The Calendar Anomalies of Stock Return in Thailand

Transcription

1 Journal of Modern Accounting and Auditing, ISSN June 2011, Vol. 7, No. 6, The Calendar Anomalies of Stock Return in Thailand Nopphon Tangjitprom Assumption University, Thailand This paper aims to examine the existence of calendar anomalies including month-of-year effect, turn-of-month effect, and weekend effect in Thai stock market. The stock return is computed from SET index during 1988 to 2009, and the SET50 index gathered since it was created in The unit root test is performed to ensure that the stock return series have no unit root. The multiple regression techniques using dummy variables are employed to test the difference of the return during each calendar anomalies period. If the regression model suffers from conditional heteroskedasticity, the will be used instead of normal ordinary least square regression. It was found that the calendar anomalies exist in Thai stock market. The return is abnormally high during December and January, which can be addressed to be the turn-of-year effect. The return during the turn-of-month period, which can be defined as the last trading day and the first four trading days of the following months, is also abnormally high. Finally, the return is also abnormally high on Fridays but abnormally low on Mondays, which is addressed as weekend effect. This may create the opportunity to make above-average profit to investors exploiting these calendar anomalies. Although these calendar anomalies may be difficult to be exploited in practice because of transaction costs and ability to replicate the stock index, the existing evidence of calendar anomalies can help investors as the clue for the timing of investment. Keywords: investment, calendar anomalies, turn-of-year effect, weekend effect Introduction Calendar anomaly is one of the phenomena in the financial market, especially in the stock market. The calendar anomalies suggested that the stock return is abnormally high during some specific trading days. Many previous studies tried to figure out the existence of calendar anomalies and the result confirmed the existence of these phenomena. Many researchers tried to address these anomalies to specific reasons. Although there is no completed explanation for the reasons behind these anomalies, they are proven to exist in many financial markets, especially stock markets, around the world in many empirical studies. There are many kinds of calendar anomalies noticed and captured by previous research. Among those phenomena, there are month-of-year effect, turn-of-month effect, and day-of-week effect: (1) -of-year effect refers to the stock return that is abnormally high during some specific months of the year (e.g., January); (2) Turn-of-month effect refers to the stock return that is abnormally high for some specific trading days within a month; (3) Day-of-week effect refers to the stock return that is abnormally high during some specific day-of-week Nopphon Tangjitprom, lecturer, Department of Finance and Banking, Martin de Tours School of Management and Economics, Assumption University.

2 566 THE CALENDAR ANOMALIES OF STOCK RETURN IN THAILAND (e.g., Friday). This study aims to examine the existence of these calendar anomalies in Thailand stock market. We examine the daily stock return based on the SET index and SET50 index. The parametric statistics is used in this study by multiple regression analysis. If these calendar anomalies really do exist, they can be exploited as the investment opportunity to either create or not create above-average return. Literature Review -of-year effect is the situation when the stock market shows the abnormally high return during any specific month. Normally, this would happen in January which is known as the January effect or turn-of-year effect. Rozeff and Kinney (1976) showed that there was the seasonality in US stock market that the stock return was abnormally high in January. Haugen and Jorion (1996) confirmed that the January effect in US Stock market still existed. However, this phenomenon was not only observed in the US but also in other stock markets all over the world. It was addressed by many researches in other countries like Canada (Tinic, Barone-Adesi, & West, 1987), Japan (Kato & Schallheim, 1985), Ghana (Ayadi, Dufrene, & Chatterjee 1998), Malaysia and Singapore (Chan, Khanthavit, & Thomas, 1996). However, the January effect did not exist in some countries like India (Raj & Kumari, 2006), Nigeria and Zimbabwa (Ayadi, Dufrene, & Chatter 1998), Jordan (Maghayereh, 2003), Bangladesh (Bepari & Mollik, 2009), Greece (Floros, 2008), Thailand (Chan, Khanthavit, & Thomas, 1996). Claessens, Dasgupta, and Glen (1995) examined the seasonal effect for many emerging countries and found that only few countries showed evidence for January effect like Republic of Korea, Turkey, and Mexico. However, many countries had shown the abnormal return in other months besides January (e.g., April in Brazil, October in Argentina and Chile, December in Pakistan, etc.). Many researchers tried to explain this phenomenon and proposed many possible hypotheses. The most popular one was tax-loss selling hypothesis citing that people will realize the loss in December in order to get the tax benefit for that year (Branch, 1977). This could create the selling pressure for the stocks and make the stock price and return reduce in December and reverse back in January, creating abnormal return in January month. Givoly and Ovadia (1983) showed that the tax loss selling was the major explanation of abnormal return in January for US stock market. Reinganum and Shapiro (1987) showed that January effect in UK stock market was totally addressed by tax effect because there was no January effect before 1965, which was the year that capital gain taxes were imposed. Window dressing hypothesis is another proposed explanation of the January effect, which mentions that institutional investors would try to get rid of the loss stocks before the end of each quarter, especially the last quarter of the year in order to make their portfolio look good (Lakonishok, Shleifer, Thaler, & Vishny, 1991). Ng and Wang (2004) found the evidences to support the above hypothesis that institutional investors would sell the loser stocks extremely in the last quarter and buy many stocks in the following quarter, which creates the turn-of-year effect or January effect. Another possible explanation of this January effect in the stock market is that the effect is transferred from the seasonality in Macroeconomics variables. Kramer (1994) showed that there was no abnormal return in January after he used the macroeconomics multi-factor model to estimate the abnormal return. Therefore, the seasonality of the shift in the stock return during the year was induced by macroeconomics factors. Turn-of-month effect or monthly effect occurs when the stock return shows different return during some

