The Lunar New Year Effect on Stock Market Returns and Volatility: Evidence from Ho Chi Minh Stock Exchange

1. Introduction

The Lunar New Year is not only the biggest festival in the culture of several East Asian countries but also has a profound impact on the psychology and behavior of investors in stock markets. Several studies have shown that due to optimistic sentiment, individual investors often engage in more trading during the early days of the Lunar New Year, leading to higher stock prices on those days (Bergsma & Jiang, 2016; Teng & Yang, 2018; Huang et al., 2022). Additionally, before the Lunar New Year, investors typically buy stocks to take advantage of prices increases at the beginning of the following year. As a result, stock prices tend to rise in the final days of the Lunar New Year. This increase in stock prices during the last and the beginning days of the Lunar New Year is referred to as the Lunar New Year effect. This effect has been observed in the stock markets of China, Hong Kong, Taiwan, South Korea, Japan, Thailand, Malaysia, and Singapore (Yen et al., 2001; Wu, 2013; Yuan & Gupta, 2014; Chia et al., 2015; Bergsma & Jiang, 2016; Teng & Yang, 2018; Cui, 2024).

Like several countries in East Asia, the Lunar New Year (Tet Nguyen Dan in Vietnamese) plays a crucial role in Vietnamese culture and it is the longest holiday of the year according to the Vietnamese Labor Law. Therefore, the Lunar New Year effect can exist in the Vietnam stock market. The existing literature on seasonal effects in financial markets has documented various phenomena, such as the January effect, Halloween effect and holiday effects, but the specific effect of the Lunar New Year on stock market returns remains underexplored, particularly in frontier markets like Ho Chi Minh stock Exchange (HOSE). Therefore, this study is devoted to filling this gap in the literature by investigating the Lunar New Year effect for the HOSE. This study contributes to the existing literature in the following aspects. First, by analyzing stock performance around the Lunar New Year, this study offers insights into how cultural events can influence market dynamics. Second, this study enhances our understanding of investor sentiment and behaviour during festive periods, bridging gaps in the literature regarding seasonal anomalies in frontier markets. Finally, the findings of this study provide practical implications for investors in navigating market fluctuations tied to cultural events.

The remainder of the paper is structured as follows. Section 2 summarizes the empirical findings of previous studies regarding the Lunar New Year effect. The data employed in the study and the research methodology are presented Section 3. The empirical results of the study are reported in Section 4. Finally, conclusions of the study are presented in Section 5.

2. Literature Review

Although calendar effect in stock returns has been extensively documented in the financial literature over the last several decades, the Lunar New Year effect has not been explored extensively. It is found that some studies investigated the Lunar New Year effect on stock market returns in East Asian countries. Specifically, Yen et al. (2001) tested the hypothesis of Lunar New Year effect for six stock markets, including Hong Kong, Japan, South Korea, Malaysia, Singapore, and Taiwan for the period from 1991 to 2000. The researchers found that market returns tend to increase in the 15 days before and after the Lunar New Year across all markets. In other words, the Lunar New Year effect exists in all studied stock markets. Similarly, Yuan & Gupta (2014) explored the Lunar New Year effect for six stock markets in East Asia, namely China, Hong Kong, Japan, Malaysia, South Korea, and Taiwan. The results derived from the GARCH(1,1) model indicate that market returns in 3 days before Lunar New Year is higher than on other days of the year in all studied stock markets. Moreover, this study found that market volatility in 3 days before Lunar New Year is higher than other days of the year for the Chinese stock market, but there was no differences for the remaining stock markets studied. In addition, Chia et al. (2015) examined the existence of the Lunar New Year effect on the Hong Kong stock market from January 1988 to July 2012. Using GARCH-M, TGARCH-M, and EGARCH-M models, the researchers found that the average market returns in the two days before and one day after the Lunar New Year are higher than the average market returns on other days of the year. Besides, this study confirmed that the volatility of market returns in the days following the Lunar New Year is higher than in the days before the Lunar New Year. According to the researchers, these findings can be explained by arguments derived from behavioral finance, where traditional culture and Chinese beliefs may shape investors’ risk attitudes and influence their decision-making in stock trading. Moreover, Teng & Yang (2018) investigated the Lunar New Year effect for the Shanghai and Shenzhen stock exchanges during the period from 1993 to 2015 and found that market returns in the three sessions before and one session after the Lunar New Year are higher than on other days of the year. In a recent study, Cui (2024) reexamined the Lunar New Year effect on the Shanghai and Shenzhen stock exchanges. The results derived from GARCH-M and EGARCH-M models confirmed that market returns in the two trading days, three trading days before, and two trading days after the Lunar New Year are higher than the average returns on other days of the year for the Shanghai Stock Exchange. However, the Lunar New Year effect did not exist on the Shenzhen Stock Exchange. In another aspect, Wu (2013) explored the effect of the Lunar New Year on stock returns of Chinese companies listed on the U.S. stock market during the period from 1993 to 2011. This study documented that the returns of stocks in the five days preceding the Lunar New Year are higher than on other days of the year by 0.226%, while the returns in the five days following the Lunar New Year were lower by 0.032%.

