Leakage-Aware Time-Based Top-K Start-Up Ranking for Venture Capital Investment Success Under Severe Class Imbalance Conditions: A Screening Evaluation Framework

Many real-world screening tasks in venture capital must rank large start-up candidate pools under conditions of tight review capacity, time-varying information, and rare investment success outcomes. When datasets are constructed retrospectively, post-decision updates can leak into features and inflate performance, especially with random splits. This study proposes a leakage-aware, time-based evaluation framework for capacity-constrained screening formulated as a top-K ranking problem. Using a dataset of 117,141 early-stage firms as an empirical testbed, features were constructed strictly as of a reference time (Formula presented.), a 180-day temporal embargo was enforced around the train–test boundary, and generalization was assessed with time-ordered splits. Because venture capital decisions are made on a shortlist, evaluation emphasizes ranking quality using PR-AUC, Lift@K, Precision@K/Recall@K, and NDCG@K, reported with bootstrap confidence intervals. Under this leakage-aware protocol and with strong class imbalance, maturity-related signals achieve the strongest PR-AUC (0.0144), while team and combined signals yield the best top-50 shortlist concentration. Finally, probability calibration substantially improves reliability for threshold planning (Brier score reduced from 0.0972 to 0.0161 with sigmoid calibration) while leaving ranking essentially unchanged. Overall, the study provides a leakage-aware evaluation template and an interpretable baseline for time-dependent venture capital screening tasks involving start-up selection, investment success prediction, leakage risk, and limited review capacity.