Estimating discretionary accruals using a grouping genetic algorithm

A number of different models have been suggested for detecting earnings management but the linear regression-based model presented by Jones (1991) is the most frequently used. The underlying assumption with the Jones model is that earnings are managed through accounting accruals. Typically, the companies for which earnings management is studied are grouped based on their industries. It is thus assumed that the accrual generating process for companies within a specific industry is similar. However, some studies have recently shown that this assumption does not necessarily hold. An alternative approach which returns a grouping which is, if not optimal, at least very close to optimal is the use of genetic algorithms. The purpose of this study is to assess the performance of the cross-sectional Jones accrual model when the data set firms are grouped using a grouping genetic algorithm. The results provide strong evidence that the grouping genetic algorithm method outperforms the various alternative grouping methods.