行为金融学文献13Does the Stock Market Overreact

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行为金融学在证券市场的应用

行为金融学在证券市场的应用1. 引言行为金融学是一门研究人们在金融决策中行为特征和心理偏差的学科。

它揭示了投资者在做出金融决策时受到心理因素的影响，与传统金融理论的理性假设形成对比。

行为金融学的研究结果对于解释证券市场的投资行为和价格形成有重要意义。

本文将分析行为金融学在证券市场的应用，并探讨相关案例和实践经验。

2. 投资者心理偏差行为金融学认为，投资者在进行金融决策时，会受到多种心理偏差的影响。

其中包括过度自信、风险厌恶、注意偏差、羊群效应等。

这些心理偏差导致投资者在决策过程中产生认知偏差，从而影响他们的投资决策和行为。

3. 证券市场的异常现象行为金融学的研究结果显示，证券市场存在一些异常现象，无法用传统金融理论来解释。

例如，股票价格的过高或过低，并不能仅仅归因于市场信息的变化。

行为金融学认为，这些异常现象往往是由投资者的心理偏差所致。

4. 案例分析：股票市场的过度自信过度自信是投资者常见的心理偏差之一。

研究表明，投资者在判断自己的投资能力时存在普遍的高估现象。

在证券市场中，过度自信可能导致投资者高估自己的股票选择能力，过度交易和频繁买卖股票。

这种行为可能导致交易成本增加，并对投资者的收益产生负面影响。

5. 实践经验：避免心理偏差对投资决策的影响在证券市场中，投资者可以通过采取一些策略来避免心理偏差对投资决策的影响。

首先，投资者应该保持冷静，避免过度自信和冲动交易。

其次，他们应该重视市场信息的分析和研究，减少注意偏差的影响。

此外，投资者还可以通过建立投资组合和多元化来分散风险，以降低心理偏差对投资决策的影响。

6. 行为金融学的意义和局限性行为金融学的研究结果对于理解证券市场的投资行为和价格形成具有重要意义。

然而，行为金融学也存在一些局限性，例如个体行为的预测能力有限，无法解释市场整体的波动等。

因此，在应用行为金融学的研究成果时，需要进行综合分析并结合其他相关理论。

7. 结论行为金融学在证券市场中的应用为我们提供了一种全新的视角来解释投资者的行为和市场价格形成。

行为金融学文献22JFE 2012Investor attention,psychologicalanchors,andstock return predictability [2]

