英文标题：
《Uncovering networks amongst stocks returns by studying nonlinear
  interactions in high frequency data of the Indian Stock Market using mutual
  information》
---
作者：
Charu Sharma and Amber Habib
---
最新提交年份：
2019
---
英文摘要：
  In this paper, we explore the detection of clusters of stocks that are in synergy in the Indian Stock Market and understand their behaviour in different circumstances. We have based our study on high frequency data for the year 2014. This was a year when general elections were held in India, keeping this in mind our data set was divided into 3 subsets, pre-election period: Jan-Feb 2014; election period: Mar-May 2014 and :post-election period: Jun-Dec 2014. On analysing the spectrum of the correlation matrix, quite a few deviations were observed from RMT indicating a correlation across all the stocks. We then used mutual information to capture the non-linearity of the data and compared our results with widely used correlation technique using minimum spanning tree method. With a larger value of power law exponent {\\alpha}, corresponding to distribution of degrees in a network, the nonlinear method of mutual information succeeds in establishing effective network in comparison to the correlation method. Of the two prominent clusters detected by our analysis, one corresponds to the financial sector and another to the energy sector. The financial sector emerged as an isolated, standalone cluster, which remain unaffected even during the election periods.
---
中文摘要：
在这篇论文中，我们探索了在印度股市中发现协同效应的股票集群，并了解它们在不同情况下的行为。我们的研究基于2014年的高频数据。这是印度举行大选的一年，牢记这一点，我们的数据集分为3个子集，选举前阶段：2014年1月至2月；选举期间：2014年3月至5月；选举后期间：2014年6月至12月。在分析相关矩阵的频谱时，观察到与RMT有相当多的偏差，表明所有股票都存在相关性。然后，我们使用互信息来捕获数据的非线性，并将我们的结果与广泛使用的使用最小生成树方法的相关技术进行比较。由于幂律指数{\\alpha}的值较大，对应于网络中的度分布，与相关方法相比，互信息非线性方法成功地建立了有效的网络。在我们的分析中发现的两个显著集群中，一个对应于金融部门，另一个对应于能源部门。金融部门作为一个孤立的、独立的集群出现，即使在选举期间也不受影响。
---
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
--

---
PDF下载：
-->