Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison
Round 1
Reviewer 1 Report
This paper uses Support Vector Machine and artificial neural networks methods to predict bankruptcy in Czech Republic. The results show that artificial neural networks are much more accurate than SVM in predicting possible bankruptcy.
I find the paper well developed and written. The two approaches are applied and contrasted. I suggest including additional macroeconomic indicators in the predictive model to account for business cycles or institutional changes. Moreover, how do the models perform in the out-of-sample forecasting?
For the comparison of the two kinds of models, you may want to provide more explanations for the strengths and weakness, and show why NM would outperform SVM.
Author Response
Dear reviewer,
Please find attached all necessary information.
Thank you for your feedback.
With kind regards
Jakub Horak
Author Response File: Author Response.pdf
Reviewer 2 Report
See file attached
Comments for author File: Comments.pdf
Author Response
Dear reviewer,
Please find attached all necessary information.
Thank you for your feedback.
With kind regards
Jakub Horak
Author Response File: Author Response.pdf