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This paper aims to develop new techniques to describe joint behavior ofstocks, beyond regression and correlation. For example, we want to identify theclusters of the stocks that move together. Our work is based on applying KernelPrincipal Component Analysis(KPCA) and Functional Principal ComponentAnalysis(FPCA) to high frequency data from NSE. Since we dealt with highfrequency data with a tick size of 30 seconds, FPCA seems to be an idealchoice. FPCA is a functional variant of PCA where each sample point isconsidered to be a function in Hilbert space L^2. On the other hand, KPCA is anextension of PCA using kernel methods. Results obtained from FPCA and GaussianKernel PCA seems to be in synergy but with a lag. There were two prominentclusters that showed up in our analysis, one corresponding to the bankingsector and another corresponding to the IT sector. The other smaller clusterswere seen from the automobile industry and the energy sector. IT sector wasseen interacting with these small clusters. The learning gained from theseinteractions is substantial as one can use it significantly to develop tradingstrategies for intraday traders.

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