Regarding the Taiwanese stock market, Huang et al. (2022) investigated the influence of culture on investors’ behavior by examining whether individual investors trade more aggressively during the early days of the Lunar New Year. Using a sample of 129,397 observations collected from 854 branches of 63 securities companies from January 2013 to December 2016, they found that individual investors trade stocks more than usual during the early days of the Lunar New Year due to feelings of happiness. Moreover, this study affirmed that individual investors incurred losses when buying and holding stocks for 1 to 5 days after the Lunar New Year. However, the findings of the study indicate that the Lunar New Year effect did not exist for the trading behavior of institutional investors. Especially, Chien & Chen (2017) measured the impact of the Lunar New Year on the January effect for the Taiwan’s stock market from 1971 to 2004 and found that the January effect exists only when the Lunar New Year fall in February. Based on empirical evidence, the researchers concluded that culture plays a significant role in adjusting the seasonal behavior of investors in the Taiwan’s stock market.

Similar to Chien & Chen (2017), Truong & Friday (2021) investigated the influence of the Lunar New Year on the January anomaly for the HOSE during the period from January 7th, 2009 to December 26th, 2018. The empirical findings obtained from OLS and GARCH(1,1) revealed that the January effect is present on the market for the entire studied period. However, the January effect disappeared in years when the Lunar New Year fall in January. These findings suggest that Lunar New Year has a significant impact on the January anomaly in the Vietnam stock market. Based on the findings, the researchers concluded that the Lunar New Year influences the January effect in Ho Chi Minh stock exchange.

In a broader context, Bergsma & Jiang (2016) investigated the New Year effect on 11 stock markets across 6 different cultures where celebrate New Year holidays do not fall on January 1st. This study documented that the market returns around a cultural New Year are higher than the average returns on other days of the year. In addition, the researchers found that market returns in the first month of the New Year according to the calendar of the countries in the study are higher than the market returns in the remaining months of the year. They argued that the New Year effect is attributed to the optimistic sentiment of investors during the New Year celebrations.

In conclusion, the Lunar New Year effect has been found in all stock markets in countries with a tradition of celebrating the Lunar New Year. Specifically, market returns on the days before and after the Lunar New Year are higher than on other days of the year. The Lunar New Year effect can be explained by the optimism that alters investors’ attitudes toward risk and influences their decision-making in stock trading during the days before and after the Lunar New Year.

3. Data and Research Methodology

The data employed in this study is primarily the daily VN30-Index series for the period from February 6, 2012 (the date the VN30-Index was officially launched) to December 31, 2024 that are obtained from the investing.com (www. investing.com, accessed on April 22, 2025). Then, a natural logarithmic transformation is conducted for the data to generate a time series of continuously compounded returns. Specifically, the market returns are computed by the following equation:

$$R_t=\log (I_t)-\log (I_{t-1})=\log (I_t/I_{t-1})$$

where:

- R_t is the market return of trading day t;

- I_t is the VN30-Index at the end of trading day t;

- I_t-1 is the VN30-Index at the end of trading day t-1.

