CLASSIFICATION OF NIFTY STOCKS BASED ON PIVOT POINTS USING THE PRINCIPLE OF NEAREST NEIGHBOURHOOD

Abstract

Investing in stocks oils the economic wheels of a country due to its impact on business investing, financial investing, government investing and consumer spending. In the perception of a trader, stock investing is a mind-boggling process mainly due to the availability of too many alternatives and too many indicators. The success in the process of investing depends on the usage of an ideal combination of indicators. Even if an investor has an ideal combination of indicators, the application of the same requires a statistical model which has the capability to sense the prospective Buy and Sell positions. Due to this reason many classification models of Statistics are gaining more and more importance in the field of Stock market investment. This work analyses three different methods of computing pivots namely standard method, DeMark method and Woodie's method. The objective of this work is to identify the most competing method of computing the pivot. Since the usage of any one technical indicator is not considered a good idea, the study identifies a combination of technical indicators to be used with the pivot points based on the statistical tests. Three combinations of the technical indicators are used to classify the stocks based on K-nearest neighbor. The study identifies the DeMark method as the most competing method and the result is also theoretically justified because this method gives more importance to the recent price action. The identification of the most competing method is done based on accuracy of the model, specificity and sensitivity as derived from the confusion matrix.