C4.5: programs for machine learning
C4.5: programs for machine learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Rule extraction: using neural networks or for neural networks?
Journal of Computer Science and Technology
Knowledge based descriptive neural networks
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
IEEE Transactions on Neural Networks
ANN-DT: an algorithm for extraction of decision trees from artificial neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Semiparametric ARX neural-network models with an application to forecasting inflation
IEEE Transactions on Neural Networks
Risk-neutral density extraction from option prices: improved pricing with mixture density networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting
Expert Systems with Applications: An International Journal
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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 use a neural network rule extraction method to extract knowledge from 2222 dividend initiation and resumption events. 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 small firms with high dividend ratios. 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 different across different types of firms. Thus, drawing the conclusions from the whole dividend event data may leave some important information unexamined. This study shows that neural network rule extraction method can reveal more knowledge from the data.