A Proposed Framework for Venture Capital Investment Analysis Using Cox Proportional Hazards Model and SHAP-Based Explainability

This paper has proposed a conceptual framework for venture capital investment analysis that integrates the Cox Proportional Hazards model and SHAP-based explainability as two complementary analytical tools. The Cox model addresses the temporal dimension of startup risk, providing survival probability estimates that capture the dynamic, time-dependent nature of company failure in ways that static classification models cannot. SHAP addresses the interpretability dimension of investment AI, providing principled, game-theoretically grounded attributions that make the predictions of opaque ensemble classifiers transparent, auditable, and actionable for investment decision makers. The two techniques are argued to be complementary rather than redundant: the Cox model answers how long a company is likely to survive, while SHAP answers why a company is predicted to succeed or fail. Together, they produce a more complete investment risk summary — survival trajectory and explained acquisition score — that is more informative for investment practice than either technique provides in isolation. The proposed framework addresses two critical gaps in existing quantitative venture capital analysis: the absence of temporal modeling in most predictive pipelines, and the opacity of high-performing machine learning models that limits their adoption in professional investment workflows. By combining statistical rigor, predictive performance, and principled interpretability in a single integrated pipeline, the framework offers a practical pathway toward data-driven investment analysis that meets the transparency and accountability standards of modern investment governance. Future work should focus on empirical validation of the framework on real-world startup datasets, investigation of conditional SHAP estimation for correlated financial features, integration of time-varying covariates into the Cox model component, and development of a user-facing decision support interface that makes the framework accessible to investment professionals without statistical expertise.

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