文献综述：我国证券投资基金羊群行为研究

羊群行为是金融市场中“市场异象”之一，它难以用建立在“理性人”假设基础之上的传统金融理论进行合理解释。行为金融学的诞生为羊群行为的研究另辟了蹊径。随着行为金融学日益受到重视，特别是在最近十几年来金融危机频繁爆发的背景下，羊群行为是金融学领域中一个重要和特殊的研究方向。     

羊群行为及其对金融市场的影响综述

系统概述金融市场中的羊群行为,分析羊群行为的分类、羊群行为对市场的影响、羊群行为的模型以及羊群行为的检验方法,并且总结国内股市关于羊群行为的实证研究,希望对我国的投资者和监管机构有一定的益处。     

关于羊群行为的分析及在我国股市的实证检验

本文首先对羊群行为作了一个简单的介绍,然后描述了羊群行为在金融市场上的几种模式.文章也对羊群行为给了个数字量度,在中国股票市场进行测试.     

机构投资者交易行为中的羊群效应

“羊群行为”是金融市场上一个令人困惑的现象，“信息不对称”，“名誉的考虑”和“投资经理激励机制”被认为是导致“羊群效应”的主要原因。对美国金融市场的研究表明，机构投资者在进行投资时存在典型的羊群效应。随着我国公募基金的迅速发展，透明的披露机制的压力以及基金的“相对业绩评价标准”导致我国证券投资基金在交易中也出现了羊群效应下的从众行为。     

中国证券市场羊群行为的实证研究

"羊群行为"是金融市场上一个令人困惑的现象.从个体意义上来说,如果一个投资者原本计划实施某项投资决策,但由于观察到其他投资者没有进行此项投资,因而自己也取消了计划中的投资,这种行为就称为"羊群行为".个体的"羊群行为"引致投资者之间的学习与模仿,从而在某段时间内买卖相似的股票或同时进出股市,出现群体意义上的"羊群行为".     

我国股票市场中的羊群行为研究

羊群效应是行为金融学中用来描述金融市场中投资者从众行为的术语,源自于生物学中对动物聚群行为的研究。在证券市场诸多异象之中,当属"羊群效应"最为明显、典型。由于诸多原因,我国证券市场"羊群效应"更为严重。本文试对"羊群效应"的产生做简单介绍,从不同角度结合我国特殊国情对产生这一现象进行解释,并提出一些简单的对策。     

机构投资者与个人投资者投资行为羊群效应比较分析

一、引言羊群效应是金融市场尤其是证券市场上的一种特殊现象,是投资者行为复杂化的典型反应。在我国证券市场"羊群效应"广泛存在,这是由于我国的证券市场较于西方证券市场还很不成熟,无论是机构投资者还是个人投资者都欠缺理性,更容易出现"羊群效应"。证券市场上的"羊群效应"是指在证券投资过程中,投资者在     

羊群行为下的中小投资者操作策略分析

近年来,国内外金融界对羊群行为进行了深入的理论分析和广泛的实证检验。文章在界定研究对象为我国股市中现有＂故意羊群行为＂,并进行理论和实证综述的基础上,对羊群行为下的中小投资者投资策略进行分析并提出建议。     

A股、H股市场羊群行为实证研究

本文采用分散度指标和偏离度指标对2005-2008年间交叉上市A股和H股市场的羊群行为进行了实证检验．发现A股市场存在羊群效应,但未发现H股市场上的羊群效应。本文认为两地市场在投资者理念、市场结构、信息披露、政府监管方面的差异是造成这种差异的根源,并在此基础上提出了政策建议。本文的研究将对企业在两地市场上投融资,各类投资者规避风险获得稳定收益,两地金融市场的一体化乃至中国资本市场的长远发展具有一定指导意义。     

羊群行为和信息串联

羊群行为是一种常见、多发的人类行为，它在金融市场深刻地影响着资产定价，因而具有重要的研究价值。本文回顾了多种羊群模型，将它们工为三类：基于收益外部性、基于信息外部性以及两者兼备的声誉模型，并对三类模型进行了比较分析，同时也对一些重要的概念和信息串联，战略延迟等做出了定义阐述。     

Herding in the strategic allocations of Spanish pension plan managers

This paper empirically examines herding behavior in the strategic style allocations of Spanish pension plan managers. The study uses both the standard metric used in financial literature to capture institutional herding and a new approach to address some shortcomings of this traditional measure. Concretely, some authors have highlighted that the traditional measure does not take into account that the probability that a manager buys rather than sells a certain stock depends on both the initial holding in the stock and the asset flows. As a consequence, this study proposes a new approach, which can be applied to other financial markets and provides more accurate values of the probability to increase (or decrease) the style exposures bearing in mind the previous exposure of each portfolio. The study confirms the existence of herding behavior by using both methods. Although the strength of this behavior decreases using the new approach, the herding levels detected in this study of style herding of Spanish pension plans are higher than those of previous research analyzing portfolio holdings in other countries. Additionally, herding levels are higher in periods of low volatility while market returns does not seem to influence herding levels.     

Animal Behavior in Capital markets: Herding formation dynamics,trading volume,and the role of COVID-19 pandemic

This paper provides new evidence on herding behavior. Using daily frequency data for 336 US listed firms over a five-year period, we investigate three important elements of financial herding behavior. First, trading volume, representing market interest, as a significant variable in capital markets apart from stock prices. Second, herding dynamics since herding formation is a dynamic process. Third, the reaction of possible financial herding to exogenous events-threats, as we use the pandemic event in order to investigate a market under stress. Even though the benchmark herding model used does not provide evidence of herding behavior, our results verify the significance of the above herding elements. We also find that trading volume and positive changes in trading volume result in increased cross-sectional absolute deviation (CSAD). Most importantly, we find that herding behavior is evident during the COVID-19 pandemic confirming that investors tend to herd during major crisis periods.     