3 THE CALENDAR ANOMALIES OF STOCK RETURN IN THAILAND 567 periods within a particular month. Ariel (1987) showed that the stock returns at the beginning or during the first half of the month would have positive average while the return for the remaining period of the month would be zero average. He also pointed out that this difference was not from the outlier data but from the slight shift in the distribution mean. Furthermore, the effect was very outstanding during the last trading day and the first four trading days of the following month. This period was so called turn-of-month (TOM) period. Ogden (1990) addressed this phenomenon with the preferred habitat result and turn-of-month liquidity hypothesis. It came from the standard of national payment system that the huge flow of fund would arise during the end of the month like salary payment. This created the large amount of short-term investable fund and pushed the demand of investors. Boudreaux (1995) studied this monthly effect in various countries and found out that many countries in Europe showed the higher return during the beginning of the month compared to the remainder of the month. Denmark, Germany, and Norway showed significantly higher return during the beginning of the month while France, Spain, and Switzerland also showed the similar but insignificant results. Interestingly, Singapore and Malaysia showed the reverse result where the return during the beginning of the months showed significantly lower than the remainder of the month. Kunkel and Compton (1998) showed that individual investors could generate the above average return for their retirement fund by switching strategies between money market and stock market by exploiting the existence of turn-of-month effect. Individuals could switch to stock market during the turn-of-month period and switch back to money market after the turn-of-month period. Day-of-week effect occurs when the stock return shows the different return during some specific day within the week. French (1980) addressed this as weekend effect and pointed out that the return on Fridays was positive while the return on Mondays was abnormally negative. He pointed out that this effect came from the fact that normally the firm would announce the unfavorable news on weekends to avoid panic selling of the stocks. Damodaran (1989) showed that news announced on Fridays tended to be more negative than other weekdays. Usually, the bad news came out after the market had closed on Friday so the effect spilled over to next trading day, which was Monday. However, he pointed out that this could account for only part of weekend effect. Without the bad news announcement on Friday, the return on Monday was still less than other weekdays. Lakonishok and Maberly (1990) studied the trading pattern by using the odd-lot sales and purchases. They found that the trading volume was lowest on Mondays. Moreover, people tended to sell rather than buy on Mondays. They found that the difference between odd-lot sales and purchases on Mondays were 29% higher than other weekdays on average. This selling pressure made the below-average return on Mondays. The studies about these calendar anomalies, even though emphasized in stock market, were levered into other financial markets. Tschoegl (1987) studied about the seasonality in gold price but the result showed that there is no seasonality in the gold market. Maxwell (1998) studied the January effect in Bond market and found that January effect is significant only for high-yield bonds. Starks, Yong, and Zheng (2006) found that the January effect in the municipal bonds closed-end mutual funds and related this January effect with the tax-loss selling behaviors. Compton, Johnson, and Krunkel (2006) showed the monthly effect in real estate investment trusts (REITs) that the return during turn-of-month was significantly higher than the return during rest-of-month. Furthermore, the calendar anomalies were found to have relationship with other asset return behaviors. Schultz (1985) found that the size of the firms was related to the January effect that the small firms showed larger abnormal return in January. Jegadeesh (1991) found that there was mean reversion pattern of stock price. However, the mean reversion will concentrate primarily in January. De Bondt and Thaler (1987) investigated

