Predicting financial distress for Romanian companies

    Gheorghe Ruxanda Affiliation
    ; Cătălina Zamfir Affiliation
    ; Andreea Muraru Affiliation


Using a moderately large number of financial ratios, we tried to build models for classifying the companies listed on the Bucharest Stock Exchange into low and high risk classes of financial distress. We considered four classification techniques: Support Vector Machines, Decision Trees, Bayesian logistic regression and Fisher linear classifier, out of which the first two proved to have the highest prediction accuracy. Classifiers were trained and tested on randomly drown samples and on four different databases built starting from the initial financial indicators. As the literature related to the topic on Romanian data is very scarce, our study, by using a variety of methods and combining feature selection and principal components analysis, brings a new approach to predicting financial distress for Romanian companies.


Keyword : Support Vector Machines, Bayesian Analysis, financial distress prediction, data mining, discriminant analysis, logistic regression

How to Cite
Ruxanda, G., Zamfir, C., & Muraru, A. (2018). Predicting financial distress for Romanian companies. Technological and Economic Development of Economy, 24(6), 2318-2337.
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Dec 14, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.


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