Wanglaoshi (no track changes)

The holiday effect in Chinese stock markets – evidence from Shanghai and Shenzhen

First Author1[0000-1111-2222-3333] and Second Author2[1111-2222-3333-4444]

1 Princeton University, Princeton NJ 08544, USA

[email protected]


The holiday effect is a subtype of the calendar effect, which refers to the abnormal return or volatility of the stock market before and after a vacation. The holiday impact is explicable from a behavioral finance standpoint. To take advantage of the vacation impact and gain extra profits, investors should adopt an active investing approach. This article studies the impact of holidays on the Shanghai and Shenzhen stock markets between January 2008 and August 2022. Standard OLS, GARCH-M, and EGARCH-M models are used to examine the presence of holiday impacts in the Chinese mainland market before and after the Chinese New Year and the National Day. The data indicate that the Shanghai market has a considerably favorable Chinese New Year impact two days before, three days before, and two days after the holiday. In Shenzhen, there are no or very minimal holiday effects.

Keywords: Holiday effect, Chinese holiday, Chinese markets, GARCH-M, EGARCH-M.


The holiday impact is the propensity for stock market asset returns to model before and after holidays. Fama’s (1970) efficient market hypothesis (EMH) states that anomalous gains are impossible since share prices represent all information. EMH can’t completely explain the stock market (Lakonishok and Smidt, 1988; Ritter, 2003). They believed that investors may attain abnormal returns by timing the January impact, day-of-the-week effect, and other calendar influences (Mitchell & Ong, 2006). Holiday influence is less emphasized. Lakonishok and Smidt (1988) found that stock prices are higher before holidays. Holiday impacts are universal, according to Gao and Lin (2011). Asians are more vulnerable to holiday impacts than westerners (Yates et al., 1997), as shown in Chinese financial markets (Brown and Mitchell, 2008). Chen and Chien (2011) and Huang et al. (2021) show the Chinese New Year influence in Taiwan. More corporations have invested in China’s financial industry, although less research have been done on this. This research examines the holiday influence on China’s stock market. An effort to broaden current research that demonstrate better market returns before Chinese New Year failed.

Literature Review

Since the 1980s, investors have studied holidays’ economic impact. McGuinness and Harris (2011) found that the holiday effect’s significant returns occur the day prior. Pre-holiday gains account for about one-third of market returns. Foreign trading practices and settlement processes don’t affect the holiday impact. Monopolistic systems, corporate features, and dominating institutional investors cannot produce a holiday impact. Pantzalis and Ucar (2014) evaluated CRSP data and found unfavorable pre-holiday impacts. They reported no vacation impacts in Spain. Comparatively few research have examined the impact of Chinese New Year on stock performance. Investors welcome the vacation impact as more Asian nations with large Chinese populations celebrate Chinese New Year (Chia et al., 2015). There is a strong effect of Chinese New Year vacations in Hong Kong, Singapore, and Malaysia using 1970-1985 stock market data. Stocks saw above-average gains before Chinese New Year (Chia et al., 2015). There is also a considerable effect of Chinese New Year holidays in Singapore and Malaysia as it affects the Chinese and Hong Kong economies (Bergsma and Jiang. 2016).

Mainland China and Hong Kong Chinese New Year impact economic performance. According to Yuan and Gupta (2014), the Chinese New Year has a bigger impact than other Chinese festivals. Cao et al. (2007) tested Chinese New Year, Labour Day, National Day, and New Year’s Day using OLS regressions. Chinese New Year has a statistically and economically substantial influence on market returns, unlike other holidays. Casalin (2018) confirms that holiday impacts are positive, time-varying, and have no declining tendency. The Chinese New Year holiday impact is important since it doesn’t decline like the US holiday effect (Keef and Roush, 2005).


This study examined the impact of the holiday effect in China by using the daily Shanghai Composite Index (SSEC) and Shenzhen Composite index (SZI) which would be representative of the Shanghai Stock Market and the Shenzhen Stock Market for a total of 3548 trading days from January 2008 to August 2022. This allows the market to have 15 Chinese New Year and 14 National Day holidays to be examined. On 14 December 2007, the Chinese State Council amended the ‘holiday arrangements for holidays and commemorative days in China’, adjusting the vacation time for the New Year, Labour Day, and other holidays in China. It means that starting from 2008, National Day on 1 October become the only longest holiday in China (7 days) apart from the Chinese New Year. Therefore, it is reasonable to believe that data from January 2008 to August 2022 (a total of 3548 trading days) would provide a more reliable result.

