Machine Learning for Corporate Default Risk: Improving Prediction Accuracy in an Era of Globalization and Digitalization
Bibliographic record
Abstract
In the recent era of financial market uncertainty increase gradually, corprotae default risk becomes a key factor to affect financial market's stability. Therefore, identifying and predicting default risk effectively represents a core challenge for financial institutions and regulatory agencies. Machine learning methods gradually applied for financial market prediction compared to traditional methods, it not only preforms higher predictive accuarcy but also showing unqiue strength in featrure extraction and variable adaptability espacially nonlinear relationships and mult-dimensional variables for corporate risk mangement. This paper aims to explore machine learning's role in predicting corporate default risk and evaluat the applicability and performance of different models in financial markets. Specifically, it compares the Logit, Random Forest, Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM)’s predictive ability by using data from 2010-2023’s China's A-share corporate bond market dataset. And results indicate that Random Forest and XGBoost have significantly better perform and identify potential risk effectively. While Logit shows value in interpretability and LSTM have potential power in time series modeling, their overall effectiveness indicates limited. The conclusion emphasizes machine learning holds high, practical value in default risk prediction and provides reference for future risk management's implementation, investment decision-making, and financial stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".