Studying the Factors Affecting the Performance of Stocks by Using the Logistic Regression: Applied Study on Jordan Stock Exchange

Obaid Mahmmood  Mohsin Alzawbaee; Munadhil Abd-Aljabar  Alsalim

doi:10.25098/6.1.33

Authors

Obaid Mahmmood Mohsin Alzawbaee Business Administration Department, Faculty of Administration and Financial Sciences, Cihan University- Sulaimaniya, Sulaimaniya, Iraq
Munadhil Abd-Aljabar Alsalim Department of Accounting, Faculty of Administrative and Financial Sciences, Cihan University- Sulaimaniya, Sulaimaniya, Iraq

DOI:

https://doi.org/10.25098/6.1.33

Keywords:

Stock Exchange, Multiple Regression, Binary Model

Abstract

The use of Multiple Binary Logistic Regression Analysis is considered as a development in data analysis methods, especially in the field of finance and business. Stock markets thought to be one of the important sectors of the economy because they affect it directly or indirectly by providing investing and financing opportunities. Making investment decisions that are related to the acquisition of shares for investment requires the use of the available financial statements correctly. Due to the complexity of forecasting the performance of shares, one of the methods used to analyze the performance of shares is to convert the financial data contained in the annual financial reports to financial ratios, which are widely used in forecasts of the performance of shares of companies.

In this research, a logistic regression analysis is used to classify the shares’ performance of listed companies on the Amman / Jordan stock exchange by constructing a model used to predict the performance of these shares. This research provides an effective investment tool to make the right decision regarding the purchase or sale of shares as well as to take corrective decisions in a timely manner. A sample of (62) Jordanian companies was adopted and (8) independent variables were studied to explain the dependent variable. Financial ratios have been adopted to determine the value of each variable whether the dependent variable or the independent variables. Finally, the research concluded with a number of conclusions and recommendations.

References

Abbas, A. K. (2012). “Use of logistic regression model in the prediction of functions with economic variables of specific nature”. Journal of Kirkuk University for Administrative Sciences and Economics. 2(1).pp 234-253

Ahan, A.E; & Okafor, R. (2010): Application of logistic regression model to graduating (CGPA of University Graduate-University of Lagos). Journal of Modern Mathematics and Statistics 2010, 2(2), pp. 58 – 62. Medwel publishing and scientific research publishing company.

Brown, C.E (1998): Applied Multivariate, statistics in Geohydrology and related sciences, Springer – verlag. Berlin Heidelberg, chapter 6, multiple regression. pp. 62-66

Dutta, A., Bandopadhyay, G. and Sengupta, S. (2012). "Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression", International Journal of Business and Information- Vol. 7, No. 1, June. Pp-105-136

Farhoud, S. H. (2014). “Use of logistic regression to study the factors affecting the performance of shares - a practical study on the Kuwait Stock Exchange”. Journal of Al Azhar university-Gaza (Natural Sciences), pp.16, 47-68.

Ghanem, A. & Al-Ga'ouni, F. K. (2011). “Use of binary regression technique in the study of the most important economic determinants of family income efficiency”. 27(1), 113-132.

King, J.E (2003). "Running A Best-Subsets Logistic Regression: An Alternative to Stepwise Methods", Educational and Psychological Measurement, Vol. 63, No. 3, June, 392-403

King, J.E. (2002). " Logistic Regression: Going beyond point-and-click", Paper presented at the annual Meeting of the American educational Research Association, New Orleans, LA, April

Lee, S. (2004). "Application of likelihood ratio and logistic regression models to landslide susceptibility mapping using GIS", Environmental Management, Vol. 34, No. 2, 223-232

Li, H., Sun, J. and Wu, J. (2010). "Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods", Expert Systems with Applications, Vol. 37, No. 8, August, 5895- 5904.

Poston, D.L (2004). "Sociological Research: Quantitative Methods (Lecture notes, Lecture 7)", Spring

Saleh, A. H. (2013). “Analysis of logistic regression to study the survival time of patients with leukemia”. Management and Economics Journal/ University of Mustansiriya. 3(8).pp112-127