我国股票市场羊群行为的实证研究

本文运用CCK模型,对我国股票市场存在的羊群行为进行了实证研究.首先检验了短时期的涨跌现象会不会引发我国股市的羊群行为;接着检验了在股票市场长期的上涨或者下跌趋势中是否存在显著的羊群行为现象;同时也检验了不同周期不同阶段的上涨和下跌阶段的羊群行为.最后,对羊群行为的成因及控制对策进行了初步探讨.     

我国P2P网络借贷的羊群行为效应——基于“拍拍贷”平台的实证研究

P2 P网络借贷是一种发生在网上的资金借贷交易活动。由于P2 P网络借贷投资无抵押无担保,投资者面临由于信息不对称性带来的较大的投资风险,因此许多投资者在做出自己的投资决策前往往会参照其他的投资者的投资行为,或直接跟随其他投资者做出相同的投资决策,产生了羊群行为。本文主要验证了P2 P网络借贷投资者的羊群行为,探究产生羊群影响因素,并探讨投资者的羊群行为对P2 P网络借贷投资者的影响,为中国P2 P网络借贷投资者提供决策建议。     

高管过度自信、过度悲观与股利羊群行为研究

以股改后的2007-2011年A股上市公司为研究样本,考察高管过度自信、过度悲观的非理性行为与股利羊群行为之间的关系。结果发现：存在过度自信和过度悲观心理行为高管的上市公司发生股利羊群行为的可能性较小,而存在适度理性高管的上市公司发生股利羊群行为的可能性较大。进一步对不同产权性质下二者的关系进行分析发现,与国有上市公司相比,非国有上市公司中高管过度自信与过度悲观对公司股利羊群行为的影响更为显著,而适度理性的高管却总是倾向于股利羊群行为。股利羊群行为是上市公司高管的理性选择。     

机构投资者的羊群行为能降低投资风险吗?——来自中国上市公司的证据

机构投资者的羊群行为对投资风险影响的实证分析结果表明: 机构投资者存在羊群行为,卖出羊群行为的程度大于买入羊群行为的程度,而且卖出羊群行为加大了投资风险、降低了投资回报率; 买入羊群行为减少了投资风险、提高了投资回报率。因此,加大监管机构投资者卖出羊群行为有利于降低投资风险,并促进证券市场稳定发展。     

基于改进SDH法的证券投资基金羊群行为实证研究

以2010年第四季度至2011年第四季度的数据作为样本,利用改进的SDH法,对证券投资基金羊群行为进行实证分析。实证结果表明,我国证券投资基金羊群行为存在较为严重的羊群行为,且程度上买方羊群行为要略大于卖方羊群行为,牛市中羊群行为的表现要强于熊市中的表现,股票所属行业类型及公司规模大小对羊群行为程度产生一定影响。     

互联网金融市场中的“羊群”行为

本文总结了"羊群"行为,尤其是理性"羊群"行为的定义,并回顾其在互联网金融领域的存在性与测度方法。就目前而言,我国互联网金融领域的"羊群"行为研究主要集中于P2P与众筹领域并着重讨论其对于互联网金融的之前的研究采取多种测度方法证明了"羊群"行为在互联网金融市场的存在性,但关于"羊群"行为的测度方式仍然存在争议。"羊群"行为的存在意味着我国政府应当采取有效措施监管并完善互联网市场。     

股票市场羊群行为的激发机制探析

羊群效应在股票市场上极为常见,行为金融用其来解释股市波动、非理性繁荣等金融现象,本文从心理学角度,在探讨羊群行为背后各种心理机制关联性的基础上,提出羊群行为的激发三阶段倾向——权衡——消除疑虑,并对影响羊群行为的其他因素进行分析,希望对进一步研究投资者行为提供参考。     

我国农产品期货价格过度波动中的羊群行为研究

商品期货在牛熊转换过程中多会出现价格过度波动的情况,且这种过度变化往往脱离经济基本面掌控,与市场上出现羊群行为有关。国内学者尚未对我国农产品价格出现巨幅变化条件下的羊群行为展开研究。本文以大豆、玉米、小麦、白糖、早籼稻和棉花为分析对象,并运用CSAD模型进行实证分析后发现,在2008年的下跌市场和整体市场以及2010年的上涨市场、下跌市场和整体市场中均存在羊群行为。据此,本文剖析了造成羊群行为的结构性和体制性原因,并提出了减少羊群行为的若干建议。     