4 568 THE CALENDAR ANOMALIES OF STOCK RETURN IN THAILAND the overreaction biases implying that the loser stocks will generate higher return than winner stocks in the following period. They showed that this overreaction could be explained partly by January effect. The reversal of losers return came from the tax-loss selling that investors wanted to sell the loser stocks in order to capture benefits from taxes while the reversal of winners return arose from the capital gain tax lock-in effect that investors postponed capital gain taxes by avoid selling winners during year ended. Data and Methodology Stock Exchange of Thailand (SET) is the main stock market in Thailand. There is SET index, which is the market value-weighted stock index computed from all stocks trading in the Stock Exchange of Thailand. There is also SET50 index, which is computed from the top 50 companies based on market capitalization. SET50 index is important because it is the underlying asset for derivatives securities in Thailand, such as SET50 index futures and SET50 index options, and it is also the benchmark for the index fund like Exchange-Traded Fund (ETF). The historical daily data is gathered and used in this study. For SET index, we use the data from 1988 to For SET50 index, we use the data since the index had started on August 16, 1995 until the end of The stock return is computed by the log-difference of the stock index as: Pt R t =ln (1) Pt-1 where R t is the continuous compound return of day t, P t is the index of day t. Before each calendar anomaly would be tested, the stock return should have stationary property. The time-series regression with non-stationary or unit root could create the spurious result (Granger & Newbold, 1974). Therefore, the unit root test based on Augmented Dickey-Fuller Test (ADF test) should be performed to confirm that the data has no unit root problem. However, the ADF test statistic would be different from the general student s t distribution. Therefore, the critical value in this ADF test would be based on McKinnon (1996). After the confirmation of no unit root problem, various tests for calendar anomaly would be conducted. -of-year effect would be examined based on the regression model. The model is defined as: 12 R = β D + ε (2) t i it t i =1 where R t is the return of day t; D 1 to D 12 is the month dummy variable representing January to December; ε t is the white noise residual term of the regression. In order to specifically examine the effect of each specific month, the dummy variable representing that month will be excluded from the model. The model would be defined as: 11 R = β + β D + ε (3) t 0 i it t i =1 where β 0 represents the return on the month that is dropped from the model, which is called base month; β i represents the return difference between the month specified by dummy variable and base month. The hypothesis testing would be conducted to test whether the slope coefficient β i is different from zero. The turn-of-month period will be determined by five days around the turn-of-month, which are last trading days of the month and the first four trading days of the following month. The regression model for turn-of-month period will be defined as: Rt = β0 + β1dtom,t + ε t (4) where D TOM represents the dummy variable for turn-of-month period; β 0 represents the return on the

5 THE CALENDAR ANOMALIES OF STOCK RETURN IN THAILAND 569 non-turn-of-month; β 1 represents the return difference between turn-of-month period and non-turn-of-month period. The hypothesis testing would be conducted to test whether the slope coefficient β 1 is different from zero. Day-of-week effect would be examined by the model: 5 Rt = βidit + εt (5) i=1 where D 1 to D 5 are the dummy variables representing Monday to Friday. To examine the effect of any specific weekday, the dummy variable of that day will be excluded and the model is defined as: 4 Rt = β0 + βidit + εt (6) where β 0 represents the return on the day that is dropped from the model, which is called base day; β i represents the return difference between the day specified by dummy variable and base day. The hypothesis testing would be conducted to test whether the slope coefficient β i is different from zero. The above time series regression model may suffer from the heteroskedasticity as the stock market may be characterized by volatility clustering firstly discussed by Mandelbrot (1963). Volatility clustering is the tendency that the large change will be followed by large change while small change will be followed by small change. In other words, the volatility is persisted for some periods. This volatility clustering can be captured by the Autoregressive Conditional Heteroskedastics (ARCH) model which was proposed by Engle (1982). Bollerslev (1986) introduced the general model to improve the efficiency of ARCH model named Generalized Autoregressive Conditional Heteroskedastics or GARCH, which is widely used for financial time series model. In this research, GARCH (1, 1) will be used as the variance equation together with the original regression as mean equation. The variance equation for GARCH (p, q) is: i =1 q p t = + i t-i + i t-i i=1 i=1 (7) σ ω α ε β σ 2 2 where ε t i represents ARCH term and σ t i represents GARCH term. The ARCH-LM test and Ljung-Box s Q statistics on square residuals are employed to detect the existence of conditional heteroskedastics. Analysis and Result The Augmented Dickey-Fuller test is conducted to see whether the stock return series has unit root. Table 1 shows that the null hypothesis which is the stock return series, both SET index and SET50 index, have unit root can be rejected. It means the stock return has no unit root and it is stationary; so the stock return series can be used in regression analysis. Table 1 Augmented Dickey-Fuller Test of Unit Root Model SET SET50 No intercept and trend ** ** Only intercept ** ** Both intercept and trend ** ** Notes. ** significant at 5%. -of-year Effect The outcome for SET index shows different results from many previous researches in which the return is

