南橘北枳:A股市场的经济关联与股票回报

本文系统检验并比较了中国A股市场中行业动量、区域动量、供应链动量以及科技关联动量等经济关联动量的显著性及预测周期。本文发现,中国股票市场中经济关联因子呈现出与美国股票市场不同的规律,在月度层面行业动量显著,而科技关联因子只在周度上具有显著的预测能力。进一步分析科技关联动量发现,中国股票市场中科技关联因子能预测目标公司未来1-3周的股票收益和未来基本面的变化,据此构建的多空策略能够产生周度0.16%的超额收益(年化8.67%);机制检验发现,科技关联因子预测期短的原因是由于中国股票市场中存在较多具有博彩倾向的散户投资者;有限注意和市场摩擦两个机制检验证明科技关联动量源自错误定价。进一步检验发现,科技关联动量在国有企业和创新政策颁布后更加显著。本文补充了现有A股市场的动量研究,有助于理解中国股票市场规律、提升资本市场有效性。     

我国公司法定类型的反思及逻辑建构

自公司诞生以来,公司这一组织形式就受到各国商业实践和立法的特别重视。各国商事立法均对公司类型加以规定,并分门别类地对公司设立、股东权利、公司治理等进行相应规范。虽然商事逻辑的共通性拉近了各国立法上的公司类型,但各国在公司法定类型及相应公司规制上的差异仍旧鲜明。这也反映在我国学界对公司法定类型的争议上。我国公司法定类型改革应坚持本土商业实践逻辑,并确保改革成本最小化。由此,保留有限责任公司和股份有限公司,并按公司规模大小进一步区分出大型公司与小型公司,就成为理性选择的结果。具体而言,在有限责任公司中,增设大型公司,明确大型公司认定标准,并在公司机关设置、财务会计制度等方面给予相对严格的例外规定;在股份有限公司中,增设小型公司,对其赋予更加灵活、便捷的自治,并将上市公司扩增为公众公司。     

股票流动性与中国企业创新策略:流水不腐还是洪水猛兽？

中国情境下股票流动性对企业创新的影响是激励机制还是压力机制占主导地位？本文基于上市公司分类专利的申请、授权、终止数据研究发现:一方面,股票流动性使企业发明专利申请显著增加,但能通过实质审查的授权增长极少,说明申请质量明显下滑;另一方面,股票流动性使创新含量较低的实用新型与外观设计授权显著增加,且这些专利拖累了企业盈利表现,法律效力提前终止的数量也明显更多,揭示企业实际上是以“策略性创新”来应对资本市场压力,加剧了“专利泡沫”问题。分样本检验发现,“重数量轻质量”的创新策略集中体现于民营、传统行业及长期机构投资者持股较少的企业。稳健性检验替换关键指标构造和模型估计方法、构造工具变量克服潜在内生性问题,前述结论仍然成立。本文启示,金融制度设计应防范资本市场压力对企业创新的“意外伤害”,更好地实现“以金融助实体、以改革促发展”的目标。     

World equity markets and COVID-19: Immediate response and recovery prospects

Following the spread of the COVID-19 pandemic, most global equity market indices experienced significant falls. Recognizing the severe economic impacts of the pandemic, starting from mid-March, many governments announced unprecedented economic rescue packages, which appear to restore investors’ confidence, given the recoveries recorded in most stock markets. However, the recovery performance significantly varies across countries. This paper provides an empirical analysis on what may explain this variation in the recovery performance observed in equity markets across countries. We find that among different types, fiscal stimulus supports seem to be strongly and positively associated with higher recovery that may justify more targeted fiscal supports for the real sector firms to restore investors’ confidence. We also find that the severity of the outbreak, reliance more on natural resource and tourism revenues are negatively associated with countries’ stock market recovery performance.     

Nonlinear tail dependence in cryptocurrency-stock market returns: The role of Bitcoin futures

We investigate the median and tail dependence between cryptocurrency and stock market returns of BRICS and Developed countries using a newly developed nonparametric cumulative measure of dependence over the period January 4, 2016 – December 31, 2019 as well as before and after the introduction of Bitcoin futures on December 17, 2017. The new measure is model-free and permits measuring tail risk. The results highlight the leading role of S&P500, Nasdaq and DAX 30 in predicting BRICS and developed countries’ stock market returns. Among BRICS countries, BVSP shows a starring role in predicting stock market returns. BSE 30 is the most predictor of cryptocurrencies, which have a little predictability on stock market returns. Ethereum has the leading role in predicting cryptocurrencies and stock market returns followed by Bitcoin. Tail dependence shows substantial role of S&P500, Nasdaq and BVSP in predicting stock market returns. Subsample analysis show the role of Bitcoin futures in reshaping the mean and tail dependence between cryptocurrency and stock market returns. Our results have important policy implications for portfolio managers, hedge funds and investors.     

