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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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A novel method for financial distress prediction based on sparse neural networks with regularization.

Ying Chen¹, Jifeng Guo², Junqin Huang³

¹International Business College, South China Normal University, Guangzhou, 510631 China.

International Journal of Machine Learning and Cybernetics

|May 2, 2022

Summary

Accurate corporate financial distress prediction is crucial. A new sparse neural network model (FDP-SNN) effectively uses both financial and non-financial variables, outperforming existing methods.

Keywords:

Features selection Financial distress prediction Sparse neural networks

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Area of Science:

Corporate Finance
Machine Learning
Accounting Theory

Background:

Corporate financial distress poses significant risks to enterprises and stakeholders.
Existing prediction models are often limited by variable selection or reliance solely on financial data.
Integrating diverse predictors is essential for robust financial distress prediction.

Purpose of the Study:

To develop a novel financial distress prediction (FDP) model using sparse neural networks (FDP-SNN).
To investigate the predictive power of both financial and non-financial variables in corporate financial distress.
To enhance prediction accuracy and model interpretability.

Main Methods:

Screening financial and non-financial variables based on accounting and finance theory.
Developing a sparse neural network (FDP-SNN) with L1 regularization for variable selection.
Comparing FDP-SNN performance against classic prediction models.

Main Results:

Non-financial variables (e.g., investor protection, governance) are key predictors, especially for longer forecast periods.
The FDP-SNN model demonstrates superior accuracy, precision, and AUC compared to traditional methods.
The model's sparsity enhances interpretability by identifying important predictors.

Conclusions:

Sparse neural networks offer a powerful approach for financial distress prediction.
Non-financial factors significantly contribute to predicting corporate financial distress.
FDP-SNN provides a more accurate, interpretable, and robust method for financial risk assessment.