• R. Subathra Government Arts College (Autonomous), Tamil Nadu, India



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.


Average True Range (ATR), Chaikin’s Oscillator, Confusion Matrix, Momentum Indicator, Moving Average Convergence and Divergence (MACD), Relative Strength Index (RSI), Trend Indicator, Volatility Indicator, Woodie’s Pivot Point, K-Nearest Neighbor


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Bessembinder, H. & Chan, K. (1998). The profitability of technical trading rules in the Asian stock markets. Pacific-Basin Finance Journal, 3(2-3), pp 257-284.

Brock, W., Lakonishok, J. & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), pp 1731-1764.

Di, X. (2014). Stock Trend Prediction with Technical Indicators using SVM. SCPD student from Apple Inc. Retrieved From:,%20Stock%20Trend%20Prediction%20with


Ellis, C. & Parbery, S.A. (2005). Is smarter better? A comparison of adaptive, and simple moving average trading strategies. Research in International Business and Finance, 19(3), pp 399-411.

Fama, E.F. & Blume, M.E. (1966). Filter Rules and Stock Market Trading Profits. Journal of Business, 39(1), pp 226-341.

Fama, E.F. (1965). The Behavior of Stock-Market Prices. The Journal of Business, 38(1), pp 34-105.

Fama, E.F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), pp 383-417.

Imandoust, S.B. & Bolandraftar, M. (2014). Forecasting the direction of stock market index movement using three data mining techniques: the case of Tehran Stock Exchange, International Journal of Engineering Research and Applications, 4(6), pp 106-117.

Jensen, M & Benington, G. (1970). Random Walks and Technical Theories: Some Additional Evidence. The Journal of Finance, 25(2), pp 469-482.

Kwon, K.Y. & Kish, R.J. (2002). Technical trading strategies and return predictability: NYSE. Applied Financial Economics, 12(9), pp 639-653.

Metghalchi, M, Chang, Y.H. & Marcucci, J. (2008). Is the Swedish stock market efficient? Evidence from some simple trading rules. International Review of Financial Analysis, 17(3), pp 475-490.

Nayak, R.K., Mishra, D, & Rath, A.K. (2015). A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices. Applied Soft Computing, 35(1), pp 670-680.

Son, Y., Noh, Dj. & Lee, J. (2012). Forecasting trends of high-frequency KOSPI200 index data using learning classifiers. Expert Systems with Applications, 39(14), pp 11607-11615.

Subathra, R. (2020). Discriminating the Pivot Points using Discriminant Function. Data Mining and Knowledge Engineering, 12(6), pp 109-114.

Teixeira, L.A. & de Oliveira, A.L.I. (2009). Predicting stock trends through technical analysis and nearest neighbor classification. IEEE International Conference on Systems, Man and Cybernetics, USA. Retrieved From: pdf

Teixeira, L.A. & de Oliveira, A.L.I. (2010). A method for automatic stock trading combining technical analysis and nearest neighbour classification. Expert Systems with Applications, 37(10), pp 6885–6890.



How to Cite

R. Subathra. (2021). CLASSIFICATION OF NIFTY STOCKS BASED ON PIVOT POINTS USING THE PRINCIPLE OF NEAREST NEIGHBOURHOOD. International Journal on Recent Trends in Business and Tourism (IJRTBT), 5(2), 18-24.