Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 7 August 2020 / Approved: 7 August 2020 / Online: 7 August 2020 (04:08:44 CEST)

How to cite: Jiang, Z.; Gupta, R.; Subramaniam, S.; Yoon, S. The Effect of Air Quality and Weather on the Chinese Stock Market: Evidence from Shenzhen Stock Exchange. Preprints 2020, 2020080171 (doi: 10.20944/preprints202008.0171.v1). Jiang, Z.; Gupta, R.; Subramaniam, S.; Yoon, S. The Effect of Air Quality and Weather on the Chinese Stock Market: Evidence from Shenzhen Stock Exchange. Preprints 2020, 2020080171 (doi: 10.20944/preprints202008.0171.v1).

Abstract

We investigate the impact of air quality and weather on the stock market returns of the Shenzhen Exchange. To capture the air quality and weather effects, we apply dummy variables generated by applying a moving average and moving standard deviation. Our study provides several interesting results. First, in the whole sample period (2005–2019), we find that high air pollution and extremely high temperature have significant and negative effects on the Shenzhen stock 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 effects 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 more the Shenzhen stock returns drop, the greater the effect of the abnormal 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 compared to the quantile regression method, suggesting that the quantile regression method is more suitable in analysing 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; Behavioural finance

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