Classification of Indian Stock Market Data Using Machine Learning Algorithms
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 9
Abstract
Classification of Indian stock market data has always been a ertain appeal for researchers. In this paper, first time ombination of three supervised machine learning algorithms, lassification and regression tree (CART) , linear discriminant nalysis (LDA) and quadratic discriminant analysis (QDA) are roposed for classification of Indian stock market data, which gives simple interpretation of stock market data in the form of binary tree, linear surface and quadratic surface respectively. These resulted forms help market analyst to make decision on selling, purchasing or holding stock for a particular company in Indian stock market. In section IV and V, experimental results and performance comparison section show that classification and regression tree misclassification rate is only 56.11% ereas LDA and QDA show 74.26% and 76.57% respectively. maller misclassification reveals that CART algorithm erforms etter classification of Indian stock market data as compared to DA and QDA algorithms.
Authors and Affiliations
Sneha Soni , Shailendra Shrivastava
Keywords
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