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

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The Future of Risk Management, Volume II

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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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Notes

  1. 1.

    For the training dataset the starting year is 2011 and for the validating one is the 2014.

  2. 2.

    For an introduction to the validation framework see (Sobehart et al. 2001).

  3. 3.

    See https://cran.r-project.org/.

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Author information

Authors and Affiliations

  1. Green Arrow Capital SGR, Milan, Italy

    Pasquale Merella

  2. Department of Management, University of Turin, Turin, Italy

    Roberto Schiesari

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  1. Pasquale Merella
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  2. Roberto Schiesari
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Correspondence to Pasquale Merella .

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Editors and Affiliations

  1. University of Turin, Turin, Italy

    Paola De Vincentiis

  2. Department of Management, University of Turin, Turin, Italy

    Francesca Culasso

  3. University of Turin, Turin, Italy

    Stefano A. Cerrato

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Merella, P., Schiesari, R. (2019). A Nonlinear Approach to Assess the Risk–Reward Ratio Using the Machine Learning Technique. In: De Vincentiis, P., Culasso, F., Cerrato, S. (eds) The Future of Risk Management, Volume II. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-16526-0_8

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