Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock Exchange

Version 1 : Received: 12 December 2020 / Approved: 14 December 2020 / Online: 14 December 2020 (13:13:36 CET)

A peer-reviewed article of this Preprint also exists.

Jiang, Z.; Gupta, R.; Subramaniam, S.; Yoon, S.-M. The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock Exchange. Sustainability 2021, 13, 2931. Jiang, Z.; Gupta, R.; Subramaniam, S.; Yoon, S.-M. The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock Exchange. Sustainability 2021, 13, 2931.

Journal reference: Sustainability 2021, 13, 2931
DOI: 10.3390/su13052931

Abstract

We investigate the impact of air quality and weather on the equity returns of the Shenzhen Exchange. To capture the air quality and weather effects, we use dummy variables created by employing a moving average and moving standard deviation. The important results are as follows. First, in the whole sample period (2005–2019), we find that high air pollution and extremely high temperature have significant and negative influence on the equity returns. In the sub-period I (2005–2012), the 11-day model and 31-day model show that high air pollution have significant and negative impacts on the Shenzhen stock returns. Second, the results of the quantile regression show that high air pollution have significant and negative effects during bullish market phase, and extremely high temperature have significant and negative effects during bearish market phase. This implies that the air quality and weather effects are asymmetric. Third, the weather effect of the abnormal temperature on the stock returns is greater in severe bearish market. Whereas the effect of the air pollution on the stock returns is greater in the bullish market. Fourth, the least squares method underestimates the air quality and weather effects compared to the quantile regression method, suggesting that the quantile regression method is more suitable in analyzing these effects in a very volatile emerging market such as the Shenzhen stock market.

Subject Areas

air quality; extreme weather; MA-MSD method; investor sentiment; behavioral finance

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