Accounting Choice for Measuring Investment Properties. Data Mining Techniques Contribution to Determine Decision Patterns
DOI:
https://doi.org/10.46661/revmetodoscuanteconempresa.2695Keywords:
elección contable, valor razonable, NIIF, redes neuronales, árboles de decisión, accounting choice, fair value, IFRS, neural networks, decision treesAbstract
International Accounting Standard 40 (IAS 40 - Investment properties) offers an ideal setting for research on accounting choice as it represents a paradigmatic case choosing between the fair value and the historical cost as the measurement criteria. In this paper, we take the opportunity of this standard to provide additional evidence in a multinational and multi-context on the determinants that explain the accounting choice. Furthermore, in this paper, we introduce and compare the use of artificial neural networks and decision trees in order to assess the predictive capability of these methodologies, compared to other techniques commonly used to solve classification problems in this area such as the logistic regression. The classification results indicate that both neural networks and decision trees can be an interesting alternative to classical statistical methods such as the logistic regression. In particular, both methods outperformed the logistic regression in terms of predictive ability, although no significant differences were found between both.
Downloads
References
Ball, M. (2005). RICS European Housing Review. RICS, Londres.
Ball, R. (2006). International Financial Reporting Standards (IFRS): pros and cons for investors. Accounting and Business Research. International Accounting Forum, 36: 5-27.
Barlev, B.; Fried, D.; Haddad, J.R. & Livnat, J. (2007). Reevaluation of Revaluations: A Cross-Country Examination of the Motives and Effects on Future Performance. Journal of Business Finance and Accounting, 34(7-8): 1025-1050.
Brown, P.; Izan, H.Y. & Loh, A.L. (1992). Fixed Assets Revaluations and Managerial Incentives. Abacus, 28(1): 36-57.
Christensen, H.B. & Nikolaev, V.V. (2013). Does fair value accounting for non-financial assets pass the market test? Review of Accounting Studies, 18: 734-775.
Cotter, J. (1999). Asset Revaluations and Debt Contracting. Abacus, 35(3): 268-285.
Cotter, J. & Zimmer, I. (1995). Asset Revaluation and Assessment of Borrowing Capacity. Abacus, 31(2): 136-151.
Cybenko, G. (1989). Approximation by Superpositions of a Sigmoidal Function. Mathematics of Control, Signals and Systems, 2: 303–314.
De Andrés, J.; Landajo; M., Lorca, P. & Ordoñez, P. (2010). Assessing the Liquidity of Firms: Robust Neural Network Regression as an Alternative to the Current Ratio. En Lytras, M.D.; Ordonez De Pablos, P.; Ziderman, A.; Roulstone, A.; Maurer, H. & Imber, J.B. (eds.). Knowledge Management, Information Systems, E-learning & Sustainability Research, Springer, Berlín, pp. 537-544.
De Vicente, M.; Molina, H. & Ramírez, J.N. (2013). Inversiones inmobiliarias: la elección contable valor razonable versus coste en los grupos cotizados españoles. Cuadernos de contabilidad, 14(34): 25-51.
Fields, T.D.; Lyz, T.Z. & Vincent, L. (2001). Empirical research on accounting choice. Journal of Accounting & Economics, 31: 255-307.
Florez-Lopez, R. (2007). Modelling of insurers’ rating determinants. An application of machine learning techniques and statistical models. European Journal of Operational Research, 183(3): 1488–1512.
Gaeremynck, A. & Veugelers, R. (1999). The revaluation of assets as a signalling device: a theoretical and an empirical analysis. Accounting and Business Research, 29(2): 123-138.
Godfrey, J.M. & Jones, K.L. (1999). Political cost influences on income smoothing via extraordinary item classification. Accounting and Finance, 39: 229-254.
Hervás Oliver, J.L. (2005). La revalorización de activos fijos. Contraste empírico de un modelo financiero de elección contable. Investigaciones Europeas de Dirección y Economía de la Empresa, 11(1): 31-51.
Hornik, K. (1991). Approximation Capabilities of Multilayer Feedforward Networks. Neural Networks, 4(2): 251–257.