投资者网络社交互动下的新羊群效应剖析

在移动互联网时代,信息传递正随网络社交平台的广泛应用发生巨大的变化,这对由于信息不充分导致的羊群行为会带来什么样的影响?文章从投资者的角度分析网络社交互动背景下的羊群效应。     

我国P2P网络借贷投资人羊群效应行为分析

首先简单介绍了现在我国P2P网贷市场的简单概况。在信息不对称的情况下,投资者的行为之间会产生相互的影响关系。会产生如"聚集效应"、"羊群效应"等现象。之后用信息披露较全的拍拍贷平台来分析投资者的羊群效应行为。运用stata软件对当期贷款金额、前期累计投标次数、前期贷款金额进行描述性统计分析,并且验证了在P2P网贷市场中羊群效应行为的存在性。并发现当期贷款金额与前期累计投标次数存在负相关的关系。最后对于P2P平台和网贷投资者提出对策建议。     

Monetary policy,exchange rate fluctuation,and herding behavior in the stock market

Interest rate and exchange rate are two important macroeconomic variables that exert considerable effects on the stock market. In this study, we investigate whether variations in interest and exchange rates induce herding behavior in the Chinese stock market. Empirical results indicate that interest rate increase and Chinese currency (CNY) depreciation will induce herding and this phenomenon is mainly manifested in down markets. Moreover, the herding level of the highest idiosyncratic volatility quintile portfolio is twice that of the lowest quintile portfolio which we consider evidence of intentional herding. This result is consistent with those of previous studies, which report that retail investors prefer and overweigh lottery-type stocks. Finally, we investigate the effects of monetary policy announcements and extreme exchange rate volatility on herding because these events elicit considerable public attention and may trigger collective behavior in the aggregate market.     

沪深300指数羊群效应研究

通过研究行为金融学的经典现象——羊群效应,并运用CCK模型和CH模型对沪深300指数成分股的羊群效应进行实证检验。研究发现,在所选的数据区间内,沪深300指数成分股之间的羊群效应现象并不明显;在对上升市和下降市的子样本检验过程中发现,上升市中存在一定的羊群效应,下降市中没有明显的羊群效应现象。     

软信息能否缓解P2P网贷市场中的羊群行为？

文章以“人人贷”网络借贷平台2011年8月15日至2014年8月20日交易数据为样本,通过Python编写网络爬虫程序抓取14936个有效借款订单,采用线性回归模型,按投标记录进行等时间段划分,分析借款人提供的借款陈述等软信息能否缓解P2P网贷市场投资者的羊群行为。实证表明,借款陈述的文本长度与羊群行为存在负相关关系,即借款描述等软信息的增加可适度缓解投资者的羊群行为。对此,建议国内网贷平台应借鉴国外P2P网贷平台的运行经验,提供更多的软信息以引导投资者合理决策。     

Did Investors Herd during the Financial Crisis? Evidence from the US Financial Industry

We examine the herding behavior of investors in the US financial industry, especially commercial banks, S&Ls, investment and insurance firms during global financial crisis of 2008 towards own sub‐sector and market consensus using augmented cross sectional absolute deviation of returns (CSAD) model. After distinguishing between fundamental and non‐fundamental information, we find a greater influence of global financial crisis on spurious herding for commercial and investment banks, and such herding increases in the down market and with conditional volatility of returns, but adverse herding is prevalent among investors during normal period in response to fundamental information. We also find that herding intensity on fundamental information is relatively high with market consensus for all financial institutions except insurance firms in high volatility regime, and intentional herding is only significant and limited to S&Ls and investment banks in high volatility regime. Our findings suggest limited spillover effects of herding when investors face non‐fundamental information.     

