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Assessing Banks' Distress Using News and Regular Financial Data
Paola Cerchiello1, Giancarlo Nicola1, Samuel Rönnqvist2
1Department of Economics and Management, University of Pavia, Pavia, Italy.
Frontiers in Artificial Intelligence
|June 20, 2022
Summary
This study enhances bank distress classification by integrating financial news using deep learning. Results show news data provides valuable insights beyond traditional financial figures.
Area of Science:
- Financial analytics
- Natural Language Processing (NLP)
- Machine Learning (ML)
Background:
- Traditional bank distress prediction relies on financial figures.
- Integrating unstructured data like financial news can improve predictive accuracy.
- Natural Language Interpretation (NLI) and media analysis are key challenges.
Discussion:
- A deep learning approach was employed, utilizing Doc2Vec for text representation.
- A feed-forward neural network combined news data representations with financial figures.
- The model aims to classify banks as distressed or tranquil.
Key Insights:
- Financial news data significantly improves bank distress classifier performance.
- News-derived features offer unique information not present in standard financial variables.
- The study validates the utility of NLP in financial risk assessment.
Outlook:
- Further research can explore advanced NLP techniques for richer feature extraction.
- This methodology can be adapted for predicting distress in other financial institutions.
- The integration of diverse data sources offers a promising avenue for robust financial modeling.
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