The fairness–efficiency frontier in data-driven VC: Alternative data for inclusive, high-return portfolios

Predictive models identify promising startups (Potanin et al., 2023; Ferrati & Muffatto, 2021), but they’re rarely evaluated for equity alongside returns. This project adds features shown to reduce information asymmetry: founder and startup social media engagement (Wang et al., 2023), alternative data in low-documentation contexts (mobile transactions, geospatial activity; Nwangele et al., 2024), and non-financial monitoring signals (Bratfisch et al., 2023). We train multi-objective models that map a Pareto frontier between capital growth and inclusion (e.g., share of women or low-network founders), then backtest using rigorous portfolio simulation akin to Potanin et al. We also probe sectoral heterogeneity (Singh et al., 2024) to see where fairness constraints have the smallest efficiency costs. The novelty is shifting from single-objective prediction to portfolio design under explicit fairness constraints—grounded in alternative data that capture “hidden traction.” If successful, this provides LPs and GPs with transparent trade-offs, potentially revealing regimes where inclusion and performance are complements rather than substitutes.

References:

1. Social Media Alleviates Venture Capital Funding Inequality for Women and Less Connected Entrepreneurs. Xiaoning Wang, Lynn Wu, L. Hitt (2023). Management Sciences.
2. Impact of Venture Capital on Startup Success Rates Across Indutry: An Emperical Study. Dr. Dharmendra Singh, Dibyansh Rai, Dr. Anil Kumar Yadav (2024). European Economic Letters.
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