Abstract
The companies involved in all areas of the business and industry can due to the unfavourable financial situation or inappropriate investments face financial problems resulting in bankruptcy of the company. The ability to foresee imminent bankruptcy helps managers and stock holders to take the corrective actions. In this paper, we analyze annual reports of thousands of limited liability companies and propose the bankruptcy prediction model. The available dataset is strongly imbalanced that corresponds to the real-world situation where bankrupt companies constitute only a small fraction of all companies. The proposed model is based on single-class least-squares anomaly detection classifier achieving as high as 91% prediction accuracy.
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This work was supported by the Slovak Research and Development Agency under contract No. APVV-15-0358.
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Drotár, P., Gnip, P., Zoričak, M., Gazda, V. (2018). Single-Class Bankruptcy Prediction Based on the Data from Annual Reports. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_37
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