Analyzing dividend events with neural network rule extraction

Over the last two decades, artificial neural networks (ANN) have been applied to solve a variety of problems such as pattern classification and function approximation. In many applications, it is desirable to extract knowledge from trained neural networks for the users to gain a better understanding of the network's solution. In this paper, we apply REFANN (rule extraction from function approximating neural networks) in dividend events study. Based on our study of 1530 dividend initiations and 692 resumptions events from April 1965 to December 2000, we find that the positive relation between the short-term price reaction and the ratio of annualized dividend amount to stock price is primarily limited to 96 firms that have high dividend ratio and small firm size. The results suggest that the degree of short-term stock price underreaction to dividend events may not be as dramatic as previously believed. The results also show that the relations between the stock price response and firm size is also different across different types of firms. It is suggested that drawing the conclusions from the whole dividend events data may leave some important information unexamined. Our rule extraction method may shed some lights on further empirical research in corporate events studies because more information can be drawn from the data.