Accounting method selection using neural networks and multi-criteria decision making
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Date
2021-01-05
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1550
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Abstract
The selection of accounting methods has significant impacts on companies’ accounting results and strategic goals. However, this selection problem has not been effectively addressed by existing studies. To fill this important gap, we propose a novel approach for evaluating two accounting method alternatives, namely Full Cost (FC) and Successful Effort (SE) with an empirical case of an oil and gas company. Neural networks (NNs), fuzzy multi-criteria decision making (MCDM) with optimal weighting are applied to evaluate the consequent effects of FC and SE on strategic goals of the case company. The empirical study conducted demonstrates the effectiveness of the proposed approach. Methodologically, this paper provides a structured approach for evaluating accounting method alternatives in a rational and informed manner. Empirically, the evidence obtained from applying the proposed approach can be used to support the case company’s decision on accounting method selection.
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Machine Learning and Predictive Analytics in Accounting, Finance, and Management, accounting method, accounting results, company strategic goals, multi-critiera decision making, neural networks
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10 pages
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Proceedings of the 54th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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