“注册制”改革后，新股弃购现象逐渐增多，已经成为新股市场中一个不容忽视的重要现象，对影响新股弃购因素的研究将有助于加深对投资者行为的理解，具有重要的现实和理论意义。本文以科创板个人投资者新股中签后的弃购行为作为研究场景，从“信息效应”和“关注效应”的视角结合发行人基本面、资本市场情绪和投资者关注度三个代表性指标研究了个人投资者新股中签后弃购决策的影响因素。研究发现：（1）发行人基本面、缴款时市场情绪和投资者关注度均对个人投资者弃购产生重要影响，尤其是市场情绪是导致个人投资者弃购最为显著的影响因素。（2）2021年9月份科创板定价制度显著提升了个人投资者的弃购率，并且造成投资者弃购决策更加依赖市场情绪。（3）弃购率高的企业IPO后续表现较差，说明科创板投资者趋于理性，并且参与弃购的个人投资者是聪明的投资者。本文结果表明企业层面，市场层面和网络信息层面的信息能够影响个人投资者交易行为，并且科创板的个人投资者中存在聪明投资者。因此，监管机构应完善科创板主承销商包销制度，允许聪明投资者弃购新股将有效制约科创板的“三高现象”。

国有股权在我国地方性商业银行股权结构中占据主导地位。要准确分析银行关联贷款问题，必须纳入对国有股东行为和动机的考察。本文使用国内较为全面的地方性商业银行关联贷款数据，实证检验了银行国有股权对关联贷款的影响及其潜在机制。结果表明，地方性商业银行国有股权占比越高，关联贷款的规模越大、价格水平越高；这种影响的潜在机制是地方银行信贷投放偏好地方政府主导的基建项目，而非地方政府利用国有股权通过低价关联贷款掠夺银行利益。本文是对地方性商业银行关联贷款与股权结构的首次系统研究，填补了国内相关研究领域的空白。

本文针对股票涨跌停板的磁吸效应现象提出了一个新的成因——非理性投资者的追涨杀跌和过度交易行为。我们使用投资者密度构建了包含异质理性和非理性投资者的均衡价格模型，发现非理性投资者供需密度超过一定阈值时，股票的期望价格变动会出现磁吸效应，非理性投资者供需密度进一步增加时，磁吸效应会减弱。我们使用中国创业板市场股票的分钟级数据实证检验，用创业板注册制改革事件作为自然实验，发现投资者供需密度的变化可以解释改革前后上涨和下跌磁吸效应变化方向不一致的现象，补充了流动性需求无法解释的空白。研究还发现信息披露质量更差的股票，其磁吸效应更弱，这和理论模型结果是一致的。

机器人投资顾问（RIA）能为投资者提供信息，帮助其决策，已出现在多种投资平台上。利用2020和2021年中国最大投资平台的账户数据，本文检验了机器人投资顾问提供的不同服务。总体上看，机器人投顾的使用率越高，未来的净收益和风险调整后收益也越高，资产更多元，风险也越高。和信息服务相比，建议服务能对未来交易行为产生更大影响；这可能是因为机器投顾的建议比较简单，容易理解，因此能更好地被遵循，这与信息服务提供的海量信息形成对比。本文没有发现现有机器人投顾能移除行为偏差的证据。

知情交易者、套利交易者和噪声交易者构成期货市场的三大交易主体，共同决定期货价格。本文在两期市场中构建含知情交易者、跨市场套利者和噪声交易者的理性预期均衡模型，揭示期货聚集私人信息的微观机制、探究套利行为对现货和期货均衡价格的影响。研究发现：现货和期货市场的知情交易行为共同决定期货的价格发现水平，第二期（期货交割期）现货价格聚集私人信息的能力越强，第一期期货市场的知情交易意愿越强；套利交易对期现价格的冲击程度与知情交易者面临的交易风险正相关；套利交易有利于提高现货和期货市场的流动性，同时增加噪声冲击的跨市场传播。研究结论有助于增进投资者对期货价格发现功能和套利行为影响的理解，对提高投资者的理性交易观念、加强市场风险管理有指导作用。

