Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.

Interpretation of Services Marketing Concepts

Scholarly research has produced conceptual knowledge that is based on real-life marketing phenomena. An initial aim of past research has been to produce marketing knowledge as a base for efficient business operation and for the improvement of productivity. Thus, an assumption has been that the knowledge would be applied by organisations. This study focuses on understanding the use of marketing knowledge within the field of service marketing. Hence, even if marketing knowledge about service-oriented principles and marketing of services is based on empirical research, there is a lack of knowledge on how this marketing knowledge is in fact applied by businesses. The study focuses on four essential concepts of services marketing knowledge, namely service quality, servicescape, internal marketing, and augmented service offering. The research involves four case companies. Data is based on in depth interviews and questionnaire-based surveys conducted with managers, employees, and customers of these companies. All organisations were currently developing in a service-oriented and customer-oriented direction. However, we found limitations, gaps, and barriers for the implementation of service-oriented and customer-oriented principles. Hence, we argue that the organisations involved in the study exploited conceptual knowledge symbolically and conceptually, but the instrumental use of knowledge was limited. Due to the shortcomings found, we also argue that the implementation of the various practices and processes that are related to becoming service-oriented and customer-oriented has not been fully successful. Further, we have come to the conclusion that the shortcomings detected were at least in some respect related to the fact that the understanding and utilisation of conceptual knowledge of service-oriented principles and marketing of services were somewhat limited.