Prediction of Delisting Using a Machine Learning Ensemble
Bibliographic record
Abstract
Despite the significant consequences of delisting, there have been only a few studies of business failure prediction that concentrate on delisting, particularly for the U.S. market. This study aims to predict delisting using quarterly financial ratios of 8,870 companies ever listed in the major U.S. exchanges from 1970 to 2022, along with key economic indices. We construct our data set to build a delisting prediction model that is robust across different company phases and economic conditions. To enhance predictive performance, we build an ensemble model to predict delisting, integrating five widely used machine learning methods : Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, and Neural Network. We compare the predictive performance of the five base learners and the ensemble model. The ensemble model achieves an accuracy of 0.836 and MCC of 0.457, demonstrating comparable accuracy and an improved MCC relative to the top-performing base learner, Random Forest. We leverage MCC, a reliable measure for imbalanced response data, to determine a classification threshold  and comprehensively evaluate the prediction results. We find that the Price-Earnings ratio, profitability ratios and inflation are among the most informative predictors for forecasting, aligning with prior research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".