英文标题：
《Sectoral co-movements in the Indian stock market: A mesoscopic network
  analysis》
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作者：
Kiran Sharma, Shreyansh Shah, Anindya S. Chakrabarti and Anirban
  Chakraborti
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最新提交年份：
2016
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英文摘要：
  In this article we review several techniques to extract information from stock market data. We discuss recurrence analysis of time series, decomposition of aggregate correlation matrices to study co-movements in financial data, stock level partial correlations with market indices, multidimensional scaling and minimum spanning tree. We apply these techniques to daily return time series from the Indian stock market. The analysis allows us to construct networks based on correlation matrices of individual stocks in one hand and on the other, we discuss dynamics of market indices. Thus both micro level and macro level dynamics can be analyzed using such tools. We use the multi-dimensional scaling methods to visualize the sectoral structure of the stock market, and analyze the comovements among the sectoral stocks. Finally, we construct a mesoscopic network based on sectoral indices. Minimum spanning tree technique is seen to be extremely useful in order to separate technologically related sectors and the mapping corresponds to actual production relationship to a reasonable extent.
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中文摘要：
在本文中，我们回顾了从股市数据中提取信息的几种技术。我们讨论了时间序列的递归分析、聚合相关矩阵的分解以研究金融数据中的协同运动、股票水平与市场指数的部分相关性、多维标度和最小生成树。我们将这些技术应用于印度股市的日收益时间序列。该分析允许我们一方面基于单个股票的相关矩阵构建网络，另一方面，我们讨论市场指数的动态。因此，可以使用这些工具分析微观和宏观层面的动态。我们使用多维标度方法来可视化股票市场的部门结构，并分析部门股票之间的协同运动。最后，我们构建了一个基于部门指数的介观网络。最小生成树技术在分离技术相关部门方面非常有用，并且映射在合理程度上与实际生产关系相对应。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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