Cost-Sensitive Ensemble Learning for Venture Capital Exit Prediction

This study presents a methodologically robust framework aimed at refining the precision of Venture Capital (VC) exit prediction. Focused on addressing the inherent data imbalance in VC exit forecasting, our approach strategically integrates both data-level and algorithm-level methodologies. Data-level methods encompass data sampling and cost-sensitive learning to mitigate class imbalance, while algorithm-level techniques employ Stacking and Cascading strategies to alleviate bias towards the majority class. The research specifically aims to forecast VC exits within the Chinese landscape through Initial Public Offering (IPO) or Merger and Acquisition (M&A) events spanning the years 2014 to 2017, with a forecasting horizon extending into the subsequent four years. Our findings highlight that an optimal algorithmic fusion of cost-sensitive learning with a Stacking Classifier outperforms traditional models such as Logistic Regression, Extra Tree, XGBoost, and Random Forest. This optimized approach achieves a superior recall rate of 81.9% coupled with an F2-score of 80.8%. Utilizing SHAP (Shapley Additive Explanations) analysis, we delve into the significance of explanatory features, elucidating the key factors influencing VC exit determinations. This study underscores the efficacy of employing cost-sensitive learning methods and resampling techniques in conjunction with ensemble learning to enhance the accuracy of VC exit forecasting. The insights provided serve as valuable references for practitioners involved in the management of VC projects, emphasizing the importance of robust methodologies in predictive modeling.