To test for the presence of the Lunar New Year effect on market returns and volatility for the HOSE, the ordinary least square (OLS) regression is first employed in this study. Specifically, the model takes the following forms:

$$R_t={\alpha }_0+{\alpha }_{1}PRE{2}_t+{\alpha }_{2}POST{2}_t+{\beta }_1{D}_{1t}+{\beta }_2{D}_{2t}+{\beta }_3{D}_{3t}+{\beta }_4{D}_{4t}+{\beta }_5{D}_{5t}+{\epsilon }_t$$

(1)

$$R_t={\alpha }_0+{\varphi }_{1}PRE{5}_t+{\varphi }_{2}POST{5}_t+{\beta }_1{D}_{1t}+{\beta }_2{D}_{2t}+{\beta }_3{D}_{3t}+{\beta }_4{D}_{4t}+{\beta }_5{D}_{5t}+{\epsilon }_t$$

(2)

where:

- PRE2_t is a dummy variable, taking the value of 1 if observation t falls within the last 2 trading days before the Lunar New Year, and 0 otherwise.

- POST2_t is a dummy variable, taking the value of 1 if observation t falls within the first 2 trading days following the Lunar New Year, and 0 otherwise.

- PRE5_t is a dummy variable, taking the value of 1 if observation t falls within the last 5 trading days before the Lunar New Year, and 0 otherwise.

- POST5_t is a dummy variable, taking the value of 1 if observation t falls within the first 5 trading days following the Lunar New Year, and 0 otherwise.

- D_1t, D_2t, D_3t, D_4t and D_5t are dummy variables for Monday, Tuesday, Wednesday, Thursday and Friday, respectively (i.e., D1_t takes the value of 1 if observation t falls on Monday and 0 otherwise). The dummy variables for days of the week are used as control variables for the day-of-the week effect that can be present in the HOSE.

The selection of the PRE2 and POST2 variables to measure the Lunar New Year effect on the HOSE is based on Vietnam’s current stock trading regulation, which follows a T+2 settlement system. This means that transactions (buying and selling) executed on day T are only completed after 2 business days, allowing the seller to receive payment and the buyer to receive stocks. Therefore, if a transaction occurs on the last two days of the lunar year, the money and stocks will only be available investors’ accounts during the first two trading days after the Lunar New Year. Given this trading characteristic, market returns during the last two trading days before the Lunar New Year break may differ from those on other days. In addition, this study investigates the Lunar New Year effect through the PRE5 and POST5 variables in order to broaden the scope of the study and enhance the robustness of the findings. The selection of these windows aligns with previous studies of Wu (2013) and Huang et al. (2022).

It is important to note that the assumption of constant variance of errors over time in the OLS model does not often hold for time series data in finance. According to Brooks (2002), if this assumption is violated and the OLS model is still applied, the standard errors may be incorrect, leading to potentially biased conclusions. To address this issue, Engle (1982) proposed the ARCH (Autoregressive Conditional Heteroscedasticity) model, which allows the variance of errors to change over time as a function of past errors. Then, Bollerslev (1986) generalized the ARCH model into the GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model, which permits conditional variance to depend on its own previous lags. Therefore, if the heteroscedasticity exists in the OLS model, the GARCH model is considered more appropriate than the OLS model.

Although the standard GARCH model addresses the issue of heteroscedasticity in regression models, it does not account for asymmetry in shocks that often occur in financial time series data. To deal with this issue, Nelson (1991) proposed the EGARCH (exponential generalized auto-regressive conditional heteroskedasticity) model to measure the asymmetric volatility of a financial asset in response to positive and negative shocks (leverage effects). Therefore, this study employs the EGARCH(1,1) model to address the issue of heteroskedasticity if it exists in the model and the asymmetric effect of shocks on the market return volatility. Specifically, the EGARCH(1,1) takes the following forms:

$$\begin{gathered} R_t={\alpha }_0+{\alpha }_{1}PRE{2}_t+{\alpha }_{2}POST{2}_t+{\beta }_1{D}_{1t}+{\beta }_2{D}_{2t}+{\beta }_3{D}_{3t}+{\beta }_4{D}_{4t}+{\beta }_5{D}_{5t}+{\epsilon }_t \\ \ln ({\sigma }_t^2)=\omega +\delta \ln ({\sigma }_{t-1}^2)+\gamma \left[{\displaystyle \frac{\left|{\epsilon }_{t-1}\right|}{\sqrt{{\sigma }_{t-1}^2}}}-\sqrt{{\displaystyle \frac{2}{\pi }}}\right]+\varphi {\displaystyle \frac{{\epsilon }_{t-1}}{\sqrt{{\sigma }_{t-1}^2}}} \end{gathered}$$

