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Knowledge discovery in corporate events by neural network rule extraction

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Abstract

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.

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Author information

Authors and Affiliations

  1. Machine Vision and Pattern Recognition Laboratory, Department of Computer Science, Wayne State University, Detroit, MI, 48202, USA

    Ming Dong

  2. School of Business, SUNY—Institute of Technology, Utica, NY, 13504, USA

    Xu-Shen Zhou

Authors
  1. Ming Dong

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  2. Xu-Shen Zhou

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Correspondence toMing Dong.

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