International Accounting Standards Board (IASB) (2013). International Accounting Standard 40, Investment Property (Part A). IASB, Londres.
Kirkos, E., Spathis, C. & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32: 995–1003.
Kirkos, E.; Spathis, C. & Manolopoulos, Y. (2008). Support vector machines, Decision Trees and Neural Networks for auditor selection. Journal of computational Methods in Sciences and Engineering, 8: 213–224.
Koh, H-C. (2004). Going concern predictions using data mining techniques. Management Auditing Journal, 19(3): 462–476.
Kvaal, E. & Nobes, C. (2010). International differences in IFRS policy choice: a research note. Accounting and Business Research, 40(2): 173-187.
La Porta, R.; López-de-Silanes, F. & Shleifer, A. (1999). Corporate Governance Around the World. The Journal of Finance, vol. LIV (2): 471-515.
La Porta; R., López de Silanes, F.; Shleiffer, A. & Vishny, R. (1998). Law and Finance. Journal of Political Economy, 106: 1113-1155.
Lin, Y.C. & Peasnell, K.V. (2000). Fixed Asset Revaluation and Equity Depletion in the UK. Journal of Business Finance & Accounting, 27(3-4): 359-394.
Linsmeier, T.J. (2013). A Standard setter’s framework for selecting between fair value and historical cost measurement attributes: a basis for discussion of ‘‘Does fair value accounting for nonfinancial assets pass the market test?’’, Review of Accounting Studies, 18: 776-782.
Martens, D.; Bruynseels, L.; Baesens, B.; Willekens, M. & Vanthienen, J. (2008). Predicting going concern opinion with data mining. Decision Support Systems, 45(4): 765–777.
Missonier-Piera, F. (2007). Motives for fixed-asset revaluation: An empirical analysis with Swiss data. The International Journal of Accounting, 42: 186-205.
Muller, K.A.; Riedl, E.J. & Sellhorn, T. (2008). Causes and Consequences of Choosing Historical Cost versus Fair Value. Working paper, Pennsylvania State University, Harvard Business School and Ruhr-Universität Bochum.
Muller, K.A.; Riedl, E.J. & Sellhorn, T. (2011). Mandatory Fair Value Accounting and Information Asymmetry: Evidence from the European Real Estate Industry. Management Science, 57(6): 1138-1153.
Quagli, A. & Avallone, F. (2010). Fair Value or Cost Model? Drivers of Choice for IAS 40 in the Real Estate Industry. European Accounting Review, 19(3): 461-493.
Quinlan, J.R. (2005). C4.5: programs for machine learning. Morgan Kaufmann Publishers, San Francisco.
Stone, M. & Rasp, J. (1991). The Tradeoffs in the Choice between Logit and OLS in Accounting Choice Studies. The Accounting Review, 66(1): 170-187.
Sun, J.; Li, H.; Huang, Q-H. & He, K-Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowdledge Based Systems, 57: 41-56.
Whittred, G. & Chan, Y.K. (1992). Asset Revaluation and the Mitigation of Underinvestment. Abacus, 28(1): 58-74.
Zeff, S.A. (2012). The Evolution of the IASC into the IASB, and the Challenges it Faces. The Accounting Review, 87(3): 807-837.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 Journal of Quantitative Methods for Economics and Business Administration
![Creative Commons License](http://i.creativecommons.org/l/by-sa/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submission of manuscripts implies that the work described has not been published before (except in the form of an abstract or as part of thesis), that it is not under consideration for publication elsewhere and that, in case of acceptance, the authors agree to automatic transfer of the copyright to the Journal for its publication and dissemination. Authors retain the authors' right to use and share the article according to a personal or instutional use or scholarly sharing purposes; in addition, they retain patent, trademark and other intellectual property rights (including research data).
All the articles are published in the Journal under the Creative Commons license CC-BY-SA (Attribution-ShareAlike). It is allowed a commercial use of the work (always including the author attribution) and other derivative works, which must be released under the same license as the original work.
Up to Volume 21, this Journal has been licensing the articles under the Creative Commons license CC-BY-SA 3.0 ES. Starting from Volume 22, the Creative Commons license CC-BY-SA 4.0 is used.