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- W2785664634 abstract "In order to forecast the price movement with the correlation between two different stocks, the model of Stock Social Network (SSN) is proposed to represent and analyze the intrinsic complex relationship. We choose 313 stocks from 9 industries to build an evolution model of SSN, which predicted that some stocks clusters are isolated and the nodes and edges in SSN are decreasing distinctly step by step with the change of threshold δ from 0.7, 0.75 and 0.8, respectively. Meanwhile, the coverage rate of nodes in SSN arrives 0.2076 at δ = 0.8, in reverse, the 79.24% nodes is trimmed during the process of evolution of SSN. Based on these results, we design a new portfolio strategy based on new index, named CSSNI, to optimize the asset pricing model. The results show that the ratio of return is 0.92666 based on the CSSNI, which is much better than the result by traditional strategy." @default.
- W2785664634 created "2018-02-23" @default.
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- W2785664634 date "2016-10-01" @default.
- W2785664634 modified "2023-09-27" @default.
- W2785664634 title "Social Network-Based Stock Correlation Analysis and Prediction" @default.
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- W2785664634 doi "https://doi.org/10.1109/iiki.2016.102" @default.
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