6 570 THE CALENDAR ANOMALIES OF STOCK RETURN IN THAILAND abnormally high in January. In this research, even though the return is high in January, the December result shows the highest return compared to any other months. The daily return in December is at %, which is around 12 times higher than the average daily return of %. We divide the data into two sub-periods before and after the 1997 crisis. The result is consistent in the part where the December return shows the highest compared to other months as shown in Table 2. Therefore, we will test the December effect if the return in December will be significantly higher than other months by linear regression with dummy variable. Table 2 The Average Daily Return From SET Index SET daily return (%) ALL ( ) Before-crisis ( ) After-crisis ( ) January February March April May June July August September October November December Average Table 3 The Test of Abnormal Return in December for SET Index Return difference from December (%) Constant January ** February * ** March ** ** April ** May * ** June * ** July ** ** August ** ** September ** ** October * ** November ** ** ARCH-LM (5) ** LB (10) LB (15) LB (20) ** ** ** AIC SIC Notes. ** significant at 5%; * significant at 10%.

7 THE CALENDAR ANOMALIES OF STOCK RETURN IN THAILAND 571 Looking at the significance of ARCH test and Ljung-Box statistics of square residual in Table 3, it can be seen that the suffers from conditional heteroskedastics. Therefore, GARCH (1, 1) model is more preferable to be used in the analysis rather than the normal ordinary least square. Akaike Information Criteria (AIC) and Schwarz Information Criteria (SIC) also confirm that GARCH (1, 1) is the better model. The result shows that the difference between each month return and December return is statistically significant. Therefore, December return shows abnormally higher return than any other months. Then, we examine whether the above abnormal return in December and high return in January is just the turn-of-year effect. The turn-of-year period is defined as the last trading week of December (after December 24) and the first trading week of January (before January 9). From Table 4, the average daily return during December is % and the average return during January 2-8 is %. The average daily return for the whole turn-of-year period is around % which is higher than the average daily return in December mentioned earlier. Therefore, the significantly high return in December and January is just the result of turn-of-year effect that the return during last week of December and first week of January (December 25 January 8) is abnormally high. Table 4 The Turn-of-Year Effect for SET Index Period Return difference from non-toy period (%) Constant December ** ** January ** ** ARCH-LM (5) ** LB (10) ** LB (15) ** LB (20) ** AIC SIC Notes. ** significant at 5%. For SET50 index, the result looks slightly different from SET index. The highest return is in January, which is consistent with many previous studies around the world. However, the result from Table 5 shows that, even if the return in January is higher than other months, it is not significant, especially after adjusting the conditional heteroskedastic by GARCH (1, 1). Even though the January effect cannot be confirmed for SET50 index, the returns in January and December were higher than other months. Therefore, we examine the turn-of-year effect based on SET50 index. The result in Table 6 shows that the average return during the turn-of-year period is abnormally high at %. Although, the coefficient test based on GARCH (1, 1) shows that the turn-of-year return in January is not significant, the average return during the whole turn-of-year period is significantly higher than non turn-of-year period. This result confirms the existence of turn-of-year effect for SET50 index.

