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行为金融学之投资者情绪对证券市场的影响作者：洪志东来源：《现代经济信息》2009年第23期摘要:从二十世纪六十年代以来,行为金融学逐渐成为一门新兴的发展学科。
投资者情绪理论作为行为金融学对证券市场群体研究的一个分支愈来愈受到重视,本文试着从另一方面浅述投资者情绪对证券市场的影响。
关键词:投资者情绪行为金融学20世纪50年代,Markowitz在“Journal of Fi.nance”发表的“Portfolio Selection”一文标志着现代金融学理论的开端。
Markowitz借助于统计技术发展的均值一方差模型研究了投资者的最优投资决策问题。
此后的30年里,理论创新不断涌现,金融学理论得到了空前的发展。
同样在20世纪50年代。
Modigliani和Miller通过对金融市场上证券供给问题的研究提出了著名的“MM 定理”,奠定了现代公司财务理论的基础。
Sharpe(1964),Lintner(1965)和Black(1972)在资产组合理论的相关假设前提下,在一般均衡框架下研究了单个投资者最优投资决策条件下的整体市场均衡, 提出了资本资产定价模型(CAPM),揭示了资本市场的价格形成机制,认为市场风险是决定收益的唯一不确定性因素。
ROSS(1976) 等学者又进一步提出了多因素资产定价模型——套利定价模型(A ),进而丰富了资本市场价格理论, 并与CAPM 共同构成了资产定价理论框架。
Fama (1970) 在Samuelson (1965) 和Roberts(1967)等学者研究的基础上系统总结了有效市场理论,构建了有效市场假说(EMH)。
Black、Scholes和Merton等人于20世纪70年代在MM 定理和CAPM的基础上发展了期权定价理论(O ),并被广泛应用于金融市场实践,为随之而来的金融产品的大量创新提供了坚实的理论和技术基础。
20世纪80年代,Ross、Grossman和Stiglitz等人另辟蹊径将博弈论和信息经济学引进到金融市场的分析当中, 进一步丰富了现代金融学理论。
中国股市投资者情绪测量研究_CICSI的构建近年来，中国股市的投资者情绪变化巨大，往往对股市波动产生重要影响。
为了准确捕捉中国股市投资者情绪的变化，并对市场走势进行预测，研究人员提出了中国股市投资者情绪指数（China Investor Confidence and Sentiment Index，简称CICSI）。
本文将介绍CICSI指数的构建过程和其在股市中的重要意义。
CICSI指数是基于投资者交易数据、媒体舆情以及市场研究的综合评估指标。
在构建过程中，研究人员将从多个维度入手，以保证指数的全面性和准确性。
首先，研究人员利用投资者交易数据来衡量市场的投资者情绪。
通过统计投资者的买卖行为，可以得出投资者对市场的信心水平。
例如，当投资者积极买入股票时，往往意味着他们对市场的情绪较好，认为股票价格将上涨；而当投资者大量卖出股票时，可能表示他们对市场的情绪较差，预期股票价格将下跌。
通过分析投资者的买卖行为，可以获取其对市场走势的预期和情绪变化。
其次，研究人员考虑到媒体对投资者情绪的影响。
媒体在股市中扮演着重要角色，可以直接或间接地影响投资者的情绪。
通过对媒体新闻报道的情感分析，可以得出股市投资者情绪的整体倾向。
例如，当媒体报道主要集中在股市利好消息时，可能会引起投资者的乐观情绪；而当媒体报道多为负面消息时，投资者的情绪往往会悲观。
最后，研究人员充分利用市场研究的成果。
通过对市场调研数据的分析，可以了解投资者对市场的整体看法和情绪变化。
市场调研可以以问卷调查、访谈等形式进行，以获取投资者的意见和预期。
通过对这些调研数据的分析，可以进一步细化投资者情绪的构建。
通过以上三个维度的数据综合分析，研究人员最终得出了CICSI指数，这是一个全面、准确地反映了中国股市投资者情绪的评估指标。
CICSI指数的构建不仅有助于了解投资者心理和情绪，也为投资者提供了重要的市场参考依据。
投资者可以根据CICSI指数的变化，调整自己的投资策略，以应对市场的波动。
投资者情绪对股票价格影响综述陈聪;赵玉平【摘要】随着行为金融学的盛行，投资者情绪对股票价格的影响已成为学术界的研究重心，非理性情绪成为重要的资产定价因素。
国内外对此均进行了一系列研究。
但是相对于国外而言，我国的研究起步较晚，且侧重点有所不同：国外研究主要集中于情绪对资产价格产生的总体效应及横截面效应，而我国对此的研究尚未深入。
本文对国内外的研究成果进行梳理，最终进行总结与展望。
%As behavioral finance is becoming popular,the impact of investor sentiment on stock price has been the focus of academic research and irrational sentiment has become an important asset pricing factor. Therefore,scholars at home and abroad have carried out a series of studies. However,compared with foreign studies,domestic researches start late and each has different emphases. Foreign studies mainly focus on the general effects of sentiment on asset price and the cross-sectional effects while China still lacks a further study of this subject. The article makes a review of research results at home and abroad,draws some conclusions and provides outlook for future direction.【期刊名称】《天津商业大学学报》【年(卷),期】2016(036)006【总页数】7页(P54-59,67)【关键词】投资者情绪;股票价格;文献综述【作者】陈聪;赵玉平【作者单位】武汉大学经济与管理学院，武汉430072;天津商业大学经济学院，天津 300134【正文语种】中文【中图分类】F832学术界对金融领域的研究自20世纪80年代就开始转向新的视角，学者试图将人类心理因素引入资产定价模型，研究该因素对资产价格的影响。
研究论文：投资者情绪对我国股票市场的效用研究109485 投资决策论文投资者情绪对我国股票市场的效用研究1研究背景及意义股票市场为经济生活提供一种独特的融资、资源配置、调控、反映和导向机制。
中国股票市场作为国民经济的重要组成部分和经济发展的重要推动器，在资本市场上发挥着至关重要的作用。
投资者情绪理论主要基于以下两个假设：一是市场是非有效的；二是投资者是不完全理性的。
对于像中国股市这样不成熟的资本市场实际状况而言，用投资者情绪理论更能客观准确地发现影响股票价格的深层次原因，为投资决策的实际操作提供理论支持。
2文献综述和心理学证据Andreassen（1990）发现，在向人们展示真实的股票价格序列后，让他们在模拟的市场中进行交易，当价格表现出一定趋势时，他们倾向于用过去的价格变化来进行交易；Koutmos（1997）对六个主要工业化国家的股票市场进行了研究，发现对于短期股票收益来说，反馈交易是影响股票收益的一个重要因素；Toshiaki（2002）对日本股票市场进行实证检验的结果也证明了反馈交易是造成股价波动的重要因素反馈交易并不限于非理性投资者，理性投资者也会表现出反馈交易的特征；赵鹂举和刘玉敏（2009）构建了一个包括反馈交易者和理性投机者的模型，发现反馈交易行为能造成收益的自相关和尖峰厚尾的特征。
3实证分析本文的变量包括投资者新增账户数、市盈率、换手率、消费者信心指数、上证指数收益率，均为月度数据。
选取的样本期间为2006年1月至20xx年10月。
收益的计算是根据价格的自然对数的一阶差分。
根据相关系数，我们可以发现开户数与市盈率之间的相关性最强，达到0.787。
其次是市盈率与换手率，相关系数为0.745。
相关性最弱的是新开户数和消费者信心指数，但是其数值也达到0.432。
由此我们得出结论，变量间存在着密切的正向关系，这符合一般经济理论。
本文通过建立合适的ARIMA 模型对投资者情绪进行探讨。
对于时间序列，在利用Eviews 6.0 对投资者情绪进行建模前需要平稳性检验。
投资者情绪与股票市场波动检验一、变量与数据1. 投资者情绪指标（SENT ）本文通过引入六个情绪代理变量，采用主成分分析法（PCA ），构建投资者情绪指标（SENT ）。
代理变量包括：（1）封闭式基金折价率。
该指标是投资者情绪基本代理变量之一，本文采用封闭式基金月末折价率加权平均值。
（2）市场换手率。
该指标是场内投资者情绪代理变量，本文采用市场整体交易量和流通市值之比衡量市场换手率，以此说明股市交易频繁程度，换手率越高，市场情绪水平越高。
（3）投资者新增开户数。
该指标是场外人士的市场情绪，该指标越高表示场外人士入市动力越强，市场情绪越高。
（4）上涨下跌家数比。
该指标采用统计区间内上涨家数与下跌家数的月度比值。
（5）A 股平均市盈率。
市场估值水平高低是投资者情绪变化的市场表现，在此引入市盈率指标作为A 股市场情绪代理变量之一。
（6）上证指数振幅。
本文采用上证指数的振幅作为市场情绪代理变量。
以上所有数据来自wind 金融数据库，均为2006年6月至2011年11月的月度数据。
本文采用主成分分析将上述六个情绪变量进行降维处理，在损失较少数据信息的基础上，提取出其中相同的受到情绪影响的部分，把多个情绪指标转化为投资者情绪综合指标。
前四个主成分的累计解释百分比达到88.5%，可以很好地解释大部分信息。
因此选取前四个主成分，按照各自解释百分比进行加权构造投资者情绪综合指标（SENT ）。
表1: 情绪代理变量主成分分析特征向量 123 4 5 6 封闭式基金折价率 0.008 0.791-0.406-0.019 0.452 0.061 换手率 0.583 -0.125 -0.216 -0.029 0.116 -0.764 市盈率 0.276 0.252 0.811 -0.360 0.269 -0.006 上涨下跌比 0.349 0.018 0.212 0.876 0.163 0.196 上证指数振幅 0.476 -0.396 -0.283 -0.304 0.342 0.572 新增开户数 0.485 0.370 -0.071 -0.093 -0.752 0.219 百分比 0.390 0.201 0.152 0.142 0.077 0.039 累计百分比 0.390 0.591 0.743 0.885 0.961 1.000 特征值2.342 1.2030.9140.8490.4600.2322. 信念调整变量：基金仓位（FUNDS ）股票型基金仓位在一定程度上可以代表机构投资者信念。
关于大学生投资股票市场的看法作文英语English:Investing in the stock market can be a beneficial financial strategy for college students, as it can provide opportunities for long-term growth and the potential to earn significant returns on their investments. However, it is crucial for college students to conduct thorough research and understand the risks involved in stock market investing before diving in. They should also consider factors such as their financial goals, risk tolerance, and time horizon when making investment decisions. Additionally, it is important for college students to start with a diversified portfolio to reduce risk and protect against market volatility. While investing in the stock market can be a valuable learning experience, college students should also seek guidance from financial advisors or experienced investors to help them navigate the complexities of the market.Translated content:大学生投资股票市场可以成为一种有益的理财策略，因为它可以为他们提供长期增长的机会，并有可能获得投资的重大回报。
本份文档包含：关于该选题的外文文献、文献综述一、外文文献标题: INVESTOR REACTION IN STOCK MARKET CRASHES AND POST-CRASH MARKET REVERSALS作者: Folkinshteyn, Daniel; Meric, Gulser; Meric, Ilhan期刊: The International Journal of Business and Finance Research 卷9；期5，年份：2015；页码: 57-70.INVESTOR REACTION IN STOCK MARKET CRASHES ANDPOST-CRASH MARKET REVERSALSABSTRACTWe study investor overreaction using data for five major stock market crashes during the 1987-2008 periods. We find some evidence of investor overreaction in all five stock market crashes. The prices of stocks investors bid down more than the average during crashes tend to increase more than the average in post-crash market reversals. In line with CAPM, we find that high beta stocks lose more value in crashes and gain more value in post-crash market reversals relative to low beta stocks. We further find that smaller firms and those with a low market-to-book ratio lose more value in stock market crashes. However, they do not gain more value in post-crash market reversals, implying that investor reaction against these firms in stock market crashes is not an overreaction. In examining industry-specific behavior, our results indicate that investors overbid down the prices of high-tech stocks in the 1997 crash and manufacturing stocks in the 2008 crash relative to other stocks. However, the prices of stocks in these industries increased more than other stocks in the post-crash market reversals, implying investor overreaction for these industries in these stock market crashes.KEYWORDS: Stock Market Crash, Post-Crash Market Reversal, Determinants of Stock Returns, Investor OverreactionINTRODUCTIONIn this paper, we study investor overreaction using data for five major stock market crashes during the 1987-2008 period. A stock market crash is commonly defined as a sudden dramatic decline of stock prices across a significant cross-section