It has been long that literature in financial academics focuses mainly on price and return but much less on trading volume. In the past twenty years, it has already linked both price and trading volume to economic fundamentals, and explored the behavioral implications of trading volume such as investor’s attitude toward risks, overconfidence, disagreement, and attention etc. However, what is surprising is how little we really know about trading volume. Here we show that trading volume probability represents the frequency of market crowd’s trading action in terms of behavior analysis, and test two adaptive hypotheses relevant to the volume uncertainty associated with price in China stock market. The empirical work reveals that market crowd trade a stock in efficient adaptation except for simple heuristics, gradually tend to achieve agreement on an outcome or an asset price widely on a trading day, and generate such a stationary equilibrium price very often in interaction and competition among themselves no matter whether it is highly overestimated or underestimated. This suggests that asset prices include not only a fundamental value but also private information, speculative, sentiment, attention, gamble, and entertainment values etc. Moreover, market crowd adapt to gain and loss by trading volume increase or decrease significantly in interaction with environment in any two consecutive trading days. Our results demonstrate how interaction between information and news, the trading action, and return outcomes in the three-term feedback loop produces excessive trading volume which includes various internal and external causes. Finally, we reconcile market dynamics and crowd’s trading behaviors in a unified framework by Shi’s price-volume differential equation in stock market where, we assume, investors derive a liquidity utility expressed in terms of trading wealth which is equal to the sum of a probability weighting utility and a reversal utility in reference to an outcome. JEL Classifications: G12, G02, D83 （长期以来，金融学术领域里的文献只注重价格和收益率，却较少研究交易量。在最近的二十年里，金融学术文献已经开始研究价格和交易量两者与经济基本量之间的相互关系，并且探讨交易量的行为内涵，例如投资者对风险的态度、过度自信、不同观点以及关注程度等等。然而，我们还是对交易量的认识知之甚少。本文根据行为分析，用交易量概率来表示市场群体的交易频率，并且通过我国股市来实证检验涉及交易量与价格之间不确定关系的两种适应性假说。实证结果表明：市场群体在每日交易的时间窗口内除了采用简单的经验法则之外，同时还采用有效的适应性方式来从事股票交易，并且逐步倾向于形成一个结果和认同的资产价格；无论该资产价格是否明显地被高估或低估，市场群体在相互作用和竞争的过程中往往能够形成这样一个稳态的均衡价格。这表明了资产价格不仅包含了基本价值同时还包含了非公开信息、投机、情绪、关注、赌博和娱乐等价值。此外，在任意两个连续交易日之间，市场群体在与市场环境的相互作用过程中，通过交易量的增加或减少来有效地适应盈亏。我们的研究结果说明了在由信息、交易与收益结果三项构成的反馈环中，它们之间的相互作用是如何导致了过度交易的，这其中包含了导致过度交易的各种内外因素。最后，我们假设股票市场中的投资者是通过交易财富来产生流动性效用，它等于概率加权效用与相对于结果为参照系的反转效用之和，从而推导出Shi氏价-量微分方程，将市场动力学行为与群体交易行为协调在一个统一的框架体系。）

It has been long that literature in financial academics focuses mainly on price and return but much less on trading volume. In the past twenty years, it has already linked both price and trading volume to economic fundamentals, and explored the behavioral implications of trading volume such as investor’s attitude toward risks, overconfidence, disagreement, and attention etc. However, what is surprising is how little we really know about trading volume. Here we show that trading volume probability represents the frequency of market crowd’s trading action in terms of behavior analysis, and test two crowd’s trading behavioral hypotheses relevant to the volume uncertainty associated with price in China stock market. The empirical work reveals that market crowd trade in simple heuristics and efficient adaptation, gradually tend to achieve agreement on an outcome or an asset price widely on a trading day, and generate such a stationary equilibrium price very often in interaction among themselves no matter whether it is highly overestimated or underestimated, suggesting that asset prices include not only a fundamental value but also private information, speculative, sentiment, gamble, and entertainment values etc. In addition, market crowd adapt to gain and loss by trading volume increase or decrease significantly in interaction with environment in any two consecutive trading days. Our results demonstrate how interaction between information and news, the trading action, and return outcomes in the three-term feedback loop produces excessive trading volume which includes various internal and external causes. Finally, we reconcile market dynamics and crowd’s trading behaviors in a unified framework by Shi’s price-volume differential equation in stock market where, we assume, investors derive a liquidity utility expressed in terms of trading wealth which is equal to the sum of a probability weighting utility and a reversal utility in reference to an outcome. JEL Classifications: G12, G02, D83 (长期以来，金融学术领域里的文献只注重价格和收益率，却较少研究交易量。在最近的二十年里，金融学术文献已经开始研究价格和交易量两者与经济基本量之间的相互关系，并且探讨交易量的行为内涵，例如投资者对风险的态度、过度自信、不同观点以及关注程度等等。然而，我们还是对交易量的认识知之甚少。本文根据行为分析，用交易量概率来表示市场群体的交易频率，并且通过我国股市来实证检验交易量与价格之间不确定关系中关于群体交易行为的两个基本假说。实证结果表明：市场群体在每日交易的时间窗口内采用简单的经验法则和有效的适应方式来从事交易，并且总是逐步地倾向于形成一个结果和认同的资产价格；无论该资产价格是否明显地被高估或低估，市场群体在相互作用的过程中往往能够形成这样一个稳态的均衡价格，这表明了资产价格不仅包含基本价值同时还包含非公开信息、投机、情绪、赌博和娱乐等价值。此外，在任意两个连续交易日之间，市场群体在与市场环境的相互作用过程中，通过交易量的增加或减少来有效地适应盈亏。我们的研究结果说明了在由信息、交易与收益结果三项构成的反馈环中，它们之间的相互作用是如何导致了过度交易的，这其中包含了导致过度交易的各种内外因素。最后，我们假设股票市场中的投资者是通过交易财富来产生流动性效用，它等于概率加权效用与相对于结果为参照系的反转效用之和，从而推导出Shi氏价-量微分方程，将市场动力学行为与群体交易行为协调在一个统一的框架体系。)

