Weather effects on the returns and volatility of the Shanghai stock market

2010 ◽  
Vol 389 (1) ◽  
pp. 91-99 ◽  
Author(s):  
Sang Hoon Kang ◽  
Zhuhua Jiang ◽  
Yeonjeong Lee ◽  
Seong-Min Yoon
Keyword(s):  
Stock Market ◽  
Weather Effects

scholarly journals Relationship among weather effects, investors' moods and stock market risk: An analysis of bull and bear markets in Taiwan, Japan and Hong Kong

2018 ◽  
Vol 65 (2) ◽  
pp. 239-253
Author(s):  
Yi-Hsien Wang ◽  
Kuang-Hsun Shih ◽  
Je-Wei Jang
Keyword(s):  
Hong Kong ◽  
Stock Market ◽  
Stock Returns ◽  
Stock Price ◽  
Psychological Impact ◽  
Weather Conditions ◽  
Market Condition ◽  
Stock Market Returns ◽  
Weather Effects ◽  
Intraday Data

Literature shows that weather encourages people to engage in certain behaviors and that three factors, particularly sunshine, temperature, and humidity, have the greatest psychological impact on investors (Edgar Howarth and Michael S. Hoffman 1984). On the contrary, some results indicate that the weather has insignificant effect on investors (Ben Jacobsen and Wessel Marquering 2008; Jing Lu and Robin K. Chou 2012). Hence, our research used three weather variables, namely temperature, humidity, and cloud cover, to detect the effects of extreme weather conditions on stock returns. The sample data used in this study consisted of the intraday data, with thirty minutes stock price, of Taiwan, Japan, and Hong Kong from 2012 to 2015. By taking into consideration the effects of asymmetric volatility, we employed the GJR-GARCH model to capture stock market returns. In addition, as the volatility of the stock market is affected by a number of economic factors, this study included the market situation, whether a bear or bull market type, as an additional condition to explore whether market condition renders the weather effects more significant. The results of this research support relevant literatures and can be used as a reference for investors.

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

2020 ◽  
Author(s):  
Zhuhua Jiang ◽  
Rangan Gupta ◽  
Sowmya Subramaniam ◽  
Seong-Min Yoon
Keyword(s):  
Air Pollution ◽  
High Temperature ◽  
Air Quality ◽  
Stock Market ◽  
Quantile Regression ◽  
Stock Returns ◽  
Regression Method ◽  
Negative Effects ◽  
Weather Effects ◽  
Quantile Regression Method

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.

Predicting Stock Market Performance

2020 ◽  
pp. 1-10
Author(s):  
Thomas Plieger ◽  
Thomas Grünhage ◽  
Éilish Duke ◽  
Martin Reuter
Keyword(s):  
Stock Market ◽  
Risk Taking ◽  
Life Stress ◽  
Price Volatility ◽  
Individual Characteristics ◽  
Self Report ◽  
Financial Behavior ◽  
Market Simulation ◽  
Stock Market Performance ◽  
Risk Taking Behavior

Abstract. Gender and personality traits influence risk proneness in the context of financial decisions. However, most studies on this topic have relied on either self-report data or on artificial measures of financial risk-taking behavior. Our study aimed to identify relevant trading behaviors and personal characteristics related to trading success. N = 108 Caucasians took part in a three-week stock market simulation paradigm, in which they traded shares of eight fictional companies that differed in issue price, volatility, and outcome. Participants also completed questionnaires measuring personality, risk-taking behavior, and life stress. Our model showed that being male and scoring high on self-directedness led to more risky financial behavior, which in turn positively predicted success in the stock market simulation. The total model explained 39% of the variance in trading success, indicating a role for other factors in influencing trading behavior. Future studies should try to enrich our model to get a more accurate impression of the associations between individual characteristics and financially successful behavior in context of stock trading.

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