用可解释模型预测初创企业融资、专利和退出，效果准确且透明。
Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits
- 构建2010-2023年企业季度面板数据，结合Crunchbase与美国专利商标局信息
- 融资、专利增长、退出预测的AUROC分别达0.921、0.817、0.872
- 采用SMOTE-NC处理不平衡数据，结果可解释且适合创新金融决策
本研究构建了一个可解释的机器学习框架，用于预测初创企业的融资、专利活动及退出行为。基于Crunchbase与美国专利商标局（USPTO）数据，建立2010-2023年企业季度面板。评估三个时间窗口：未来12个月内获得下一轮融资、未来24个月内专利存量增长、未来36个月内通过IPO或并购退出。训练集（2010-2019）的预处理流程固定应用于后续周期以避免信息泄露。针对类别不平衡问题，使用逆频率权重与适用于名义与连续特征的合成少数类过采样技术（SMOTE-NC）。对比逻辑回归与树集成方法（随机森林、XGBoost、LightGBM、CatBoost），以精确率-召回率曲线下面积（PR-AUC）和受试者工作特征曲线下面积（AUROC）为评估指标。专利、融资与退出预测的AUROC分别为0.921、0.817和0.872，实现高精度且透明可复现的创新金融排名。
原文摘要 · Abstract (English)
This study develops an interpretable machine learning framework to forecast startup outcomes, including funding, patenting, and exit. A firm-quarter panel for 2010-2023 is constructed from Crunchbase and matched to U.S. Patent and Trademark Office (USPTO) data. Three horizons are evaluated: next funding within 12 months, patent-stock growth within 24 months, and exit through an initial public offering (IPO) or acquisition within 36 months. Preprocessing is fit on a development window (2010-2019) and applied without change to later cohorts to avoid leakage. Class imbalance is addressed using inverse-prevalence weights and the Synthetic Minority Oversampling Technique for Nominal and Continuous features (SMOTE-NC). Logistic regression and tree ensembles, including Random Forest, XGBoost, LightGBM, and CatBoost, are compared using the area under the precision-recall curve (PR-AUC) and the area under the receiver operating characteristic curve (AUROC). Patent, funding, and exit predictions achieve AUROC values of 0.921, 0.817, and 0.872, providing transparent and reproducible rankings for innovation finance.
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