Accounting results modelling with neural networks: the case of an international oil and gas company

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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.

Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part II
EditorsLong Cheng, Andrew Chi Sing Leung, Seiichi Ozawa
Place of PublicationCham Switzerland
PublisherSpringer
Pages275-285
Number of pages11
ISBN (Electronic)9783030041793
ISBN (Print)9783030041786
DOIs
Publication statusPublished - 2018
EventInternational Conference on Neural Information Processing 2018 - Siem Reap, Cambodia
Duration: 13 Dec 201816 Dec 2018
Conference number: 25th
https://conference.cs.cityu.edu.hk/iconip/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11302
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Neural Information Processing 2018
Abbreviated titleICONIP 2018
CountryCambodia
CitySiem Reap
Period13/12/1816/12/18
Internet address

Keywords

  • Accounting results
  • Business factors
  • Neural network modelling
  • Oil and gas company

Cite this

Duan, Y., Yeh, C-H., & Dowe, D. L. (2018). Accounting results modelling with neural networks: the case of an international oil and gas company. In L. Cheng, A. C. S. Leung, & S. Ozawa (Eds.), Neural Information Processing : 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part II (pp. 275-285). (Lecture Notes in Computer Science; Vol. 11302 ). Cham Switzerland: Springer. https://doi.org/10.1007/978-3-030-04179-3_24
Duan, Yang ; Yeh, Chung-Hsing ; Dowe, David L. / Accounting results modelling with neural networks : the case of an international oil and gas company. Neural Information Processing : 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part II. editor / Long Cheng ; Andrew Chi Sing Leung ; Seiichi Ozawa. Cham Switzerland : Springer, 2018. pp. 275-285 (Lecture Notes in Computer Science).
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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, C-H & Dowe, DL 2018, Accounting results modelling with neural networks: the case of an international oil and gas company. in L Cheng, ACS Leung & S Ozawa (eds), Neural Information Processing : 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part II. Lecture Notes in Computer Science, vol. 11302 , Springer, Cham Switzerland, pp. 275-285, International Conference on Neural Information Processing 2018, Siem Reap, Cambodia, 13/12/18. https://doi.org/10.1007/978-3-030-04179-3_24

Accounting results modelling with neural networks : the case of an international oil and gas company. / Duan, Yang; Yeh, Chung-Hsing; Dowe, David L.

Neural Information Processing : 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part II. ed. / Long Cheng; Andrew Chi Sing Leung; Seiichi Ozawa. Cham Switzerland : Springer, 2018. p. 275-285 (Lecture Notes in Computer Science; Vol. 11302 ).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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Duan Y, Yeh C-H, Dowe DL. Accounting results modelling with neural networks: the case of an international oil and gas company. In Cheng L, Leung ACS, Ozawa S, editors, Neural Information Processing : 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13–16, 2018 Proceedings, Part II. Cham Switzerland: Springer. 2018. p. 275-285. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-04179-3_24