Emotions are fundamental elements driving humans’ decision-making and information processing. Fear is one of the most common emotions influencing investors’ behaviors in the stock market. Although many studies have been conducted to explore the impacts of fear on investors’ investment performance and trading behaviors, little is known about factors contributing to and alleviating investors’ fear during the market crash (or extremely volatile periods) and their fear regulation after the crisis. Thus, the current data descriptor provides details of a dataset of 1526 Chinese and Vietnamese investors, a potential resource for researchers to fill in the gap. The dataset was designed and structured based on the information-processing perspective of the Mindsponge Theory and existing evidence in life sciences. The Bayesian Mindsponge Framework (BMF) analytics validated the data. Insights generated from the dataset are expected to help researchers expand the existing literature on behavioral finance and the psychology of fear, improve the investment effectiveness among investors, and inform policymakers on strategies to mitigate the negative impacts of market crashes on the stock market.

We propose a new investor sentiment index by estimating the differences in variance,skewness, and kurtosis from realized stock returns and option implied moments. We show that our index cannot be explained by risk factors such as market risk, firm size, value, or profitability. Furthermore, we present evidence that this correlation can be exploited for momentum strategies, which perform significantly better during high-stimulation periods. In fact, our methodology can be extended to a daily sentiment measure and stock-specific sentiment indices.

Using a large sample of over 5,000 start-ups across various industries and 524 media outlets in China between 2000 and 2016, we examine the effects of media coverage of start-ups on VC investment decisions and performance. To the best of our knowledge, for the first time in the finance literature, we have discovered that media coverage of start-ups significantly affects VC investment decisions and exit performance. Specifically, such coverage, especially positive coverage, significantly increases the probability and amount of VC investments in start-ups. It also significantly improves the exit performance of VC investments. The significant effects of media coverage of start-ups on VC investments are driven by market-oriented instead of state-controlled media. We further find that VC investments in a focal start-up are significantly influenced by the average media coverage of other start-ups in the same industry or the same city. Our results are robust to a battery of robustness tests. Our research contributes to the behavioral finance literature by showing that an increasingly prominent type of institutional investors, venture capitalists, just like individual investors, are also subject to limited attention. Our research also extends the research by You, Zhang and Zhang (2018) by revealing the heterogeneous effects of market-oriented and state-controlled media on VC investments. Last but not the least, we are the first to discover that peer start-ups’ media coverage matters for VC investments in the focal firms, thereby pushing the frontier of research on the roles of media in finance.

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。

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].

This study examines the role of information uncertainty (IU) in predicting cross-sectional stock returns. We define IU in terms of "value ambiguity", or the precision with which firm value can be estimated by knowledgeable investors at reasonable cost. Using several different proxies for IU, we show that: (1) On average, High IU firms earn lower future resturns (the "mean" effect), and (2) Price and earnings momentum effects are much stronger among high IU firms (the "interaction" effect). These findings are consistent with theoretical models that feature investor overconfidence (Daniel et al. (1998)) and information cascades (Bikhchandani et al. (1992)). Specifically, our evidence indicates that high IU exacerbates investor overconfidence and limits rational arbitrage.