Market impact and structure dynamics of the Chinese stock market based on partial correlation analysis

Li, Xing; Qiu, Tian; Chen, Guang; Liu, Zhong; Wu, Xiao-Run

doi:10.1016/j.physa.2016.11.121

Cited by 14 publications

(4 citation statements)

References 43 publications

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“…Correlation analysis of atmospheric pollutants is the simple and effective access to determine the influencing factors and trace the pollution source. In a literature review, the mainstream of correlation analysis methods includes partial correlation [14,23], principal component [15,24], and grey correlation analysis [16,25], which have been applied widely in different fields.…”

Section: Correlation Analysis Methodsmentioning

confidence: 99%

“…In partial correlation, the correlation coefficient R or R 2 is set as the criterion for the correlation degree. Li et al [23] applied partial correlation analysis to the impact of market elements on the domestic stock market. Porth et al [26] studied the nutrient resource allocation between plant growth and recuperation based on the partial correlation of gene expressions.…”

Section: Correlation Analysis Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Bai

Wang

et al. 2020

IJERPH

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Pollutant analysis and pollution source tracing are critical issues in air quality management, in which correlation analysis is important for pollutant relation modeling. A dynamic correlation analysis method was proposed to meet the real-time requirement in atmospheric management. Firstly, the spatio-temporal analysis framework was designed, in which the process of data monitoring, correlation calculation, and result presentation were defined. Secondly, the core correlation calculation method was improved with an adaptive data truncation and grey relational analysis. Thirdly, based on the general framework and correlation calculation, the whole algorithm was proposed for various analysis tasks in time and space, providing the data basis for ranking and decision on pollutant effects. Finally, experiments were conducted with the practical data monitored in an industrial park of Hebei Province, China. The different pollutants in multiple monitoring stations were analyzed crosswise. The dynamic features of the results were obtained to present the variational correlation degrees from the proposed and contrast methods. The results proved that the proposed dynamic correlation analysis could quickly acquire atmospheric pollution information. Moreover, it can help to deduce the influence relation of pollutants in multiple locations.

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Section: Correlation Analysis Methodsmentioning

confidence: 99%

Section: Correlation Analysis Methodsmentioning

confidence: 99%

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Bai

Wang

et al. 2020

IJERPH

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show abstract

“…Therefore, partial correlation analysis is utilized to measure the degree of correlation between any two reservoir state variables x 1 (t) and x 2 (t) in the reservoir. To quantify the effect of the third reservoir state variable x 3 (t) on the correlation between x 1 (t) and x 2 (t), a partial correlation coefficient [23] is introduced as…”

Section: Partial Correlation Analysis Of Reservoir Neuronsmentioning

confidence: 99%

“…Compared with correlation coefficient [22], partial correlation analysis [23] is a more accurate means of measurement. It can measure the degree of association between two variables among multiple variables while controlling for the influence of other variables.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Echo State Network with Partial Correlation Pruning for Time-Series Variables Prediction in Industrial Processes

Huang¹,

Wang²,

Yang³

et al. 2023

Meas. Sci. Technol.

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For an ordinary echo state network (ESN), redundant information in the huge reservoir will lead to degradation of the prediction performance of the network, especially when the labels of the samples are limited. To solve this problem, a semi-supervised ESN with partial correlation pruning (PCP-S2ESN) is proposed in this paper to scientifically capture the essential association between two reservoir variables while controlling for the influence of other factors. In this way, redundant neurons and their connection weights in the reservoir are eliminated, so that the prediction accuracy is significantly enhanced by optimizing the network structure. Moreover, anunsupervised pre-training procedure is introduced to modify the input weight matrix and reservoir connection weight matrix of the ESN, which successfully achieves precise prediction of time-series variables with limited labels. The superiority of the PCP-S2ESN model is demonstrated through two benchmark prediction tasks and the fed-batch penicillin cultivation (FBPC) process.

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Portfolio Management by Time Series Clustering Using Correlation for Stocks

Mitra¹,

Das

Goswami

et al. 2019

Communications in Computer and Information Science

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Market impact and structure dynamics of the Chinese stock market based on partial correlation analysis

Cited by 14 publications

References 43 publications

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Semi-supervised Echo State Network with Partial Correlation Pruning for Time-Series Variables Prediction in Industrial Processes

Portfolio Management by Time Series Clustering Using Correlation for Stocks

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