Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Principles of Corporate Finance with Cdrom
Principles of Corporate Finance with Cdrom
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Computers and Operations Research
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
Decision support for foreign investment strategy under hybrid uncertainty
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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), 1-40.] 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.