Development of stock correlation network models using maximum likelihood method and stock big data

Xue Guo, Hu Zhang, Feng Jiang, Tianhai Tian

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

1 Citation (Scopus)

Abstract

Stock correlation network uses stock return to study the relationship between different stocks traded in the stock market. The method of general threshold is a basic idea to use the highest values of correlation coefficients to develop stock network. However, there are two disadvantages. First, the correlation coefficient can only quantify linear relationship. In real case, there would be more non-linear relationships. Additionally, the general threshold favours strong correlations, which will lead to neglect of some information. To address these issues, this work will introduce a new method to measure the relationship between two variables. And maximum likelihood method will be applied to select the optional correlation relationship. Using 280 stocks traded at the Shanghai Stocks Exchange in China during from year 2014 to 2016, we first compare the function of correlation coefficient and mutual information to measure the proximity between two objects. Based on mutual information and correlation coefficient, we then develop two stock networks by two different construction mechanism and study the topological properties of these networks.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publication2018 IEEE International Conference on Big Data and Smart Computing
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages455-461
Number of pages7
ISBN (Electronic)9781538636497
DOIs
Publication statusPublished - 25 May 2018
EventInternational Conference on Big Data and Smart Computing (BIGCOMP) 2018 - Crowne Plaza Shanghai Fudan, Shanghai, China
Duration: 15 Jan 201818 Jan 2018
http://www.bigcomputing.org/

Conference

ConferenceInternational Conference on Big Data and Smart Computing (BIGCOMP) 2018
Abbreviated titleBigComp.2018
CountryChina
CityShanghai
Period15/01/1818/01/18
Internet address

Keywords

  • correlation coefficient
  • maximum likelihood estimation
  • Mutual information
  • threshold

Cite this

Guo, X., Zhang, H., Jiang, F., & Tian, T. (2018). Development of stock correlation network models using maximum likelihood method and stock big data. In Proceedings: 2018 IEEE International Conference on Big Data and Smart Computing (pp. 455-461). [8367153] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BigComp.2018.00073
Guo, Xue ; Zhang, Hu ; Jiang, Feng ; Tian, Tianhai. / Development of stock correlation network models using maximum likelihood method and stock big data. Proceedings: 2018 IEEE International Conference on Big Data and Smart Computing. IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 455-461
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abstract = "Stock correlation network uses stock return to study the relationship between different stocks traded in the stock market. The method of general threshold is a basic idea to use the highest values of correlation coefficients to develop stock network. However, there are two disadvantages. First, the correlation coefficient can only quantify linear relationship. In real case, there would be more non-linear relationships. Additionally, the general threshold favours strong correlations, which will lead to neglect of some information. To address these issues, this work will introduce a new method to measure the relationship between two variables. And maximum likelihood method will be applied to select the optional correlation relationship. Using 280 stocks traded at the Shanghai Stocks Exchange in China during from year 2014 to 2016, we first compare the function of correlation coefficient and mutual information to measure the proximity between two objects. Based on mutual information and correlation coefficient, we then develop two stock networks by two different construction mechanism and study the topological properties of these networks.",
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Guo, X, Zhang, H, Jiang, F & Tian, T 2018, Development of stock correlation network models using maximum likelihood method and stock big data. in Proceedings: 2018 IEEE International Conference on Big Data and Smart Computing., 8367153, IEEE, Institute of Electrical and Electronics Engineers, pp. 455-461, International Conference on Big Data and Smart Computing (BIGCOMP) 2018, Shanghai, China, 15/01/18. https://doi.org/10.1109/BigComp.2018.00073

Development of stock correlation network models using maximum likelihood method and stock big data. / Guo, Xue; Zhang, Hu; Jiang, Feng; Tian, Tianhai.

Proceedings: 2018 IEEE International Conference on Big Data and Smart Computing. IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 455-461 8367153.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

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AB - Stock correlation network uses stock return to study the relationship between different stocks traded in the stock market. The method of general threshold is a basic idea to use the highest values of correlation coefficients to develop stock network. However, there are two disadvantages. First, the correlation coefficient can only quantify linear relationship. In real case, there would be more non-linear relationships. Additionally, the general threshold favours strong correlations, which will lead to neglect of some information. To address these issues, this work will introduce a new method to measure the relationship between two variables. And maximum likelihood method will be applied to select the optional correlation relationship. Using 280 stocks traded at the Shanghai Stocks Exchange in China during from year 2014 to 2016, we first compare the function of correlation coefficient and mutual information to measure the proximity between two objects. Based on mutual information and correlation coefficient, we then develop two stock networks by two different construction mechanism and study the topological properties of these networks.

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Guo X, Zhang H, Jiang F, Tian T. Development of stock correlation network models using maximum likelihood method and stock big data. In Proceedings: 2018 IEEE International Conference on Big Data and Smart Computing. IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 455-461. 8367153 https://doi.org/10.1109/BigComp.2018.00073