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A Nonlinear Approach to Assess the Risk–Reward Ratio Using the Machine Learning Technique

  • Pasquale MerellaEmail author
  • Roberto Schiesari
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Abstract

The objective of our work is to assess the reliability of the machine learning techniques estimating the default probabilities. In our work, we followed an alternative nonlinear separation method known as the Support Vector Machine (SVM) for the default risk analysis. We did not need any parameter restrictions or prior assumptions in our estimates. We analysed more than 42,000 Italian companies, using the AIDA dataset spanning the years 2011 through to 2016, proposing an SVM model based on the performance measures such as ratios of leverage, liquidity and activity. The results of our work point out that using nonlinear techniques for predicting bankruptcy allows to achieve better performances than traditional statistical ones and, moreover, shows the important predictors to estimate default probabilities.

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Copyright information

© The Author(s) 2019

Authors and Affiliations

  1. 1.Green Arrow Capital SGRMilanItaly
  2. 2.Department of ManagementUniversity of TurinTurinItaly

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