Determinants of intangible assets value: The data mining approach

• Published in: Knowledge-based systems Vol. 31; pp. 67 - 77
• Main Authors: Tsai, Chih-Fong, Lu, Yu-Hsin, Yen, David C.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-07-2012
• Subjects: Companies; Data mining; Feature selection; Firm value; Intangible assets value; Neural networks; Valuation; Intangible assets value; Feature selection; Firm value; Data mining; Neural networks
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2012.02.007

It is very important for investors and creditors to understand the critical factors affecting a firm’s value before making decisions about investments and loans. Since the knowledge-based economy has evolved, the method for creating firm value has transferred from traditional physical assets to intangible knowledge. Therefore, valuation of intangible assets has become a widespread topic of interest in the future of the economy. This study takes advantage of feature selection, an important data-preprocessing step in data mining, to identify important and representative factors affecting intangible assets. Particularly, five feature selection methods are considered, which include principal component analysis (PCA), stepwise regression (STEPWISE), decision trees (DT), association rules (AR), and genetic algorithms (GA). In addition, multi-layer perceptron (MLP) neural networks are used as the prediction model in order to understand which features selected from these five methods can allow the prediction model to perform best. Based on the chosen dataset containing 61 variables, the experimental result shows that combining the results from multiple feature selection methods performs the best. GA∩STEPWISE, DT∪PCA, and the DT single feature selection method generate approximately 75% prediction accuracy, which select 26, 22, and 7 variables respectively.