of a stock market. There is no generally accepted threshold for duration or magnitude for the decline in stock prices. Wang et al. (2009) and Gulko (2002) define a stock market crash as 5% or greater decrease in stock prices in a single trading day. In this paper, we study the stock market crashes with a minimum of 9.8% cumulative decline in stock prices in several consecutive trading days.Stock market crashes are generally followed by several days of sharp market reversal. If there is an overreaction towards stocks with certain financial characteristics during a crash, the reaction is reversed with a sharp market correction during the post-crash market reversal period. For instance, Wang et al. (2013) find that investors overreacted to the technical insolvency risk and bankruptcy risk characteristics of firms by bidding down their stock prices sharply in the 2008 crash. These stocks gained more value relative to other stocks in the post-crash market reversal. In this study, following the methodology used in Wang et al. (2013), we compare the crash and post-crash market reversal periods to determine if there was any investor overreaction in the five most important stock market crashes of the 1987-2008 period. The crash and post-crash market reversal periods included in the study are presented in Table 1.Our research makes several important contributions to the literature. We document a consistent pattern of investor overreaction in a large cross-sectional sample across five of the most significant stock market crashes of the past three decades. We also find that different stock characteristics had varying impact on the magnitude of overreaction among the events included in our study. The paper is organized as follows: The next section examines prior related literature. We then provide information about our data and methodology, and follow with a presentation of our results on stock market crashes during the crash and recovery periods. In the final section, we conclude the paper and note suggestions for future research. LITERATURE REVIEWStock market crashes have received considerable attention in finance literature. Arshanapalli and Doukas (1993) and Lau and McInish (1993) study the co-movements of the world's stock markets before and after the 1987 stock market crash. Roll (1988) and Pan et al. (2001) study the effects of emerging markets in stock market crashes. Wang et al. (2009 and 2010) study the determinants of stock returns in stock market crashes. De Bondt and Thaler (1985, 1987) argue that investors tend to overreact to economic events. Chopra et al. (1992), Rozeff and Zaman (1998), Bauman et al. (1999) and others provide empirical evidence for investor overreaction. In a recent article, Wang et al (2013) demonstrate that investors overreacted to the bankruptcy risk and technical insolvency risk characteristics of firms in the 2008 stock market crash. There are many possible explanations for this 'investor overreaction' phenomenon, from behavioral sentiment issues (Baker, Wurgler, 2006; Barberis, et al., 1998), to herding (Puckett, Yan, 2008) to market microstructure constraints (Park, 1995; Kaul, Nimalendran, 1990; Atkins, Dyl, 1990), to appropriate response to changing risk (Brown et al., 1993).Dreman and Lufkin (2000) conclude that investor overreaction is psychological. Amini et al. (2013) present an overview of the literature on price reversals. Analysis of price reversals accompanying issuespecific public news or lack thereof on a shorter time frame tends to find evidence of overreaction (e.g. Chan, 2003; Larson, 2003; Bremer, Sweeney, 1991). Chopra et al. (1992) examine a longer time frame and also conclude there is evidence of overreaction. While some researchers attempt to measure investor sentiment directly (Baker, Wurgler, 2006), in this study we follow most prior research and focus on the price movements and company financials to investigate the issue of investor overreaction in general stock market crashes with data for the five most important stock market crashes during the 1987-2008 period.DATA AND METHODOLOGYThe daily stock trading prices, used in the calculation of daily returns, are obtained from the Center for Research in Security Prices (CRSP) database. The 'crash return' is defined as the cumulative return over several consecutive daily price decreases in the S&P 500 index during the crash event. The 'recovery return' isdefined as the cumulative return over several consecutive daily price increases in the S&P 500 index immediately following the crash event. We use the event study methodology and calculate the cumulative stock returns during the crash and recovery windows using trading price data. We compute the CAPM betas of the stocks using the daily stock returns for the past 90 calendar days and the CRSPprovided returns on a value-weighted index which includes NYSE, NASDAQ, and ARCA securities. Firms with missing trading prices on key event dates and those with fewer than 30 trading quotes in the past 90 calendar days are excluded from the sample. Following Wang et al. (2009), we also exclude firms with a trading price of less than one dollar. We use the Research Insight (COMPUSTAT) quarterly database to collect balance-sheet information on the individual securities. For each security and each event, we select the latest available COMPUSTAT quarterly observation within the year prior to the start of the event. Firms with missing data are excluded from the sample.The study by Wang et al. (2013) finds that investors overreacted to the technical insolvency risk and bankruptcy risk characteristics of firms in the 2008 stock market crash. The current ratio measures the ability of a firm to meet its maturing obligations and is a standard measure of technical insolvency risk (see: Wang et al, 2013). The debt ratio is commonly used in empirical studies as a measure of firm bankruptcy risk (see, e.g., Mitton, 2002; Baek et al., 2004; Wang et al., 2013). The current ratio and the debt ratio are the two key financial ratios used in Ohlson's (1980) bankruptcy prediction model. We use these two financial ratios in our empirical tests to study investor reaction to technical insolvency risk and bankruptcy risk characteristics of firms in the five stock market crashes and post-crash market reversals included in the paper. Sharpe's CAPM has been generally tested with data for normal time periods. Beta has not been studied sufficiently as a determinant of stock returns in stock market crashes and post-crash market reversals. We use beta as a control variable in our empirical tests and we study if it was a significant determinant of stock returns in the stock market crashes and post-crash market reversals included in the paper. In their three-factor CAPM, in addition to beta, Fama and French (1992, 1993) use firmsize and the market-to-book ratio as determinants of stock returns. In our empirical tests, we also use these two variables as controls and investigate if they were significant determinants of stock returns in the stock market crashes and post-crash market reversals included in the study.Industry dummy variables are commonly used in cross-sectional studies of stock returns (see, e.g., Mitton, 2002; Baek at al., 2004; Wang et al., 2009). To control for the industry effect, we construct five broad industry portfolios (French, 2008) based on SIC codes. The portfolios are 'cnsmr', including consumer durables, nondurables, wholesale, retail, and some services; 'manuf', including manufacturing, energy, and utilities; 'hitec', including business equipment, telephone and