The Effect of Air Quality and Weather on the Chinese

We investigate the impact of air quality and weather on the stock market returns of the 13 Shenzhen Exchange. To capture the air quality and weather effects, we apply dummy variables 14 generated by applying a moving average and moving standard deviation. Our study provides 15 several interesting results. First, in the whole sample period (2005–2019), we find that high air 16 pollution and extremely high temperature have significant and negative effects on the Shenzhen 17 stock returns. In the sub-period I (2005–2012), the 11-day model and 31-day model show that high 18 air pollution have significant and negative effects on the Shenzhen stock returns. Second, the results 19 of the quantile regression show that high air pollution have significant and negative effects during 20 bullish market phase, and extremely high temperature have significant and negative effects during 21 bearish market phase. This implies that the air quality and weather effects are asymmetric. Third, 22 the more the Shenzhen stock returns drop, the greater the effect of the abnormal temperature is. 23 Whereas, the more the Shenzhen stock returns increase, the greater the effect of the abnormal air 24 quality is. Fourth, the least squares method underestimates the air quality and weather effects 25 compared to the quantile regression method, suggesting that the quantile regression method is more 26 suitable in analysing these effects in a very volatile emerging market such as the Shenzhen stock 27 market. 28


Introduction
index returns. However, Lepori [15] confirmed that this negative effect only exists when stock 135 exchange facilities use trading floor technology.

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Recently, Chinese scholars have actively conducted the research on this issue, and reported 137 several results. For example, Guo and Zhang [5] found that air quality may affect stock market 138 participants and ultimately affect stock market through the channels of emotion, policy and expect.

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This study empirically analysed how whether and air quality affects stock market by using the data 144 Wu et al. [26] explored the relationship between air pollution and stock prices of locally 145 headquartered firms using firm-level data in China. They found that severe air pollution results in low returns, turnover, volatility, and low liquidity. They also found that the relationship between air pollution and local firms' performance is insignificant, implying that the air pollution effects can be of public awareness of environment on stock market in China. They showed that enhanced public 150 environmental awareness negatively influences trading activities in stock market. All these studies 151 suggest that both actual air quality and awareness of environmental problems can influence investor 152 behaviour and stock market.

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As shown above, all previous studies focus only one of the weather effect and air quality effect 154 in their analysis. However, we will incorporate these two effects simultaneously in the analysis. And 155 most previous studies use least squares method, whereas we use the quantile regression method to 156 capture the nonlinearity and asymmetry in the relationship of very volatile market.     206 Table 3 shows the composition of investors in the Shenzhen stock market. As shown in this

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Guangdong, and Guangzhou account for 20% of the transactions, which provides a basis for us to 247 study whether the weather and air quality index impact the order-driven stock trading behaviour.

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Since the proportion of local individual investors in the Shenzhen stock market is high, the market is 249 expected to be sensitive to local air quality and weather conditions.       289     (1) where is the daily values of air quality and three weather variables-AQI, SUNSH, TEMP, and substantially augment the weather (or air quality) effects on stock returns than normal conditions, 307 two dummy variables using each raw variable were generated as follows: where represents a dummy variable for extremely below-average weather (or air quality) and 309 is a dummy variable for extremely above-average weather (or air quality). The air quality and 310 weather dummies used in the study are summarized in Table 7.

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Using the air quality and weather dummy variables generated in the above section, we estimated 315 the following model for analysing the effect of air quality and weather conditions on stock returns: In this equation, denotes the daily returns of the Shenzhen stock market; denotes the 317 coefficients of air quality and weather dummies; and denote the dummies for varying heteroskedasticity in the error of the above model using the following GARCH(1,1) model: where denote an independent time series with a zero mean and an unconditional variance, ,

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and ℎ denotes the conditional variance. All parameters ( , , and ) are expected to be positive for 322 non-negativity of variance, and the sum of ( + ) indicates the persistence of shocks to volatility.

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The GARCH(1,1) model can capture the feature of volatility clustering in the return dynamics of continuously developed as a very important research topic in applied economics as well as in econometrics, due to its advantages of providing detailed information about the conditional 328 distribution of dependent variable and nonlinearity and asymmetry in the relationship.

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For quantile regression, Eq. (8) is transformed into the following form: where ( = 1, 2, ⋯ , 8) represents the parameters that needs to be estimated, represents the 331 quantile point, and represents the quantile regression estimate. High quantile implies bull market, 332 while low quantile implies bear market. We will compare the results between the high and low 333 quantiles.

Effects of Air Quality and Weather on Shenzhen Stock Returns
336 Table 8 shows the effects of air quality and weather on the returns of the SZI using 11-day MA-  Notes: log and denote the calculated values of log-likelihood and Akaike information criterion, respectively. *** and ** indicate significance at the 1% and 5% levels, respectively.  Notes: log and denote the calculated values of log-likelihood and Akaike information criterion, respectively. *** and ** indicate significance at the 1% and 5% levels, respectively. Notes: log and denote the calculated values of log-likelihood and Akaike information criterion, respectively. *** *** and ** indicate significance at the 1% and 5% levels, respectively.

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The test results of Tables 8-10 are summarized in Table 11. As shown in this

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The volatility of Chinese stock markets was extremely high from 2005 to 2009, as shown in Figure   388 1. Chinese stock market experienced the 'roller-coaster' effect during that period, which is affected

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In Table 12

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This implies that the air quality and weather effects exist, but are asymmetric in the Shenzhen stock 416 market.

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In Table 13

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In Table 14

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If we put the above results together, the air pollution has negative influence on the SZI in the 429 high quantile ( ≥ 0.7; bull market), whereas the extremely high temperature has negative influence 430 on the SZI in the low quantile ( ≤ 0.3; bear market). This suggests that the air quality and weather 431 have asymmetric effects in the Shenzhen stock market.6 Interestingly, in several cases, we can find 432 that the Monday effect is significant, however the sign of the effect is also asymmetric.

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implies that the influence of air quality and weather conditions on the Shenzhen stock returns are temperature is. Whereas, the more the Shenzhen stock returns increase, the greater the effect of the abnormal air quality is. Fourth, the least squares method underestimates the air quality and weather effects on the stock returns compared to the quantile regression method, suggesting that the quantile 493 regression method is more suitable in analysing these effects in a very volatile emerging market such 494 as the Shenzhen stock market.

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The efficient market hypothesis (EMH) implies that stock prices are unpredictable. However, quality and weather conditions play different roles in predicting the stock price movement. Investors 499 need to know that they may make biased decisions due to poor air quality and weather problems 500 rather than rational economic prospects. Our findings are helpful for investors in correcting biases in 501 their investment behaviour.

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As part of future research, it would be interesting to extend our analysis to check if air pollution 503 tends to have higher-moment effects, for instance on volatility, which in turn is an important input