A COMPARATIVE STUDY ON THE METHODS OF COMPUTING PIVOT POINTS USING LOGISTIC REGRESSION

Authors

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

Abstract

Proper prediction of potential turning points is the key to success for the traders in futures, commodities and stock markets. Many technical analysis tools serve this purpose and one such tool is the pivot point. It is used by the traders to predict the support and resistance level in the current and upcoming trading sessions. The standard pivot point is the simple average of high, low and close prices of the previous trading session. However some other variations to this approach are in practice. This work applies logistic regression to compare the performances of the pivot points computed using Standard method, Woodie’s method and DeMark’s method.  With the pivot points computed with these three methods, the categorical trend variables are generated. Since using multiple indicators is a common practice, identification of the most competing method of computing the pivot becomes necessary. This study utilizes Logistic Regression analysis to identify the most competing method of pivot to be used with other technical indicators. The credibility of the results is tested with various performance measures and out of sample tests of the fitted logistic regression models.

Keywords:

Pivot Point, DeMark”s Pivot, Woodie’s Pivot, Bootstrapping, Logistic Regression, Chaikin’s Oscillator, Relative Strength Index

Downloads

Download data is not yet available.

References

Altman, E.I. (1968). Financial Ratios, Discriminant Analysis, and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), pp 589-609.

Connor, M.C. (1973). On the Usefulness of Financial Ratios to Investors in Common Stock. The Accounting Review, 48(2), pp 339-352.

Haines, L.M., Kabera, M.G., Ndlovu, P. & O'Brien, T.E. (2007). D-optimal Designs for Logistic Regression in Two Variables. 8th International Workshop in Model-Oriented Design and Analysis. Almagro, Spain, 4th–8th June. Retrieved From: file:///C:/Users/kabir/Desktop/2007_Bookmatter_MODa8-AdvancesInModel-Oriented.pdf

Horrigan, J.O. (1965). Some Empirical Bases of Financial Ratio Analysis. The Accounting Review, 43(2), pp 284-294.

Huang, Q., Cai, Y. & Peng, J. (2007). Modeling the Spatial Pattern of Farmland Using GIS and Multiple Logistic Regression: A Case Study of Maotiao River Basin. Guizhou Province, China. Environmental Modeling and Assessment, 12(1), pp 55-61.

Kumar, P.R. & Ravi, V. (2007). Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent techniques: A review. European Journal of Operation Research, 180(1), pp 1-28.

Lee, S. (2004). Application of Likelihood Ratio and Logistic Regression Models to Landslide Susceptibility Mapping Using GIS. Environmental Management, 34(2), pp 223-232.

Lee, S. Ryu, J. & Kim, L. (2007). Landslide Susceptibility Analysis and Its Verification Using Likelihood Ratio, Logistic Regression, and Artificial Neural Network Models: Case Study of Youngin, Korea. Landslides, 4(4), pp 327–338.

McConnell, D., Haslem, J.A. & Gibson, V.R. (1986). The President’s Letter to Stockholders. Financial Analysts Journal, 42(5), pp 66-70.

Melnyk, Z.L. & Iqbal, M. (1972). Business risk homogeneity: A multivariate application and evaluation. Proceedings of the 1972 Midwest AIDS Conference.

Nepal, S.K. (2003). Trail Impacts in Sagarmatha (Mt. Everest) National Park, Nepal: A Logistic Regression Analysis. Environmental Management, 32(3), pp 312-321.

Ohlson, J.A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), pp 109-31.

Pardo, J.A., Pardo L. & Pardo, M.C. (2005). Minimum Ө-divergence Estimator in Logistic Regression Models, Statistical Papers, 47(1), pp 91-108.

Zavgren, C. (1985). Assessing the Vulnerability to Failure of American Industrial Firms: A Logistic Analysis. Journal of Business Finance and Accounting, 12(1) pp 19-45.

Zmijewski, M.E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, pp 59-82.

Downloads

Published

2020-10-01

How to Cite

R. Subathra. (2020). A COMPARATIVE STUDY ON THE METHODS OF COMPUTING PIVOT POINTS USING LOGISTIC REGRESSION. International Journal on Recent Trends in Business and Tourism (IJRTBT), 4(4), 9-15. Retrieved from https://ejournal.lucp.net/index.php/ijrtbt/article/view/1209