Abstract
Accounting results are crucial information closely monitored by managers, investors and government agencies for decision making. Understanding various endogenous and exogenous business factors affecting accounting results is an essential step in managing them. However, how to model the relationship between accounting results and their business factor antecedents remains an unresolved issue. To address this issue, this paper develops neural network (NN) models for modelling complex interactions between the business factors and accounting results. Based on empirical data from an international leading oil and gas company, 15 original data points, 8 inputs and 6 outputs are used, and 4 NN architectures in 2 training settings are tested. The experiments conducted show satisfactory results. Comparisons of various training settings suggest that a recurrent NN architecture with multiple outputs is best suited for accounting results modelling. The relative contribution factor analysis with the best-performing NN model provides new insights in understanding crucial business factors for the case company and accounting professionals to manage accounting results. As a pilot study, this paper contributes to business, accounting and finance research by providing a promising approach for accounting results modelling.
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Duan, Y., Yeh, CH., Dowe, D.L. (2018). Accounting Results Modelling with Neural Networks: The Case of an International Oil and Gas Company. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_24
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DOI: https://doi.org/10.1007/978-3-030-04179-3_24
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