This paper uses high frequency option data to investigate the information content of option trading of AH cross listed stocks (A-shares traded in mainland China and H-shares traded in Hong Kong) and the role of the Shanghai-Hong Kong Connect in this issue. Measuring the informed trading with order imbalance, we find that the order imbalance of stock options traded in Hong Kong contains incremental information that predicts the return of corresponding A-shares traded in Shanghai after controlling for the cross-market return and volume factors proposed by Gagnon and Karolyi (2009). More important, this predictive power strengthens after the Shanghai-Hong Kong Connect, which is also supported by the evidence of comparison between the two stock crashes exactly before and after the connection. During the 2015 stock crash, the spillover effect of the two markets is significantly stronger than that during the 2008 financial crisis.

Although China has the largest trading volume of commodity futures, limited studies have been devoted to the term structure of Chinese commodity futures. This paper takes the tools in functional data analysis to understand the term structure of commodity futures and forecast its dynamic changes at both short and long horizons. Functional ANOVA has been applied to examine the calendar e_ect of term structure in level and _nd the seasonality in the commodity futures of coking coal and polypropylene. We use an h-step functional autoregressive (1) model to forecast the dynamic change of term structure. Comparing with native predictor, in-sample and out-of-sample forecasting performance indicate that additional forecasting power is gained by using the functional autoregressive structure. Although the dynamic change at short horizons is not predictable, the forecasts appear much accurate at long horizons due to the stronger temporal dependence. The predictive factor method has a better in-sample _tting, but it cannot outperform the estimated kernel method for out-of-sample testing, except for 1-quarter-ahead forecasting.

This paper studies the presence of informed trading in Taiwan stock index options (TXO) and analyzes the informational role of foreign institutions in incorporating information into Taiwan stock index futures (TX). We have found that only the option-induced part (OOI) of the total TX order imbalance can predict future TX prices, and the OOI calculated from open-buy TXO, defined by Ni et al. (2008), provides incremental predictability. This finding shows that the price predictability stems from the information flow resulting from option transactions rather than from liquidity pressure. We conclude further that option transactions from foreign institutions provide the most significant predictability, out-of-the-money option transactions in particular. These empirical results show that option transactions conducted by foreign institutions have played the primary role in conveying the information inherent in the TXO market to the TX market, foreign institutions being delta-informed traders. Retail investors, the major players in both the TXO and TX markets, have done almost nothing of significance with regard to TXO information transmission into the TX market, with the exception of some near-the-money and out-of-the-money options.

Abstract: Traditional research about the passive investors and index were mainly focus on the tracking error and the performance of mutual funds. However, they ignored that, deceptive by name, the passive investors, such as index-tracking funds and ETFs, may have an active impact on the value of the company through large-scale transactions of these passive investors. Focused on the Chinese stock market, this paper investigates whether specific passive investors, the funds and ETFs that track MSCI China index, will actively influence the market valuation after MSCI Index Rebalance. When the passive shareholders, which are always the mutual funds, exceeds a threshold, I find that firms added to the index will have a significant positive return, about X%, to the index itself. Also, I find the firms eliminated out to the index have a significant negative return, about X%, to the index itself. One potential interpretation of these results is that index-rebalancing will lead the index-trackers to buy those stocks added to the index, and these transactions represent a large buy power that will lead the demanding of those stocks to exceed the selling power and this dynamic of trading plus the following transactions of other investors eventually cause a premium and positive return. The firm size will also have an impact on stock performance when the index get rebalanced, partially in that the weight of the index is calculated according to the market value, a calculate method that leads to the higher weight of large companies. If large companies are added to or removed from the index, the trading volume will be larger, causing more transactions dynamic on those stocks.

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。

This paper examines the predictive power of trading volume for Chinese equity premium. High (low) trading volume significantly predicts subsequent high (low) equity premium in Chinese stock market in- and out-of-sample. The predictability of trading volume remains significant after controlling for a large number of China economic variables. The predictive power of trading volume is economically important from an asset allocation perspective. Overall, our study suggests that trading volume should be used in conjunction with economic variables to further enhance the Chinese equity premium predictability.

为了减少市场冲击成本，高频交易者通常会将大额指令拆分为小额指令择机逐次提交，但同时也存在指令不能全部成交的风险。针对现有文献主要考虑指令全部成交的不足，本文提出了一种估计交易策略机会成本的方法，在最小化总交易成本的目标下，分析了投资者同时考虑市场冲击成本和机会成本的情形与投资者同时考虑市场冲击成本、机会成本和择时风险情形下如何制定最优交易策略的问题。研究结论表明，如果投资者只考虑冲击成本，或者虽然也同时了考虑机会成本但若所有交易时期指令的成交概率都相等，那么投资者最优的算法交易策略仍然是采用著名的交易量加权平均价格（Volume Weighed Average Price，VWAP）交易策略；如果投资者同时考虑市场冲击成本和机会成本，且对于所有交易时期指令的成交概率不一致但交易者能预先预期总的可执行指令大小的特殊情况，本文得到了此问题解的解析解，对于各交易时期指令成交概率不一致的一般情形，通过数值示例发现，对于不同交易时期指令的成交概率分别为递增、递减和U型三种不同情形，投资者同时关注市场冲击成本和机会成本时最优指令提交策略（Market Impact and Opportunity Cost，MIOC）的总交易成本均小于VWAP交易策略；如果投资者同时考虑市场冲击成本、机会成本和择时风险，通过数值示例发现，对于各交易时期指令成交概率分别为递增、递减和U型的三种不同情形，投资者最优指令提交策略（Market Impact, Opportunity Cost and Timing Risk，MIOCTR）的总交易成本均小于MIOC交易策略和VWAP交易策略；

This is a contribution to a conference volume for the 2010 National University of Singapore Symposium on ―Law and Development in China - The Legal Dimension of China’s Development Model.‖ The paper aims to shed some light on the way forward for the Chinese securities markets. It examines the desirability of financial markets for broader economic development, and presents the national market system implemented by the US and the norms in the EU’s MiFID that promote competition among and integration of securities markets and alternative trading venues. The paper assesses the current state of the Chinese securities markets, where according to law trading is concentrated on traditional exchanges, against the backdrop of the rapidly evolving (disintegrating) US and EU markets. Clearly, China will be able to learn much from the experiences of the US and EU, but the route that China will take for further evolution of its national market for securities can at this point be neither predicted nor projected.