television transmission; 'hlth', including healthcare, medical equipment, and drugs; and 'allother', which includes mines, construction, building materials, transportation, hotels, business services, entertainment, and finance. Utility firms' financial decisions are affected by regulation, while financial firm financial ratios are not comparable to those of other firms. Therefore, following Fama and French (2001, 2002), Gadarowski et al. (2007), and Wang (2009 and 2013), we exclude utilities (SIC code 4900-4999) and financial firms (SIC code 60006999) from our sample. The data items used in the study from the CRSP and COMPUSTAT databases are presented in Table 2. We list the variables constructed with the data in Table 3. After excluding observations with missing values, we winsorize extreme values using robust median-based measures of center and scale. At the end, we have 2591 observations for 1987, 4642 observations for 1997, 4443 observations for 1998, 4442 observations for 2000, and 3277 observations for 2008, a total of 19395 observations for the entire sample.The descriptive statistics for the sample are presented in Table 4. The statistics in the table show a pattern of growing firm size over time, both in terms of total assets and market cap. Mean total assets gradually increases from 968 million in 1987 to 5,184 million in 2008, which is expected given the general growth of the economy as well as dollar inflation over the time period. The mean current ratio steadily grows from 2.8 in 1987 to 3.06 in 2000, then drops back down to 2.77 in 2008. The mean debt-to-equity ratio starts out high at 1.34 in 1987, decreases to 1.25 in 1997, thengradually increases to 1.29 by 2008. The crash and recovery returns are highly variable within each event sample showing that, even during significant overall market moves, there is wide variation in the performance of individual stocks.We use the following multivariate regression model for each of the five crash events with the dependent variable as the crash return: (1)where a0 is a constant (the intercept term), ? is the error term, and a1, a2, ... a9 are the regression coefficients. The independent variables in the model are beta (beta), size (TCap), market-to-book ratio (mkbk), debt-to-equity ratio (dr), current ratio (cr), and the dummy variables for the industry portfolios (cnsmr, hitec, hlth, and manuf). The effect of the fifth portfolio, allother, is left in the intercept. We use the following multivariate regression model for each of the five post-crash market reversal events with the dependent variable as the recovery return: (2)where b0 is a constant (the intercept term), e is the error term, and b1, b2, ... b10 are the regression coefficients. The independent variables of Model (2) are the same as in Model (1) except the crash returns (crash.return) variable. Crash returns are used as an independent variable in Model (2) to determine if crash returns can explain post crash returns.RESULTS AND DISCUSSIONCrash PeriodsThe multivariate regression analysis results for the five crash periods using Equation (1) are presented in Table 5. The F statistics indicate that all five regressions in the table are statistically significant. The explanatory power of the model varies between the events with the adjusted R-squared ranging from a low of 8.5 percent for 1997 to 44.9 percent for 2000. For all the events, the regression coefficient of beta is significant and negative, which indicates that stocks with a higher beta lost more value in all five stock market crashes relative to lower beta stocks. This result is in line with the CAPM, which predicts that stocks with higher betas would lose more value in down markets relative to low beta stocks. The regression coefficient of thesize (TCap) variable is significant with a negative sign for the 1987 crash and with a positive sign for the 1998, 2000, and 2008 crashes. It is not statistically significant for the 1997 crash. The Fama and French (1992, 1993) three-factor CAPM argues that large firms are less risky than smaller firms. Therefore, the TCap variable should have a positive sign in a stock market crash. The 1998, 2000, and 2008 results confirm the prediction of the model. However, our results indicate that larger firms lost more value compared with smaller firms in the 1987 crash. There was a major market correction in stock prices in the 1987 crash. Investors might have thought that large firm stocks were more overvalued compared with small company stocks prior to the crash.The regression coefficient of the market-to-book (mkbk) variable is significant with a negative sign for the 1987, 1997, 1998 and 2000 crashes and with a positive sign for the 2008 crash. According to Fama and French's (1992, 1993) three-factor CAPM, the market-to-book ratio is a risk factor in capital-asset pricing. A low market-to-book ratio implies that the firm may be in financial distress. These firms are expected to perform worse compared with high market-to-book firms in stock market crashes. Our regression result for the 2008 crash confirms the theory's prediction. However, the results for the 1987, 1997, 1998, and 2000 crashes do not support the theory's prediction. Wang et al. (2013) determine that bankruptcy risk was a serious concern for investors in the 2008 crash. Therefore, we find that firms with a low market-to-book ratio and greater risk lost more value in the 2008 crash. In the 1987, 1997, 1998, and 2000 crashes, however, perhaps investors considered firms with a high market-to-book ratio to be overvalued prior to crash and they simply bid down their prices more relative to low market-to-book ratio firms during the crash.Our findings with the debt-to-equity (dr) variable also confirm our conclusion with the size (TCap) and mkbk variables. Bankruptcy risk was a significant concern for investors in the 2008 crash. Therefore, firms with a smaller size, lower mkbk, and higher dr lost more value in the 2008 crash. However, the dr variable is significant with a positive sign in the 1997, 1998, and 2000 crashes. Firms with a high dr performed better in these crashes relative to low dr firms. Since technical insolvencyrisk and bankruptcy risk were significant concerns for investors in the 2008 crash, like the dr variable, the current ratio (cr) variable is also significant for the 2008 crash with a positive sign. Firms with a higher cr (i.e., firms with a better ability to meet their maturing obligations) lost less value in the 2008 crash. We find a similar result for the 1987 crash. However, the sign of the cr variable is significant but negative for the 1998 and 2000 crashes. Firms with more investment in less profitable liquid assets lost more value in these crashes.The regression coefficients for the industry dummy variables indicate that there is significant variation ithe industry effect between the five crash events. With the exception of the 1987 crash, it appears that the consumer goods industry segment (cnsmr) generally performed better during crashes with positive and significant regression coefficients. Firms in the 'hitec' industry group performed better than the average in the 2008 crash and worse than the average in the 1997, 1998 and 2000 crashes. The healthcare (hlth) industry regression coefficient is significant only for the 1987 and 2000 crashes with firms in this industry underperforming the average in the former and outperforming the average in the latter event. The regression coefficient for the manufacturing (manuf) industry segment is positive and significant for 1997, 1998, and 2000, and