Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits

Battle Count: 78

The paper is primarily focused on startup finance, venture capital screening, and innovation prediction rather than traditional quantitative trading. However, the interpretable ML pipeline methodology, leakage-safe temporal splits, class imbalance handling, and SHAP-based diagnostics are transferable to quantitative trading contexts (e.g., predicting stock exits, IPO timing, or sector rotation signals). The ranked target lists and calibration analysis have parallels in trading signal generation. The connection is indirect but methodologically relevant.

The pipeline involves multiple stages: data merging (Crunchbase-USPTO), quarterly panel construction, leakage-safe preprocessing, five model families with two imbalance variants each, SHAP analysis, calibration diagnostics, and out-of-time scoring. While individual components use standard Python libraries (scikit-learn, XGBoost, LightGBM, CatBoost, SHAP), the orchestration of temporal splits, persisted feature lists, and the full model zoo requires careful engineering. The complexity is moderate-to-high due to the breadth of the pipeline rather than any single algorithmic challenge.

The paper emphasizes exact reproducibility: all steps scripted in Python, deterministic seeds, persisted feature lists for column alignment, fixed filenames and manifests, development-only preprocessing applied unchanged to later splits, and non-overlapping time splits to prevent leakage. The pipeline design (Boxes 1-5) is fully documented with explicit leakage controls.