Accurate Prediction of Financial Distress with Machine Learning Algorithms
نویسندگان
چکیده
Prediction of financial distress of companies is analyzed with several machine learning approaches. We used DIANE, a large database containing financial records of 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, increase the prediction accuracy. In particular, fluctuations of some financial ratios are found to be crucial. Due to the inclusion of a large amount of inputs particular attention is given to feature selection. An accuracy of up to 94% is achieved with the best models.
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