Neural Networks and Empirical Research in Accounting
概述神经网络方法在会计实证研究中的潜在作用，指出会计任务领域与神经网络原始应用领域的差异，提供非技术性指南、示例及未来研究方向，帮助会计研究者识别其使用中的风险与优势。
Abstract This article seeks to provide an overview of the potential role of neural network (connectionist) methodology in empirical accounting research. It highlights how the accounting task domain differs substantially from those for which neural network techniques were originally developed. A non-technical overview of neural network methodology is given, along with guidelines to help accounting researchers interested in applying these new tools to recognise the potential dangers and strengths underlying their use. An illustrative example is provided. The paper suggests research areas in accounting where neural network approaches could make a potential contribution. Explicit recommendations for prospective authors are made.