DATA
The research analyzes daily data from the VN-Index, HNX-Index, and UPCOM, sourced from the Ho Chi Minh Stock Exchange, Hanoi Stock Exchange, and UPCoM Stock Exchange This data spans from January 4, 2010, to January 22, 2021.
JANUARY EFFECT
Hypothesis
The Literature Review highlights that the Tet holiday and investor risk aversion contribute to the presence of the "January effect" in the Vietnamese Stock Market To validate this assertion, we will test the following hypotheses.
H0: à1 = à2 (The average daily log return of January is equal the daily log return of rest of the months);
- à1 = the average daily log return of January;
- à2 = the average daily log return of rest of the months.
Model
To test whether there is January in returns of 3 stock market, we use OLS regression model:
R t = α + β 2 * FEB + β 3 MAR+ β 4 *APR+ β 5 *MAY+ β 6 *JUN+ β 7 *JUL+ β 8 *AUG+ β 9 *SEP+ β 10 *OCT+ β 11 *NOV+ β 12 *DEC
- Rt: The daily return of each index in t times Rt = ln(Rt/Rt-1)*100;
- FEB is dummy variables For FEB = 1, if month t is February and 0 otherwise;
- MAR is dummy variables For MAR = 1, if month t is March and 0 otherwise;
- APR is dummy variables For APR = 1, if month t is April and 0 otherwise;
- MAY is dummy variables For MAY = 1, if month t is May and 0 otherwise;
- JUN is dummy variables For JUN = 1, if month t is June and 0 otherwise;
- JUL is dummy variables For JUL = 1, if month t is July and 0 otherwise;
- AUG is dummy variables For AUG = 1, if month t is August and 0 otherwise;
- SEP is dummy variables For SEP = 1, if month t is September and 0 otherwise;
- OCT is dummy variables For OCT = 1, if month t is October and 0 otherwise;
- NOV is dummy variables For NOV = 1, if month t is November and 0 otherwise;
- DEC is dummy variables For DEC = 1, if month t is December and 0 otherwise;
- Β2 to β12: the difference in return between month t and January and the i month with i runs from 2 to 12.
DAY OF THE WEEK EFFECT
Hypothesis
The "Friday effect" in the Vietnamese Stock Exchanges is attributed to the weekend closure and investor risk aversion To investigate this phenomenon, we propose several hypotheses for analysis.
H0: à1= à2 (The average daily log return of investigated day is equal the daily log return of other weekdays);
- à = the average daily log return of the investigated day;
- à2 = the average daily log return of the other weekdays.
Model
The research utilized daily data from the VN-Index, HNX-Index, and UPCOM, sourced from the Ho Chi Minh Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX) This data collection spanned from January 4, 2010, to January 22, 2021.
The following formula is used to calculate daily returns:
- Rt: the return over the period t;
- Pt: the daily closed share price index of day t;
- Pt-1: the daily closed share price index of day t-1.
To examine the impact of each day of the week on the stock returns in Vietnamese stock exchange, we establish the following OLS regression model:
- Ri: the daily return of each index;
- MON: dummy variable on Monday (MON = 1 for the observation on Monday; otherwise MON = 0);
- TUE: dummy variable on Tuesday (TUE = 1 for the observation on Tuesday; otherwise TUE = 0);
- WED: dummy variable on Wednesday (WED = 1 for the observation on
- THU: dummy variable on Thursday (THU = 1 for the observation on Thursday; otherwise THU = 0);
- Β1 to β4: the difference between the return on Friday and the returns on other days of the week.
JANUARY EFFECT
Descriptive data
Table 1 Descriptive statistic of the 3 indexes for 12 months
VN-Index HNX-Index UPCOM
Standard deviation Mean Standard deviation Mean Standard deviation Mean
The descriptive statistics reveal that the VN-Index experiences the highest average return in January, while the HNX-Index and UPCOM do not share this trend, with their peak average returns occurring in May and November, respectively Notably, the average return for UPCOM in January is negative, indicating that investors may incur losses during this month Consequently, the January effect appears to be exclusive to the VN-Index.
