Abstract
The purpose of the paper was to compare the accuracy of traditional bankruptcy prediction models with the Random Forest method. In particular, the paper verifies 2 research hypotheses (verification was based on the representative sample of Polish companies): [H1]: The Random Forest algorithm (trained on a representative set of companies) is more accurate than traditional bankruptcy prediction methods: logit and linear discriminant models, and [H2]: The Random Forest algorithm efficiently uses normalized financial statement data (there is no need to calculate financial ratios).
The paper is supported by the Cracow University of Economics research grant.
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Wyrobek, J., Kluza, K. (2018). Efficiency of Random Decision Forest Technique in Polish Companies’ Bankruptcy Prediction. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_41
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