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Financial Fraud Identification Based on Stacking Ensemble Learning Algorithm: Introducing MD&A Text Information.

Zhiheng Zhang¹, Yong Ma¹, Yongjun Hua²

¹School of Accounting, Chongqing University of Technology, Banan 400054, Chongqing, China.

Computational Intelligence and Neuroscience

|September 30, 2022

Summary

This summary is machine-generated.

This study introduces a stacking ensemble learning model to identify financial fraud using text analysis from annual reports. The model significantly improves fraud detection accuracy by incorporating financial, non-financial, and text-based variables.

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Area of Science:

Financial analysis
Machine learning
Text analytics

Background:

Increasing incidents of financial fraud necessitate improved detection methods.
Maintaining capital market order is a key concern for researchers and practitioners.
Existing financial fraud identification models may lack comprehensive variable integration.

Purpose of the Study:

To develop an advanced financial fraud identification model.
To evaluate the efficacy of stacking ensemble learning in fraud detection.
To assess the contribution of text-based variables to model performance.

Main Methods:

Construction of a financial fraud identification model using stacking ensemble learning.
Integration of financial and non-financial variables.
Inclusion of text variables: sentiment polarity, emotional tone, and readability from the Management Discussion and Analysis (MD&A) chapter.

Main Results:

The stacking ensemble learning model significantly outperforms individual classifiers.
Incorporating text variables demonstrably enhances the model's fraud recognition capabilities.
The combined approach using financial, non-financial, and text data yields superior results.

Conclusions:

The developed stacking ensemble model offers a more effective method for financial fraud identification.
Textual analysis of annual reports provides valuable insights for fraud detection.
This approach holds promise for improving capital market integrity.