A neural fuzzy system approach to assessing the risk of earnings restatements.
Recent high-profiled accounting scandals (e.g., Enron and WorldCom) have called into question the quality of financial reporting in the U.S. These accounting scandals have resulted in massive restatements of corporate earnings and market value losses to investors. While earnings restatements have become more prevalent and costly in recent years, detection or prediction of earnings restatement has been badly lagging. Several recent studies have evaluated the usefulness of various computer technologies such as fuzzy logic and neural networks in business and industrial applications. The purpose of this paper is to evaluate the utility of an integrated neural fuzzy system (NFS) in assessing the risk of earning restatements. The integrated NFS outperforms a baseline Logit model, especially in the prediction of restatement cases.
International Association for Computer Information Systems
Lin, J.W., Hwang, M.I., & Li, J.F. (2004). A neural fuzzy system approach to assessing the risk of earnings restatements. Issues in Information Systems, 5(1), 201-207.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.