Forecasting decisions on dividend policy of South Korea companies listed in the Korea Exchange Market based on support vector machines

In this study, performance of classification techniques is compared in order to predict dividend policy decisions. We first analyzed the feasibility of all available companies listed in the Korea Exchange (KRX) market as dividend data sets by using classification techniques. Then we developed a prediction model based on support vector machines (SVM). We compare the classification accuracy performance between our SVM model and artificial intelligence techniques, and suggest a better dividend policy forecasting model to help a chief executive officer (CEO) or a board of directors (BOD) make better decision in a corporate dividend policy. The experiments demonstrate that the SVM model always outperforms other models in the performance of dividend policy forecasting, and hence 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 correct valuation of a company, and hence those people from investors to financial managers to any decision makers of a company can make use of the SVM model for the better financing and investing decision making which can lead to higher profits and firm values eventually. Moreover, this is particularly important for people who want to obtain a high level of accuracy in advanced areas such as financial decision makings.