Explainable Artificial Intelligence for identifying profitability predictors in Financial Statements
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913669855576064 |
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| author | Piazza, Marco Passacantando, Mauro Magli, Francesca Doni, Federica Amaduzzi, Andrea Messina, Enza |
| author_facet | Piazza, Marco Passacantando, Mauro Magli, Francesca Doni, Federica Amaduzzi, Andrea Messina, Enza |
| contents | The interconnected nature of the economic variables influencing a firm's performance makes the prediction of a company's earning trend a challenging task. Existing methodologies often rely on simplistic models and financial ratios failing to capture the complexity of interacting influences. In this paper, we apply Machine Learning techniques to raw financial statements data taken from AIDA, a Database comprising Italian listed companies' data from 2013 to 2022.
We present a comparative study of different models and following the European AI regulations, we complement our analysis by applying explainability techniques to the proposed models. In particular, we propose adopting an eXplainable Artificial Intelligence method based on Game Theory to identify the most sensitive features and make the result more interpretable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17676 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable Artificial Intelligence for identifying profitability predictors in Financial Statements Piazza, Marco Passacantando, Mauro Magli, Francesca Doni, Federica Amaduzzi, Andrea Messina, Enza Machine Learning The interconnected nature of the economic variables influencing a firm's performance makes the prediction of a company's earning trend a challenging task. Existing methodologies often rely on simplistic models and financial ratios failing to capture the complexity of interacting influences. In this paper, we apply Machine Learning techniques to raw financial statements data taken from AIDA, a Database comprising Italian listed companies' data from 2013 to 2022. We present a comparative study of different models and following the European AI regulations, we complement our analysis by applying explainability techniques to the proposed models. In particular, we propose adopting an eXplainable Artificial Intelligence method based on Game Theory to identify the most sensitive features and make the result more interpretable. |
| title | Explainable Artificial Intelligence for identifying profitability predictors in Financial Statements |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.17676 |