(3)

$$\begin{gathered} R_t={\alpha }_0+{\varphi }_{1}PRE{5}_t+{\varphi }_{2}POST{5}_t+{\beta }_1{D}_{1t}+{\beta }_2{D}_{2t}+{\beta }_3{D}_{3t}+{\beta }_4{D}_{4t}+{\beta }_5{D}_{5t}+{\epsilon }_t \\ \ln ({\sigma }_t^2)=\omega +\delta \ln ({\sigma }_{t-1}^2)+\gamma \left[{\displaystyle \frac{\left|{\epsilon }_{t-1}\right|}{\sqrt{{\sigma }_{t-1}^2}}}-\sqrt{{\displaystyle \frac{2}{\pi }}}\right]+\varphi {\displaystyle \frac{{\epsilon }_{t-1}}{\sqrt{{\sigma }_{t-1}^2}}} \end{gathered}$$

(4)

Finally, to test for the presence of the Lunar New Year effect on the market return volatility, dummy variables capturing the Lunar New Year effect are included in the conditional variance equation. Specifically, the model measuring the Lunar New Year effect on market return volatility takes the forms as follows:

$$\begin{gathered} R_t={\alpha }_0+{\alpha }_{1}PRE{2}_t+{\alpha }_{2}POST{2}_t+{\beta }_1{D}_{1t}+{\beta }_2{D}_{2t}+{\beta }_3{D}_{3t}+{\beta }_4{D}_{4t}+{\beta }_5{D}_{5t}+{\epsilon }_t \\ \ln ({\sigma }_t^2)=\omega +\delta \ln ({\sigma }_{t-1}^2)+\gamma \left[{\displaystyle \frac{\left|{\epsilon }_{t-1}\right|}{\sqrt{{\sigma }_{t-1}^2}}}-\sqrt{{\displaystyle \frac{2}{\pi }}}\right]+\varphi {\displaystyle \frac{{\epsilon }_{t-1}}{\sqrt{{\sigma }_{t-1}^2}}}+{\lambda }_{1}PRE{2}_t+{\lambda }_{2}POST{2}_t \end{gathered}$$

(5)

$$\begin{gathered} R_t={\alpha }_0+{\varphi }_{1}PRE{5}_t+{\varphi }_{2}POST{5}_t+{\beta }_1{D}_{1t}+{\beta }_2{D}_{2t}+{\beta }_3{D}_{3t}+{\beta }_4{D}_{4t}+{\beta }_5{D}_{5t}+{\epsilon }_t \\ \ln ({\sigma }_t^2)=\omega +\delta \ln ({\sigma }_{t-1}^2)+\gamma \left[{\displaystyle \frac{\left|{\epsilon }_{t-1}\right|}{\sqrt{{\sigma }_{t-1}^2}}}-\sqrt{{\displaystyle \frac{2}{\pi }}}\right]+\varphi {\displaystyle \frac{{\epsilon }_{t-1}}{\sqrt{{\sigma }_{t-1}^2}}}+{\tau }_{1}PRE{5}_t+{\tau }_{2}POST{5}_t \end{gathered}$$

(6)

4. Empirical Results

As presented in Section 3, this study employs a set of regression models to investigate the Lunar New Year effect on the market returns and volatility for the HOSE. First, the results of the OLS model summarized in Table 1 indicate that the average return in the last two days (PRE2) and five days (PRE5) before the Lunar New Year are significantly higher than the average market returns on other days of the year. However, the findings shown in Table 1 confirm that the average return in the first two trading days (POST2) and five trading days (POST5) after the Lunar New Year are not significantly higher than the average market returns on other days of the year. On the basis of these findings, it can be concluded that pre-Lunar New Year effect exists in the HOSE. However, this conclusion does not take into account the ARCH effect that is suspected to be present in the model. To test for the existence of ARCH effects, the Lagrange Multiplier approach, developed by Engel (1982), is employed. The results of ARCH-LM test presented in Table 3 confirm that ARCH effects is present in the OLS model because the test statistic of the model is higher than the LM-critical value at the one percent significance level. Due to ARCH effect in the OLS models, the EGARCH(1,1) models are employed in this study.

