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

Cao, Guangxi; Han, Yan

doi:10.1142/s0217979214502361

Cited by 20 publications

(6 citation statements)

References 34 publications

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“…However, the results are conflicting with Yoon and Kang (2009), Floros (2011) and Brahmana et al (2012) that have proven that temperature has negative relationship with stock returns. Moreover, Yoon and Kang (2009), Tuna (2014) and Cao and Han (2015) have found significant relationship of humidity with stock returns. Our findings show evidence that stock return is positively affected by temperature.…”

Section: Regression Model Resultsmentioning

confidence: 96%

“…Consistent with Dowling and Lucey (2005), wind is found to be positive in all days of the week. However, Yoon and Kang (2009), Tuna (2014) and Cao and Han (2015) suggested negative relationship between humidity and stock returns.…”

Section: Weather and Dowamentioning

confidence: 96%

“…Although there have been voluminous studies examining the impact of weather on stock returns in both developed and emerging markets, the mixed results are reported. Some studies found the existence of positive relationship between weather and stock returns (see Dowling & Lucey, 2005) while some observed a negative relationship between weather and stock returns (see Shu & Hung, 2009;Floros, 2011;Yoon & Kang, 2009;Cao & Han, 2015). There are also studies show evidence of no significant relationship between the variables (see Keef & Roush, 2007;Saporoschenko, 2011;Wang et al 2011).…”

Section: Introductionmentioning

confidence: 99%

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Weather, Mood and Stock Market Returns in Argentina

Karim¹,

Shukri²,

Yuin³

2018

AFR

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The paper examines the relationship between weather and stock market returns in the Argentina’s stock market using daily data from 2001 to 2014 and regression models. The data consists of stock market returns, temperature, humidity and wind. The empirical findings show that all weather variables (temperature, humidity and wind) have significant relationship with stock market returns in some of the trading days in the week. We also find evidence of the existence of day-of-week effect in the stock market. On average, the highest return falls on Friday and lowest return falls on Monday. Temperature is considered very significant in influencing the stock market returns in Argentina. Our findings suggest that the stock market returns are higher when the temperature is higher. This phenomenon is related to the seasonal affective disorder (SAD). We can conclude that stock market of Argentina is not informational efficient. The results have major implications for traders, individual investors, fund managers and financial institutions to make investment planning in the Argentina’s stock market.

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Section: Regression Model Resultsmentioning

confidence: 96%

Section: Weather and Dowamentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Weather, Mood and Stock Market Returns in Argentina

Karim¹,

Shukri²,

Yuin³

2018

AFR

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“…Financial time series analyses have played an important role in developing some of the fundamental economic theories. Furthermore, the understanding and analysis of financial time series, especially the evolution of stock markets, has been attracting the close attention of economists, statisticians, and mathematicians for many decades [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Recent research mostly focuses on the long-term average behavior of a market, and thus sheds little light on the temporal changes of a market.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Multiscale Predictability of Financial Time Series by an Information-Theoretic Approach

Zhao

Liang

Zhang

et al. 2019

Entropy

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Making predictions on the dynamics of time series of a system is a very interesting topic. A fundamental prerequisite of this work is to evaluate the predictability of the system over a wide range of time. In this paper, we propose an information-theoretic tool, multiscale entropy difference (MED), to evaluate the predictability of nonlinear financial time series on multiple time scales. We discuss the predictability of the isolated system and open systems, respectively. Evidence from the analysis of the logistic map, Hénon map, and the Lorenz system manifests that the MED method is accurate, robust, and has a wide range of applications. We apply the new method to five-minute high-frequency data and the daily data of Chinese stock markets. Results show that the logarithmic change of stock price (logarithmic return) has a lower possibility of being predicted than the volatility. The logarithmic change of trading volume contributes significantly to the prediction of the logarithmic change of stock price on multiple time scales. The daily data are found to have a larger possibility of being predicted than the five-minute high-frequency data. This indicates that the arbitrage opportunity exists in the Chinese stock markets, which thus cannot be approximated by the effective market hypothesis (EMH).

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“…[1][2][3][4][5][6][7] Concepts and methods in the field of complex networks are applied to describe the feature of financial market networks. 8,9 For example, the power-law (scale-free) property, the topological phase transitions, and the hierarchical structure of financial market networks are widely studied. [10][11][12][13][14][15][16] Besides the aforesaid studies, only a few works devote effort to investigate the stability of financial market networks, especially the stock market networks.…”

Section: Introductionmentioning

confidence: 99%

Stock market network’s topological stability: Evidence from planar maximally filtered graph and minimal spanning tree

Yan

Xie

Wang

2015

Int. J. Mod. Phys. B

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We study the topological stability of stock market network by investigating the topological robustness, namely the ability of the network to resist structural or topological changes. The stock market network is extracted by minimal spanning tree (MST) and planar maximally filtered graph (PMFG). We find that the specific delisting thresholds of the listed companies exist in both MST and PMFG networks. In comparison with MST, PMFG provides more information and is better for the aim of exploring stock market network's robustness. The PMFG before the US sub-prime crisis (i.e., from June 2005 to May 2007) has a stronger robustness against the intentional topological damage than the other two sub-periods (i.e.). We also find that the nonfractal property exists in MSTs of S&P 500, i.e., the highly connected nodes link with each other directly, which indicates that the MSTs are vulnerable to the removal of such important nodes. Moreover, the financial institutions and high technology companies are important in maintaining the stability of S&P 500 network. networks, the minimum spanning tree (MST) and the planar maximally filtered graph (PMFG) are introduced in previous studies. 1,2 The MST method is widely used in different financial market networks. 11,23-26 In a MST, only N − 1 links out of the original N (N − 1)/2 links are kept to connect all the nodes together with a minimal total distance. It should be pointed out that the completely connected network contains more information than the hierarchical MST. To give filtered graphs that contain more information, PMFG is proposed by Tumminello et al., which is a network with triangular loops and four-element cliques. 2,27 In this paper, we aim to investigate the topological stability of stock market network which is filtered by the PMFG and MST algorithms. The topological stability is the ability of the network to resist structural or topological changes. 28 In the case of stock market network, the topological robustness is the ability of the network to resist the collapse of the listed companies (i.e., the removal of nodes). As briefed in the method proposed by Albert et al., 29 when the nodes are removed from network, clusters which connect to the removed nodes fall apart from the network. The removal of nodes can collapse the network and lead to catastrophic 1550161-2 Int. J. Mod. Phys. B Downloaded from www.worldscientific.com by UNIVERSITY OF GEORGIA on 08/12/15. For personal use only.

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

Cited by 20 publications

References 34 publications

Weather, Mood and Stock Market Returns in Argentina

Weather, Mood and Stock Market Returns in Argentina

Quantifying the Multiscale Predictability of Financial Time Series by an Information-Theoretic Approach

Stock market network’s topological stability: Evidence from planar maximally filtered graph and minimal spanning tree

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