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Classification of Stock Index movement using k-Nearest Neighbours (k-NN) algorithm

Many research studies are undertaken to predict the stock price values, but not many aim at estimating the predictability of the direction of stock market movement. The advent of modern data mining tools and sophisticated database technologies has enabled researchers to handle the huge amount of data generated by the dynamic stock market with ease. In this paper, the predictability of stock index movement of the popular Indian Stock Market indices BSE7SENSEX and NSE7NIFTY are investigated with the data mining tool of k7Nearest Neighbours algorithm (k7NN) by forecasting the daily movement of the indices. To evaluate the efficiency of the classification technique, the performance of k7NN algorithm is compared with that of the Logistic Regression model. The analysis is applied to the BSE7SENSEX and NSE7NIFTY for the period from January 2006 to May 2011.