Neural Networks and Empirical Research in Accounting |
| |
Authors: | Duarte Trigueiros Richard Taffler |
| |
Affiliation: | 1. Department of Business Studies , ISCTE , Lisbon;2. Accounting and Finance , City University Business School , London |
| |
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. |
| |
Keywords: | accounting earnings scalars deflators boundary conditions parametric and nonparametric density Estimation |
|