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Mutual Funds as Venture Capitalists? Evidence from Unicorns

Review of Financial Studies 2021 34(5), 2362-2410
“Founder-friendly” venture financings and nontraditional venture investors have both flourished over the past decade. Using detailed contract data, we study open-end mutual funds investing in private venture-backed firms. We posit that conflicts between early-stage venture investors and liquidity-constrained later-stage ones influence the classic agency problems affecting entrepreneurs and investors. We find that mutual funds with more stable funding are more likely to invest in private firms and that financing rounds with mutual fund participation have stronger redemption, stronger IPO-related rights, and less board representation. These findings are consistent with our conceptual framework.

Intellectual Property Rights Protection, Ownership, and Innovation: Evidence from China

Review of Financial Studies 2017 30(7), 2446-2477
Using a difference-in-differences approach, we study how intellectual property right (IPR) protection affects innovation in China in the years around the privatizations of state-owned enterprises (SOEs). Innovation increases after SOE privatizations, and this increase is larger in cities with strong IPR protection. Our results support theoretical arguments that IPR protection strengthens firms’ incentives to innovate and that private sector firms are more sensitive to IPR protection than SOEs. Received June 17, 2015; editorial decision November 23, 2016 by Editor Andrew Karolyi.

Missing Financial Data

Review of Financial Studies 2025 38(3), 803-882
We document the widespread nature and structure of missing observations of firm fundamentals and show how to systematically handle them. Missing financial data affects more than 70% of firms that represent about half of the total market cap. Firm fundamentals have complex systematic missing patterns, invalidating traditional approaches to imputation. We propose a novel imputation method to obtain a fully observed panel of firm fundamentals that exploits both time-series and cross-sectional dependency of data to impute missing values and allows for general systematic patterns of missingness. We document important implications for risk premiums estimates, cross-sectional anomalies, and portfolio construction.