Intangible assets evaluation: The machine learning perspective

• Different machine learning techniques for intangible assets evaluation are compared.
• Particularly, single classifiers, classifier ensembles and hybrid classifiers are compared.
• Hybrid classifiers by k-means+DT ensembles provide the best performance.

The lack of regulations and disclosures regarding intangible capital has made it rather difficult for investors and creditors to evaluate a firm׳s intangible value before making the associated investment and loan decisions. This study represents an initial attempt to compare/contrast different types of machine learning techniques and identify the optimal prediction model for intangible assets. In addition, this paper shows that machine learning can be used effectively for the problem of intangible assets evaluation. To be specific, five classification algorithms are considered: decision trees (DT), artificial neural networks (ANN), naïve Bayes, support vector machines (SVM) and k-Nearest Neighbors (k-NN). Consequently, thirty prediction models are constructed for comparison, including five single classifiers, boosting and bagging based classifier ensembles, and the combination of k-means clustering, single classifiers and classifier ensembles. The experimental results show that prediction models combining k-means with boosting/bagging based classifier ensembles perform much better than the other methods in terms of prediction accuracy, ROC Curve, as well as Type I and II errors. In particular, while the best single classifier, k-NN provides 78.24% prediction accuracy, k-means+bagging based DT ensembles provide the best performance to predict intangible assets with a prediction accuracy of 91.60%, 96.40% of ROC Curve and 18.65% of Type I and 6.34% of II errors, respectively.