This paper shows that the empirical distribution of cross-sectional analyst coverage in China's stock markets follows an exponential law in a given month from 2011 to 2020. The findings hold in both the emerging (Shanghai) and the developed market (Hong Kong). Moreover, the unique distribution parameter (i.e., mean) is directly related to the amount of market-wide information. Average analyst coverage exhibits a significant negative predictive power for stock-market uncertainty, highlighting the role of security analysts in diminishing the total uncertainty. The exponential law can be derived from the maximum entropy principle (MEP). When analysts, who are constrained by average ability in generating information (i.e., the first-order moment), strive to maximize the amount of market-wide information, this objective yields the exponential distribution. Contrary to the conventional wisdom that security analysts specialize in the generation of firm-specific information, empirical findings suggest that analysts primarily produce market-wide information for 25 countries. Nevertheless, it remains unclear why cross-sectional analyst coverage reflects market-wide information, this paper provides an entropy-based explanation.

投资者情绪与股票价格波动是行为金融领域的前沿问题.文章选取A股上市公司2011—2020年交易数据,构造一个全新的日频情绪指标,并基于该指标研究短期内投资者情绪的累积变化对股票收益的影响.结果表明:投资者情绪具有累积效应,该效应最初带来显著的正收益,但会在投资者情绪累积到一定水平后,引致较大的价格修正,这对难以套利的股票影响更为明显;高、低情绪的累积效应具有显著的非对称性,在持续的高情绪累积下,市场中的资金会涌入波动水平低、流动性好的资产;而在持续的低情绪累积下,资金会选择投机性更强的资产.

We establish a model in which speculators use feedback trading characteristics to infer the behavior of irrational investors and induce them to trade. We also discuss the stability and time series of asset prices. Our results show that: (1) speculators have speculation and arbitrage demands and make "noise" to induce irrational investors to trade, (2) the time series of asset prices show stable momentum and a reversal effect when fundamental traders dominate the market, and (3) momentums are unstable and perform poorly under extreme circumstances. Our article offers a unique approach to understanding the micro mechanism of different momentum effects in various markets and suggests a plausible theoretical framework to illustrate such differences.

This article constructs a behavioral financial model that provides feedback on both historical prices and company fundamentals without considering asset liquidation to discuss the long-term impact of investor sentiment and feedback trading on asset price fluctuations. The research conclusion shows that the abnormal volatility of asset prices is captured by the value effect, the cognitive bias effect, the sentiment shock effect, and the trading inductive effect. The value effect is the volatility of asset prices that is completely determined by fundamental factors; the higher the degree of cyclical fluctuations in fundamental factors, the higher the volatility of prices. The bias effect refers to investors' misreading of basic information and trends in asset prices; the greater the instability of emotional shocks, the greater the abnormal volatility of asset prices. The trading inductive effect is also called the Keynes effect, which reflects the role played by rational traders.

This paper investigates whether sell-side analyst attention effect extends to the aggregate stock market. Our findings demonstrate that: (i) aggregate analyst coverage negatively predict future stock market returns while controlling for a rich set of predictors related to economic fundamentals and investor sentiment. (ii) higher aggregate analyst coverage also precede lower market-wide cash-flow volatility, cash holdings, absolute magnitude of unexpected earnings, investor information-search demand, and greater capital expenditures, long-term debt, indicating that the negative return premium stems from less compensation for lower expected market uncertainty. (iii) long-short profits of overreaction-related and lottery-related anomalies significantly decrease following high levels of aggregate analyst attention. Our results highlight the important role of aggregate analyst attention in reducing stock market uncertainty and enhancing investor rationality, thereby mitigating anomalies induced by psychological biases.