Results of OLS estimation
The Augmented Dickey-Fuller Test is employed to determine the stationarity of a time series The test results indicate a p-value of less than 0.01, suggesting that the time series is stationary.
Therefore, the regression is stationary.
We use OLS estimation to test whether there is January effect in Vietnam stock market. The result of the estimation is shown in the table below.
Table 2 Estimated results of the January effect model
VN-Index HNX-Index UPCOM
Estimate* t-value Estimate* t-value Estimate* t-value
(Intercept) 0.233469** 2.7338 0.132533 1.5107 0.0035012 0.0619FEB - 0.156440 - 1.1925 0.039279 0.2779 0.0090929 0.0903MAR - 0.311855* - 2.5709 - 0.167697 - 1.3004 0.0684987 0.7058APR - 0.115381 - 0.9786 - 0.106099 - 0.8419 - 0.0522764 - 0.5317MAY - 0.296289* - 2.2795 - 0.264742 - 1.8633 - 0.0442464 - 0.4349JUN - 0.229670* - 2.0745 - 0.125833 - 1.0719 - 0.0472160 - 0.5752JUL - 0.219105* - 2.0047 - 0.187227 - 1.5942 0.0047613 0.0550AUG - 0.230275* - 2.0006 - 0.146640 - 1.1442 0.0257391 0.3184SEP - 0.196005 - 1.9139 - 0.108236 - 0.9372 0.0851334 1.0212
The analysis of the VN-Index indicates the presence of the January effect on the Ho Chi Minh Stock Exchange (HOSE), as all variables, except for February, are statistically significant, confirming the validity of the coefficients Notably, the negative coefficients suggest that the average returns in other months are lower than those in January, making January the month with the highest returns of the year on HOSE Furthermore, March experiences the lowest stock returns, which are 0.31% lower than those recorded in January.
The analysis of the HNX-Index and UPCOM reveals that only two explanatory variables are statistically significant, with some coefficients being positive This indicates that January does not statistically yield the highest returns for the Hanoi Stock Exchange and UPCOM Consequently, there is insufficient evidence to support the existence of the January effect in these markets.
FRIDAY EFFECT
Descriptive data
Table 3 Statistic description of VN-Index daily return
N Range Minimum Maximum Mean Std Deviation Variance
N Range Minimum Maximum Mean Std Deviation Variance
Table 4 Statistic description of HNX-Index daily return
N Range Minimum Maximum Mean Std Deviation Variance
MON 539 12.16348 -6.657003 5.506476 -0.15716103 1.606730 2.581581 TUE 552 10.46084 -5.379204 5.081638 -0.06947156 1.285794 1.653267 WED 554 8.57737 -4.597660 3.979711 0.12009790 1.248582 1.558956 THU 559 11.40035 -6.615097 4.785252 0.00998943 1.298576 1.686298 FRI 554 11.12944 -6.754663 4.374778 0.14406921 1.123859 1.263059
Table 5 Statistic description of UPCOM daily return
N Range Minimum Maximum Mean Std Deviation Variance
N Range Minimum Maximum Mean Std Deviation Variance
The descriptive statistics reveal insights into the distribution of daily returns throughout the week, indicating that all three indices experience the highest returns on Monday However, Monday also records the lowest and negative average returns, making it one of the only two days, alongside Tuesday, with a negative mean value In contrast, Friday boasts the highest mean daily returns compared to other weekdays and demonstrates the most stability with the lowest standard deviation, while Monday exhibits the highest standard deviation.
Results of OLS estimation
We employ OLS estimation to analyze the influence of each day of the week on stock returns within the Vietnamese stock market, with the findings presented in the tables below.
Table 6 Estimated results of the Friday effect model
VN-Index HNX-Index UPCOM
Estimated* t-value Estimated* t-value Estimated* t-value
Our analysis of the Vietnam stock market reveals a notable "Friday effect," indicating market inefficiency, as all variables exhibit negative coefficients, suggesting that returns on other days are lower than those on Friday In the case of the HNX-Index and VN-Index, all variables, except for Wednesday, show statistical significance For the UPCOM, only the variable MON is significant, making it the most statistically relevant among the explanatory variables Overall, our findings demonstrate that Friday yields the highest mean returns of the week, with greater statistical significance for the VN-Index and HNX-Index compared to UPCOM, aligning with previous studies by Gibbons and Hess (1981) and Keim and Stambaugh (1984).