negative for 2008, indicating that manufacturing firms performed better than the average in the 1997,1998, and 2000 crashes and worse than the average in the 2008 crash. We observe a pattern of opposition between the effects of the hitec and manuf industry sectors in stock market crashes. Whenever the regression coefficient of one is positive the other is negative, and vice versa. The cnsmr sector appears to perform consistently better than the average in crashes.Post-Crash Market Reversal PeriodsThe results of the regressions specified by Equation (2) are shown in Table 6. The F statistics indicate that all five regressions are statistically significant. The explanatory power of the model varies between the events but is on average higher than in the crash regressions with the adjusted R-squared ranging from a 14.7 percent for 1998 to 36.5 percent for 2000.For all the events, the regression coefficient of the crash return variable issignificant with a negative sign indicating that firms that experience a larger negative return during the crash period make up for it with a larger positive return in the post-crash market reversal. It implies investor overreaction during the crash period with a significant market correction in the post-crash market reversal.The regression coefficient of beta is significant with a positive sign for all events except the 1987 postcrash market reversal event. The 1997, 1998, 2000, and 2008 results are in line with the prediction of the CAPM. The model predicts that stocks with higher betas earn higher returns relative to low beta stocks in up markets. The regression coefficient of size (TCap) is significant with a positive sign for all post-crash market reversal events (i.e., large company stocks outperform small company stocks in all post-crash market reversals). This result is in line with the prediction of the Fama-French (1992, 1993) three-factor CAPM. The regression coefficient of the market-to-book ratio (mkbk) is positive and significant for all post-crash market reversal events except the 1987 event. The 1997, 1998, 2000, and 2008 results are in line with the prediction of the Fama-French model. The model predicts that high market-to-book stocks would outperform low market-to-book stocks in up markets.The regression coefficient of the debt-to-equity ratio (dr) is negative and significant for the 1997, 1998, and 2000 market reversal events. Since these coefficients are positive and significant during the crash, it implies that investors overreacted by bidding down the prices of low dr firm stocks too much in these crashes, which resulted in a significant market correction after the crash. The regression coefficients for the current ratio (cr) variable are statistically insignificant in all five post-crash market reversal events. It implies that there was no investor overreaction to the cr variable in the market crashes which would have resulted in a significant market correction in the post-crash market reversal. The signs and significance of the regression coefficients for the industry portfolios vary between the events. The industry effect appears to be generally less significant in the post-crash market reversal period than in the crash period. The results imply investor overreaction in the hitec industry in the 1997 crash and in the manuf industry in the2008 crash. Stocks that lost more value than the average in the hitec industry in the 1997 crash and in the manuf industry in the 2008 crash gained more value than the other stocks in the post-crash market reversal.Combined Data for All Five CrashesAlthough the five stock market crash events have a number of distinct characteristics, running regressions with the combined sample may provide some useful insights about the overall mean effects of the variables across all crashes. We present our regression results with the entire data set for all five market crash and post-crash market reversal events in Table 7. The F statistics indicate that both the crash regression and the post-crash market reversal regression are statistically significant at the 1-percent level.The regression coefficient of the crash return variable is significant with a negative sign for the post-crash market reversal. It indicates that stocks that lose more value in crashes tend to gain more value in postcrash market reversals. It implies investor overreaction in stock market crashes. The regression coefficient of beta is significant in both regressions and it has a negative sign for the crash and a positive sign for the post-crash market reversal. It implies that stocks with higher betas lose more value in crashes and they gain more value in post-crash market reversals relative to low beta stocks. This finding is in line with the prediction of the CAPM that high beta stocks lose more value in down markets and gain more value in up markets relative to low beta stocks. The regression coefficient of size (TCap) is significant with a positive sign in both regressions. It implies that stocks with larger market capitalization perform better compared with stocks with smaller market capitalization both in crashes and in post-crash market reversals. This finding is in line with the Fama-French (1992, 1993) three-factor CAPM, which argues that large firms are less risky and investors require lower returns from these firms.When the regression coefficient of a variable is significant with different signs in the crash and post-crash market reversal periods, it implies investor overreaction during the crash. The regression coefficient of the mkbk variable is significant in both regressions and it has a negative sign for the crash and a positive sign for thepost-crash market reversal. It implies that investors consider high mkbk stocks to be overvalued prior to crashes and they bid down their prices more relative to low mkbk stocks in stock market crashes. However, there is a significant market correction for the prices of high mkbk stocks in the post-crash reversals implying investor overreaction towards these stocks during the crash.The regression coefficient of the debt-to-equity ratio (dr) variable is significant in both regressions and it has a positive sign for the crash and a negative sign for the post-crash market reversal implying investor overreaction during the crash period. The result implies that the stocks of firms with higher debt ratios generally perform better in crashes (excluding the 2008 crash when investors had a serious concern with bankruptcy risk) but they perform worse in post-crash market reversals compared with the stocks of firms with lower debt ratios. The regression coefficient of current ratio (cr) is significant with a negative sign in both regressions. It implies that the stocks of firms with more investment in less profitable current assets generally perform worse both in crashes and in post-crash market reversals. However, this is an aggregate result for all crashes. Because technical insolvency risk was a major concern for investors, low cr firms lost more value relative to high cr firms in the 1987 and 2008 stock market crashes (see Table 6). All regression coefficients for the industry dummy variables are statistically significant for the crash period. The sign of the regression coefficient for the cnsmr, hlth, and manuf industries is positive. It implies that the stocks of firms in these industries generally perform better than the average in stock market crashes. The sign of the regression coefficient for the hitec industry is negative in the crash regression. It implies that the stocks in this industry generally