Initial stage clustering when estimating accounting quality measures with self-organizing maps

Abstract

This study introduces self-organizing maps as a clustering approach for several measures in accounting that rely on local linear regression-based estimation models with an initial and essential clustering phase. Clustering by industry is the most frequently used approach in prior literature when estimating measures such as real activities manipulation or accruals quality. However, this approach has been subject to criticism due to its association with sample attrition and biased outcome measures. The purpose of our study is to develop and evaluate the performance of a self-organizing map (SOM) local regression-based estimation model for several measures of accounting quality. The SOM is built by utilizing general firm characteristics such as regular balance sheet items as cluster variables instead of model specific variables. According to the results, our SOM local regression models outperform previously suggested clustering methods. Simulation tests show that estimation models based on SOM clustering with general firm characteristics detect abnormality in the accounting quality measures much better than previously used clustering methods. By utilizing the SOM approach, the estimation process of the measures is significantly improved which results in more accurate outcome measures that can be used in various contexts including expert systems designed for auditors and investors.