The results obtained from the EGARCH(1,1) models are summarized in Table 2. These findings consistently confirm that pre-Lunar New Year effect exists in the HOSE. Specifically, it is observed from Table 2 that the average return in the last two days (PRE2) and five days (PRE5) before the Lunar New Year are higher than the average market returns on other days of the year by 0.26 percent and 0.22 percent respectively. The differences in market returns are statistically significant at the five percent level. These findings are in line with previous findings of Yen et al. (2001), Yuan & Gupta (2014), Chia et al. (2015), Teng & Yang (2018) and Cui (2024). However, the results presented in Table 2 indicate that the average return during the first two trading days (POST2) and five trading days (POST5) following the Lunar New Year are not significantly different from the average market returns on other days throughout the year.

The pre-Lunar New Year effect on market returns in the HOSE can be attributed to several factors. First, the Lunar New Year is a major cultural event in Vietnam, often associated with renewal and prosperity. Therefore, investors may feel more optimistic as the Lunar New Year approaches. The optimism can lead to increased buying activity, driving stock prices up. In addition, many individual investors prefer to start the new year with profitable positions, which can drive up stock prices as they trade more aggressively in the days leading up to the holiday in anticipation of positive market movements. Moreover, in the Vietnamese culture, many companies issue bonuses before the Lunar New Year holiday, providing investors with additional capital to invest in stocks. This influx of cash can drive up demand and prices of stocks. Finally, traders often engage in speculative buying in anticipation of price increases after the Lunar New Year, further contributing to rising stock prices.

Regarding the Lunar New Year effect on the market volatility, the results of EGARCH(1,1) model presented in Table 3 show that the coefficients of the dummy variables for the Lunar New Year effect (PRE2, PRE5, POST2 and POST5) in the conditional variance equation of Model 5 and Model 6 are not statistically significant. These findings imply that the Lunar New Year effect on the market volatility is not present for the HOSE. This evidence is in line with previous findings of Yuan & Gupta (2014) for stock markets in Hong Kong, Japan, Malaysia, South Korea, and Taiwan. In addition, the results obtained from the studied models consistently confirm that the day-of-the-week effect is not present for the HOSE. This finding aligns with the earlier finding of Nguyen et al. (2022). Moreover, the results of the EGARCH(1,1) model confirm that the leverage effect on the market volatility exists in the HOSE. Specifically, the results presented in Table 2 and Table 3 consistently show that the leverage effect coefficient is statistically negative at the one percent significance level, implying that negative market shocks lead to greater volatility than positive shocks of the same magnitude.

Table 3. Results of the Lunar New Year effect on the market return volatility.

Variable	Model 5		Model 6
	Coefficient	z-Statistic	Coefficient	z-Statistic
Conditional mean equation
${\alpha }_0$ (constant)	-0.00107	-0.20	-0.00107	-0.20
${\alpha }_1$ (PRE2)	0.00249	1.76*	-	-
${\alpha }_2$ (POST2)	0.00006	0.07	-	-
${\varphi }_1$ (PRE5)			0.00199	2.72***
${\varphi }_2$ (POST5)			0.00019	0.30
${\beta }_1$ (Monday)	0.00079	0.15	0.00077	0.14
${\beta }_2$ (Tuesday)	0.00123	0.23	0.00122	0.23
${\beta }_3$ (Wednesday)	0.00163	0.30	0.00160	0.30
${\beta }_4$ (Thursday)	0.00108	0.20	0.00106	0.20
${\beta }_5$ (Friday)	0.00139	0.26	0.00140	0.26
Conditional variance equation
$\omega$	-0.52486	-10.86***	-0.51969	-10.65***
${\lambda }_1$ (PRE2)	0.08610	0.57	-	-
${\lambda }_2$ (POST2)	0.01545	0.09	-	-
${\tau }_1$ (PRE5)			0.07063	1.60
${\tau }_2$ (POST5)			-0.04408	-1.07
$\gamma$ ( ARCH effect)	0.20586	15.41***	0.20382	15.30***
$\delta$ (GARCH effect)	0.96548	228.85***	0.96581	227.67***
$\varphi$ (Leverage effect)	-0.04788	-7.20***	-0.04749	-7.17***

