Cluster Analysis of Indian Stock Market during the 90 Day Period after 4 June 2024 Crash

Abstract

4, June 2024 can be considered as a unique market event in the history of Indian stock markets. Prior to this event, Indian share markets were upbeat and steadily growing due to a prevailing market sentiment that a third term election win for the BJP/NDA alliance is certain. This sentiment was supported by multiple opinion polls and odds data released by illegal betting markets. However, as vote counting started on June 4, 2024 and live results started percolating, markets realized that BJP will not be in a position to cross the midway mark and obtain a simple majority to form the government. Stock markets crashed by the end of the day at 3.30 pm on Tuesday,  ​BSE Sensex ​lost 4389.73 points (-5.74%)  to close at 72,079.05, and ​Nifty 50​ ended at 21,884.50, lower by 1,379.40 (-5.93%). Both NIFTY and SENSEX indices recovered very fast and within a few days regained their last traded levels of 3, June, 2024. Recovery of all the stocks is not uniform and the market event resulted in triggering a correction phase. This paper is a detailed analysis of the stock market from 4, June 2024 to 4 September 2024. Detailed cluster analysis is done and results are reported. Clustered Heat maps (Double Dendrograms) are presented along with traditional quantitative analysis. Heat Maps and Dendrograms can be considered as visual statistics/analytical tools.  The objective of this research is to study clusters of shares and industry segments and the pattern of their recovery. Historically, Indian stock markets went through many such crashes. However, only high-level details are available in the literature. It is very difficult for individual retail investors to obtain stock market historical data which is older than three years. This research paper attempts to document granular details which can be used in the future. The study of outliers in the data will give insights into specific stocks/ industry sectors and their resilience. The results of the analysis presented in this paper can be used by investors and financial institutions to handle future crashes, reduce panic selling and limit their losses.