Modelling and Predicting the Stock Price of the Pharmaceutical and Chemical Companies Listed on the Stock Exchange via New Methods and Models
O. Aman Dad, Mahdi Salehi, Marjan Fallahi · DOAJ (DOAJ: Directory of Open Access Journals) · 2015
Introduction: In this research, econometrics and Radial Base Function neural networks have been used to increase the effectiveness, decrease time and costs for predicting the stock price of the material industries and pharmaceutical products, and the medical and optical measuring instruments companies by the method of fundamental analysis. Method: The current research is an applied one and it has a quasi-empirical design. The statistical population of the research consist of 30 companies listed on the Tehran Stock Exchange from 2005 to 2011. Designing the model and analyzing data have been done through Eviews Software Version 7, and Clementine Version 12. Results: The results of the research indicate that the selected model includes PC1 (the sum of current assets, and the sum of liabilities), PC2 (current ratio, quick ratio, the ratio of tangible fixed assets turnover, gross profit margin, operational profit margin, and net profit margin), return on equity and earnings per share has a high explanatory ability to predict the stock price. Conclusion: Neural network has a good accuracy in predicting the stock price. Moreover, the comparison of these two models state that the Radial Base Function neural network is more accurate than the model of econometrics of panel data in predicting the stock price.