Behaviours of Bursa Malaysia: a multidimensional network analysis
In current practice, the similarities between two or more univariate time series of stocks are determined by using the Pearson correlation coefficient (PCC). However, the economic information might be misleading if the analysis applies only the univariate time series of stock price, as each stock is...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
Science Publishing Corporation
2018
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4385/ |
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| Summary: | In current practice, the similarities between two or more univariate time series of stocks are determined by using the Pearson correlation coefficient (PCC). However, the economic information might be misleading if the analysis applies only the univariate time series of stock price, as each stock is denoted by four types of prices. Therefore, multidimensional of stocks are taken into account in this paper. The similarities between two or more multi-dimensional of stocks are quantified by using Random Vector (RV) coefficient. Additionally, an algorithm is proposed due to the computational of RV coefficient is tedious and time-consuming when a large number of stocks are included. In this paper, the Malaysian stock network analysis in univariate and multivariate setting are conducted and analysed by using the PCC, RV coefficient, forest of all possible MSTs and centrality measures. In summary, there is some important economic information could not be brought out by univariate network analysis alone. |
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