It has been long that literature in finance focuses mainly on price and return but much less on trading volume, even completely ignoring it. There is no information on supply-demand quantity and trading volume in neoclassical finance models. Contrary to one of the clearest predictions of rational models of investment in a neoclassical paradigm, however, trading volume is very high on the world’s stock market. Here we extend Shi’s price-volume differential equation, propose a notion of trading conditioning, and measure the intensity of market crowd trading conditioning by accumulative trading volume probability in the wave equation in terms of classical and operant conditioning in behavior analysis. Then, we develop three kinds of market crowd trading behavior models according to the equation, and test them using high frequency data in China stock market. It is hardly surprising that we find: 1) market crowd behave coherence in interaction widely and reach agreement on a stationary equilibrium price between momentum and reversal traders; 2) market crowd adapt to stationary equilibrium price by volume probability increase or decrease in interaction between market crowd and environment (or information and events) in an open feedback loop, and keep coherence by conversion between the two types of traders when it jumps and results in an expected return from time to time, the outcome of prior trading action; 3) while significant herd and disposition “anomalies” disappear simultaneously by learning experience in a certain circumstance, other behavioral “anomalies”, for examples, greed and panic, pronounce significantly in decision making. Specifically, a contingency of return reinforcement and punishment, which includes a variety of internal and external causes, produces excessive trading volume. The behavioral annotation on the volume probability suggests key links and the new methods of mathematical finance for quantitative behavioral finance.长期以来，金融的学术文献主要关注价格和回报率，很少考虑甚至完全忽视了交易量。新经典金融模型就没有供需量和交易量的信息。然而，与新经典框架理性投资模型的预计结果不同，交易量在世界的股票市场上是非常大的。我们基于Shi的价-量微分方程，根据行为分析中的经典性和操作性条件反射，提出了交易性条件反射的概念，并且用该方程中的累计交易量概率来计量市场群体交易性条件反射的强度。由该方程，我们得到三种市场群体的交易行为模型，并且用我国股市的高频数据进行实证分析。不难发现：1）市场群体在相互作用的过程中普遍地表现出相互一致的行为特征，趋势和反转交易者之间存在着一个大家都能够接受的稳态均衡价格；2）交易行为有时会导致稳态均衡价格出现跳跃、带来预期收益率，这时，市场群体在开放的反馈环中，通过与环境（或信息和事件）之间的相互作用，由成交量概率的增加或减少来适应该均衡价格的变化，趋势和反转交易者也会通过相互转换保持市场群体行为的相互一致性； 3）尽管在某特定环境下市场群体通过学习实践，羊群和处置行为同时消失了，但是其他行为“异象”，例如贪婪与恐慌，在决策中却表现的十分显著。特别地，收益率强化和惩罚过程，其中包含各种内外因素，导致过度交易量。累计交易量概率的行为诠释为计量行为金融学提供了关键性的纽带作用和数学金融的新方法。

本文结合最近的实证文献所描述的市场表现，脱离本领域典型的投资者非理性研究，以投资者交易行为作为中间环节，对资产价格波动非对称性这个经典问题迚行新的阐释。本文从交易者个体自适应角度出发，借鉴Hommes等人的思想，在经典的圣塔菲人工股票市场上迚行简单的修改，找到了造成资产价格波动丌对称新的因素——投资者策略转换倾向的时变性。本文通过新兴的计算实验方法迚行建模，实验，最后通过EGARCH模型迚行实证检验，证明本文找到的因素显著地影响资产价格波动丌对称性。

长期以来国内研究者一直忽视了对我国股市中系统流动性变动来源的研究。本文利用 Coughenour & Saad(2004)两步检验方法，实证检验了我国股市中机构投资者非自愿性交易行 为与系统流动性变动之间的关系，研究结果表明作为一个投资者群体的开放式基金，其非自 愿性交易行为是导致我国股市系统流动性变动的一个重要来源。本文的研究结论从新的角度 丰富了对我国股市中系统流动性变动来源的解释。