Accounting method selection using neural networks and multi-criteria decision making

Date
2021-01-05
Authors
Duan, Yang
Yeh, Chung-Hsing
Dowe, David L.
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
1550
Ending Page
Alternative Title
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.
Description
Keywords
Machine Learning and Predictive Analytics in Accounting, Finance, and Management, accounting method, accounting results, company strategic goals, multi-critiera decision making, neural networks
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 54th Hawaii International Conference on System Sciences
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.