Predicting venture capital exit strategies through explainable machine learning: governance, decision intelligence and organisational implications

Purpose This study examines venture capital (VC) exit prediction as a multi-class classification problem by integrating venture finance theory with explainable machine learning techniques. This study aims to identify the organisational, governance and timing-related drivers underlying heterogeneous VC exit outcomes across IPOs, mergers and acquisitions, secondary sales, reverse takeovers, buybacks and write-offs. Design/methodology/approach Using a data set of 17,042 global VC exit transactions extracted from 2010 to 2025, this study implements eight supervised machine learning models spanning linear, probabilistic, non-parametric and ensemble-learning approaches. Model performance is evaluated using macro-averaged precision, recall, F1-score, ROC-AUC, confusion matrices and stratified cross-validation. Explainable AI techniques are further used to interpret feature importance and link predictive outcomes to theory-guided mechanisms. Findings Ensemble tree-based models, particularly random forest and gradient boosting, consistently outperform linear and probabilistic alternatives in predictive accuracy and robustness. The findings indicate that VC exits are shaped by identifiable governance and timing mechanisms rather than isolated financial indicators alone. Variables such as exit timing, acquirer identity, equity structure, purchase price and sectoral affiliation emerge as key predictors of heterogeneous exit outcomes. Sector-specific patterns further reveal that pharmaceutical ventures exhibit stronger IPO orientation, whereas technology-oriented firms are more likely to exit through mergers and acquisitions. Research limitations/implications This study, though methodologically innovative, has certain limitations. It relies on secondary deal-level data that may not fully capture qualitative dimensions such as founder intent or investor–entrepreneur dynamics. While eight supervised machine learning models were used, deep learning or hybrid ensemble techniques could further enhance accuracy, particularly for rare exits. The cross-sectional design constrains temporal generalisation across economic cycles. Moreover, although explainable AI improves interpretability, it does not establish causality. Future studies could integrate behavioural and institutional variables, adopt longitudinal designs and explore hybrid analytical frameworks to deepen understanding of VC exit mechanisms. Practical implications Findings provide actionable insights for venture capitalists, limited partners and policymakers to improve deal structuring, liquidity planning and exit governance. Predictive analytics can enhance decision-making transparency, risk mitigation and capital recycling efficiency. Social implications This study holds strong social relevance for entrepreneurial ecosystems and inclusive financial development. By improving the predictability of VC exits, it enhances transparency, reduces information asymmetry and fosters trust among investors, entrepreneurs and regulators. Predictive analytics can help emerging markets address exit bottlenecks, enabling faster capital recycling and broader innovation diffusion. Explainable AI strengthens governance accountability and data-driven decision-making, ensuring responsible deployment of financial technologies. Importantly, these insights can support equitable access to funding and liquidity opportunities, empowering diverse entrepreneurial participants and promoting sustainable market development aligned with long-term innovation and social inclusion goals. Originality/value To the best of the author’s knowledge, this study is among the first to reframe VC exit prediction as a multi-class classification problem using explainable machine learning. By integrating eight supervised algorithms with canonical theories of agency, real options and signalling, it bridges predictive analytics and entrepreneurial finance. The approach advances methodological rigour beyond traditional econometric models and enhances interpretability through explainable AI. The findings not only uncover the key organisational and transactional drivers of exit outcomes but also provide actionable intelligence for fund managers, limited partners and policymakers. Thus, the study contributes both theoretical innovation and practical value to data-driven decision-making in VC ecosystems.