Accounting Results Modelling with Neural Networks: The Case of an International Oil and Gas Company

  • Yang DuanEmail author
  • Chung-Hsing Yeh
  • David L. Dowe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11302)


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.


Neural network modelling Accounting results Business factors Oil and gas company 


  1. 1.
    Fields, T.D., Lys, T.Z., Vincent, L.: Empirical research on accounting choice. J. Account. Econ. 31, 255–307 (2001)CrossRefGoogle Scholar
  2. 2.
    Watts, R.L., Zimmerman, J.L.: Positive accounting theory: a ten year perspective. Account. Rev. 65, 131–156 (1990)Google Scholar
  3. 3.
    Kim, J.-B., Zhang, L.: Accounting conservatism and stock price crash risk: firm-level evidence. Contemp. Account. Res. 33, 412–441 (2016)CrossRefGoogle Scholar
  4. 4.
    Tkáč, M., Verner, R.: Artificial neural networks in business: two decades of research. Appl. Soft Comput. 38, 788–804 (2016)CrossRefGoogle Scholar
  5. 5.
    Groot, T.d.: Accounting choices of controllers: an insight into controller deliberations. vol. Doctor of Philosophy. Tilburg University. CentER, Center for Economic Research, Tilburg (2015)Google Scholar
  6. 6.
    Dichev, I.D., Li, F.: Growth and accounting choice. Aust. J. Manag. 38, 221–252 (2013)CrossRefGoogle Scholar
  7. 7.
    Aharony, J., Lin, C.-J., Loeb, M.P.: Initial public offerings, accounting choices, and earnings management. Contemp. Account. Res. 10, 61–81 (1993)CrossRefGoogle Scholar
  8. 8.
    Cazavan-Jeny, A., Jeanjean, T., Joos, P.: Accounting choice and future performance: the case of R&D accounting in France. J. Account. Public Policy 30, 145–165 (2011)CrossRefGoogle Scholar
  9. 9.
    Huang, T.-L., Wang, T., Seng, J.-L.: Voluntary accounting changes and analyst following. Int. J. Account. Inf. Manag. 23, 2–15 (2015)CrossRefGoogle Scholar
  10. 10.
    Gietzmann, M., Ireland, J.: Cost of capital, strategic disclosures and accounting choice. J. Bus. Financ. Account. 32, 599–634 (2005)CrossRefGoogle Scholar
  11. 11.
    Watts, R.L., Zimmerman, J.L.: Towards a positive theory of the determination of accounting standards. Account. Rev. 53, 112–134 (1978)Google Scholar
  12. 12.
    Zhang, W.: CEO Tenure and Aggressive Accounting, vol. 3421490, p. 63. The University of Texas at Dallas, Ann Arbor (2010)Google Scholar
  13. 13.
    Balsam, S.: Discretionary accounting choices and CEO compensation. Contemp. Account. Res. 15, 229–252 (1998)CrossRefGoogle Scholar
  14. 14.
    Oler, M.: Determinants of the length of time a firm’s book-to-market ratio is greater than one. Rev. Quant. Financ. Acc. 45, 509–539 (2015)CrossRefGoogle Scholar
  15. 15.
    Friedlan, J.M.: Accounting choices of issuers of initial public offerings. Contemp. Account. Res. 11, 1–31 (1994)CrossRefGoogle Scholar
  16. 16.
    Lennox, C., Lisowsky, P., Pittman, J.: Tax aggressiveness and accounting fraud. J. Account. Res. 51, 739–778 (2013)CrossRefGoogle Scholar
  17. 17.
    Hanlon, M., Slemrod, J.: What does tax aggressiveness signal? Evidence from stock price reactions to news about tax shelter involvement. J. Public Econ. 93, 126–141 (2009)CrossRefGoogle Scholar
  18. 18.
    Hirshleifer, D., Kewei, H., Teoh, S.H., Yinglei, Z.: Do investors overvalue firms with bloated balance sheets? J. Account. Econ. 38, 297–331 (2004)CrossRefGoogle Scholar
  19. 19.
    Gardner, M.W., Dorling, S.: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32, 2627–2636 (1998)CrossRefGoogle Scholar
  20. 20.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Egnlewood Cliffs (2003)zbMATHGoogle Scholar
  21. 21.
    Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design. PWS Pub, Boston (1996)Google Scholar
  22. 22.
    Coakley, J.R., Brown, C.E.: Artificial neural networks in accounting and finance: modeling issues. Int. J. Intell. Syst. Account. Financ. Manag. 9, 119–144 (2000)CrossRefGoogle Scholar
  23. 23.
    Callen, J.L., Kwan, C.C.Y., Yip, P.C.Y., Yuan, Y.: Neural network forecasting of quarterly accounting earnings. Int. J. Forecast. 12, 475–482 (1996)CrossRefGoogle Scholar
  24. 24.
    PwC: Financial reporting in the oil and gas industry - International Financial Reporting Standards (2017)Google Scholar
  25. 25.
    Dechow, P.M., Hutton, A.P., Sloan, R.G.: Economic consequences of accounting for stock-based compensation. J. Account. Res. 34, 1–20 (1996)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of Information TechnologyMonash UniversityClaytonAustralia

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