| 数据挖掘方法在政治风险评估中的应用研究 |
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| 引用本文： | 陈建先,陈浩. 数据挖掘方法在政治风险评估中的应用研究[J]. 当代经济管理, 2011, 33(4): 1-4 |
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| 作者姓名： | 陈建先 陈浩 |
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| 作者单位： | 中国社会科学院研究生院,北京,102488 |
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| 摘 要： | 2008年金融危机过后,中国企业将很有可能进入另一波海外收购的浪潮。然而以往的失败案例为中国企业开展海外投资敲响了警钟。基于此背景,应用K-means聚类方法,提取国家透明组织2007年CPI(Corruption Perceptions Index)数据和世界银行2007年"良好政府"(good government)数据,运用两次聚类方法,得出了50个存在高政治风险的国家列表。同时采用2007年的FDI对该列表进行了进一步的检验。此列表可供"走出去"的中国企业作为参考。
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| 关 键 词： | 政治风险 腐败 “良好政府” K-means算法 外商直接投资 |
Application of Data Mining in Assessing Political Risk |
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| Chen Jianxian,Chen Hao. Application of Data Mining in Assessing Political Risk[J]. Contemporary Economic Management, 2011, 33(4): 1-4 |
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| Authors: | Chen Jianxian Chen Hao |
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| Affiliation: | Chen Jianxian,Chen Hao(Graduate School,the Chinese Academy of Social Sciences,Beijing 102488,China) |
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| Abstract: | After the financial crisis,it is very likely that Chinese enterprises will enter a new wave of overseas acquisition.However,the failure examples of past overseas acquisition gave an alarm to Chinese enterprises.In the paper,CPI data from Transparency International and good government data from World Bank are used.K-means algorithm is utilized to discover high political countries from 177 countries.Finally,50 countries with high political risk are classified.This conclusion is further tested by FDI in 2007.T... |
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| Keywords: | political risk corruption good government K-means algorithm FDI |
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