Sentiment and extrapolation are ubiquitous in the financial market, and they are not only the embodiment of human nature, but also the primary drivers of asset price bubbles. In this study, we first constructed a theoretical model that included fundamental traders and extrapolated investors, and we assessed the time series characteristics of asset prices under different types of information shocks. According to the research results, good news about the fundamentals can lead to positive asset price bubbles, and correspondingly, bad news can lead to negative asset price bubbles; however, the decrease in asset prices in the case of negative bubbles is not as substantial as the increase in prices in the case of positive bubbles, and the time for prices to reverse is also long, which can be explained by the short-selling constraints. According to the comparative static analysis, the scales of the positive and negative foams depend on the proportion of investors in the market and the extrapolation coefficient. We verified the conclusion of the theoretical model from two aspects: (1) we analyzed the relationship between investor sentiment and the prevalence of informed trading, and according to the results, the increase (decrease) in investor sentiment can reduce the information content of asset prices and increase price volatility; however, the impact of low sentiment is not substantial, which preliminarily tests the conclusion of the theoretical model; (2) we examined the relationship between the cumulative change in investor sentiment and future portfolio returns, and we found that the cumulative increase in investor sentiment can have a positive impact on future portfolio returns at the initial stage, and depress future portfolio returns in the long term, which forms positive asset price bubbles. The cumulative depression of investor sentiment can depress the future portfolio returns at the initial stage, and positively influence the future portfolio returns in the long term, which forms negative asset price bubbles. Moreover, these two nonlinear relationships exhibit cross-sectional differences in different types of asset portfolios, which further validates the key proposition of the theoretical model.

Using the Two Control Zones policy in China, we analyze the impact of tougher environmental policy on the employment of industrial enterprises through DID and PSM-DID methods. We find that tougher environmental policy has a positive (negative) employment effect for industrial enterprises above (below) designated size. In the heterogeneity tests, the positive effect is more significant for enterprises with bigger size, stronger R&D capability, less financial constraint, owned by foreign shareholders, and located in eastern provinces. As for the enterprises in heavily polluting industries, tougher environmental regulation may hinder their development.

This paper discusses the impact of noise trader risk on total consumption and investor consumption. The model predicts that: (1) If noise traders show optimistic beliefs, they will have a restraining effect on the total consumption when the noise trading intensity is high enough, they will expand consumption at t = 1 and reduce consumption at t = 2, and rational investors will reduce consumption at t = 1 and expand consumption at t = 2; (2) if the beliefs of noise traders do not show bias, the consumption of rational investors is always higher than that of noise traders and exceeds the market benchmark; (3) the relative consumption of rational investors and noise traders depends on the risk, risk aversion, fundamental risk and market ratio of noise traders; (4) based on the reasonable range of noise traders' beliefs, the lifetime consumption of noise traders will be higher than that of rational investors and the market, and the excess consumption will change with a series of parameters.

本文拓展了De Long等的模型,研究了正反馈交易者与理性投机者之间的关系.研究表明:第一,如果市场中出现一个准确信号,在正反馈交易者的反馈系数具有递减性、引入额外的无附加信息交易时期的情形下,理性投机会产生稳定效应.第二,如果这一信号含有噪音,理性投机可以在信号所含噪音成分较大的情况下产生稳定效应.第三,如果市场依次出现正确信号与噪音信号,资产价格会在不同时期对第一个信号表现出相反的反应;如果市场中缺乏理性投机者,资产价格会产生反转效应.基于研究结论,本文从遏制噪音交易比例等角度提供政策建议.

不同于标准期权,可转债转股条款的实质是一份交换期权.考虑了市场中的杠杆交易限制,利用远期风险中性测度原理和超复制方法,重新构建了一个可转债交换期权模型.结合2015年2月至2015年10月的可转债市场数据,通过比较基于套利限制的交换期权定价方法与基于无套利原理的标准期权定价方法的计算结果,发现前者能够为可转债的真实价值确定一个参考区间,而后者仅仅是前者的一个特例,且后者大致相当于价值参考区间的下界.通过调整杠杆交易限制的参数,发现:一方面,随着市场杠杆交易限制的增加,转股权(从而转债)的价值会由于其复制成本的上升而增加;另一方面,当转股权处于深度虚值或深度实值状态时,转股权(从而转债)的理论价值区间会缩小.这就为杠杆交易限制与转债价格无套利区间之间所存在的正向关系,以及转股权实/虚值程度与转债价格无套利区间之间所存在的反向关系提供了理论上的依据.

