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.

Exploring Marketing in Micro Firms

Purpose - The purpose of the paper is to explore the practice of marketing in micro firms. Which are the challenges micro firms encounter and how do they handle them? Methodology - The research methodology is based on the theory-in-use approach (Zaltman, Heffring & LeMasters 1982) in order to inductively explore the practice of marketing in micro firms. The empirical findings rest on ten case studies, where data has been generated through repeated interactions with each case. Findings - The empirical findings show that micro firms handle their marketing challenges in a distinctive manner, by creatively using available resources and network relations. Marketing in micro firms is largely about a long-term, gradual development of a position on the market. This process we label germinal marketing. Two key dimensions of germinal marketing were identified: “earning your position” and “being your brand”. Research limitations and implications - The findings rest on an explorative study consisting of ten cases and the general applicability of the results need to be validated by further studies. These cases are however sufficient to illuminate the need for further research into the area. Value of the paper - The value of the paper is twofold. First, it expands the theory-in-use approach, and presents a research method for successful inductive empirical studies of small firm phenomena. Secondly, the paper widens our understanding of the marketing reality and practice of micro firms, identifying new dimensions of marketing and revealing the strategic implications of ordinary business activities.