英文标题：
《Cluster analysis of stocks using price movements of high frequency data
  from National Stock Exchange》
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作者：
Charu Sharma (Shiv Nadar University, UP), Amber Habib (Shiv Nadar
  University, UP), Sunil Bowry (Shiv Nadar University, UP)
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最新提交年份：
2018
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英文摘要：
  This paper aims to develop new techniques to describe joint behavior of stocks, beyond regression and correlation. For example, we want to identify the clusters of the stocks that move together. Our work is based on applying Kernel Principal Component Analysis(KPCA) and Functional Principal Component Analysis(FPCA) to high frequency data from NSE. Since we dealt with high frequency data with a tick size of 30 seconds, FPCA seems to be an ideal choice. FPCA is a functional variant of PCA where each sample point is considered to be a function in Hilbert space L^2. On the other hand, KPCA is an extension of PCA using kernel methods. Results obtained from FPCA and Gaussian Kernel PCA seems to be in synergy but with a lag. There were two prominent clusters that showed up in our analysis, one corresponding to the banking sector and another corresponding to the IT sector. The other smaller clusters were seen from the automobile industry and the energy sector. IT sector was seen interacting with these small clusters. The learning gained from these interactions is substantial as one can use it significantly to develop trading strategies for intraday traders.
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中文摘要：
本文旨在开发新的技术来描述股票的联合行为，超越回归和相关性。例如，我们想要识别一起移动的股票集群。我们的工作基于将核主成分分析（KPCA）和功能主成分分析（FPCA）应用于NSE的高频数据。由于我们处理的高频数据的刻度大小为30秒，FPCA似乎是一个理想的选择。FPCA是PCA的一个函数变体，其中每个采样点被视为希尔伯特空间L^2中的一个函数。另一方面，KPCA是使用核方法对PCA的扩展。从FPCA和高斯核PCA得到的结果似乎是协同的，但有滞后性。在我们的分析中，有两个突出的集群，一个对应于银行业，另一个对应于IT业。其他较小的集群来自汽车行业和能源部门。IT部门被视为与这些小型集群相互作用。从这些互动中获得的知识是非常重要的，因为人们可以利用这些知识为日内交易者制定交易策略。
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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：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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