Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction

ACL ARR 2026 January Submission9886 Authors

Keywords: Venture Capital Investment, Large Language Model, Fintech, Financial Intelligence

Abstract:

Most venture capital (VC) investments fail, while a few deliver outsized returns. Predicting startup success requires synthesizing relational evidence across company fundamentals, investor track records, and investment networks through explicit reasoning, which traditional machine learning and graph neural networks lack. Large language models excel at reasoning, but applying them to VC prediction must address: selecting compact evidence subgraphs from large investment networks, one-sided label noise where failures may be latent
successes, and grounding decisions in structured VC domain knowledge. We present MIRAGE-VC, an evidence-grounded reasoning framework with three innovations. First, an information-gain-driven retriever distills networks
into compact evidence subgraphs. Second, a dual-layer knowledge base grounds reasoning in VC principles. Third, a noise-aware mechanism down-weights mislabeled negatives via improved Positive-Unlabeled (PU) estimation.
MIRAGE-VC achieves +5.9\% F1 and +22.1\% Precision@5 over state-of-the-art baselines. Expert evaluation confirms professional-quality rationales.
We further validate our approach on public data with consistent improvements.
Code and reasoning results available.\footnote{\url{https://anonymous.4open.science/r/MIRAGE-VC-323F}}

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: financial/business NLP, Financial NLP

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 9886