我国科技企业融资的决定因素研究——基于科创板企业的机器学习分析  

Determinants of Financing for High-tech Enterprises in China:Machine Learning Analysis Based on the STAR Market

作　　者：陆瑶[1] 施函青 LU Yao;SHI Hanqing(School of Economics and Management,Tsinghua University)

机构地区：[1]清华大学经济管理学院,北京100084

出　　处：《金融研究》2022年第9期132-151,共20页Journal of Financial Research

基　　金：国家自然科学基金(71722001);清华大学中国现代国企研究院项目(iSOEYB202211);清华大学自主科研计划文科专项项目(2021THZWYY09)资助。

摘　　要：本文以手工收集的2019年7月至2020年12月所有申报科创板上市的企业为研究对象,研究了企业层面五大维度数十个因素对我国科技企业融资的预测效果,通过传统的OLS和机器学习降维排序方法,为科创板增量改革的实践效果提供了直接证据。研究发现:企业能否在科创板上市方面,企业研发水平、成长性和公司治理水平这三类反映企业未来发展潜力的特征占据主导作用,且研发水平最为关键,盈利的重要性最低;在众多研发变量中,企业研发人员人数占总人数比最为重要;企业能否在科创板上市和上市后二级市场表现的变量重要性排序各不相同,甚至相反;公司治理方面,国有股份占比的重要性强于其他治理机制。结合研究结论,本文从重视企业未来可持续发展、构建科研人才队伍培育和激励机制以及统筹考虑上市后的市场表现等角度,为科创企业发展提供了政策建议。China’s economy has entered a new era,transforming from high-speed growth to high-quality growth.In this new era,scientific and technological innovations are a matter not only of development but also of survival.The construction of an effective multi-level capital market to enable an innovative and developed real economy is currently an important topic.The establishment of the STAR board and a pilot registration system for high-quality capital market transformation and reforms are warranted to integrate science and technology with the capital market.The study sample includes all of the enterprises that applied for listing on China’s STAR Market from July 2019 to December 2020.The financial data of the samples that went public successfully are obtained from the CSMAR and Wind databases,and the data of the samples that failed to go public are manually collected from their preliminary prospectuses.We construct a high-dimensional enterprise characteristic research index with dozens of factors related to enterprise research and development(R&D),corporate governance,growth,profitability,and risk levels.Based on global stakeholders’perspectives,we explore all of these factors’predictive effects on the listing performance of technology enterprises(e.g.,whether to be listed,duration from declaration to listing,amount of funds raised through the listing)and their market performance after listing(stock return and liquidity 3 months after listing and return on assets 1 year after listing).This paper provides comprehensive and direct empirical evidence regarding the practical effects of the incremental reforms of the Science and Technology Innovation Board obtained using the traditional OLS approach and a machine learning dimensionality reduction algorithm.We use the Boosting regression tree model.The basic idea of this model is that we first set a regression tree from the initial training and then train a new basis regression tree to achieve a loss function that gradually decreases with an increase in iterations.Fina

关 键 词：技术创新 科创板 IPO 机器学习 

分 类 号：F832.51[经济管理—金融学] F276.44F275