Logistic regression to determine significant factors associated with share price change

• Main Author: Muchabaiwa, Honest
• Other Authors: Muchengetwa, S.
• Format: Others
• Language: en
• Published: 2014
• Subjects: Logistic regression; Binary logistic regression; Share price; Stock exchange; Akaike’s Information Criterion; Wald Test; Score test; Enter method; Stepwise logistic regression; 519.536; Logistic regression analysis; Market share; Akaike Information Criterion
• Online Access: http://hdl.handle.net/10500/13229

This thesis investigates the factors that are associated with annual changes in the share price of Johannesburg Stock Exchange (JSE) listed companies. In this study, an increase in value of a share is when the share price of a company goes up by the end of the financial year as compared to the previous year. Secondary data that was sourced from McGregor BFA website was used. The data was from 2004 up to 2011. Deciding which share to buy is the biggest challenge faced by both investment companies and individuals when investing on the stock exchange. This thesis uses binary logistic regression to identify the variables that are associated with share price increase. The dependent variable was annual change in share price (ACSP) and the independent variables were assets per capital employed ratio, debt per assets ratio, debt per equity ratio, dividend yield, earnings per share, earnings yield, operating profit margin, price earnings ratio, return on assets, return on equity and return on capital employed. Different variable selection methods were used and it was established that the backward elimination method produced the best model. It was established that the probability of success of a share is higher if the shareholders are anticipating a higher return on capital employed, and high earnings/ share. It was however, noted that the share price is negatively impacted by dividend yield and earnings yield. Since the odds of an increase in share price is higher if there is a higher return on capital employed and high earning per share, investors and investment companies are encouraged to choose companies with high earnings per share and the best returns on capital employed. The final model had a classification rate of 68.3% and the validation sample produced a classification rate of 65.2% === Mathematical Sciences === M.Sc. (Statistics)