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

Kang, Sang Hoon; Jiang, Zhuhua; Lee, Yeonjeong; Yoon, Seong‐Min

doi:10.1016/j.physa.2009.09.010

Cited by 56 publications

(40 citation statements)

References 22 publications

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“…Several authors have found that weather is one of the factors affecting economic and commercial behavior [4][5][6][7]. In order to predict future behaviors or relationships, there is a subset of artificial intelligence called machine learning, which includes building classification and regression models [8].…”

Section: Introductionmentioning

confidence: 99%

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

2018

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Persea americana, commonly known as avocado, is becoming increasingly important in global agriculture. There are dozens of avocado varieties, but more than 85% of the avocados harvested and sold in the world are of the Hass one. Furthermore, information on the market of agricultural products is valuable for decision-making; this has made researchers try to determine the behavior of the avocado market, based on data that might affect it one way or another. In this paper, a machine learning approach for estimating the number of units sold monthly and the total sales of Hass avocados in several cities in the United States, using weather data and historical sales records, is presented. For that purpose, four algorithms were evaluated: Linear Regression, Multilayer Perceptron, Support Vector Machine for Regression and Multivariate Regression Prediction Model. The last two showed the best accuracy, with a correlation coefficient of 0.995 and 0.996, and a Relative Absolute Error of 7.971 and 7.812, respectively. Using the Multivariate Regression Prediction Model, an application that allows avocado producers and sellers to plan sales through the estimation of the profits in dollars and the number of avocados that could be sold in the United States was created.

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Section: Introductionmentioning

confidence: 99%

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

2018

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“…However, additional analyses need to be done in the future to extend our study. Kang et al 15 studied weather effects on the returns and volatility of A-share and B-share of the Shanghai stock market; therefore, the stock market can be further subdivided in later studies. Moreover, we can discuss more weather variables, such as precipitation 8 and cloud cover 2,9 and their effects on the stock market.…”

Section: Discussionmentioning

confidence: 99%

“…They found that stock returns are significantly related to the length of daylight through the fall and winter and that the economic magnitude of the SAD effect is large. Kang et al 15 found that the weather effect exists in the A-share returns and that weather effects have strong influence on the volatility of both A-and B-share returns. However, Lu and Chou 16 came up with a different conclusion: in an order-driven market, environmental impacts on sentiment are likely to affect trading activities but not returns.…”

Section: Introductionmentioning

confidence: 99%

Does the weather affect the Chinese stock markets? Evidence from the analysis of DCCA cross-correlation coefficient

Cao

Han

2014

Int. J. Mod. Phys. B

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Recent studies confirm that weather affects the Chinese stock markets, based on a linear model. This paper revisits this topic using DCCA cross-correlation coefficient (ρ DCCA (n)), which is a nonlinear method, to determine if weather variables (i.e., temperature, humidity, wind and sunshine duration) affect the returns/volatilities of the Shanghai and Shenzhen stock markets. We propose an asymmetric ρ DCCA (n) by improving the traditional ρ DCCA (n) to determine if different cross-correlated properties exist when one time series trending is either positive or negative. Further, we improve a statistical test for the asymmetric ρ DCCA (n). We find that cross-correlation exists between weather variables and the stock markets on certain time scales and that the cross-correlation is asymmetric. We also analyze the cross-correlation at different intervals; that is, the relationship between weather variables and the stock markets at different intervals is not always the same as the relationship on the whole. PACS number: 89.65.Gh 1450236-1 Int. J. Mod. Phys. B 2015.29. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/05/15. For personal use only. 1450236-3 Int. J. Mod. Phys. B 2015.29. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/05/15. For personal use only. 1450236-4 Int. J. Mod. Phys. B 2015.29. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/05/15. For personal use only. 1450236-6 Int. J. Mod. Phys. B 2015.29. Downloaded from www.worldscientific.com by MCGILL UNIVERSITY on 02/05/15. For personal use only.

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“…Furthermore, statistically significant correlations between weather variables and stock market returns are documented in the prior literature. For example, the significant influence of wind (Wellington's weather) on the returns of stocks listed on the New Zealand Stock Exchange (Keef and Roush, 2002); a significant relationship between mood proxy variables (rain) and daily Irish stock returns (Dowling and Lucey, 2005); the significant effects of temperature and cloud cover on Taiwan's stock market returns (Chang et al, 2006); a negative correlation between temperature and the daily returns of Australian stock indices (Keef and Roush, 2007b), a relationship between UK equity markets and climatic conditions, such as temperature and wind speed (Dowling and Lucey, 2008), the existence of the weather effect-as measured by temperature, humidity, and sunshine duration-on the Shanghai A-and B-share indexes (Kang et al, 2010); a negative influence of temperature on the PSI 20 index returns of the Lisbon Stock Exchange (Floros, 2011); and the association between the Israeli Stock Exchange index returns and natural phenomena variables, including wind velocity, temperature, rain, and earthquakes (Nissim et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of Intraday Weather Changes on Asset Returns and Volatilities

Shim¹,

Kim²,

Ryu³

2017

Zb.rad.Ekon.fak.Rij.

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Analyzing the intraday dataset on weather and market information with the use of the extended GJR-GARCH framework, this study explores in depth the weather effects on the asset returns and volatilities of the Korean stock and derivatives markets. Our intraday analyses contribute to the existing literature by going beyond the attempt of prior studies to capture the weather effects using the average daily observations alone. The empirical results document a modest presence of the weather effect on the returns and volatilities, though the significance of its impact is found to vary across different market conditions and indices.We also find that the return and volatility respond asymmetrically to extremely good and bad weather conditions. The intraday analyses show that the weather effect on the returns and volatilities is more statistically significant at the beginning of the working day or the lunch break, indicating the intraday weather effects on the financial market.

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

Cited by 56 publications

References 22 publications

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

Does the weather affect the Chinese stock markets? Evidence from the analysis of DCCA cross-correlation coefficient

Effects of Intraday Weather Changes on Asset Returns and Volatilities

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