This research highlights the varying results of seasonality in the Vietnamese stock market, influenced by different data sets, techniques, and methodologies It serves as a reminder for those interested in seasonality research that findings are unique and should be interpreted with caution Our study reveals that the results from the VN-Index are particularly significant, confirming the presence of seasonality effects, notably the "January effect" and "Friday effect," across three distinct stock exchanges.
The "January effect" has been attributed by many researchers to institutional factors, particularly the tax system; however, in Vietnam, the favorable taxation laws significantly lower the tax burden for citizens and corporations compared to other countries This supportive tax environment, which aligns with citizens' income levels, suggests that taxation is not the primary cause of the "January effect." Instead, it is proposed that the extended holiday periods and the risk preferences of investors are more influential underlying factors contributing to this phenomenon.
During the Tet holiday, the Vietnamese stock exchanges close for an extended and unpredictable period, which can heighten investor anxiety The COVID-19 pandemic significantly impacted the global economy and particularly affected Vietnam during the Tet holiday of 2019, leading to dramatic fluctuations in stock prices This uncertainty makes it riskier for investors to hold stocks, as they cannot react to market changes Consequently, many investors choose to sell their stocks at the end of the year and actively trade to repurchase them at the beginning of the new year.
The "Friday effect" indicates that Fridays yield the highest mean returns in the week, likely due to investors' risk aversion as the Vietnamese Stock Exchanges close on weekends This leads to a surge in stock sales on Fridays, resulting in higher returns compared to other weekdays, presenting potential profit opportunities for investors in the Vietnamese market Conversely, Mondays experience the most significant fluctuations, attributed to the two-day market closure This volatility arises from investor reactions to events that occur over the weekend, which can lead to selling on Mondays if negative news emerges Thus, the interplay of the "Friday effect" and weekend uncertainties underscores the unique dynamics of the Vietnamese stock market.
Investors' potential to achieve high profits from seasonality effects in the Vietnamese market is limited due to several factors Firstly, while the market may appear less efficient, rational investors can conduct their own research on stocks and financial statements, leading to informed decisions Consequently, when seasonality is recognized, it becomes challenging to secure significant abnormal returns, as investors often rely on collective opinions Secondly, the reliance on historical data for modeling stock prices introduces unpredictability, as future price movements are influenced by various factors Although awareness of seasonality is crucial for investors in assessing investments and developing portfolio strategies, it does not guarantee abnormal returns.
This research paper presents exclusive findings that underscore our strong belief in the promising future of the Vietnamese stock market We aim for this work to serve as a valuable reference for future researchers interested in this topic Given the current global and domestic circumstances, we believe that further exploration of this subject is both relevant and worthwhile.
Al-Saad *, K., & Moosa, I A (2005) Seasonality in stock returns: evidence from an emerging market Applied Financial Economics, 15(1), 63–71. doi:10.1080/0960310042000281185
Ariel, R A (1987) A monthly effect in stock returns Journal of Financial Economics,
Assar Lindbeck and Dennis J Snower (1990) The insider-outsider theory of employment and unemployment Long Range Planning, 23(2), 120 doi:10.1016/0024-6301(90)90230- 2.
Ayadi, O F., Dufrene, U B., & Chatterjee, A (1998) Stock return seasonalities in low- income African emerging markets Managerial Finance, 24(3), 22-33.
Bonin, J M., & Moses, E A (1974) Seasonal Variations in Prices of Individual Dow Jones Industrial Stocks The Journal of Financial and Quantitative Analysis, 9(6), 963. doi:10.2307/2329730
Chan, K (1985) An exploratory investigation of the firm size effect Journal of Financial
Chang, E C., & Huang, R D (1990) Time-Varying Return and Risk in the CorporateBond Market The Journal of Financial and Quantitative Analysis, 25(3), 323.doi:10.2307/2330699
Chang, E C., & Pinegar, J M (1986) Return seasonality and tax-loss selling in the market for long-term government and corporate bonds Journal of Financial Economics,
Chung Tien Luu, Cuong Hung Pham & Long Pham (2016) Seasonality Effect on the Vietnamese Stock Exchange International Journal of Financial Research, 7(3), 28-40. doi:10.5430/ijfr.v7n3p28
D’Mello, Ranjan, Stephen P Ferris, and Chuan Yang Hwang (2003) The Tax-Loss Selling Hypothesis, Market Liquidity, and Price Pressure around the Turn-of-the-Year.
