An alternative model for the analysis of detecting electronic industries earnings management using stepwise regression, random forest, and decision tree
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- 作者：Fu-Hsiang Chen ; Hu Howard
- 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
- 出版年：2016
- 出版时间：May 2016
- 年：2016
- 卷：20
- 期：5
- 页码：1945-1960
- 全文大小：1,148 KB
- 刊物类别：Engineering
- 刊物主题：Numerical and Computational Methods in Engineering
Theory of Computation
Computing Methodologies
Mathematical Logic and Foundations
Control Engineering - 出版者：Springer Berlin / Heidelberg
- ISSN：1433-7479
- 卷排序：20
文摘
This study attempts to diagnose the detecting electronic industries’ earnings management by integrating suitable soft computing methods. Accounting earnings information is a very crucial element for corporate stakeholders to determine their stock prices and evaluate their supervision and management authority’s performance, while it is also essential information for measuring corporate value. Hence, whether an enterprise can faithfully express its true economic meaning over its financial statements and how the management handles its earnings have turned out to be a popular issue widely discussed by researchers. Detecting public companies’ earnings management is an important and challenging issue that has served as the impetus in many academic studies over the last few decades. Data mining technique and machine learning methods have also been commonly applied by accounting and financial personnel to other fields of studies. The study used the stepwise regression and random forest techniques to screen the variables in the first place, followed by adopting three kinds of decision trees including Chi-squared automatic interaction detector, classification and regression trees and C5.0 to establish a model and find out if the tested enterprise had extreme earnings manipulation. The results show that the proposed hybrid approach (RF+C5.0) has the optimal classification rate (the accuracy rate is 91.24 %) and the lowest occurrence of Type I error and Type II error. Also, as discovered from the rules set of the final additional testing, an enterprise’s operating cash flow, times interest earned ratio and previous period’s discretionary accruals play a decisive role in affecting its extreme earnings management.KeywordsEarnings managementRandom forestStepwise regressionCHAIDCARTC5.0Decision tree