Elección contable para la valoración de las inversiones inmobiliarias. Contribución de las técnicas de minería de datos para determinar patrones de decisión // Accounting Choice for Measuring Investment Properties. Data Mining Techniques Contribution to Determine Decision Patterns

Autores/as

  • Marta De Vicente Lama Departamento de Economía Financiera y Contabilidad Universidad Loyola Andalucía
  • Horacio Molina Sánchez Departamento de Economía Financiera y Contabilidad Universidad Loyola Andalucía
  • Jesús N. Ramírez Sobrino Departamento de Economía Financiera y Contabilidad Universidad Loyola Andalucía
  • Mercedes Torres Jiménez Departamento de Métodos Cuantitativos Universidad Loyola Andalucía

Palabras clave:

elección contable, valor razonable, NIIF, redes neuronales, árboles de decisión, accounting choice, fair value, IFRS, neural networks, decision trees

Resumen

La normativa contable internacional ofrece con la Norma Internacional de Contabilidad 40 (NIC 40) "Inversiones inmobiliarias" un caso referente para investigar la decisión que toman las empresas cuando se les ofrece el valor razonable o el coste histórico como criterios alternativos de valoración. En este trabajo aprovechamos la oportunidad que ofrece esta norma para aportar evidencia adicional en un contexto multinacional y multisectorial sobre cuáles son los motivos que explican la elección contable. Además, en este trabajo introducimos y comparamos el uso de las redes neuronales artificiales y los árboles de decisión, con el objetivo de evaluar la capacidad predictiva de estas metodologías, frente a la tradicionalmente utilizada regresión logística para la resolución de problemas de clasificación en este área. Los resultados de la clasificación indican que tanto las redes neuronales como los árboles de decisión pueden ser una alternativa interesante a los métodos clásicos estadísticos como la regresión logística. En particular, las dos metodologías mostraron una mayor capacidad predictiva frente a la regresión logística aunque no se encontraron diferencias significativas entre ambas. 

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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.

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Publicado

2017-07-01

Cómo citar

De Vicente Lama, M., Molina Sánchez, H., Ramírez Sobrino, J. N., & Torres Jiménez, M. (2017). Elección contable para la valoración de las inversiones inmobiliarias. Contribución de las técnicas de minería de datos para determinar patrones de decisión // Accounting Choice for Measuring Investment Properties. Data Mining Techniques Contribution to Determine Decision Patterns. Revista De Métodos Cuantitativos Para La Economía Y La Empresa, 23, Páginas 234 a 256. Recuperado a partir de https://www.upo.es/revistas/index.php/RevMetCuant/article/view/2695

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