A Corporate Dividend Policy UJsing Human Knowledge Process Model

Dividend policy is one of most important managerial decision makings affecting the firm value. Although there are many studies regarding financial decision-making problems, such as bankruptcy prediction and credit scoring, there is no research, to our knowledge, about dividend prediction or dividend policy forecasting using machine learning approaches in spite of the significance of dividends. For dealing with the above issues, we suggest a knowledge refinement model that can refine the multiple rules extracted through rule-based algorithms from dividend data sets using GA. The new technique, called ´GAKR (genetic algorithm knowledge refinement)´, aims to combine the advantages of both knowledge consolidation and genetic algorithm. The experiments show that GAKR model always outperforms other models in the performance of dividend policy forecasting; we can predict future dividend policy more correctly than any other models. This enhancement in predictability of future dividend policy can significantly contribute to the valuation of a company, and hence from investors to financial managers to any decision makers of a company can make use of GAKR model for the better financing and investing decision makings which can lead to higher profits and firm values eventually.