Abstract
Prediction of financial distress of companies is analyzed with several machine learning approaches. We used Diane, a large database containing financial records from small and medium size French companies, from the year of 2002 up to 2007. It is shown that inclusion of historical data, up to 3 years priori to the analysis, increases the prediction accuracy and that Support Vector Machines are the most accurate predictor.
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Vieira, A.S., Duarte, J., Ribeiro, B., Neves, J.C. (2009). Accurate Prediction of Financial Distress of Companies with Machine Learning Algorithms. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_58
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DOI: https://doi.org/10.1007/978-3-642-04921-7_58
Publisher Name: Springer, Berlin, Heidelberg
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