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本文探讨了2020年8月创业板市场进行的三项改革(放宽涨跌幅限制、设置“价格笼子”、引入盘后定价交易)对股票定价效率的影响。通过使用2018～2022年A股上市公司季度数据,结合双重纠偏机器学习法与双重、三重差分模型提供可信的因果证据。实证结果表明,放宽盘中涨跌幅限制显著降低了定价效率;“价格笼子”对定价效率影响不显著;盘后定价交易显著提高了定价效率。然而,三大机制的净影响为负。本文分析进一步揭示,改革后创业板公司信息需求和信息供给均显著增加,信息披露也更为及时,排除了信息供需恶化导致定价效率下降的可能。本文对完善基础交易制度以活跃资本市场、强化价格信号的资源配置功能,走好中国特色金融发展之路具有启示意义。
Abstract：This paper studies the impact of three trading mechanism reforms implemented in the GEM market in August 2020(relaxation of the limit on stock price fluctuations,introduction of the "price cage" and after-hours trading) on stock pricing efficiency.The paper provides credible causal evidence by using quarterly data of A-share listed companies from 2018 to 2022,and combining the double debiased machine learning method with DID and DDD models.The empirical results show that the relaxation of intraday price limit significantly reduces the pricing efficiency,while the "price cage" has no significant impact on the pricing efficiency,while the after-hours fixed-price transaction significantly improves the pricing efficiency.The three mechanisms have negative net effects.Based on textual analysis,it is revealed that after the reform,the information demand and supply of GEM listed companies increased significantly and disclosure is timelier,ruling out the possibility of the deterioration of information environment leads to the decline of pricing efficiency.These findings have implications for the improvement of basic transaction system to activate the capital market,strengthen the resource allocation function of price signals,and also helps China to better take the path of financial development with Chinese characteristics.
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(1)在2023年2月后,创业板对超出报价范围的订单由“缓存”机制改革为“拒单”机制。
(1)注意Frequency_i不具有下标t,是一个股票层面上不随时间变化的前定变量,外生于交易制度冲击。非流动性指标Illiquidity_i同理。这两个变量均被企业固定效应所吸收,不在模型中单独出现。
(1)注意盘后交易强度是事后变量,与基于事前指标形成的外生交互项Treat×Post×Frequency、Treat×Post×Illiquidity不同,但本文控制了企业固定效应和时间固定效应,Afterhours的系数反映的是“组内”(within-group)效应——对于一只平均意义的股票,假如它属于处理组的改革后期间,其盘后交易强度的一单位变化能多高程度地影响自身的定价效率。
(1)科创板股票并非合适的控制组,因为:自2019年7月成立起,科创板股票每交易日涨跌幅已是20%(新股上市后的前5个交易日不设涨跌幅限制),其“价格笼子”对超出报价范围的订单执行“拒单”。
(1)为了更直观地观测处理组与控制组Idiosyn的变化趋势,本文绘制了Idiosyn2做被解释变量时的平行趋势检验图(Idiosyn1效果一致),由于篇幅原因并未放入文章,可联系作者。
(2)我们进行了DID、DDD模型的OLS回归,发现:无论是以Idiosyn1还是以Idiosyn2为被解释变量,OLS回归均显示三个机制的影响为正(其中,放宽涨跌幅限制与盘后交易显著,“价格笼子”不显著)。假如这些结果真实可信,则净影响应显著为正,但Treat×Post均为负,这样的矛盾结果说明OLS不适用于机制检验。由于篇幅原因,回归结果并未放入文章中。
(3)此外,堆叠结果显示,OLS在所有堆叠模型的权重都接近于0,而神经网络算法在堆叠算法中的权重最大,故选择神经网络算法作为基准学习器。
基本信息:
中图分类号:F832.51
引用信息:
[1]林志帆,纪泽宇.交易制度改革对股票定价效率的影响研究——来自创业板准自然实验的证据[J].金融学季刊,2024,18(04):52-75.
基金信息:
国家自然科学基金青年项目(72303021); 教育部人文社会科学研究青年基金项目(22YJCZH102); 广东省高校科研平台特色创新项目(2023WTSCX185)的资助