Algorithmic Deal-Sourcing and Predictive Valuation:Evaluating the Efficacy of Machine Learning Models inMitigating Investment Risk and Identifying High-GrowthEarly-Stage Startups within the U.S. Venture CapitalEcosystem

• Abbas Ahsun

  Texas University

  Author

The U.S. venture capital (VC) ecosystem faces persistent challenges in early-stage startup
selection, characterized by information asymmetry, high failure rates exceeding 90% for seedstage
ventures, and reliance on subjective heuristics that produce variable investment outcomes.
While machine learning (ML) has demonstrated promise in financial analytics, existing
approaches treat success classification and valuation prediction as independent tasks, failing to
exploit their inherent correlations while ignoring the substantial uncertainty present in earlystage
company assessment . This study addresses this gap by developing and validating an
integrated ML framework for algorithmic deal-sourcing and predictive valuation within the U.S.
VC ecosystem. Employing a quantitative, design-based research methodology, the study
analyzes a comprehensive Crunchbase dataset comprising 623,232 companies, 799,446 founders,
and 227,172 funding events spanning 2014–2024 . The proposed multi-task learning framework
jointly predicts startup success probability and valuation through shared representations,
incorporating uncertainty quantification via evidential deep learning. Empirical evaluation
demonstrates that the model achieves 89.4% accuracy in success prediction, a 4.4% relative
improvement in AUC (0.924), and a 14.5% reduction in Mean Absolute Error for valuation
compared to state-of-the-art baselines . Feature importance analysis identifies funding levels,
syndication breadth, executive team size, and product differentiation as the strongest success
predictors. The framework's uncertainty-aware design enables routing of ambiguous cases to
human experts, enhancing decision-making reliability. This research contributes a replicable,
transparent, and risk-aware decision-support framework for VC practitioners, with implications
for improved capital allocation, reduced cognitive bias, and more inclusive founder evaluation.

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Copyright (c) 2026 Abbas Ahsun (Author)

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