8 572 THE CALENDAR ANOMALIES OF STOCK RETURN IN THAILAND Table 5 The January Effect for SET50 Index Return difference from January (%) Constant February * March ** ** April May ** June July * * August * September October ** November December ARCH-LM (5) ** LB (10) LB (15) LB (20) AIC SIC Notes. ** significant at 5%; * significant at 10%. Table 6 The Turn-of-Year Effect for SET50 Index Return difference from Period non-toy Period (%) Turn-of-year period in December and January Constant ** 1,143.2 ** 1,243.4 ** December ** January ** ** ARCH-LM (5) ** LB (10) LB (15) LB (20) AIC SIC The whole turn-of-year period Constant ** 1,179.8 ** 1,279.1 ** Turn-of-year ** ** ARCH-LM (5) ** LB (10) LB (15) LB (20) ** 1,179.6 ** 1,278.9 ** AIC SIC Notes. ** significant at 5%.

9 THE CALENDAR ANOMALIES OF STOCK RETURN IN THAILAND 573 Turn-of- Effect The result for SET index shows the daily return of % during the first half of the month (during date 1-15 of each month) is higher compared to the remaining half of the month (during date of each month), which is %. We examine whether the higher return of first half of the month is actually turn-of-month effect, which the return during turn-of-month period will be significantly higher than the remaining period. The result in Table 7 shows that the return during the turn-of-month period is %, which is statistically significant while the return of non turn-of-month period shows the negative return at %. The result for SET50 index also shows that same as the return during the first half of the month is % and the return during the second half is %. The result has shown in Table 8 that the return during the turn-of-month period is %, which is significant while the non turn-of-month shows a negative return. Therefore, the turn-of-month effect has existed in Thai stock market, which can be examined from both SET index and SET50 index. Table 7 The Turn-of- Effect for SET Index Period Return difference from non-tom period (%) Constant normal OLS regression Turn-of-month ** ** ARCH-LM (5) ** LB (10) ** LB (15) 1,208.7 ** LB (20) 1,280.0 ** AIC SIC Notes. ** significant at 5%. Table 8 The Turn-of- Effect for SET50 Index Period Return difference from Non-TOM period (%) Constant Normal OLS regression Turn-of-month ** ** ARCH-LM (5) ** LB (10) ** LB (15) 1,175.1 ** LB (20) 1,275.7 ** AIC SIC Notes. ** significance at 5%. Day-of-Week Effect The result shows the consistent result with previous research that the daily return will be abnormally high on Friday and abnormally low on Monday. We examine this weekend effect to show whether the returns on Fridays

10 574 THE CALENDAR ANOMALIES OF STOCK RETURN IN THAILAND and Mondays are different from other weekdays. The result in Table 9 shows that the average return on Friday is % which is higher than any other weekdays. The result of coefficient test from also confirms this significance. Furthermore, the Monday return is tested to be determined that it is lower than any other days of the week. The result is shown in Table 10. The average Monday return is and the result of test based on GARCH (1, 1) also confirms that Monday return is significantly lower than any other weekdays. Table 9 The test of Abnormally High Return on Friday for SET Index Return difference from Friday (%) Constant Monday ** ** Tuesday ** ** Wednesday ** ** Thursday ** ** ARCH-LM (5) ** LB (10) ** LB (15) 1,253.0 ** LB (20) 1,323.4 ** AIC SIC Notes. ** significance at 5%. Table 10 The Test of Abnormally Low Return on Monday for SET Index Return difference from Monday (%) Constant Tuesday * ** Wednesday ** ** Thursday ** ** Friday ** ** ARCH-LM (5) ** LB (10) ** LB (15) 1,253.0 ** LB (20) 1,323.4 ** AIC SIC Notes. ** significance at 5%; * significance at 10%. The result from SET50 index is also consistent with SET index as shown in Table 11 and Table 12. The weekend effect in Thailand is confirmed. The average return on Friday will be abnormally high and the average return on Monday will be abnormally low.

