Panel Data Clustering and Its Application to Discount Rate of B Stock in China

2009 ◽  
Author(s):  
Tao Zheng ◽  
Donghua Zhu ◽  
Xuefeng Wang ◽  
Bo Yu
Keyword(s):  
Panel Data ◽  
Discount Rate ◽  
Data Clustering

scholarly journals Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hong Li ◽  
Yuantao Xie ◽  
Juan Yang ◽  
Di Wang
Keyword(s):  
Panel Data ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Semiparametric Estimation ◽  
Similarity Measurement ◽  
Model Estimation ◽  
Similarity Coefficients ◽  
Clustering Model ◽  
Compound Model

This paper proposed a panel data clustering model based on D-vine and C-vine and supported a semiparametric estimation for parameters. These models include a two-step inference function for margins, two-step semiparameter estimation, and stepwise semiparametric estimation. In similarity measurement, similarity coefficients are constructed by a multivariate Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models, which are HNAC and D-vine compound model and HNAC and C-vine compound model. Estimation solutions and models evaluation are given for these models. In the case study, the clustering results of HNAC and D-vine compound model and HNAC and C-vine compound model are given, and the effect of different copula families on clustering results is also discussed. The result shows the models are effective and useful.

Three way decisions based grey incidence analysis clustering approach for panel data and its application

Kybernetes ◽  
2019 ◽  
Vol 48 (9) ◽  
pp. 2117-2137 ◽  
Author(s):  
Yong Liu ◽  
Jun-liang Du ◽  
Ren-Shi Zhang ◽  
Jeffrey Yi-Lin Forrest
Keyword(s):  
Panel Data ◽  
Data Clustering ◽  
Threshold Value ◽  
Content Type ◽  
Clustering Problem ◽  
Proposed Model ◽  
Clustering Approach ◽  
Grey Incidence Analysis ◽  
Incidence Analysis ◽  
Extract Information

PurposeThis paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.Design/methodology/approachBecause of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.FindingsThe results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.Practical implicationsThe novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.Originality/valueThe proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

scholarly journals An Improved Grey Clustering Model with Multiattribute Spatial-Temporal Feature for Panel Data and Its Application

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jian Li ◽  
Wan-ming Chen
Keyword(s):  
Panel Data ◽  
Data Clustering ◽  
High Tech ◽  
Absolute Amount ◽  
Clustering Method ◽  
Clustering Model ◽  
Proposed Model ◽  
Grey Clustering ◽  
Feature Information ◽  
Temporal Feature

Due to the complexity and uncertainty of the objective world and the limitation of cognition, it is difficult to extract the information and rules contained in the panel data effectively based on the traditional panel data clustering method. Given this, considering that the absolute amount level, increasing amount level, and volatility level are the main indicators to represent the spatial-temporal feature of the panel data, a novel grey clustering model with the multiattribute spatial-temporal feature of panel data is established, and then it is applied in the regional high-tech industrialization in China. The results show that the proposed model can make full use of the spatial-temporal feature information of the panel data, identify the problems existing in the clustering objects, and make the clustering results more objective and practical.

scholarly journals Avaliação de Ações e Números Contábeis: Aplicação dos modelos de Zhang (2000) e Zhang e Chen (2007) no mercado brasileiro

2011 ◽  
Vol 9 (1) ◽  
pp. 131
Author(s):  
Fernando Caio Galdi ◽  
Rodrigo Falco Lopes
Keyword(s):  
Panel Data ◽  
Stock Returns ◽  
Discount Rate ◽  
Capital Markets ◽  
Capital Market ◽  
Net Income ◽  
Cross Sectional ◽  
Empirical Results ◽  
The Us ◽  
Empirical Tests

This paper investigates how accounting variables explain cross-sectional stocks returns in Brazilian capital markets. The analysis is based on Zhang (2000) and Zhang and Chen (2007) models. These models predict that stock returns are a function of net income, change in profitability, invested capital, changes in opportunity growths and discount rate. Generally, the empirical results for the Brazilian capital market are consistent with the theoretical relations that models describe, similarly to the results found in the US. Using different empirical tests (pooled regressions, Fama-Macbeth and panel data) the results and coefficients remain similar, what support the robustness of our findings.

Panel data clustering analysis based on composite PCC: a parametric approach

2018 ◽  
Vol 22 (S4) ◽  
pp. 8823-8833 ◽  
Author(s):  
Juan Yang ◽  
Yuantao Xie ◽  
Yabo Guo
Keyword(s):  
Panel Data ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Parametric Approach

scholarly journals Credit card customer analysis based on panel data clustering

2010 ◽  
Vol 1 (1) ◽  
pp. 2489-2497 ◽  
Author(s):  
Guangli Nie ◽  
Yibing Chen ◽  
Lingling Zhang ◽  
Yuhong Guo
Keyword(s):  
Panel Data ◽  
Data Clustering ◽  
Credit Card ◽  
Customer Analysis

Using Discount Rate to Predict Treatment Effect and Relapse

2007 ◽  
Author(s):  
Yun Zhang ◽  
Min Yu ◽  
Xinyue Zhou ◽  
Jingjing Lin ◽  
Yi Ni
Keyword(s):  
Discount Rate ◽  
Treatment Effect
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