The impact of COVID-19 on the stock market crash risk in China

This study investigates the impact of the COVID-19 pandemic on the stock market crash risk in China. For this purpose, we first estimated the conditional skewness of the return distribution from a GARCH with skewness (GARCH-S) model as the proxy for the equity market crash risk of the Shanghai Stock Exchange. We then constructed a fear index for COVID-19 using data from the Baidu Index. Based on the findings, conditional skewness reacts negatively to daily growth in total confirmed cases, indicating that the pandemic increases stock market crash risk. Moreover, the fear sentiment exacerbates such risk, especially with regard to the impact of COVID-19. In other words, when the fear sentiment is high, the stock market crash risk is more strongly affected by the pandemic. Our evidence is robust for the number of daily deaths and global cases.     

Does corporate eco-innovation affect stock price crash risk?

We examine the effect of corporate environmental innovation (hereafter eco-innovation) on stock price crash risk and document a significant negative association. Utilising a large sample of publicly listed U.S. firms for the period 2003 to 2017, we find that an increase in eco-innovation from the 25th to the 75th percentile is associated with 17.62% reduction in stock price crash risk. This outcome remains robust to a variety of sensitivity tests and after accounting for potential endogeneity concerns. Eco-innovative firms attract more institutional investors and equity analyst following and disclose more information leading to lower stock price crash risk. Additional tests reveal that the negative effect of eco-innovation is contingent on the political leadership's ideology and environmental sensitivity. Our paper contributes to the ongoing discourse on the costs and benefits of eco-innovation, documenting the value-enhancing perspective of eco-innovation.     

Can Warren Buffett forecast equity market corrections?

Warren Buffett suggested that the ratio of the market value of all publicly traded stocks to the Gross National Product could identify potential overvaluations and undervaluations in the US equity market when this ratio deviates above 120% or below 80%. We investigate whether this ratio is a statistically significant predictor of equity market corrections and rallies. We find that Buffett's decision rule does not deliver satisfactory forecasts. However, when we adopt a time-varying decision rule, the ratio becomes a statistically significant predictor of equity market corrections. The two time-varying decision rules are: (i) predict an equity market correction when the ratio exceeds a 95% one-tail confidence interval based on a normal distribution, and (ii) predict an equity market correction when the ratio exceeds a threshold computed using Cantelli's inequality. These new decision rules are robust to changes in the two key parameters: the confidence level and the forecasting horizon. This paper also shows that the MV/GNP ratio performs relatively well against the four most popular equity market correction models, but the ratio is not a particularly useful predictor of equity market rallies.     

Which institutional investor types are the most informed?

We examine the informativeness of quarterly disclosed portfolio holdings across four institutional investor types: hedge funds, mutual funds, pension funds and private banking firms. Overweight positions outperform underweight positions only for hedge funds. By decomposing holdings and stock returns, we find that hedge funds are superior to other institutional investors both at picking industries and stocks and that they are better at forecasting long‐term as well as short‐term returns. Furthermore, our results show that hedge funds, mutual funds and pension funds are able to successfully time the market. The outperformance of hedge funds is not explained by a liquidity premium.     

异质信念对股价波动的非对称性影响研究

本文利用上市公司的相关数据,实证研究了异质信念对我国股票市场股价波动的 影响,探讨了在不同的市场行情和企业性质下异质信念对股价波动的非对称性影响,并对融资 融券推出前后异质信念对股价波动的非对称性影响进行了研究。研究结果表明：（1）股票投 资者异质信念的差异程度越大,股价偏离其股票的内在价值就越大,进而导致股价的波动程度 更剧烈;（2）异质信念对股价波动的影响呈非对称性,在熊市行情下异质信念对股价波动的 影响要高于牛市,在国有企业背景下异质信念对股价的波动程度大于非国有企业;（3）融资 融券推出后在一定程度上可以减缓异质信念对股价波动的影响。