To avoid the effect of extreme values, daily stock market returns adopted Eidinejad and Dahlem (2021) ’s method. The daily returns rate is as:

Rt = 100 Pricet- Pricet-1 Pricet-1(1)

The Rt is the return on time t, and price t and price t-1 are the stock composite index in time t and t-1 respectively. Stock market non-trading days are used to compute the day following non-trading day’s return. This research defines holidays as trade breaks. To expand McGuinness and Harris’s (2011) research and examine the vacation effect in China. Three days before and two days after market close were studied. This research classified Pre1-CNY as one day before, Pre2-CNY as two days before, and Pre3-CNY as three days before the Chinese New Year to evaluate the pre-holiday impact, and Post1-CNY as one day after, Post2-CNY as two days after to examine the post-holiday effect. Similarity, comparing Pre1-NH, Pre2-NH, Pre3-NH, Post1-NH, and Post2-NH. Rest of trade day handled like other days.

Table SEQ Table * ARABIC 1. Statistics for the Shanghai and Shenzhen Composite Index.

Table 1 displays the Shanghai and Shenzhen Composite index daily returns pre- and post-Chinese New Year and National Day, including the mean, maximum, minimum, standard deviation, Jarque-Bera normality test, and Augmented Dickey-Fuller (ADF) test. The Shanghai and Shenzhen composite indices have a p-value less than 1% in the ADF test. This shows data smoothness and doesn’t need data modification. The Jarque-Bera normality test rejects the null hypothesis of normal distribution at 1% significance, revealing daily stock returns are not normal. Classical linear regression models violated normality. It matches prior research’ findings.


standard ordinary least square

Most prior studies employed OLS regression to evaluate if stock returns are different before and after vacations. Simple OLS regression equation using dummy holiday variables:

Rt = ɑ1 + β1DChinese New Year holiday effect + β2DNational Day holiday effect + ηt + εt(2)

Where Rt is the daily market return (Shanghai and Shenzhen) at time t. DChinese New Year holiday effect and DNational Day holiday effect represent the dummy variables for the Chinese New Year and National Day. DChinese New Year holiday effect included Pre1-CNY, Pre2-CNY, Pre3-CNY, Post1-CNY, and Post2-CNY. DNational Day holiday effect included Pre1-NH, Pre2-NH, Pre3-NH, Post1-NH, and Post2-NH. The dummy variables take the value of 1 if the daily return is on a corresponding day, and take the value of 0 on other trading days. ɑ1 is the intercept term that shows the percentage average return for other trading days. β1 and β2 are the estimated coefficient, ηt is the monthly fixed effect and εt is the error term. If the estimated coefficient of the dummy variables is significant, which shows the holiday effect in mainland china exists. The daily market returns before and after the Chinese New Year and National Day are significantly different from other trading days.

OLS demands that stock return error terms be homogenous, normally distributed, and uncorrelated. It’s hard to do and may lead to inaccurate findings (Chien et al.,2002, Yuan and Gupta, 2014, Chiu,2020). OLS can’t capture time-varying market volatility (Chai et al., 2015). Using OLS to examine stock market seasonality may not be appropriate.


GARCH-M was utilized to overcome OLS’s flaws. GARCH-M links stock returns with volatility and captures time-varying error term variance. Bollerslev et al. (1986) note that the GARCH model is appropriate for most financial series when p and q are 1.

Therefore, the GARCH-M (1,1) model is adopted in this study and the equation writes as:

Rt = ɑ1 + β1DChinese New Year holiday effect + β2DNational Day holiday effect + β3σ2t + εt(3)

Whereσ2t = γ1 + β1*DChinese New Year holiday effect + β2*DNational Day holiday effect + γ2σ2t-1 + γ3 ε2t-1(4)

Compared to Eq.(2), the return rate (Rt) in Eq.(3) depends on the conditional variance and error term εt with a mean of zero. β3 values the ratio of return to risk. In addition, Eq.(4) shows the conditional variance in a linear regression term. γ1 is the interception,γ2 and γ3 are the coefficients to capture the heteroskedasticity present in the daily stock return.

Engle (1993) found that markets respond asymmetrically to positive and negative shock, with negative news causing more volatility. To observe the asymmetric link between returns and volatility, the research employed Nelson’s (1991) EGARCH model. EGARCH-M provides more accurate findings. EGARCH-M model equation: Rt = ɑ1 + β1DChinese New Year holiday effect + β2DNational Day holiday effect + logβ3σ2t + εt(5)

The conditional variance in the EGARCH-M model is nature logarithm, and express as:

logσ2t= γ1 + β1*DChinese New Year holiday effect + β2*DNational Day holiday effect + γ2σ2t-1 + ( γ3|εt-1σt-1 – 2/π | + φ1 εt-1σt-1)(6)

Where γ2 explains the persistence, γ3 explains the volatility clustering, and φ1 explains the leverage effects. φ1 equal to 0 if the impact of external shocks on the stock market is symmetric, and not equal to 0 if the impact is symmetrical. In addition, the negative φ implies that financial markets are more affected by negative news than positive news and that leverage effects exist.