基于沪深两市二级市场2012年1月1日至2016年12月31日的数据,通过横截面分析发现,在研报发布之前,标的股的价格已经出现显著变化,且涨幅更大的股票具有更加乐观的分析师评级,非理性情绪在研报发布日前后出现激增;标的股的前期价格上涨时,分析师情绪可使其后续价格出现显著的正向波动;在牛市中,分析师情绪可以诱发更大幅度的价格波动.这证实了A股分析师大概率地通过研报传递着“虚情假意”的分析师情绪,意味着股市中诱导与欺骗行为的存在,也说明了股票定价在本质上是一种“欺骗均衡”.

Leveraged trading exhibits the characteristics of “strong margin trading and weak short selling” in the Chinese stock market. On the basis of monthly data on leveraged trading in the Chinese stock market from January 2014 to December 2016, we aim to empirically examine the relationship between leveraged trading and investor sentiment, and analyze the characteristics of investor sentiment contained in the leverage ratio. The results show that (1) as the leverage ratio increases, the pattern of investor trading changes from the positive feedback trading of “chasing up and down” to the negative feedback trading of “selling high and buying low”; (2) leveraged trading has the typical characteristics of irrational sentiment; (3) inverse arbitrage strategies based on leverage ratios is effective in one month in the Chinese market. The findings in this paper provide empirical support for clarifying the influence mechanism between leveraged trading and investor sentiment, and can serve as a useful reference for reducing the impact of leveraged trading on volatility and maintaining the sustainability of the stock market.

The stock price crash constitutes one part of the complexity in the stock market. We aim to verify the threshold effect of leveraged trading on the stock price crash risk from the perspective of feedback trading. We empirically demonstrate that leveraged trading has a threshold effect on the stock price crash risk on the basis of monthly data on leveraged trading in the Chinese stock market from January 2014 to December 2016. At a low leverage ratio, leveraged trading reduces the stock price crash risk; however, as the leverage ratio increases and exceeds a certain threshold, leveraged trading asymmetrically increases the stock price crash risk. These findings provide new insights in understanding the complexity in the Chinese stock market.

本文基于中国A股市场融资融券数据,运用TVP-SV-VAR模型对杠杆交易、市场流动性和资产价格波动性之间的反馈机制进行实证检验.结果表明:"杠杆率——市场流动性—资产价格波动性—杠杆率"反馈环显著存在,并且具有时变性和结构突变性特征;无论是市场处于暴涨还是暴跌状态,资产价格波动都会显著恶化市场流动性状况,但在市场处于暴跌状态时会导致市场流动性状况更加迅速恶化.中国监管部门在市场价格崩盘后及时为市场注入宝贵的流动性,对于减弱去杠杆冲击对金融市场的负面影响非常重要.

交易量之谜是一种典型的金融异象.采用主成分分析法构造投资者情绪指数,基于滚动回归法提取个股的情绪beta,通过建立横截面回归模型,研究市场层面的"热点"产生和风格轮动现象.研究发现:股票的情绪beta最能捕捉到市场的"热点"产生和风格轮动特征,市场中"热点"的切换很大程度上存在于高情绪beta组合和低情绪beta组合之间;中期收益是市场风格偏好发生动态转换最为重要的影响因素,前6个月平均收益越低的情绪beta极端组合越有可能成为当前市场的"热点";情绪beta风格偏好动态转换对资产组合收益有系统性的影响,这种影响在不同的情绪状态下会表现出不同的特征.