perform worse than the average in crashes. The regression coefficients of all four industries are insignificant for the post-crash market reversal period. This implies that the stocks in all four industries generally perform similarly in post-crash market reversals with no major market correction for any industry to correct an overreaction during the crash periods.CONCLUSIONIn this paper, we study the determinants of stock returns in five major stockmarket crashes and post-crash market reversals during the 1987-2008 period to investigate if there was any investor overreaction in these crashes. Using daily closing prices we calculate cumulative returns for the crash and reversal periods for the events listed in Table 1, and regress crash and reversal returns on a number of firm characteristics. The regression coefficient of the crash return variable is statistically significant with a negative sign in all post-crash market reversal regressions. This result implies that there is investor overreaction in stock market crashes. Stocks that lose more value in crashes tend to gain more value after the crash with a significant market correction in the post-crash market reversal. Sharpe's CAPM predicts that high beta firms lose more value in down markets and gain more value in up markets compared with low beta firms. As predicted by the theory, in this paper, we find that high beta companies lose more value in stock market crashes and gain more value in post-crash market reversals.In the Fama-French (1992, 1993) three-factor CAPM, in addition to beta, firm size and market-to-book ratio are also market risk factors and determinants of stock returns. The model argues that smaller firms and those with lower market-to-book ratios are riskier. Therefore, investors would require a higher rate of return with a larger risk premium when valuing these firms. As predicted by the theory, we find that smaller firms and those with lower market-to-book ratios lose more value in stock market crashes. However, the sign of the regression coefficients for these variables does not change in the post-crash market reversals. It implies that investor reaction against smaller and lower market-to-book ratio firms in stock market crashes is not an overreaction. The regression coefficient of the debt ratio (dr) variable is significant with a positive sign in the 2008 crash. Since bankruptcy risk was a serious concern for investors, high-dr firms lost more value relative to low-dr firms in the 2008 crash. However, the regression coefficient for the dr variable is not significant in the post-crash market reversal. It implies that investors' bidding down the prices of high dr firms was not an overreaction in the 2008 crash.Our crash regressions show that, because technical insolvency risk was an important concern for investors, firms with a higher current ratio (cr) and thus greater。
投资者情绪对股票收益影响的研究综述投资者情绪对股票收益的影响一直是金融研究热点问题之一。
自从2001年"9·11"事件发生以来，以及2008年全球金融危机爆发以来，金融市场的波动变得更加剧烈，投资者情绪也更加波动不定。
本文综述了国内外有关投资者情绪对股票收益影响的研究，主要结论如下：一、投资者情绪与股票收益呈现明显正相关或负相关关系。
早期研究发现，投资者情绪对股票收益的影响主要表现为"反应迟滞、短期性"等特点。
但随着研究的深入，不少学者发现，投资者情绪与股票收益之间存在深层次关联，具有"前瞻性"等特点。
例如，Fama和French (1988)的研究表明，投资者情绪对股票收益具有显著负面影响；而Baker和Wurgler(2006)的研究结果显示，过度乐观的投资者情绪会导致股票的高估值，并最终引起市场下跌。
另外，Kim和Shamsuddin(2008)的研究发现，市场上过度悲观的投资者情绪会导致股票收益率的显著下跌。
从而可以看出，投资者情绪对股票收益存在着显著的正或负相关性。
二、投资者情绪对不同行业股票的影响存在差异。
投资者情绪对不同行业股票的影响存在差异，这是因为不同行业公司的盈利模式、市场风险和资本结构等差异较大。
例如，Ling和Raviv(2001)的研究发现，市场上乐观情绪较强的科技股票表现超过其他行业的股票；而Wu和Lin（2014）的研究则表明，在市场悲观情绪强烈时，基本面优良的金融股表现超过其他行业股票。
不同市场的股票对投资者情绪的反应也是存在差异的。
早在20世纪90年代，Kumar （1995）的研究表明，美国市场的股票对投资者情绪的反应具有"抗压能力"；而Edmans，Garcia和Nogales(2007)的研究发现，欧洲市场的股票对投资者情绪的反应较为敏感。
自从1990年代，以Momentum效应为代表的发现引起了广泛的关注。
近五年来有关投资者情绪影响企业投资行为的参考文献以下是关于投资者情绪影响企业投资行为的近五年的参考文献：1. Baker, M., & Wurgler, J. (2019). Behavioral corporate finance: An updated survey. Journal of Financial Economics, 133(2), 597-616.2. Bernstein, S., Giroud, X., & Müller, H. M. (2016). Corporate financial policies and the impact of the 2007–2008 financial crisis. Journal of Financial Economics, 119(3), 429-447.3. Chemmanur, T. J., Hu, G., & Huang, Y. (2017). The role of institutional investors in seasoned equity offerings. Journal of Corporate Finance, 45, 1-24.4. Dou, Y., Hu, Y., & Li, B. (2017). Investor sentiment, corporate investment, and productivity. Journal of Corporate Finance, 46,346-362.5. Hirshleifer, D., Li, D., & Zhang, Y. (2020). Investment sentiment and firm equity issuance. Journal of Financial Economics, 136(2), 379-394.这些文献涵盖了投资者情绪对企业投资行为的影响，包括投资决策、财务政策和股权发行等方面的研究。
关于投资者情绪的影响因素研究王保庆，黄解宇，孙维峰（运城学院经济管理系，山西运城044000）摘要：基于投资者情绪的行为金融学理论，利用我国消费者信心指数构建投资者情绪指标，建立VAR 模型，分析投资者情绪影响因素与情绪指标的关系。
同时利用格兰杰因果检验说明了三个因素对投资者情绪的影响。
此研究一方面可以使得投资者更加了解他们自身的情绪，减少非理性的投资行为；另一方面，也可以为政府相关金融监管机构制定相关政策提供一些借鉴。
关键词：投资者情绪；行为金融学；股票收益；货币政策中图分类号：F83文献标识码：Ａ文章编号：1005-913X （2020）02-0017-06Analysis of Research on the Influencing Factors ofInvestor SentimentWang Baoqing ，Huang Jieyu ，Sun Weifeng （Yuncheng University ，Yuancheng Shanxi 044000）Abstract ：Based on the behavioral finance theory of investor sentiment,this paper constructs the investor sentiment index by using China's consumer confidence index （CCI ）,establishes the VAR model,and analyses the relationship between the influ-encing factors of investor sentiment and the sentiment index.Finally,the Granger causality test is used to illustrate the impact of three factors on investor sentiment.On the one hand,this study can make investors more aware of their own emotions and reduce irrational investment behavior;on the other hand,it can also provide some reference for the relevant government finan-cial regulators to formulate relevant policies.Key words:Investor Sentiment;Behavioral Finance;Stock Returns;Monetary Policy收稿日期：2019-11-11基金项目：运城学院重点学科研究项目(XK-201708)；山西省“1331”重点创新团队建设计划资助；山西省哲学社会科学规划课题作者简介：王保庆(1979-)，男，山西晋中人，副教授，博士，硕士研究生导师，研究方向：金融发展理论；黄解宇(1966-)，男，山西运城人，教授，博士，博士研究生导师，研究方向：金融衍生品风险管理、金融集聚；孙维峰(1980-)，男，山东临沂人，教授，博士，硕士研究生导师，研究方向：产业经济学与金融集聚。
University of Massachusetts Boston ScholarWorks at UMass BostonFinancial Services Forum Publications Financial Services Forum 2-1-2005Measuring Investor Sentiment in Equity Markets Arindam BandopadhyayaUniversity of Massachusetts Boston , arindam.bandopadhyaya@Anne Leah JonesUniversity of Massachusetts Boston , anne.jones@Follow this and additional works at:/financialforum_pubsPart of the Finance and Financial Management CommonsThis Occasional Paper is brought to you for free and open access by the Financial Services Forum at ScholarWorks at UMass Boston. It has been accepted for inclusion in Financial Services Forum Publications by an authorized administrator of ScholarWorks at UMass Boston. For moreinformation, please contact library.uasc@ .Recommended CitationBandopadhyaya, Arindam and Jones, Anne Leah, "Measuring Investor Sentiment in Equity Markets" (2005).Financial Services Forum Publications.Paper 6./financialforum_pubs/6Measuring Investor Sentiment in Equity MarketsAbstractRecently, investor sentiment has become the focus of many studies on asset pricing. Research has demonstrated that changes in investor sentiment may trigger changes in asset prices, and that investor sentiment may be an important component of the market pricing process. Some authors suggest that shifts in investor sentiment may in some instances better explain short-term movement in asset prices than any other set of fundamental factors. In this paper we develop an Equity Market Sentiment Index from publicly available data, and we then demonstrate how this measure can be used in a stock market setting by studying the price movements of a group of firms which