The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock Exchange
We investigated the impact of air quality and weather on the equity returns of the Shenzhen Exchange. To capture the air quality and weather effects, we used dummy variables created by employing a moving average and moving standard deviation. The important results are as follows. First, in the whole...
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doaj-47f466d5d04f44e2a9e9fbfd6c5f85652021-03-09T00:05:05ZengMDPI AGSustainability2071-10502021-03-01132931293110.3390/su13052931The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock ExchangeZhuhua Jiang0Rangan Gupta1Sowmya Subramaniam2Seong-Min Yoon3Division of Chinese Foreign Affairs and Commerce, Hankuk University of Foreign Studies, Seoul 02450, KoreaDepartment of Economics, University of Pretoria, Pretoria 0002, South AfricaIndian Institute of Management Lucknow, Prabandh Nagar off Sitapur Road, Lucknow, Uttar Pradesh 226013, IndiaDepartment of Economics, Pusan National University, Busan 46241, KoreaWe investigated the impact of air quality and weather on the equity returns of the Shenzhen Exchange. To capture the air quality and weather effects, we used 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.https://www.mdpi.com/2071-1050/13/5/2931air qualityextreme weatherMA-MSD methodinvestor sentimentbehavioral finance |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhuhua Jiang Rangan Gupta Sowmya Subramaniam Seong-Min Yoon |
spellingShingle |
Zhuhua Jiang Rangan Gupta Sowmya Subramaniam Seong-Min Yoon The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock Exchange Sustainability air quality extreme weather MA-MSD method investor sentiment behavioral finance |
author_facet |
Zhuhua Jiang Rangan Gupta Sowmya Subramaniam Seong-Min Yoon |
author_sort |
Zhuhua Jiang |
title |
The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock Exchange |
title_short |
The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock Exchange |
title_full |
The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock Exchange |
title_fullStr |
The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock Exchange |
title_full_unstemmed |
The Effect of Air Quality and Weather on the Chinese Stock: Evidence from Shenzhen Stock Exchange |
title_sort |
effect of air quality and weather on the chinese stock: evidence from shenzhen stock exchange |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2021-03-01 |
description |
We investigated the impact of air quality and weather on the equity returns of the Shenzhen Exchange. To capture the air quality and weather effects, we used 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. |
topic |
air quality extreme weather MA-MSD method investor sentiment behavioral finance |
url |
https://www.mdpi.com/2071-1050/13/5/2931 |
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