11 THE CALENDAR ANOMALIES OF STOCK RETURN IN THAILAND 575 Table 11 The Test of Abnormally High Return on Friday for SET50 Index Return difference from Friday (%) Constant Monday ** ** Tuesday ** ** Wednesday ** ** Thursday ** ** ARCH-LM (5) ** LB (10) ** LB (15) 1,185.1 ** LB (20) 1,282.6 ** AIC SIC Notes. ** significance at 5%. Table 12 The Test of Abnormally Low Return on Monday for SET50 Index Return difference from Monday (%) Constant Tuesday * ** Wednesday ** ** Thursday ** ** Friday ** ** ARCH-LM (5) ** LB (10) ** LB (15) 1,185.1 ** LB (20) 1,282.6 ** AIC SIC Notes. ** significance at 5%; * significance at 10%. Conclusion This study examines the calendar anomalies in Thailand stock market. The result shows that the return from SET index is abnormally high during December. However, for SET50 index, the result is slightly different because the January return shows the highest return even if it is not significant. After further examination of the abnormal return during December and January, the abnormal return for both SET index and SET50 index is concentrated during the last week of December and the first week of January. Therefore, this abnormally high return can be addressed to be the turn-of-year effect. The average return during this turn-of-year period is at %, which is 28 times higher than the average stock return. Furthermore, the return of the first half of the month is shown to be significantly higher than the remaining half of the month for both SET index and SET50 index. This abnormal return is also concentrated for some

12 576 THE CALENDAR ANOMALIES OF STOCK RETURN IN THAILAND days during the last trading day and some trading days of the beginning of following month, which is known as turn-of-month effect. The return from SET index and SET50 index shows that the stock returns during this turn-of-month period is significantly higher than the remaining period. The average return during this turn-of-month period is at %, which is eight times higher than average stock return. Moreover, the weekend effect also exists for both SET index and SET50 index. The stock return is abnormally high on Friday and abnormally low on Monday. The average return on Friday is %, which is 16 times higher than average stock return Even though the calendar anomalies have existed in Thailand stock market, people may not be able to exploit this opportunity to make profit because of transaction costs. The minimum standard roundtrip commission fees in Thailand stock market is 0.3% and the average bid-ask spread computed from the tick size is around 0.53%. Both constitute the average roundtrip transaction costs at 0.83%. After considering transaction costs, exploiting the opportunity of weekend effect and turn-of-month effect is not worth doing. Only turn-of-year period still provides the daily return around 0.41% after deducting transaction costs, which is 23 times higher than average daily stock return. However, this investment opportunity is not consistent, meaning that investing in this turn-of-year period may not create above-average return in some years. Therefore, the calendar anomalies have really existed in Thailand stock market. Even if investors cannot exploit these calendar anomalies to earn above-average return, it can help the investors to make decision about the timing of investment. References Ariel, R. A. (1987). A monthly effect in stock returns. Journal of Financial Economics, 18(1), Ayadi, O. F., Dufrene, U. B., & Chatterjee, A. (1998). Stock return seasonalities in low-income African emerging markets. Managerial Finance, 24(3), Bepari, K., & Mollik, A. T. (2009). Seasonalities in the monthly stock returns: Evidence from Bangladesh Dhaka Stock Exchange (DSE). International Research Journal of Finance and Economics, 24, Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, Boudreaux, D. O. (1995). The monthly effect in international stock markets: Evidence and implications. Journal of Financial and Strategic Decisions, 8(1), Branch, B. (1977). A tax loss trading rule. Journal of Business, 50, Chan, M. W. L., Khanthavit, A., & Thomas, H. (1996). Seasonality and cultural influences on four Asian stock markets. Asia Pacific Journal of Management, 13(2), Claessens, S., Dasgupta, S., & Glen, J. (1995). The cross-section of stock returns: Evidence from emerging markets. Policy Research Working Paper Series, the World Bank, Compton, W. S., Johnson, D. T., & Kunkel, R. A. (2006). The turn-of-the-month effect in real estate investment trusts (REITs). Managerial Finance, 32(12), Damodaran, A. (1989). The weekend effect in information releases: A study of earnings and dividend announcements. The Review of Financial Studies, 2(4), De Bondt, W. F. M., & Thaler, R. H. (1987). Further evidence on investor overreaction and stock market seasonality. The Journal of Finance, 42(3), Engle, R. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica, 50(4), Floros, C. (2008). The monthly and trading month effects in Greek stock market returns: Managerial Finance, 34(7), French, K. R. (1980). Stock returns and the weekend effect. Journal of Financial Economics, 8(1), Givoly, D., & Ovadia, A. (1983). Year-end tax-induced sales and stock market seasonality. The Journal of Finance, 38(1),

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