The Analysis of 52‐Week High Investing Strategy Based on Herding Behavior

The present study develops zero‐costing strategies that are based on the 52‐week high and herding behavior. Proximity of the current price to the 52‐week high and the level of herding behavior of individual/institutional investors are the two criteria used to screen stocks. Because herding behavior affects stocks that are associated with value‐related beliefs that investors are reluctant to revise, the level‐of‐herding criterion uses the 52‐week high strategy to improve profits. The present study examines strategy profits in Taiwan, a market in which more than 70% of investors are individuals and where the level of herding among individual investors is higher than that for institutional investors. Empirical results found that profits earned using zero‐costing strategies, identified both using the 52‐week high and herding, were larger than those earned using only the 52‐week high strategy. Furthermore, stocks with values that were far from their 52‐week high made significant and positive profits through buy‐herding and by shorting sell‐herding stocks.     

Herding behaviour in an emerging market: Evidence from the Moscow Exchange

This study investigates the extent to which herding towards the market consensus for Russian stocks is driven by fundamental and non-fundamental factors. We find evidence that investors on the Moscow Exchange herd without any reference to fundamentals during unanticipated financial crises coupled with high uncertainty, in falling markets, and during days with extreme upward oil price movements. In contrast, in periods of high liquidity and on days of international sanction announcements during the Ukrainian crisis, herding behaviour is merely driven by fundamentals. In Russia, macroeconomic news releases induce both information-related herding and herding without any reference to fundamentals. These results suggest that motives of investors' herding behaviour vary under specific market conditions such as market trends, liquidity, uncertainty, arrival of new information, and oil price volatility.     

Patterns of Herding and their Occurrence in an Online Setting

When groups of consumers share information or express their opinions about products and services, their attitudes or behavior sometime align without centralized coordination, a phenomenon known as herding. Building on pattern-based explanations of herding from the cognitive science literature, we propose a framework to elucidate herding behavior based on three dimensions: the speed of contagion, i.e., the extent to which the behavior spreads in a given time, the number of individuals, i.e., the proportion of the whole population expressing the behavior, and the uniformity of direction, i.e., the extent to which the mass behavior is increasingly uniform with one variant becoming dominant. Based on these dimensions, we differentiate eight patterns of herding behavior from slowly diffusing, small and disparate groups through to rapidly spreading, massive herds expressing a convergent behavior. We explore these herding patterns in an online setting, measuring their prevalence using over four thousand streams of data from the online micro-blogging application, Twitter. We find that all eight patterns occur in the empirical data set although some patterns are rare, particularly those where a convergent behavior rapidly spreads through the population. Importantly, those occurrences that develop into the pattern we call “stampeding,” i.e., the rapid spread of a dominant opinion expressed by many people, generally follow a consistent development path. The proposed framework can help managers to identify such noteworthy herds in real time, and represents a first step in anticipating this form of group behavior.     

The case against active pension funds: Evidence from the Turkish Private Pension System

Using data on private Turkish pension funds we show that most active managers are not able to provide performance beyond what could be achieved by passive indexing. The average fund beats its benchmark by only 26 basis points, before fees. We also observe herding behavior among managers' asset allocation decisions which can potentially explain their lack of overperformance. Our results strongly support the need for low-cost index funds in emerging market countries that are reforming their pension schemes. We further recommend regulatory oversight on the “activeness” of funds and introduction of default plans with more balanced asset allocations.     

房地产市场异常现象的行为金融学研究

房地产市场在发展的过程中出现了一些异常现象,传统金融理论面对这些异常现象无法给出令人信服的解释,现以行为金融学的噪声交易理论、反馈机制、羊群效应,对上海房地产市场出现的异常现象进行解释,并在此基础上寻求相应的对策,以促进房地产市场健康发展。     

Asymmetric Relationship between Investors' Sentiment and Stock Returns: Evidence from a Quantile Non‐causality Test

This study investigates the causal relationship between investor sentiment and stock returns in the USA by conducting a quantile Granger non‐causality test. Employing two proxies for investor sentiment – the sentiment index developed by Baker and Wurgler in 2007 and the University of Michigan Consumer Survey, a consumer confidence index – we find that the causal relationship between investor sentiment and stock returns strengthens when a tail quantile interval is considered. This finding implies that the investor sentiment could provide the incremental predictability for the stock returns under the extreme market situation, which cannot be found using a traditional Granger causality test. Interestingly, the findings can be explained by investors' loss aversion and herding behavior.