Cluster analysis of stocks using price movements of high frequency data from National Stock Exchange

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 ea...