Using China's Strategic Emerging Industries Classification (CSEIC), which is enacted in 2018, as a quasi-natural experiment, this study investigates its impact on corporate innovation behaviors. In basic research, we find that: (1) The CSEIC significantly enhances both substantive and strategic innovation; (2) The effect of CSEIC is influenced by the characteristics of the enterprise. Specifically, in state-owned enterprises (SOEs), the CSEIC significantly enhances substantive innovation and strategic innovation, while in non-SOEs, the CSEIC only significantly enhances substantive innovation. In further research based on entrepreneurial spirit, we find that: (1) The effect of CSEIC on strategic innovation is suppressed if entrepreneurial patriotism is higher, no matter whether the enterprise is SOEs or non-SOEs; (2) The effect of CSEIC on substantive innovation is enhanced, if and only if entrepreneurial integrity is higher in SOEs or International Vision is higher in non-SOEs. These findings offer valuable insights for policymakers aiming to foster innovation through targeted support for enterprise leaders, highlighting the need for tailored approaches considering the distinct characteristics of SOEs and non-SOEs.

One commonly adopted practice in classifying retail and institutional orders is based on order size. Due to the increasing use of small orders by institutional investors, size-based classification can lead to an error rate over 20%. To improve the accuracy of the order size algorithm, we study the order patterns and uncover a higher tendency of retail investors trading in multiples of 500 shares. We modify the original order size algorithm by incorporating the feature of share roundedness. The modified algorithm substantially improves the accuracy of identifying retail and institutional investors in China. Order imbalances derived from the modified algorithm better predict future stock returns.

This paper investigates the free cash flow productivity of SOEs compared with non-SOEs and examines its possible determinants. We find that SOEs have slightly weak free cash flow productivity but significantly stronger than non-SOEs. Similar performance exists among commercial class I and II SOEs and public-benefit SOEs. Further analyses suggest that firm size, age, sales growth, ownership concentration, government subsidies, and industry monopoly factors cannot explain this phenomenon. The common driver for all types of SOEs to generate stronger free cash flows than nonSOEs is their stronger expense control capability.

This paper investigates the free cash flow productivity of SOEs compared with non-SOEs and examines its possible determinants. We find that SOEs have slightly weak free cash flow productivity but significantly stronger than non-SOEs. Similar performance exists among commercial class I and II SOEs and public-benefit SOEs. Further analyses suggest that firm size, age, sales growth, ownership concentration, government subsidies, and industry monopoly factors cannot explain this phenomenon. The common driver for all types of SOEs to generate stronger free cash flows than non-SOEs is their stronger expense control capability.

The adequate evaluation of mutual fund performance and of the fund managers’ ability to add value is an issue to which it has been given special attention in the recent financial literature. One of the traditional evaluation measures most commonly used is Carhart's alpha. However, one of the main problems of the evaluation methods that use the beta of the portfolios as a measure of risk and, therefore, Carhart's alpha is its sensitivity to the definition of the market portfolio. In this work we study the importance of defining the market portfolio using Carhart's alpha for a sample of UK mutual funds, and the influence of this market portfolio in the funds´ excess returns and in the performance ranking classification of the fund sample.

To tackle the problems in the measurement of money demand, this paper holds that: money demand could be divided into different parts including basic demands, other demands, reasonable demands and so forth; relatively accurate measurement could be made for basic demand and reasonable demand through the application of big data technology; in-depth studies on the basic functions, mutual relations and quantitative methods of the above demands would be beneficial for more accurate measurement of money demand.

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。)

This paper investigates whether Chinese equity investors price major earnings components correctly. Total earnings are decomposed into core and non-core earnings according to a classification framework of Chinese accounting principles. The results show that, as expected, core earnings are more persistent than non-core earnings. Most importantly, the market underestimates (overestimates) the value implications of changes in current core (non-core) earnings for future earnings changes. Therefore, future stock returns can be predicted based on the information that is contained in the components of current earnings. Both portfolio tests and regression analysis generate economically significant abnormal returns that are robust to sensitivity checks.

In this paper, the author observes a stationary volume/price pattern, while studying the relationship between volume and price through the amount of transaction in stock market. The probability of accumulated trading volume (actual supply/demand volume) that distributes over its price range gradually emerges the maximum around the price mean value in a transaction body system when it takes a longer trading time regardless of its price fluctuation path or time series in the time interval. The volume/price behaves a probability wave toward an actual supply/demand equilibrium price, forced by a linear central actual supply/demand potential. In terms of physics, the author establishes a transaction (actual supply/demand) energy hypothesis, defines a measurable actual supply/demand restoring force, derives a time-independent security transaction (actual supply/demand) probability wave differential equation, and obtains an explicit volume/price distribution function, the distribution of absolute zero-order Bessel eigenfunctions, in a stable transaction body system when its supply/demand is in a dynamic state. By fitting and testing the function with intraday real transaction volume/price distributions on a considerable number of individual stocks in Shanghai 180 Index, the author demonstrates its validation at this early stage, and attempts to offer a micro and dynamic actual supply/demand volume/price wave theory. JEL classification: G12; D50; C51; C52