This paper investigates the impact of two global crises, the global financial crisis and the COVID-19 crisis, on herding behavior in the Chinese and U.S. stock markets. We find no evidence of herding behavior during these two global crises in the U.S. stock market, yet significant herding emerges under the COVID-19 crisis in Chinese mainland stock market. Additionally, the observed herding behavior in mainland China is primarily driven by sentiment. Our results reveal and explain the differences in the effects of financial crisis and public health crisis on herding behavior, as well as variations between emerging and developed stock markets.

This study explores how foreign competition impacts the capital structure of domestic firms. While import competition is associated with a decrease in domestic firms’ leverage, we propose a novel perspective concerning the positive effect of inward foreign direct investment (FDI) on leverage. FDI competition can boost demand for debt via productivity spillover to domestic firms, and also increase supply of debt by inducing lenders to herd toward foreign investors. Using Chinese firm-level data, we find that the positive effects of industry inward FDI on domestic firms’ leverage are more pronounced in high-tech industries and industries where foreign investors exhibit a high degree of herding behavior. Our instrument variable approach, employing industry exchange rates and import tariffs, supports these findings. Additionally, we reveal that the positive effect of FDI on local firms’ leverage is amplified when the firms have stronger absorptive capacities, receive foreign capital, and experience more human capital transfers from foreign rivals.

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].

A substantial literature in institutional herding examines reasons for and evidence of correlated trading across institutional investors, but little has been written about the extent to which individual investor trading is correlated or why. We document that the trading of individuals is highly correlated and surprisingly persistent. Furthermore, we find that the systematic trading of individual investors is driven by their own decisions―trades they initiated―rather than by passive reactions to institutional herding. We discuss why this correlation is unlikely to stem from the same motivations as institutional herding. Correlated trading by individual is a necessary condition for the trading biases of individual investors to affect asset prices, since the trades of any particular individual are likely to be small. The preferences for buying some stocks while selling others must be shared by many individual investors if these preferences are to affect prices. We analyze trading records for 66,465 households at a large national discount broker between January 1991 and November 1996 and 665,533 investors at a large retail broker between January 1997 and June 1999. Using a variety of empirical approaches, we document that the trading of individuals is more coordinated than one would expect by mere chance. For example, if individual investors are net buyers of a stock this month, they are likely to be net buyers of the stock next month. In additional analyses, we present four stylized facts about the trading of individual investors: (1) they buy stocks with strong past returns; (2) they also sell stocks with strong past returns, though this relation is stronger than that for buys at short horizons (one to two quarters), but weaker at long horizons (up to 12 quarters); (3) their buying is more concentrated in fewer stocks than selling; and (4)they are net buyers of stocks with unusually high trading volume.

Building upon the seminal work of Easley, Kiefer, O’Hara and Paperman (1996), we develop a framework to investigate the relationship between the behavior of uninformed investors and the time-varying informed trading activities. We allow the arrival rates for uninformed traders to follow a Markov switching process where the transition probabilities depend on market fundamentals. Informed traders may match the level of the uninformed arrival rate with certain probability so as to make better use of the camouflage provided by the uninformed transactions. Our empirical estimation of NYSE stocks shows that the uninformed transition probabilities are indeed time-varying, so is the probability of information content. The estimated probability of information content predicts the opening, median and closing spreads. There is evidence that uninformed investors exhibit momentum chasing and “noise herding” behavior. There is also a positive “market spillover” effect in the uninformed trading activities. We find that the “clustering” of trading activities by uninformed and informed traders seem to be more likely on low volume days, and the uninformed trading activities are responsible for most of the stock trading volatilities.

A government policy aimed at the reduction of state shares in state-owned enterprises (SOE) triggered a crash in the Chinese stock market. The sustained depression and spillover even after the policy adjustments were over constitute a puzzle---the so called "state-share paradox". The empirical study finds evidence in two dimensions. First, a regime switching model with an absorbing state suggests that government policy switches the regime to liquidity black holes. Second, there is no evidence of flight-to-liquidity during the crash, suggesting to model the crash as an aggregate phenomenon of the whole market. To carefully match the evidence, a theoretical model is set up within the framework of market microstructure. The model shows that the Chinese stock market has distinctive features of liquidity production and price discovery. The irregularities generate an inverted-S demand curve, gives rise to potential liquidity black holes, and are key features to explain the state-share paradox. This study contributes a rational panics hypothesis to the literature. The rational panics hypothesis is neither a herding model with or without behavioral assumptions, nor a standard rational expectation model under the asymmetric information framework. It is based on homogeneous agents with incomplete information, and is consistent with the evidence of absorbing regime switching and the recent literature on state-dependent preference. Our findings have larger implications for theoretical modeling and policy design.