Eugene F Fama (1970) Efficient Capital Markets: A Review of Theory and Empirical Work Journal of Finance, 25 (2), 383-417.
Gay, G D., & Kim, T H (1987) An investigation into seasonality in the futures market.
Georgantopoulos, A G., Kenourgios, D F., & Tsamis, A D (2011) Calendar anomalies in emerging Balkan equity markets International Economics & Finance Journal, 6(1), 67-82.
Gibbons, M., & Hess, P (1981) Day of the week effects and asset returns Journal of
Granger, C W J., & Morgenstern, O (1963) Spectral analysis of New York Stock Market Prices Kyklos, 16(1), 1–27 doi:10.1111/j.1467-6435.1963.tb00270.x
H.S.Friday & N.Hoang (2015) Seasonality in the Vietnam Stock Index The International
Journal of Business and Finance Research, 9(1), 103-112
Haugen, R.A., Lakonishok, J (1987) The Incredible January Effect: The Stock Market’s
Unsolved Mystery New York, NY: Dow Jones-Irwin.
Hepsen, A (2012) Calendar anomalies and Turkish real estate investment trusts (REITs).
International Journal of Economics and Finance, 4(3), 230-236.
Huynh, T L D., Wu, J., & Duong, A T (2020) Information Asymmetry and firm value:
Is Vietnam different? The Journal of Economic Asymmetries, 21, e00147. doi:10.1016/j.jeca.2019.e00147.
Keim, B D., & Stambaugh, R F (1984) A further investigation of the weekend effect in stock returns Journal of Finance, 39, 819-840.
Keim, D B (1983) Size-related anomalies and stock return seasonality: Further empirical evidence Journal of Financial Economics, 12(1), 13-32.
Kohers, T., & Patel, J B (1996) An examination of the day-of-the-week effect in junk bond returns over business cycles Review of Financial Economics, 5(1), 31-46.
Lakonishok, J., and Smidt, S (1988) Are seasonal anomalies real? A ninety-year perspective Review of Financial Studies, 1, 403-425.
Lawson, G H., Granger, C W J., & Morgenstern, O (1971) Predictability of Stock Market Prices The Economic Journal, 81(323), 641 doi:10.2307/2229868
Malhotra, N., Tandon, K., & Tandon, D (2015) Testing the empirics of weak form of efficient market hypothesis: Evidence from Asia-Pacific markets IUP Journal of Applied
McNichols, M (1988) A comparison of the skewness of stock return distributions at earnings and non-earnings announcement dates Journal of Accounting and Economics,
Mills T, Coutts J (1995) Calendar effects in the London Stock Exchange FT-SE Indices.
Mishra, K (2009) Indian Capital Market-Revisiting Market Efficiency Indian Journal of Capital Markets, 2, 30-34.
Muradoglu, G., & Oktay, T (1993) Türk Hisse senedi piyasasında zayıf etkinlik: Takvim anomalileri Hacettepe ĩniversitesi IIBF Dergisi, 11, 51-62.
Officer, R R (1975) Seasonality in Australian capital markets Journal of Financial
Ogden, J.P (1990) Turn of Month Evaluations of Liquid Profits and Stock Returns: A‐ ‐ Common Explanation for the Monthly and January Effects The Journal of Finance,
Reilly, F K (1989) Investment Analysis and Portfolio Management, Hinsdale, Illinois: Dryden Press.
Reyes, M G (2001) Asymmetric volatility spillover in the Tokyo Stock Exchange.
Journal of Economics and Finance, 25(2), 206-213.
Richard Roll (1983) Vas ist das? The Journal of Portfolio Management Winter 1983, 9
(2) 18-28; DOI: https://doi.org/10.3905/jpm.1983.18