Scollr
Sign in Get app
Financial Distress and Bankruptcy Prediction Open access Peer reviewed

Advanced Machine Learning Techniques for Corporate Bankruptcy Prediction: A Gradient Boosting TreeNet Approach

Ali Saeedi

Intelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management | Jul 23, 2026

Open in Scollr View at source Open DOI

Scollr summary

What this paper is about

The findings highlight the high classification accuracy of TreeNet, particularly in predicting Chapter 7 cases, and its robust performance across different time horizons, and its robust performance across different time horizons.

Full abstract

Read the full abstract

ABSTRACT This study aims to enhance bankruptcy prediction models by employing the gradient‐boosting TreeNet algorithm. This research assesses the predictive accuracy of TreeNet in bankruptcy classification using a high‐dimensional approach. Specifically, it categorizes bankruptcy as nonbankrupt, Chapter 7, or Chapter 11 categories. This research utilizes a large dataset comprised of 76,069 firm‐year observations for the years between 1991 and 2019. The findings highlight the high classification accuracy of TreeNet, particularly in predicting Chapter 7 cases, and its robust performance across different time horizons. The TreeNet prediction model is a valuable tool for decision‐making and risk management in finance, auditing, and policymaking sectors.

Direct answer

What can I do from this paper page?

Use this page to scan "Advanced Machine Learning Techniques for Corporate Bankruptcy Prediction: A Gradient Boosting TreeNet Approach" quickly: start with the summary and abstract, then check the authors, source, topics, and related papers. From here, open Scollr to follow Financial Distress and Bankruptcy Prediction research, save the paper, or map adjacent work.

Authors

Researchers on this paper

Ali Saeedi

first | University of Wisconsin–Parkside | ORCID 0000-0002-6312-7127

Research areas

Follow related topics

Imbalanced Data Classification TechniquesCorporate Insolvency and GovernanceLatest Financial Distress and Bankruptcy Prediction research

Internal research mesh

These related papers are in Scollr's curated public subset and open as public HTML pages.

Citation

BibTeX

@article{Saeedi2026Advanced,
  title = {Advanced Machine Learning Techniques for Corporate Bankruptcy Prediction: A Gradient Boosting TreeNet Approach},
  author = {Ali Saeedi},
  journal = {Intelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management},
  year = {2026},
  doi = {10.1002/isaf.70044},
  url = {https://doi.org/10.1002/isaf.70044}
}

FAQ

Using this paper in a discovery workflow

How do I find related work for this paper?

Use the related papers and topic links on this page as starting points. In Scollr, you can also open the paper and build a literature map around its references, citing papers, and related work.

How can I keep up with new Financial Distress and Bankruptcy Prediction research papers?

Follow Financial Distress and Bankruptcy Prediction research in Scollr. New papers from the topic flow into a personalized feed, and you can save useful studies to revisit later.

Can I cite this paper from this page?

This page includes a static BibTeX block for Advanced Machine Learning Techniques for Corporate Bankruptcy Prediction: A Gradient Boosting TreeNet Approach. Always verify the DOI, source, and publication details against the publisher record before submitting a manuscript.

Follow this research in Scollr

Follow the topics and authors behind this paper, save useful studies, and build a literature map when you are ready to go deeper.

Get the app