Abstract
Investing in stock market has always been a riskier venture. Market participants have always tried to correctly time the market to make more money. It is not only about the timing in the market but also about a correct decision to buy or sell the stock. Big institutional players had always been more successful in making money compared to the smaller ones like retail investors. The main objective of this paper is to decode the investment pattern of these big payers like Foreign Institutional Investors (FIIs) and Domestic Institutional Investors (DIIs) using decision tree method of machine learning techniques. Using J48 technique of C4.5 classification program it was found that FIIs have more predictable and correct investment pattern compared to DIIs. The information gain ratio made High attribute as the prime node of the tree for both FIIs and DIIs with accuracy level of 66.17% and 59.35% respectively. FIIs are the net buyers if High and Close attributes are positive and net sellers if High and Low are negative. However, DIIs ironically are the net buyers with a negative High. Keywords: Decision Tree, Stock Market, FIIs, DIIs, Investment, Machine Learning.
Published Version
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