Title Accounting choice for measuring investment properties. Data mining techniques contribution to determine decision patterns
Authors DE VICENTE LAMA, MARTA, MOLINA SÁNCHEZ, HORACIO, RAMÍREZ SOBRINO, JESÚS NICOLÁS, TORRES JIMÉNEZ, MERCEDES, MOLINA SÁNCHEZ, HORACIO, RAMÍREZ SOBRINO, JESÚS NICOLÁS, TORRES JIMÉNEZ, MERCEDES, DE VICENTE LAMA, MARTA
External publication No
Means Rev. Metodos Cuantitativos Econ. Empresa
Scope Article
Nature Científica
SJR Quartile 4
SJR Impact 0.12500
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021749239&partnerID=40&md5=0c4bda4c8948f33bd44fd5468a10105f
Publication date 01/01/2017
Scopus Id 2-s2.0-85021749239
Abstract 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.
Keywords Accounting choice; Decision trees; Fair value; IFRS; Neural networks
Universidad Loyola members