Russian equity market, and April, May, and December effects in the South African equity market. No MOY anomaly is detected for the Indian and Chinese indices. Therefore, we document no January effect in the BRICS stock markets. Moreover, a TOM effect is found in several BRICS countries. The IBOVESPA shows anomalous behavior two days before the TOM, whereas the returns of the RTSI are anomalous one day after the TOM. The Chinese and Indian indices display a TOM effect one day before, one day after, and two days after the TOM. In addition, the TOM effect manifests itself one and two days after the TOM in the South African equity market. On Tuesdays, a DOW anomaly is documented in all countries, except for Brazil. Moreover, on Tuesdays, significant negative abnormal returns are documented for Russia, India, and South Africa, whereas, in China, investors obtain significant positive returns. On Mondays, only Russian and South African investors obtain significant positive abnormal results. In addition, a weak DOW effect on Fridays is documented only for Brazil. Holiday inconsistency, which is divided into a pre- and a
234 |
Bulletin of Monetary Economics and Banking, Volume 22, Number 2, 2019 |
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in some of the BRICS countries. The IBOVESPA and the RTSI do not display a holiday anomaly, whereas the Indian index shows a pre- and a
Overall, the results of this paper show that some of the calendar anomalies exist in the BRICS stock markets. Therefore, future research could conduct further tests concerning calendar anomalies for other
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