represent a stock market index. News events that affect the underlying market studied are quickly captured by changes in this measure of investor sentiment, and the sentiment measure is capable of explaining a significant proportion of the changes in the stock market index.JEL Classification: G11, G12.Key Words: Market Sentiment, Investor Sentiment and Risk Appetite.1. IntroductionTraditional research on asset pricing has focused on fundamental, firm-specific, and economy-wide factors that affect asset prices. Recently, however, some researchers have turned to investor psychology to explain asset-price behavior. It was previously assumed that there is little correlation among the sentiments of investors. The differing sentiments thus offset each other and there is no resulting effect on market prices. If, on the other hand, there is enough of a consensus among investors, their viewpoints will not offset and will instead become an integral part of the price-setting process. In fact, some researchers [e.g., Eichengreen and Mody (1998)] suggest that a change in one set of asset prices may, especially in the short run, trigger changes elsewhere because such a change engenders shifts in the market's attitude towards risk (i.e., because there is a change in investor sentiment). Such shifts in risk attitudes may explain short-term movements in asset prices better than any other set of fundamental factors [see, e.g., Baek, Bandopadhyaya and Du (2005)]. Other studies have also recognized that investor sentiment may be an important component of the market pricing process [see Fisher and Statman (2000) and Baker and Wurgler (2006)].Many investor sentiment measures have been identified in the academic literature and in the popular press. Dennis and Mayhew (2002) have used the P ut-Call Ratio, Randall, Suk and Tully (2003) utilize Net Cash Flow into Mutual Funds, Lashgari (2000) uses the Barron’s Confidence Index, Baker and Wurgler (2006) use the Issuance Percentage, Whaley (2000) uses the VIX-Investor Fear Gauge, and Kumar and Persaud (2002) employ the Risk Appetite Index (RAI). A more detailed list of studies that utilize these and other investor sentiment measures appears in Table 1.In this paper we show that the risk appetite measure developed by Persaud (1996) for currency markets can be successfully adapted to measure investor sentiment in an equity market using publicly available data. Using Persaud’s 1996 methodology we develop and quantify an Equity Market Sentiment Index (EMSI) for a group of firms in an equity market index. In prior studies, the Put-Call Ratio and the VIX-Investor Fear Gauge have been used as measures of investor sentiment in equity markets. However, as argued in Kumar and Persaud (2002), these measures could be measuring changes in the underlying risk of the market itself just as easily as they could be measuring changes in investor attitude towards that risk; it is not possible to isolate the two phenomena. The advantage of the RAI developed in Persaud (1996) and the EMSI constructed in this paper is that changes to the underlying riskiness of the market do not directly affect the proposed measure and thus these measures more accurately reflect the changes in the market’s attitude towards risk. The RAI and the EMSI speak specifically to the risk/return tradeoff embedded in prices and therefore focuses solely on the market’s willingness to accept whatever risks are inherent in the market at a given time.We construct the EMSI using stock market price data for firms listed in the Massachusetts Bloomberg Index (MBI) 1. We find that changes in our EMSI are closely related to news items regarding key firms in Massachusetts as well as to news reports on the condition of the Massachusetts economy as a whole. We also find that changes in the MBI are related to the EMSI. In fact, our results indicate that lagged values of the EMSI better explain changes in the MBI than do past changes in the MBI itself (i.e. MBI's own price momentum).1 The Massachusetts Bloomberg Index follows the performance of public companies which are either based in or do considerable business in Massachusetts. This Massachusetts Bloomberg Index closely approximates other indices that contain a larger collection of firms.The rest of the paper is organized as follows. Section 2 outlines the construction of the EMSI. Empirical results and discussion appear in Section 3. Section 4 concludes.2. The Construction of the Equity Market Sentiment IndexPersaud (1996) developed a measure of the market's attitude towards risk - a measure that he describes as the market's appetite for risk- in the context of currency markets.2 He argues that over the short run, in the foreign exchange market, the market's changing appetite for risk is a dominant force and at times is the most influential factor affecting currency returns. He goes on to suggest that if the market's appetite for risk were fixed, exchange rate changes would be driven only by unanticipated shifts in economic risk. If the appetite for risk grows and economic risks are unchanged, investors will feel overcompensated for these risk levels and the sense of overcompensation will grow as the level of risk grows.3 As investors take advantage of what they see as an improving risk-return trade off, currency values will change in line with their risk. High-risk currencies should appreciate more than low-risk ones and the riskiest currency should rally the most.4 Thus, a risk appetite index could be constructed based upon the strength of the correlation between the order of currency performance and the order of currency risk.In this paper we demonstrate that the technique developed in Persaud (1996) can be applied to an equity market setting by constructing the EMSI for a group of firms in the MBI. The MBI follows 242 firms which span more than 50 industries and range in size from $22 Persaud discusses the risk appetite in a research report published by JP Morgan Securities Ltd. This idea has received attention in the “Economics Focus” series in the Economist (1996), and in a 1998 conference on business cycles organized by the Federal Reserve Bank of Boston. Other studies [e.g., Baek, Bandopadhyaya and Du (2005)] have used Persaud’s notion of risk appetite to construct risk appetite indices applicable to different contexts.3 In Persaud, the risk of a currency is proxied by the yield on the bonds denominated in that currency.4 The reverse argument applies when the risk appetite falls. High-risk (or high yielding) currencies would be devalued more than those perceived to be safe.million to $42 billion in market capitalization. Using data over the period from July 2, 2003 to July 1, 2004, we compute daily returns for each of the securities in the MBI. For each of the securities, we also compute the average standard deviation of the daily returns over the previous five days (the “historic volatility”) for each day of the sample period.5 We then rank the daily rate of return and rank the historic volatility and compute the Spearman rank correlation coefficient between the rank of the daily returns for each firm and the rank of the historic volatility of the returns for each firm, and multiply the result by 100. The EMSI is therefore computed as follows:EMSI =()()1222()()*100ir r iv v ir r iv v R R R R R R −−⎡⎤−−⎢⎥⎣⎦∑∑∑; -100 ≤ EMSI ≤ +100 (1)where R ir and R iv are the rank of the daily return and the historical volatility for security i, respectively, and r R and v R are the population mean return and historical volatility rankings, respectively.3. Empirical Results and