Jesper Haga

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

We use self-organizing maps as a clustering tool when estimating accounting quality.SOM local regression model outperforms commonly used industry models.Overall, commonly used industry models do not perform very well.SOM should be used on large and messy data. 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.

A neural network approach to measure real activities manipulation

We use neural networks to measure three varieties of real activities manipulation.The multilayer perceptron approach outperforms traditional linear regressions.The multilayer perceptron approach performs better than self-organizing maps.Individual measures should be used instead of aggregated measures when applying linear regression. A growing body of literature is examining the concept of real activities manipulation in various contexts. In these studies, the researcher typically models abnormal real activities and draws inferences based on the output measures. Thus, the results of the studies hinge critically on the underlying models. We contribute by examining alternative approaches to measure three varieties of real activities manipulation. Neural network models based on a self-organizing map and a multilayer perceptron are used in variation to a frequently used linear approach. The purpose of the study is to examine whether the neural network models outperform linear-based models in the detection of real activities manipulation. According to the results, the multilayer perceptron models are remarkably strong while the traditional linear models are underachievers. These results are specifically evident when common comprehensive measures are used.

Are Momentum Crashes Pervasive Regardless of Strategy? Evidence from the Foreign Exchange Market

This paper studies the option-like behavior of popular momentum strategies implemented in foreign exchange markets. The results confirm those of Daniel and Moskowitz (2013) in finding strong option-like behavior for both momentum measures, based on the cumulative return from 12 and 6 months prior to the formation date to one month prior to the formation date. Surprisingly, there is no such evidence for the popular momentum strategy accounting for a one-month formation period.

Real Earnings Management in UK Private and Public Firms

This paper analyses real earnings management among private versus public firms. Using accounting data of British firms, we find that public firms overall engage in more earnings management through real operating activities. Furthermore, when clear incentives to manage earnings in a specific direction are present, such as to beat earnings targets, we also find that public firms manage their earnings in the expected direction more than private firms. Additional tests reveal that higher analyst coverage may mitigate the level of abnormal operating behaviour in certain settings while quality auditing is not a limiting factor. We also find that high managerial ownership among private firms is associated with less real earnings management. Our study contributes to the emerging literature on non-accrual earnings management and to the broader understanding about the private vis-a-vis public firm reporting and operating behaviour.