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Financial distress prediction using integrated Z-score and multilayer perceptron neural networks
Desheng Wu1, Xiyuan Ma1, David L Olson2
1University of Chinese Academy of Sciences, Beijing, People's Republic of China.
Decision Support Systems
|November 28, 2022
Summary
This study introduces a hybrid model combining the Altman Z-Score and a multi-layer perceptron artificial neural network (MLP-ANN) for improved financial risk forecasting. The new model significantly enhances prediction accuracy for enterprise crises, outperforming traditional methods.
Area of Science:
- Finance
- Artificial Intelligence
- Data Science
Background:
- The COVID-19 pandemic introduced significant financial market volatility and uncertainty.
- Accurate financial risk forecasting remains crucial for effective financial planning.
Purpose of the Study:
- To develop a novel hybrid model for predicting enterprise financial crises.
- To enhance the accuracy of financial risk forecasting by combining traditional and AI-based methods.
Main Methods:
- Integration of the Altman Z-Score model with a multi-layer perceptron artificial neural network (MLP-ANN).
- Development of a hybrid enterprise crisis warning system.
- Empirical validation using Chinese financial data.
Main Results:
- The hybrid Z-score and MLP-ANN model achieved an average correct classification rate of 99.40%.
- This accuracy surpasses the standalone Altman Z-score model (86.54%) and a pure MLP-ANN approach (98.26%).
Conclusions:
- The proposed hybrid model offers a superior method for early warning signals of corporate financial distress.
- This advanced prediction capability aids stakeholders in mitigating potential financial losses.
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