This paper asks whether a personally experienced bubble crash leaves a lasting imprint on investment style. We study this in China’s stock market, where two technology-growth booms —the 2014–2015 Internet boom and the 2025–2026 AI hardware boom—share a similar narrative but are separated by a decade of generational turnover. Following managers across the mutual funds they run from 2013Q1 to 2026Q1, we compare those who lived through the 2015 crash with otherwise similar managers who entered afterwards. We find that the caution of veteran managers reflects a scar rather than a symptom of ageing. Conditional on age and career length, managers who lived through the 2015 crash hold more value exposure and chase technology stocks less when prices have been rising, and this reluctance deepens with how much the crash hurt them—crash-window tenure, performance shortfalls, redemption pressure—rather than with age. These demand differences reach asset prices as well: stocks held disproportionately by managers without bubble memories carry significantly higher subsequent left-tail risk, whereas crash-experienced ownership stabilises the stock, particularly when ownership is broad. Heterogeneous institutional beliefs rooted in career experience therefore shape not only how managers invest but also the crash risk of the assets they hold.

In Chinese culture, digit 8 (4) is taken as lucky (unlucky). We find that the numerological superstition has a profound impact across China’s stock, bond, foreign exchange and commodities markets, affecting asset prices in both the primary and secondary markets. The superstition effect, i.e., the probability of asset price ending with a lucky (unlucky) digit far exceeds (falls short of) what would be expected by chance, is prevalent. The effect is driven by investors’ reliance on superstition as an anchor to face uncertainty in asset pricing and the overoptimism of unsophisticated investors. While the superstition effect does not lead to systemic mispricing for assets traded by sophisticated investors, it implies overpricing for assets involving more unsophisticated investors.

We investigate the impact of state ownership structure on asset prices and corporate policies. By primarily focusing on China’s corporations, we show that the relationship between expected returns and capital investment varies significantly across state owned enterprises (SOE) and private owned enterprises (POE). A portfolio that longs low investment and shorts high investment firms earns an average annual excess stock return of 5% in the SOE sector. In contrast, there is no relationship between investment and expected returns in the POE sector. We show that the difference in the link between expected returns and investment across SOE and POE firms is driven by their differential exposures to the debt issuance shocks, which captures the monetary supply shocks in China. As SOE firms have easier access to bank loans, the high investment firms in the SOE sector are more able to raise debt despite that debt supply is shrinking, and hence they are less risky. We develop a dynamic model with SOE and POE firms facing different frictions in debt markets. The economic mechanism emphasizes that heterogeneous access to the debt market is an important determinant of equilibrium risk premiums across sectors with different state ownership.

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 the dynamic and long-run relationships between monetary policy and asset prices in China using monthly data from June 2005 to September 2010. Johansen’s cointegration approach based on vector autoregression (VAR) and Granger causality test are used to identify the long-run relationships and directions of causality between asset prices and monetary variables. Empirical results show that monetary policies have little immediate effect on asset prices, suggesting that Chinese investors may be ‘irrational’ and ‘speculative’. Instead of running away from the market, investors rush to buy houses or shares whenever tightening monetary actions are taken. Such seemingly irrational and speculative behavior can be explained by various social and economic factors, including lack of investment channels, market imperfections, cultural traditions, urbanization and demographic changes. The results have two important policy implications. First, China’s central bank has not used and should not use interest rate alone to maintain macro-economic stability. Second, both monetary and non-monetary policies should be deployed when asset bubbles loom large to avoid devastating consequences when they burst.

