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摘要翻译：
在本研究中，我们研究了影响股票网络连接结构的决定因素。股票网络拓扑性质的代表性指标是与其他股票的链接数。我们使用了金融文献中广泛认可的多因素模型。在多因素模型中，公因子作为自变量，个股收益率作为因变量。我们计算了决定系数，它代表了因变量被自变量解释的程度的测量值。因此，我们在多因素模型中考察了库存网络中的链接数与决定系数之间的关系。我们使用了在韩国、日本、加拿大、意大利和英国市场指数上交易的个股。结果如下。我们发现，与其他股票有大量联系的股票的平均决定系数比那些与其他股票有少量联系的股票的平均决定系数要高。这些结果表明，在建立股票网络时，公共因素是需要考虑的决定性因素。此外，与其他股票有大量联系的股票可以更多地受到共同因素的影响。
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英文标题：
《Statistical Investigation of Connected Structures of Stock Networks in
Financial Time Series》
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作者：
Cheoljun Eom, Gabjin Oh, Seunghwan Kim
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最新提交年份：
2007
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Physics 物理学
二级分类：Data Analysis, Statistics and Probability 数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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英文摘要：
In this study, we have investigated factors of determination which can affect the connected structure of a stock network. The representative index for topological properties of a stock network is the number of links with other stocks. We used the multi-factor model, extensively acknowledged in financial literature. In the multi-factor model, common factors act as independent variables while returns of individual stocks act as dependent variables. We calculated the coefficient of determination, which represents the measurement value of the degree in which dependent variables are explained by independent variables. Therefore, we investigated the relationship between the number of links in the stock network and the coefficient of determination in the multi-factor model. We used individual stocks traded on the market indices of Korea, Japan, Canada, Italy and the UK. The results are as follows. We found that the mean coefficient of determination of stocks with a large number of links have higher values than those with a small number of links with other stocks. These results suggest that common factors are significantly deterministic factors to be taken into account when making a stock network. Furthermore, stocks with a large number of links to other stocks can be more affected by common factors.
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PDF链接：
https://arxiv.org/pdf/0709.2200
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