Investor attention,psychological anchors,and stockreturn predictability$Jun Li,Jianfeng Yu nUniversity of Minnesota,Department of Finance,3-133Carlson School of Management,32119th Avenue South,Minneapolis,MN55455,United Statesa r t i c l e i n f oArticle history:Received28September2010Received in revised form4March2011Accepted18April2011JEL classiﬁcation:G12G14Keywords:AttentionAnchorOverreactionUnderreaction52-week higha b s t r a c tMotivated by psychological evidence on limited investor attention and anchoring,wepropose two proxies for the degree to which traders under-and overreact to news,namely,the nearness to the Dow52-week high and the nearness to the Dow historicalhigh,respectively.Weﬁnd that nearness to the52-week high positively predicts futureaggregate market returns,while nearness to the historical high negatively predictsfuture market returns.We further show that our proxies contain information aboutfuture market returns that is not captured by traditional macroeconomic variables andthat our results are robust across prehensive Monte Carlo simulationsand comparisons with the NYSE/Amex market cap index conﬁrm the signiﬁcance oftheseﬁndings.&2011Elsevier B.V.All rights reserved.1.IntroductionFinancial economists have sought to identify variablesthat forecast aggregate stock market returns.This articleinvestigates the ability of the nearness to the Dow52-week high and the nearness to the Dow historical high topredict market returns.The predictors we propose in thisstudy are motivated by empirical evidence on psycholo-gical anchoring and limited investor attention.In an intriguing study,George and Hwang(2004)suggest that traders might use the52-week high as ananchor when assessing the increment in stock valueimplied by new information.They argue that a stockwhose price is at or near its52-week high is a stock forwhich good news has recently arrived,and that this maybe precisely the time when traders’underreaction to goodnews is at its peak.Hence,nearness to the52-week high ispositively associated with expected returns in the cross-section.On the other hand,Peng and Xiong(2006)showthat limited investor attention leads to category-learningbehavior,i.e.,investors tend to process more market-wideinformation thanﬁrm-speciﬁc information.Because theDow index is arguably the most widely available informa-tion about the market,investors are likely to use the Dowindex as a benchmark when evaluating new market-wideinformation.Taken together,we conjecture that nearnessto the Dow52-week high captures the extent of under-reaction,and it should forecast aggregate market returns.Grifﬁn and Tversky(1992)suggest that individualsmight underreact to sporadic news,but overreact to aprolonged record of salient performance,regardless ofContents lists available at SciVerse ScienceDirectjournal homepage:/locate/jfecJournal of Financial Economics0304-405X/$-see front matter&2011Elsevier B.V.All rights reserved.doi:10.1016/j.jﬁneco.2011.04.003$First draft:October2009.We would like to thank an anonymousreferee,Santiago Bazdresch,Frederico Belo,John Boyd,Murray Frank,Bob Goldstein,Felix Meschke,Stavros Panageas,William Schwert,JianWang,and seminar participants at the University of Minnesota and the2010Econometric Society World Congress for comments.Of course anyerrors are our own.n Corresponding author.Tel.:þ16126255498;fax:þ16126261335.E-mail addresses:lixxx353@(J.Li),jianfeng@(J.Yu).Journal of Financial Economics](]]]])]]]–]]]whether good or bad.Motivated by Grifﬁn and Tversky (1992),George and Hwang(2004),and Peng and Xiong (2006),we further conjecture that traders might also use the Dow historical high as an anchor when evaluating information.However,the effect of this anchor is expected to be opposite that of the Dow52-week high anchor.In particular,when the current price is far from its historical high,this may be precisely the time when traders’over-reaction to bad news is at its peak.Because,in this case,it is likely that there has been a series of bad news in the past,traders overreact to prolonged news.Hence,we hypothesize that nearness to the Dow historical high captures the extent of overreaction,and that it should be negatively associated with future market returns.Armed with these two psychologically motivated proxies for under-and overreaction,we then empirically explore their ability to forecast aggregate excess market ing the Dow Jones Industrial Average index,we compute nearness to the52-week high and nearness to the historical high.We show that there is no momentum in aggregate market returns when we regress future excess market returns on past excess market returns alone.How-ever,after we control for nearness to the historical high, past excess market returns signiﬁcantly predict future excess market returns.This indicates that nearness to the historical high contaminates the relationship between future returns and past performance.Furthermore,when nearness to the52-week high is included in the regression along with nearness to the historical high and past returns, the predictive ability of past returns weakens substantially while nearness to the52-week high signiﬁcantly predicts future excess market returns.This indicates that the pre-dictive ability of past market returns is dominated by nearness to the52-week high,conﬁrming the cross-sec-tionalﬁndings of George and Hwang(2004).In a horse race regression in which future excess market returns are regressed on the nearness to the52-week high,nearness to the historical high,and a set of macro variables,our proposed predictors have the greatest power and are stable across subsamples and other G7countries.Note that the negative predictive power of nearness to the historical high could also be consistent with a rational model with a mean-reverting state variable.Hence,to differentiate our limited-attention explanation with the unobservable-state-variable explanation,we replace the most visible Dow index with the economically more mean-ingful market cap from NYSE/Amex,and weﬁnd that the predictive power from the nearness to the historical high is much lower.This suggests a special role for the Dow index,probably due to its visibility and investors’limited attention,consistent with Peng and Xiong(2006).Conse-quently,an unobservable mean-reverting state variable is unlikely to account for the predictive power of nearness to the historical high.We also perform comprehensive Monte Carlo simulations to conﬁrm the signiﬁcance of theseﬁndings.We provide further support for our hypotheses in cross-sectional analysis.Weﬁrst identify a group ofﬁrms that are less likely to have experienced overreaction in the past. Speciﬁcally,weﬁnd that for stocks with only one anchor, that is,for which the52-week high equals the historical high,the momentum effect is about three times stronger.1 For stocks with two anchors,the momentum effect is no longer signiﬁcant in a simple one-way sorting by nearness to the52-week high.However,after controlling for near-ness to the historical high,the momentum effect re-emerges signiﬁcantly.A similar pattern is found for the historical high.When controlling for nearness to the 52-week high,nearness to the historical high is positively associated with expected