Abstract
Dividend is the return that an investor receives when purchasing a company's shares. The decision to pay these dividends to shareholders concerns several other groups of people, such as financial managers, consulting firms, individual and institutional investors, government and monitoring authorities, and creditors, just to name a few. The prediction and modelling of this decision has received a significant amount of attention in the corporate finance literature. However, the methods used to study the aforementioned question are limited to the logistic regression method without any implementation of the advanced and expert methods of data mining. These methods have proven their superiority in other business-related fields, such as marketing, production, accounting and auditing. In finance, bankruptcy prediction has the vast majority among data-mining implementations, but to the best of the authors’ knowledge such an implementation does not exist in dividend payment prediction. This paper satisfies this gap in the literature and provides answers that help to understand the so-called "dividend puzzle". Specifically, this paper provides evidence supporting the hypothesis that data-mining methods perform better in accuracy measures against the traditional methods used. The prediction of dividend policy determinants provides valuable benefits to all related parties, as they can manage, invest, consult and monitor the dividend policy in a more effective way.