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How Do Government Guidance Funds Cultivate “Patient Capital”? — An Empirical Study on Asymmetric Game Based on Investment Leverage and Exit Mechanism of Sci-Tech Innovation Enterprises
Journal article   Open access

How Do Government Guidance Funds Cultivate “Patient Capital”? — An Empirical Study on Asymmetric Game Based on Investment Leverage and Exit Mechanism of Sci-Tech Innovation Enterprises

Jingdong Huang, Xinyi Zhang and Hao Jin
Finance and Trade Dynamics, Vol.2(1), pp.1-18
2026
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Published VersionCC BY V4.0 Open Access

Abstract

Government Guidance Funds (GGFs) patient capital sci-tech innovation enterprises investment leverage exit mechanism risk-sharing signal certification
Against the macro backdrop of accelerating the cultivation of “new quality productive forces”, sci-tech innovation enterprises face a “valley of death” dilemma due to the term mismatch between their long-cycle, high-risk characteristics and social capital’s short-term profit-seeking preference. This paper explores how Government Guidance Funds (GGFs) transform short-term capital into “patient capital” through risk-sharing and certification effects. Using a sample of sci-tech enterprises receiving VC/PE investments from 2010 to 2024 (12,846 valid samples) and data from CVSource, Zero2IPO, and CSMAR databases, empirical analysis is conducted via Staggered DID, PSM-DID, and IV methods. The findings are: (1) GGF participation significantly increases social capital financing scale (financial leverage) and extends holding periods (time leverage), with stronger effects on early-round projects; (2) Mechanism tests show that “implicit guarantee” risk-sharing and “signal sending” certification are core channels; (3) The exit mechanism has a non-linear moderating effect, as active S-fund markets and smooth M&A channels enhance GGF effects by reducing liquidity anxiety. Marginal contributions include integrating mixed oligopoly models with signal transmission theory and using “holding period” as a direct measure of patient capital. Policy recommendations include optimizing GGF assessment, cultivating multi-level exit ecosystems, and implementing classified supervision.

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