Herding behavior,market sentiment and volatility: Will the bubble resume?

This paper aims to investigate herding behavior and its impact on volatility under uncertainty. We apply a cross-sectional absolute deviation approach as well as Quantile Regression methods to capture the herding behavior in daily and monthly frequencies in US markets over several time-periods including the global financial crisis. In a novel attempt we modify the empirical CSAD herding modeling by introducing implied volatility as a measure of agent risk expectations. Our findings indicate that herding tends to be intense under extreme market conditions, as depicted in the upper high quantile range of the conditional distribution of returns. During crisis periods herding is observed at the beginning of the crisis and becomes insignificant towards the end. The US market herding behavior exhibits time-varying dynamic trading patterns that can be attributed e.g., to overconfidence or excessive “flight to quality” features, mostly observed in the aftermath of the global financial crisis. Moreover, implied volatility reveals asymmetric patterns and plays a key role in enforcing irrational behavior.     

Herding in the bad times: The 2008 and COVID-19 crises

The objective of this paper is to analyze the imitation behavior of investors in especially convulsed periods, such as the 2008 financial crisis and the recent global pandemic, both of which could affect investors' emotions and behavior, although both have different characteristics and might have different implications. The cross-sectional dispersion of returns is used to measure the level of herding in the markets of Spain and Portugal, using a survivorship-bias-free dataset of daily stock returns during the period January 2000–May 2021, in turn divided into several sub-periods classified as pre-2008 crisis, 2008 crisis, post-2008 crisis, Covid-19 and post Covid-19. Additionally, the existence is studied of differences between days of positive and negative returns, or between days of high volatility compared to the rest, and whether the cross-sectional dispersion of returns in one market is affected by the cross-sectional dispersion of returns in the other market. The results indicate that herding appears with greater intensity in periods prior to the crisis, disappearing during the financial crisis and reappearing, although with less intensity, after it, while it is not generally detected in Covid-19 times. However, herding behavior can be observed in the market during the pandemic on high volatility days.     

Herding behavior in the cryptocurrency market during COVID-19 pandemic: The role of media coverage

This paper examines herding behavior in the cryptocurrency market during the COVID-19 pandemic using daily data and based on static and regime-switching models. Furthermore, we investigate whether herding behavior is affected by the coronavirus media coverage. Based on a sample of the top-43 cryptocurrencies in terms of market capitalization between 2013 and 2020, we find significant evidence of herding for the entire sample period only during high volatility state. Moreover, during the COVID-19 crisis, results suggest that investors in the cryptocurrency market follow the consensus. Finally, the impact of coronavirus media coverage is significant on herding among investors, explaining such behavior in the cryptocurrency market during the COVID-19 crisis. Our findings explain herding determinants that may help investors avoid such comportment, mainly during the crisis.     

Do carbon traders behave as a herd?

This paper shows the existence of herding behavior in the European Carbon Futures Market and studies its possible causes and consequences. This market is characterized by leading the carbon price discovery process and by being highly dominated by professional traders. Both features make it an appropriate environment for the existence of herding. A patterns analysis indicates that the herding level increases in speculative periods, on those days on which the price and size clustering effect is stronger, and with the arrival of carbon-related news. Regarding possible market drivers, we find that herding behavior is positively related with the number of trades, the intraday volatility and on days with extreme returns. Our results appear to support the claim that the lower the availability of information, the higher the level of herding. Finally, we show that herding increases market volatility and leads carbon traders to overreact.     

What drives herding in oil-rich,developing stock markets? Relative roles of own volatility and global factors

The main goal of this paper is to formally establish the volatility-herding link in the developing stock markets of the oil-rich GCC countries by examining how market volatility affects herd behavior after controlling for global factors. Using a regime-switching, smooth transition regression model (STR), we find significant evidence of herding in all Gulf Arab stock markets, with the market volatility being the more paramount factor governing the switches between the extreme states of non-herding and herding. The global variables comprised of the U.S. stock market performance, the price of oil and the US interest rate as well as the risk indexes including the CBOE Volatility Index (VIX) and the St. Louis Fed's Financial Stress Index (FSI) are found to be significant factors governing the transition to herding states. The findings stress the effect of contagion in financial markets, despite the restrictions established by the GCC policymakers in order to protect their markets.     

Speculative behavior and the dynamics of interacting stock markets

We develop a simple agent-based financial market model in which heterogeneous speculators apply technical and fundamental analysis to trade in two different stock markets. Speculators׳ strategy/market selections are repeated at each time step and depend on predisposition effects, herding behavior and market circumstances. Simulations reveal that our model is able to explain a number of nontrivial statistical properties of and between international stock markets, including bubbles and crashes, fat-tailed return distributions, volatility clustering, persistent trading volume, coevolving stock prices and cross-correlated volatilities. Against this background, our model may be deemed to have been validated.     

Bank herding and systemic risk

Bank herding behavior is often hypothesized to increase systemic risk, but the actual effect is unclear ex-ante from the theory and unknown ex-post from the data. We expand the literature on this topic in several dimensions – posing alternative hypotheses regarding the effects of herding in asset, liability, and off-balance sheet portfolios; developing a novel set of bank-specific, time-varying measures of herding in these portfolios; and empirically testing the relations between bank herding for all three portfolios and bank systemic risk contributions. We find nuanced empirical results that differ by portfolio, bank size class, and periods before versus after TARP.