*** and * indicate statistically significance at 1% and 10% levels respectively.

Table 3. Results of the Lunar New Year effect on the market return volatility.

Variable	Model 5		Model 6
	Coefficient	z-Statistic	Coefficient	z-Statistic
Conditional mean equation
${\alpha }_0$ (constant)	-0.00107	-0.20	-0.00107	-0.20
${\alpha }_1$ (PRE2)	0.00249	1.76*	-	-
${\alpha }_2$ (POST2)	0.00006	0.07	-	-
${\varphi }_1$ (PRE5)			0.00199	2.72***
${\varphi }_2$ (POST5)			0.00019	0.30
${\beta }_1$ (Monday)	0.00079	0.15	0.00077	0.14
${\beta }_2$ (Tuesday)	0.00123	0.23	0.00122	0.23
${\beta }_3$ (Wednesday)	0.00163	0.30	0.00160	0.30
${\beta }_4$ (Thursday)	0.00108	0.20	0.00106	0.20
${\beta }_5$ (Friday)	0.00139	0.26	0.00140	0.26
Conditional variance equation
$\omega$	-0.52486	-10.86***	-0.51969	-10.65***
${\lambda }_1$ (PRE2)	0.08610	0.57	-	-
${\lambda }_2$ (POST2)	0.01545	0.09	-	-
${\tau }_1$ (PRE5)			0.07063	1.60
${\tau }_2$ (POST5)			-0.04408	-1.07
$\gamma$ ( ARCH effect)	0.20586	15.41***	0.20382	15.30***
$\delta$ (GARCH effect)	0.96548	228.85***	0.96581	227.67***
$\varphi$ (Leverage effect)	-0.04788	-7.20***	-0.04749	-7.17***

*** and * indicate statistically significance at 1% and 10% levels respectively.

5. Conclusions

This study investigates the Lunar New Year effect on the market returns and volatility in the HOSE. Using the daily VN30-Index data from February 6th, 2012 to December 31st, 2024, we find significant evidence of a pre-Lunar New Year effect, characterized by higher average returns in the final days leading up to the Lunar New Year. Specifically, market returns in the last two trading days and five trading days before the Lunar New Year are statistically higher than those on other days of the year. However, this effect does not extend to the days immediately following the holiday, where no significant differences in returns are observed. These findings align with previous studies conducted in various East Asian markets, reinforcing the notion that cultural factors significantly influence investor behavior and market dynamics. The optimism associated with the Lunar New Year appears to drive increased trading activity and speculative buying, contributing to price surges prior to the holiday. In contrast, our analysis does not support the existence of a significant Lunar New Year effect on the HOSE’s volatility. This suggests that while returns may be influenced by cultural sentiments, the volatility does not exhibit the same patterns, aligning with findings from other regional studies. Additionally, this study find that effect of shocks on the market return volatility is asymmetric for the HOSE. Specifically, negative market shocks lead to greater volatility than positive shocks of the same magnitude.

Overall, this research contributes to the understanding of seasonal anomalies in frontier markets and highlights the importance of cultural events in shaping investor sentiment and market behavior. The insights gained from this study can aid investors in navigating the complexities of market fluctuations associated with cultural festivities. Specifically, investors can benefit from increased stock prices in the days leading up to the Lunar New Year. Understanding this seasonal trend can help investors make informed decisions about when to buy or sell stocks. Given that no significant price increases were observed in the days following the Lunar New Year, investors should be cautious about holding positions into this period. The potential for lower returns post-holiday suggests a strategy focused on short-term gains in the days leading up to the Lunar New Year.