Results and Discussion

OLS model

Table 2 displays mainland China’s OLS results for Chinese New Year and National Day. For the Chinese New Year holiday impact, the calculated coefficients of the dummy variables are considerably positive 2 days before and after the holiday in Shanghai and Shenzhen. Pre-CNY holiday impact had greater coefficients than post-CNY. 1 day before and 2 days after Chinese National Day have positive coefficients. Shanghai’s pre-holiday impact coefficient is larger than post-holiday, whereas Shenzhen indicates the reverse. The ARCH-LM test rejects the null hypothesis that the model has no ARCH impact, suggesting the OLS model’s untreated return volatility has ARCH effects. To explain the holiday influence on the Chinese stock market, the GARCH model must be used to account for such volatility.

Table SEQ Table * ARABIC 2 The OLS results

GARCH Models

Table 3 shows that 5 is not significant at a 10% level in Shanghai and Shenzhen for GARCH-M and EGARCH-M. Mean return may not rise with volatility. The EGARCH-M model demonstrates that Shanghai’s market performed well 2 days before, 3 days before, and 2 days after Chinese New Year. Results pass 5% significance. These days may have greater returns. GARCH-M demonstrated a substantial holiday impact just 2 days after Chinese New Year. In Shenzhen, only post2-CNY produced positive significant findings with a 10% significant level in GARCH-M. Post-holiday impact in both markets has greater coefficients than pre-holiday effect. Unlike the OLS model, the ARCH impact reduces the pre-holiday effect.

For the Chinese National Day, the estimated coefficients in the EGARCH-M model pass the 1% significant level 1 day before, 1 day after, and 2 days after in the Shanghai market, and 5% on 1 day before and after in the Shenzhen market, indicating the National Day effects are statistically significant in the Shanghai and Shenzhen stock markets. Post-holiday effects are greater than pre-holiday effects, similar to Chinese New Year. Shenzhen has a bigger National Day influence than Shanghai a day before and after the holiday. Shanghai has greater positive calculated coefficients. In the GARCH-M model, the National Day holiday impact only reflects post-holiday effects, not pre-holiday ones.

Table 3 The GARCH-M and EGARCH-M model results (return equation)

Conditional variance equation is in Table 4. Post1-NH has the most volatility in Shanghai and Shenzhen, while Post2-CNY and Post2-NH have the lowest. The day following Chinese New Year and National Day was more volatile than the pre-holiday influence. Shenzhen’s market was only statistically significant post-New Year’s and National Day (Post1-CNY, Post2-CNY, Post1-NH, and Post2-NH). Post-holiday, it was volatile.

Table 4 The GARCH-M and EGARCH-M model results (variance equation)

In the variance equation, the negative ARCH (γ2) parameter is allowed because the EGARCH model uses the logarithm sigma-square to confirm that the sigma-square is positive. Furthermore, the significantly positive φ1 shows the impact of the positive shocks and negative shocks on the stock market is asymmetrical, and the positive shock has a greater impact on volatility rather than the negative shocks of the same magnitude in both the Shanghai and Shenzhen stock market. Positive persistence (γ2) also indicates that investors are more likely to be susceptible to positive news, compared to negative news. This means that the volatility premium is asymmetric in the mechanism.

Table 5 The Ljung-Box and ARCH LM Tests

The diagnostic test is shown in Table 6, both GARCH-M and EGARCH-M do not have the remaining ARCH effect. All p-value is not significant at the 10% level, indicating the GARCH-M and EGARCH-M models are better than the OLS model to test the holiday effect.

The Shanghai stock market exhibited statistically significant coefficients two, three, and two days before and after Chinese New Year, indicating that holiday impacts occur before and after the holiday. Pre2-CNY and Pre3-CNY results match Yuan and Gupta (2014). The holiday has no lasting impact the day after. EGARCH-M shows no Shenzhen vacation impact, whereas GARCH-M does. Mainland China investments are different. China’s stock markets must be invested in. Chinese New Year’s impacts are greater than Labor Day, National Day, and New Year’s Day, according to Cao et al. (2007). 2 days after Chinese New Year, Shanghai’s predicted coefficient is higher than before. Shenzhen prioritizes National Day above Chinese New Year.


From a behavioral finance perspective, holiday stock returns contradict the EMH theory. Holiday-related anomalies may benefit investors. China’s vacation impact on stock returns is important for theory and practice. This helps comprehend how Chinese stock market volatility influences investor decisions. Using OLS, GARCH-M, and EGARCH-M, this research examined Shanghai and Shenzhen stock returns from 2008 through 2022. The main findings are that Shanghai had greater atypical returns before and after holidays, with more positive consequences. Second, only the Shanghai stock market shows a positive holiday impact for Chinese New Year, consistent with past results. More than National Day, Chinese New Year influences Shanghai’s market. After Chinese New Year and National Day, volatility increases. Positive shocks impact China’s volatility more than negative shocks, according to EGARCH-M. Investors must be wary with high-risk investments.


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