A knowledge integration model for the prediction of corporate dividends
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 future dividends play a key role in the pricing of a current 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 [Marsh, T. A., & Merton, R. C. (1987). Dividend behavior for the aggregate stock market. Journal of Business, 60 (1), 140.] model. Thus, if we can develop a better method than Marsh and Merton (1987) in the prediction of future dividends, it can contribute significantly to the improvement of the pricing model of a firm value. Therefore, the most important goal of this study is to develop a better model by applying artificial intelligence techniques than Marsh and Merton (1987). The effectiveness of our approach was verified by the experiments comparing with Marsh and Merton model, Neural Networks, and CART approaches.
knowledge integration marsh and merton model neural networks dividend policy rule induction