A Knowledge Integration Model for Corporate Dividend Prediction

• Main Authors: Jinhwa Kim, Chaehwan Won, Jae Kwon Bae
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial neural networks; Companies; Cost accounting; Decision trees; Dividend Policy; Equations; Knowledge Integration; Marsh and Merton Model; Mathematical model; Neural Networks; Predictive models; Rule Induction
• DOI: 10.1109/NCM.2008.144

Dividend is one of essential factors determining the value of a firm. According to the valuation theory in finance, discounted cash flow (DCF) is the most popular and widely used method for the valuation of any asset. Since dividends play a key role in the pricing of a firm value by DCF, it is natural that the accurate prediction of future dividends should be most important work in the valuation. Although the dividend forecasting is of importance in the real world for the purpose of investment and financing decision, it is not easy for us to find good theoretical models which can predict future dividends accurately except Marsh and Merton (1987) model. Thus, if we can develop a better method than Marsh and Merton in the prediction of future dividends, it can contribute significantly to the enhancement of a firm value. Therefore, the most important goal of this study is to develop a better method than Marsh and Merton model by applying artificial intelligence techniques. The effectiveness of our approach was verified by the experiments comparing with Marsh and Merton model, Neural Networks, and CART approaches.