DiscussionFigure 1 presents the EMSI for the one year sample time period. EMSI ranges from ahigh of 48.09 to a low of -35.44. It averages 4.20 for the year with a standard deviation of 16.62. We place these EMSI values into five categories. For values between -10 to +10 we classify the market as risk-neutral, for values between -10 and -30 the market is labeled moderately risk-averse, and for values less than -30 the market is considered highly risk-averse. Similarly, if EMSI falls between +10 and +30, the market is labeled moderately risk-seeking, and if the index 5 Results do not change if standard deviations of returns over a different number of days are used.exceeds +30, the market is considered highly risk-seeking. During the sample period there were seventeen days on which the market was highly risk-seeking and seventy-eight days on which the market was moderately risk-seeking. The market was risk-neutral for one hundred and nine days, and exhibited moderately and highly risk-averse behavior for forty-two and six days respectively. For a summary of these categories, refer to Table 2.Movements in the EMSI capture both positive and negative news as reported in the Boston Globe, New England’s leading newspaper, concerning Massachusetts firms and the region's economy. A sample of news events and their impact on the EMSI appear in Table 3. For example, on August 8, 2003 when the Globe reported that the local economy was building steam, the EMSI increased by 31 points in a four-day period. On September 11 of that year, when the Globe reported that the high-tech sector may be poised for new hiring, the EMSI gained 36 points in one day. When news hit that Putnam Investment’s asset values fell by $14 billion, the EMSI dropped by 51 points in two days, and when the Commonwealth later charged Prudential with illegal trading, the EMSI again declined 38 points in three days. In reaction to an April 6, 2004 Globe story which indicated that Bank of America planned to cut 12,500 jobs, the EMSI plummeted 42 points, and later in May when it appeared that the Bank ofAmerica/Fleet Bank merger might cost Massachusetts 500 jobs, the EMSI declined another 26 points. Lastly, the EMSI rose 25 points after a June 2004 story regarding a boost in hiring by Boston employers.Not only do the movements in EMSI correspond with positive and negative news events affecting firms in Massachusetts and the economy of Massachusetts, but changes in the EMSI also closely replicate changes in the MBI. The EMSI and the MBI return for the same tradingday have a significant correlation coefficient of 74.84%. To investigate the explanatory power of the EMSI in greater detail, we first posit the following equation:MBI t = β0 + β1 MBI t-1 + β2 EMSI t + εt (2) MBI t = The return on the Massachusetts Bloomberg Index from day t-1 to day tEMSI t = The Equity Market Sentiment Index (see Equation 1) on day tWhile we were unable to confirm whether EMSI Granger causes MBI return or not, results indicate that the EMSI is able to explain changes in the MBI returns. The results from an estimation of Equation (1), which appear in Table 4, indicate that a majority of the variation in MBI t is explained by the two independent variables MBI t-1 and EMSI t (R2 = 0.56). Interestingly, while MBI t-1 (the lagged value of the return in MBI) has an insignificant impact on the dependent variable MBI t, the coefficient on EMSI t is highly significant. This implies that returns in the MBI for any given day were primarily driven not by returns on the previous day but by the risk-seeking behavior of market participants for that particular day.To further investigate the impact of the EMSI on the MBI, we estimate the following equation, which includes additional lagged values of the EMSI and the MBI:6MBI t = β0 + β1 MBI t-1 + β2 MBI t-2 + β3 MBI t-3 + β4 MBI t-4 + β5 MBI t-5 + β6 MBI t-6+ δ0EMSI t + δ1EMSI t-1 + δ2EMSI t-2 + δ3EMSI t-3+ δ4EMSI t-4 +δ5EMSI t-5 + ε t (3) 6 Standard specification tests were utilized to determine the appropriate number of lags included for both variables.(MBI t and EMSI t are defined earlier). To avoid autocorrelation problems associated with estimating Equation (3) using ordinary least squares, we used the polynomial distributed lagged model (see Harvey, 1990). The results from the estimation of Equation (3) appear in Table 5.A number of important observations emerge from an examination of Table 5. A comparison of the t-ratios across the different lagged variables indicates that the most significant variables explaining MBI t are the contemporaneous and one-day lagged values of the EMSI. The second lagged value of the EMSI is significant as well. Although they are relatively less significant, the lagged values of MBI t do play a significant role in the equation; however they lose their significance after two lags. Most importantly, while the sum of all the lagged values of MBI t jointly do not significantly impact MBI t, the lagged values of EMSI t combined do play a significant role. These results suggest that the EMSI better explains MBI returns than do past returns of the MBI itself.4. ConclusionThere has been growing interest in investor psychology as a potential explanation for stock price movements. In this study, using a technique developed in Persaud (1996), we construct a measure called the Equity Market Sentiment Index (EMSI) which utilizes publicly available data to measure the market’s willingness to accept the risks inherent to an equity market at a given point in time . This measure relates the rank of a stock's riskiness to the rank of its return and therefore directly measures the market's pricing of the risk-return tradeoff.Using data for the portfolio of firms included in the Massachusetts Bloomberg Index (MBI) we find that our EMSI captures Massachusetts-related news events as reported in the Boston Globe and is highly correlated with the MBI. Moreover, daily price movements in theMBI are significantly related to investor sentiment. In fact, our results indicate that lagged values of the EMSI better explain changes in the market index value than lagged values of the market index itself. This has important implications since it appears that short-run changes in the market index value are driven primarily by investor sentiment rather than by the index’s own price momentum. Researchers and practitioners should pay close attention to investor sentiment as a determinant of changes in financial markets.ReferencesBaek, In-Mee, Arindam Bandopadhyaya and Chan Du, (2005), “Determinants of Market Assessed Sovereign Risk: Economic Fundamentals or Market Risk Appetite?”, Journal of International Money and Finance, Vol. 24, Issue 4, pp. 533-548.Baker, Malcolm and Jeremy Stein, (2002), “Market Liquidity as a Sentiment Indicator”, Harvard Institute Research Working Paper, No. 1977.Baker, Malcolm and Jeffrey Wurgler, (2006), “Investor Sentiment and the Cross-section of Stock Returns”, Journal of Finance, Forthcoming.Branch, Ben, (1976), “The Predictive Power of Stock Market Indicators”, Journal of Financial and Quantitative Analysis, Vol. 11, Issue 2, pp. 269-286.Charoenrook, Anchada, (2003), “Change in Consumer Sentiment and Aggregate Stock Market Returns”, The Owen Graduate School of Management, Vanderbilt University, WorkingPaper.Chopra, Navin, Charles M. C. 