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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 summary is machine-generated.

This study enhances bank distress classification by integrating financial news using deep learning. Results show news data provides valuable insights beyond traditional financial figures.

Keywords:

Doc2Vec classification model credit risk deep learning text analysis

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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.