Lisa Crosato, Caterina Liberati, Marco Repetto
arXiv 31 Aug 2021 · Statistics — Machine Learning
arXiv:2108.13914 · PDF · DOI · OpenAlex · Extracted main text
Academic research and the financial industry have recently paid great attention to Machine Learning algorithms due to their power to solve complex learning tasks. In the field of firms' default prediction, however, the lack of interpretability has prevented the extensive adoption of the black-box type of models. To overcome this drawback and maintain the high performances of black-boxes, this paper relies on a model-agnostic approach. Accumulated Local Effects and Shapley values are used to shape the predictors' impact on the likelihood of default and rank them according to their contribution to the model outcome. Prediction is achieved by two Machine Learning algorithms (eXtreme Gradient Boosting and FeedForward Neural Network) compared with three standard discriminant models. Results show that our analysis of the Italian Small and Medium Enterprises manufacturing industry benefits from the overall highest classification power by the eXtreme Gradient Boosting algorithm without giving up a rich interpretation framework.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Altman, E.I., Sabato, G., Wilson, N (2010) The value of non-financial information in small and medium-sized enterprise risk management | 1.000 | 8 | 3 | 100% |
| 2 | Ciampi, F., Vallini, C., Gordini, N., Benvenuti, M (2009) Are Credit Scoring Models Able to Predict Small Enterprise Default? Statistical Evidence from Italian Small Enterprises | 1.000 | 7 | 3 | 100% |
| 3 | Ciampi, F., Gordini, N (2013) Small enterprise default prediction modeling through artificial neural networks: An empirical analysis of Italian small enterpri… | 1.000 | 7 | 3 | 100% |
| 4 | Lin, S.M., Ansell, J., Andreeva, G (2012) Predicting default of a small business using different definitions of financial distress | 1.000 | 7 | 3 | 100% |
| 5 | Apley, D.W., Zhu, J (2020) Visualizing the effects of predictor variables in black box supervised learning models | 1.000 | 5 | 3 | 100% |
| 6 | Ciampi, F., Giannozzi, A., Marzi, G., Altman, E.I (2021) Rethinking SME default prediction: a systematic literature review and future perspectives | 0.928 | 4 | 4 | 100% |
| 7 | Calabrese, R., Osmetti, S.A (2013) Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model | 0.928 | 4 | 3 | 100% |
| 8 | Calabrese, R., Marra, G., Angela Osmetti, S (2016) Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model | 0.928 | 4 | 3 | 100% |
| 9 | Andreeva, G., Calabrese, R., Osmetti, S.A (2016) A comparative analysis of the UK and Italian small businesses using generalised extreme value models | 0.874 | 14 | 2 | 100% |
| 10 | Michala, D., Grammatikos, T., Filipe, S.F (2013) Forecasting Distress in European SME Portfolios | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 91 scored citations.