Indian stock market analysis using CHAID regression tree

Abstract

Data mining is the technique which utilized to extract concealed “analytical” and “predictive” facts and figures from a big set of datasets and databases. It is being applied in various research areas by data scientists and analysts such as mathematics, marketing, genetics, cybernetics, etc. In this paper, A Chi Squared Automatic Interaction Detection (CHAID) regression tree model has been proposed to infer the volatility of the Stock Exchange Sensitive Index (SENSEX) data while explicitly accounting for dependencies between multiple derived attributes. Using real stock market data, dynamic time varying graphs are constructed to further analyze how the volatility depends on various factors such as Lok Sabha Elections, Domestic Riots, Union Budget of India, Indian Monsoon and Global factors. Factors have been analyzed to understand their role in the fluctuations seen over time in the market and how the SENSEX behave over these factors.