layoff-risk-xgboost

Layoff Risk Prediction Model (XGBoost)

Binary classifier estimating the probability that a public company will execute a mass layoff (WARN Act notice) within a 90-day forward window, using fused market, fundamental, sentiment, and macro features.

• Model type: XGBoost (XGBClassifier), n_estimators=300, max_depth=6, learning_rate=0.05, subsample=0.8, colsample_bytree=0.8
• Task: Binary classification, $Y \in {0, 1}$ — layoff notice within 90 days
• Framework: scikit-learn API / XGBoost, tracked via MLflow
• Source: github.com/hongming111/layoff-risk-prediction-pipeline

Training data

13 features fused from five data domains, resampled/forward-filled onto a shared daily grid:

Ground truth labels come from state WARN Act notices (warn-scraper), entity-resolved to stock tickers via fuzzy company-name matching.

Evaluation

5-fold stratified cross-validation on the training snapshot (497 positive labels):

Known limitations

• Suspiciously high CV AUC. These numbers likely reflect the small current training set and ticker universe rather than a fully validated production signal — close (raw price level) is included as a feature, which can make separability easier than it would be in a true forward-looking deployment. Before trusting this for anything beyond a portfolio demo, it needs out-of-time (not just stratified k-fold) validation and a leakage audit.
• Small, non-representative ticker universe. Currently evaluated on a handful of large, well-known public companies — not a broad or industry-representative sample.
• Not financial, HR, or legal advice. This is a research/portfolio project. Scores should not be used to make real decisions about any company or its employees.

Intended use

Educational/portfolio demonstration of a multi-modal MLOps pipeline (entity resolution, temporal feature alignment, drift monitoring, and closed-loop prediction-vs-actual evaluation). See the linked GitHub repo for the full architecture, Airflow DAGs, and evaluation loop.