This paper investigates the dynamic and long-run relationships between monetary policy and asset prices in China using monthly data from June 2005 to September 2010. Johansen’s cointegration approach based on vector autoregression (VAR) and Granger causality test are used to identify the long-run relationships and directions of causality between asset prices and monetary variables. Empirical results show that monetary policies have little immediate effect on asset prices, suggesting that Chinese investors may be ‘irrational’ and ‘speculative’. Instead of running away from the market, investors rush to buy houses or shares whenever tightening monetary actions are taken. Such seemingly irrational and speculative behavior can be explained by various social and economic factors, including lack of investment channels, market imperfections, cultural traditions, urbanization and demographic changes. The results have two important policy implications. First, China’s central bank has not used and should not use interest rate alone to maintain macro-economic stability. Second, both monetary and non-monetary policies should be deployed when asset bubbles loom large to avoid devastating consequences when they burst.

This paper investigates the dynamic and long-run relationships between monetary policy and asset prices in China using monthly data from June 2005 to September 2010. Johansen’s cointegration approach based on vector autoregression (VAR) and Granger causality test are used to identify the long-run relationships and directions of causality between asset prices and monetary variables. Empirical results show that monetary policies have little immediate effect on asset prices, suggesting that Chinese investors may be ‘irrational’ and ‘speculative’. Instead of running away from the market, investors rush to buy houses or shares whenever tightening monetary actions are taken. Such seemingly irrational and speculative behavior can be explained by various social and economic factors, including lack of investment channels, market imperfections, cultural traditions, urbanization and demographic changes. The results have two important policy implications. First, China’s central bank has not used and should not use interest rate alone to maintain macro-economic stability. Second, both monetary and non-monetary policies should be deployed when asset bubbles loom large to avoid devastating consequences when they burst.

This paper investigates the dynamic and long-run relationships between monetary policy and asset prices in China using monthly data from June 2005 to September 2010. Johansen?s cointegration approach based on vector autoregression (VAR) and Granger causality test are used to identify the long-run relationships and directions of causality between asset prices and monetary variables. Empirical results show that monetary policies have little immediate effect on asset prices, suggesting that Chinese investors may be ?irrational? and ?speculative?. Instead of running away from the market, investors rush to buy houses or shares whenever tightening monetary actions are taken. Such seemingly irrational and speculative behavior can be explained by various social and economic factors, including lack of investment channels, market imperfections, cultural traditions, urbanization and demographic changes. The results have two important policy implications. First, China?s central bank has not used and should not use interest rate alone to maintain macro-economic stability. Second, both monetary and non-monetary policies should be deployed when asset bubbles loom large to avoid devastating consequences when they burst.

By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on imitation and herding of investors and traders and (iii) the mathematical and statistical physics of bifurcations and phase transitions, the logperiodic power law (LPPL) model has been developed as a flexible tool to detect bubbles. The LPPL model considers the faster-than-exponential (power law with finite-time singularity) increase in asset prices decorated by accelerating oscillations as the main diagnostic of bubbles. It embodies a positive feedback loop of higher return anticipations competing with negative feedback spirals of crash expectations. We use the LPPL model in one of its incarnations to analyze two bubbles and subsequent market crashes in two important indexes in the Chinese stock markets between May 2005 and July 2009. Both the Shanghai Stock Exchange Composite index (US ticker symbol SSEC) and Shenzhen Stock Exchange Component index (SZSC) exhibited such behavior in two distinct time periods: 1) from mid-2005, bursting in October 2007 and 2) from November 2008, bursting in the beginning of August 2009. We successfully predicted time windows for both crashes in advance [24, 1] with the same methods used to successfully predict the peak in mid-2006 of the US housing bubble [37] and the peak in July 2008 of the global oil bubble [26]. The more recent bubble in the Chinese indexes was detected and its end or change of regime was predicted independently by two groups with similar results, showing that the model has been well-documented and can be replicated by industrial practitioners. Here we present more detailed analysis of the individual Chinese index predictions and of the methods used to make and test them. We complement the detection of log-periodic behavior with Lomb spectral analysis of detrended residuals and (H, q)-derivative of logarithmic indexes for both bubbles. We perform unit-root tests on the residuals from the log-periodic power law model to confirm the Ornstein-Uhlenbeck property of bounded residuals, in agreement with the consistent model of ‘explosive’ financial bubbles [16].