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Kumar, Manmohan S. and Avinash Persaud, (2002), “Pure Contagion and Investors’ Shifting Risk Appetite: Analytical Issues and Empirical Evidence”, International Finance, 5:3, 401-436.Lashgari, Malek, (2000), “The role of TED Spread and Confidence Index in explaining the behavior of stock prices”, American Business Review, Vol. 18, Issue 2, pp. 9-11.Lee, Charles, Andrei Schleifer and Richard H. Thaler, (1991), “Investor Sentiment and the Closed-End Fund Puzzle”, Journal of Finance, 46, pp. 75-109.Neal, Robert and Simon M. Wheatley, (1998), “Do Measures of Investor Sentiment Predict Returns?”, Journal of Financial & Quantitative Analysis, Vol. 33, Issue 4, pp. 523-548.Persaud, Avinash, (1996), “Investors’ Changing Appetite for Risk”. J.P. Morgan Securities Ltd., Global FX Research.Randall, Maury R., David Y. Suk, and Stephen W. Tully, (2003), “Mutual Fund Cash Flows and Stock Market Performance”, Journal of Investing, Vol. 12, Issue 1, pp. 78-81.Whaley, Robert E., (2000), “The Investor Fear Gauge”, Journal of Portfolio Management, Vol.26, Issue 3.Table 1Measures of Market Sentiment Used in Prior ResearchName How Measured Studies1. Optimism/Pessimism about theEconomyIndex of Consumer Confidence Survey by Conference BoardFisher and Statman (2003)Consumer Confidence Index Survey by U Mich.- monthly Charoenrook (2003)Fisher and Statman (2003)2. Optimism/Pessimism about the StockMarketPut/Call ratio Puts outstandingCalls outstandingDennis and Mayhew (2002)Trin. Statistic Vol Decl issues/# DelVol Adv issues/# AdvNO ACADEMIC REFMutual Fund Cash positions % cash held in MFs Gup (1973)Branch (1976)Net cash flow into MF's Randall, Suk, and Tully (2003) Mutual Fund redemptions Net redemptions/total assets Neal and Wheatley (1998)AAII Survey Survey of individualinvestors Fisher & Statman (2000) Fisher & Statman (2003)Investors Intelligence Survey Survey of newsletter writers Fisher & Statman (2000) Barron's confidence index Aaa yield – Bbb yield Lashgari (2000)TED Spread Tbill futures yield –Eurodollar futures yieldLashgari (2000)Merrill Lynch Survey Wall St. sell-side analysts Fisher & Statman (2000)Fisher & Statman (2003)Table 1 (Continued)Measures of Market Sentiment Used in Prior ResearchName How Measured Studies3. Riskiness of the Stock MarketIssuance % Gross annual equities issuedGross ann. debt & equ. issuedBaker & Wurgler (2006)RIPO Avg. ann. first-day returns onIPO'sBaker & Wurgler (2006)Turnover Reported sh.vol./avg shs listedNYSE (logged & detrended)Baker & Wurgler (2006)Closed-end fund discount Y/E, value wtd. avg. disc. onclosed-end mutual fundsBaker & Wurgler (2006) Neal and Wheatley (1998) Lee, Schleifer, & Thaler(1991) Chopra, Lee, Schleifer, & Thaler (1993)Market liquidity Reported share volumeAvg # of sharesBaker & Stein (2002 WP)NYSE seat prices Trading volume orquoted bid-ask spreadKeim and Madhavan (2000)4. Riskiness of an individual stockBeta CAPM Various 5. Risk AversionRisk Appetite Index Spearman Rank correlationvolatility vs. excess returnsKumar and Persaud (2002) VIX – Investor Fear Gauge Implied option volatility Whaley (2000)Risk Categorization of Daily EMSI FiguresRange of EMSI Category Number of Days -30 and below Highly Risk Averse 6-10 to -30 Moderately Risk Averse 4210 to + 10 Risk Neutral 109+10 to +30 Moderately Risk Seeking78+30 and above Highly Risk Seeking 17News and EMSINews Fact Date Index Change (Up/ Down) From (Date) To (Date) CONFIDENCE AMONG MASS. FIRMS LEAPS 2-Jul-03 ▲ 36 (-5 to 31) 3-Jul-03 8-Jul-03 AN AILING IMAGE: DRUG INDUSTRY'S TENACIOUS PRICEPROTECTION STIRS ANGER 11-Jul-03 ▼ 56 (23 to -33) 14-Jul-03 17-Jul-03 DATA SUGGEST ECONOMY BUILDING STEAM 8-Aug-03 ▲ 31 (-3 to 34) 8-Aug-03 12-Aug-03 BAY STATE JOBLESS RATE DECLINES 16-Aug-03 ▼ 52 (36 to -16) 18-Aug-03 22-Aug-03 INVESTORS’ LOYALTY FACING TEST 10-Sep-03 ▼ 60 (30 to -30) 10-Sep-03 11-Sep-03 `NOW HIRING' RETURNING TO HIGH TECH'S VOCABULARY 11-Sep-03 ▲ 36 (-30 to 6) 11-Sep-03 12-Sep-03 A WARY EYE ON THE BULLS: The dollar could lose value 23-Sep-03 ▼ 49 (14 to -35) 23-Sep-03 24-Sep-03 STATE REVENUE UP, BUT DISAPPOINTING 2-Oct-03 ▼ 34 (37 to 3) 3-Oct-03 10-Oct-03 INVESTOR HABITS LIKELY TO CHANGE: Top executive at PutnamInvestments resigned 4-Nov-03 ▼ 47 (25 to -23) 4-Nov-03 10-Nov-03 PUTNAM ASSETS FALL BY $14B 11-Nov-03 ▼ 51 (30 to -21) 12-Nov-03 14-Nov-03 IN DIVIDENDS WE TRUST: Biggest increase in payouts 20-Nov-03 ▲ 57 (-9 to 48) 20-Nov-03 25-Nov-03 FUND INVESTORS RETHINKING THEIR STRATEGY 28-Nov-03 ▼ 50 (25 to -25) 1-Dec-03 9-Dec-03 SURVEY: MASS. LOSING ANCHOR COMPANIES 9-Dec-03 ▼ 25 (0 to -25) 9-Dec-03 10-Dec-03 STATE CHARGES PRUDENTIAL ALLOWED ILLEGAL TRADING 12-Dec-03 ▼ 38 (20 to -18) 12-Dec-03 15-Dec-03 $750B VOW FOR LENDING DRAWS FIRE 8-Jan-04 ▼ 37 (25 to -12) 8-Jan-04 9-Jan-04 MFS APPEARED AWARE OF MARKET TIMING 16-Jan-04 ▼ 29 (10 to -19) 16-Jan-04 22-Jan-04 REBUILDING A HIGH-TECH GIANT 22-Jan-04 ▲ 37 (-19 to 18) 22-Jan-04 26-Jan-04 NO BUBBLE BILLIONAIRES: Boston Scientific shares to an all-time high 5-Feb-04 ▲ 46 (-15 to 31) 5-Feb-04 6-Feb-04 GREAT NUMBERS, BUT SHOW US YOUR WORST: The mutual fund industryhas declared open season 22-Feb-04 ▲ 34 (-17 to 17) 23-Feb-04 25-Feb-04 THE GOOD AND THE BAD OF A FUND CLOSING 7-Mar-04 ▼ 29 (10 to -19) 7-Mar-04 9-Mar-04 TRUSTEES ON THE HOT SEAT 16-Mar-04 ▼ 51 (39 to -12) 17-Mar-04 23-Mar-04 MUTUAL FUND FIRMS ADDING DISCLAIMERS 22-Mar-04 ▲ 34 (-12 to 22) 23-Mar-04 25-Mar-04 BANK OF AMERICA TO CUT 12,500 JOBS 6-Apr-04 ▼ 42 (20 to -22) 6-Apr-04 14-Apr-04 EMC QUARTERLY EARNINGS AND REVENUES POST GAINS 16-Apr-04 ▲ 24 (-10 to 14) 16-Apr-04 19-Apr-04 GROWTH SOLID IN QUARTER: 4.2% RISE IN GDP 30-Apr-04 ▲ 47 (-26 to 21) 30-Apr-04 5-May-04SIGN OF REBOUND: SMALL FIRMS THINKING BIGGER 9-May-04 ▲ 46 (-35 to 11) 9-May-04 12-May-04MERGER TO CLAIM 500 JOBS: BoA SAYS LOSSES WILL HIT MASS.OVER 2 YEARS 14-May-04 ▼ 26 (10 to -16) 14-May-04 18-May-04NUMBERS DOWN, CHINS UP AT MERGED BIOTECHS 18-May-04 ▲ 48 (-16 to 32) 18-May-04 25-May-04STRATEGIC FIT: BOSTON SCIENTIFIC PAYS $740M FORMICROELECTRONIC 2-Jun-04 ▲ 35 (-15 to 20) 2-Jun-04 7-Feb-04 BOSTON EMPLOYERS ARE PLANNING TO BOOST HIRING 15-Jun-04 ▲ 25 (9 to 34) 15-Jun-04 23-Jun-04Explanation of Massachusetts Bloomberg Index ReturnsUsing Ordinary Least Squares EstimatesMBI t = β0 + β1 MBI t-1 + β2 EMSI t + εtMBI t = Massachusetts Bloomberg Index return from day t-1 to tMBI t-1 = One period lagged value of MBI tEMSI t = The Equity Market Sentiment Index on day tVariable Coefficient t-Statistic P-ValueConstant -0.001321 -2.96277 0.0033MBI t-10.040734 0.977536 0.3342EMSI t0.046143 17.78022 0.00000.561510R-SquaredAdjusted R-Squared 0.557973Durbin Watson Statistic 2.231518F Statistic 158.7884Value (F Statistic) 0.0000Explanation of Massachusetts Bloomberg Index ReturnsUsing Polynomial Distributed Lagged Model Estimates MBI t = β0 + β1 MBI t-1 + β2 MBI t-2 + β3 MBI t-3 + β4 MBI t-4 + β5 MBI t-5 + β6 MBI t-6 + δ0 EMSI t + δ1EMSI t-1 + δ2EMSI t-2 + δ3EMSI t-3+ δ4EMSI t-4 +δ5EMSI t-5 + ε t MBI t = Massachusetts Bloomberg Index return from day t-1 to tMBI t-i = i period lagged value of MBI tEMSI t = The Equity Market Sentiment Index for Massachusetts on day tEMSI t-i = i period lagged value of EMSI tVariable Coefficient t-StatisticMBI t-1 -0.24937 -4.63278**MBI t-2 -0.08360 -1.99927*MBI t-3 0.02330 0.51883MBI t-4 0.07134 1.68805MBI t-5 0.06051 1.88195MBI t-6 -0.00919 -0.22753Sum of Lags -0.18702 -1.09072Variable Coefficient t-StatisticEMSI t 0.03873 16.3857**EMSI t-1 0.02262 13.0613**EMSI t-2 0.01043 4.48360**EMSI t-3 0.00215 0.86171EMSI t-4 -0.00221 -0.93336EMSI t-5 -0.00265 -0.82559Sum of Lags 0.06908 7.47905**** Denotes significance at 1% level* Denotes significance at 5% level0.570109R-SquaredAdjusted R-Squared 0.559317Durbin Watson Statistic 1.846193F Statistic 52.82586Value (F Statistic) 0.0000The Equity Market Sentiment Index: July 2, 2003 – July 1, 2004。
