Does the Pilot of Registration System on the ChiNext Reduce the Cost of Equity Capital?: A Double Machine Learning Model

Abstract

Abstract: Studying the spillover effects of the registration-based IPO system helps optimize the capital market’s resource allocation function. Based on data from ChiNext and main board listed companies from 2016 to 2022, this study uses a double machine learning model to explore the spillover effects of the registration-based IPO reform on ChiNext. The findings reveal that the reform has significantly reduced the cost of equity capital for ChiNext-listed companies, with the spillover effect mainly driven by improvements in stock liquidity and information disclosure quality. Moreover, the impact of the registration-based IPO reform on reducing the cost of equity capital is more pronounced for companies with low analyst coverage, low audit quality and high financing constraints. This paper enriches the relevant research on the spillover effects of the registration-based IPO system and has important practical implications for comprehensively deepening the capital market reform.

Key words: the registration-based IPO system, ChiNext, double machine learning, the cost of equity capital, spillover effect

Hu Lei, He Chongyang, Zhu Pingheng. Does the Pilot of Registration System on the ChiNext Reduce the Cost of Equity Capital?: A Double Machine Learning Model[J]. Management Review, 2026, 38(4): 206-217.

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