以上证50ETF期权为研究对象,重点分析上证50ETF的隐含偏度与市场情绪之间的关系.与以往的相关研究不同,对于可能引致期权隐含分布左偏的市场情绪进行了分解,通过对所分解的情绪成分及其与左偏分布特征的关系进行实证检验,重点研究了情绪中的非理性成分在原生市场和衍生市场之间的跨市场传递,以进一步探究出影响市场稳定性的根源.研究表明:上证50ETF隐含分布的左偏程度与市场中的非理性情绪紧密相关,非理性情绪越高涨,隐含分布越呈现左偏;并且,在对已实现波动率、期权相对需求等变量加以控制后,结论依然成立.这意味着,现阶段,仅从中国ETF期权市场中提取关于ETF未来预期信息的想法不可行.

从期权价格中提取信息的传统做法是借助于隐含波动率,然而,通过与标的资产的历史数据对比发现,隐含波动率并不能比历史波动率提供更多的市场预期信息.考虑隐含波动率是利用Black-Scholes模型所导出,意味着模型设定风险也可能会影响到结论的客观性与准确性.为了克服传统方法的不足,本文尝试从一种无模型的视角,利用矩方法展开相关研究.该方法不依赖于任何模型和假设,避免了对定价核以及中性概率分布的讨论,直接由期权价格得到股票收益的隐含分布,利用状态价格来确定市场预期收益与风险厌恶.在分布曲线足够光滑(可导)的条件下,通过对行权价格求导得到标的资产未来收益的隐含风险中性概率密度,并测算出隐含分布的高阶矩特征.

本文以分析师研报数量作为分析师情绪的代理变量,依次检验了分析师情绪对股价的影响如何随时间变化、影响程度是否与荐股强度有关、不同风格特征的股票对分析师情绪的敏感性是否相同等问题.结果表明将样本股按照分析师情绪高低进行分组后,针对分析师或孤立或连续的情绪冲击所构造的套利组合均可获得显著的情绪溢价,且分析师荐股力度越大、短期溢价水平越高.随着时间的推移,上述情绪溢价会出现显著的价格反转.此外,按不同风格特征对样本股进行分组后,小盘股和成长股对分析师情绪更加敏感,呈现出明显的情绪跷跷板效应.

金融市场归根到底是人的市场,金融市场异象与人性紧密相关.情绪反馈和交易诱导是金融市场中最为普遍的现象,既是人性的体现,也会对资产价格行为造成显著影响.本文构建了一个投机者对具有反馈交易特征的非理性投资者进行信息推断和交易诱导的情绪反馈模型,对投机者的需求函数、资产价格的稳定性及时间序列特征进行了讨论.研究结果表明:(1)投机者同时具有套利需求和投机需求,当市场中“噪音”不足时,投机者会主动地制造“噪音”,诱导非理性投资者;(2)投机者对非理性投资者的诱导行为可导致资产价格的过度波动;(3)基本面交易者具有稳定市场的作用,并决定了不同市场动量的强弱.本研究为理解金融市场中的价格操纵行为和复杂性提供了参考.

Does the individual investor sentiment affect stock returns?By using the number of posts on East Money Stock Post Boards as a proxy variable, this paper analyzes the relationship among the individual investor sentiment, the network we-media effect, and the predictability of stock returns.We find that individual investor sentiment can influence stock returns through the spread of network we-media which produces a significant predictability.Specifically, stocks with the more number of posts will earn higher in short-term, showing a short-term momentum effect.When distinguishing the sample stocks by corporation identity, empirical results indicate that individual investor sentiment more significantly affects the small scale stocks, low book-to-market stocks and stocks with low institutional ownership, showing a distinguished "Sentiment Cogging Effect".Furthermore, after risk adjustment, the zero-cost arbitrage portfolio still produces significant excess returns, and those returns are derived from the previous low number of posts stocks, sentiment remains emotional.