returns.However,this effect is insigniﬁcant in a simple one-way sorting by nearness to the historical high.We also demonstrate a link between the historical high and value investing.In particular,we show that the value premium is much weaker amongﬁrms for which overreaction is less likely,that is,for which the 52-week high equals the historical high.Our paper’s main contribution to the literature is at least threefold.First,we propose two novel predictors based on the visible Dow index and show that these variables are important predictors for future aggregate market returns. Indeed,these predictors,which are based on psychological anchors,have a few advantages compared with the tradi-tional macroeconomic predictors.For instance,unlike divi-dend yield,our predictors have the greatest power at horizons of less than one year.Our proxies are therefore not subject to criticisms on long-run predictability.In addition,unlike the consumption–wealth ratio,our predic-tors have no look-ahead bias.Second,we show that the historical high is also an anchor that investors use when evaluating information.This anchor has an effect that is opposite that of the52-week high anchor.Controlling for the historical high,the momentum effect is two to three times larger.On the other hand,controlling for the52-week high,the value premium is much stronger.Finally,although there is abundant evidence on stock market over-and underreaction,most of the statistical evidence comes from the cross-section of stock returns.Whether behavioral biases can affect the aggregate market return is still under debate.We add to the literature by showing that two behavioral-bias-motivated variables have strong power to forecast future aggregate market returns.In addition,this paper contributes to the price barrier literature,2led by Donaldson(1990a,1990b)and Donaldson and Kim(1993).The concept of psychological barriers is closely related to the issue of anchoring and heuristic simpliﬁcation.Donaldson and Kim(1993)ﬁnd that multiples of100and1,000in the Dow index are difﬁcult to break through,and hence,these levels are approached as well as transgressed relatively infre-quently.Many subsequent studies conﬁrm the existence of price barriers in other asset classes.Despite the strong evidence of psychological barriers in asset prices,the evidence of the induced return predictability by price barriers is limited and mixed.This paper adds to the barrier literature by showing that two other psychological 1See Section2for more discussion on the intuition behind this.In short,in Section2we argue that,for stocks whose52-week high equals the historical high,it is less likely that there has been overreaction in the past.2We are grateful to the referee for bringing this interesting earlier literature to our attention.J.Li,J.Yu/Journal of Financial Economics](]]]])]]]–]]] 2anchors,the52-week high and the historical high,induce strong predictability in aggregate market returns.In this sense,our evidence complements the price barrier literature.The rest of the paper is organized as follows.Section2 describes the psychological and statistical evidence on under-and overreaction and provides the intuition behind our proxies.Section3presents the empirical evidence on return predictability induced by psychological anchors. Section4concludes.2.Related literature and motivationThis section summarizes the statistical and psychologi-cal evidence on under-and overreaction and provides the intuition for our proposed predictors of expected returns. The empirical work pointing to under-or overreaction in asset markets is vast,so that it is impossible to provide a comprehensive review here.Barberis,Shleifer,and Vishny (1998),Daniel,Hirshleifer,and Subrahmanyam(1998),and Fama(1998)provide excellent summaries of this literature.Psychological studies related to over-and underreac-tion are also extremely numerous.One important phe-nomenon that has been identiﬁed by many psychologists (e.g.,Edwards,1968)is conservatism.Conservatism refers to the tendency of individuals to be reluctant or slow to change their prior beliefs in the face of new information. This,of course,is consistent with underreaction in the stock market where high past returns predict high future returns(e.g.,Cutler,Poterba,and Summers,1991; Jegadeesh and Titman,1993).Tversky and Kahneman (1974)document another important phenomenon,the representativeness,which is the tendency of human beings to view events as representative of some speciﬁc class and to ignore the laws of probability.For example, when investors see that a company displays a series of high earnings growth,they may classify this company as a growthﬁrm and ignore the probability that very few companies can keep growing.This is in line with the overreaction evidence from the stock market where long-term past performance is negatively associated with future returns(e.g.,De Bondt and Thaler,1985;Zarowin, 1989).Finally,to unify conservatism and representative-ness,Grifﬁn and Tversky(1992)suggest that individuals might underreact to intermittent news,but overreact to a prolonged record of salient performance.In a nutshell,previous empirical studies suggest that stock prices tend to underreact to intermittent news such as earnings announcements,and overreact to a series of news,either good or bad.However,most of the strong evidence comes from the cross-section of stock returns, and whether behavioral biases can affect the aggregate market return is still under debate.In this paper,as mentioned in the introduction,we propose nearness to the52-week high as a proxy for underreaction,and nearness to the historical high as a proxy for overreaction. In the data,weﬁnd that our behavioral-bias-motivated variables have strong power to predict future aggregate market returns,especially for short horizons of1–12 months.Below we provide two potential justiﬁcations for our hypothesis that our proxies capture under-and overreaction in the stock market.One possible justiﬁcation is based on the stock market’s underreaction to intermittent news and overreaction to a prolonged series of news.By comparing the current price to the52-week high,it is more likely that this would pick up underreaction to sporadic past recent news.For example,if nearness to the52-week high is high,it is more likely that theﬁrm has experienced sporadic good news in the recent past.Psychological evidence on conservatism suggests that traders tend to underreact to this good news in the recent past.Similarly,if the current price is far below its52-week high,it is more likely that theﬁrm has experienced intermittent bad news in the recent past.Again,conserva-tism suggests that traders underreact to this bad news in the recent past.We therefore use nearness to the52-week high to summarize the degree of good news that the market has underreacted to in the past year,where nearness to the 52-week high is expected to be positively associated with future returns.Analogously,if the current price level is far from the historical high,it is more likely that theﬁrm has experienced a series of bad news in the past.As representa-tiveness suggests,traders tend to overreact to a series of bad news,and hence subsequent returns should be higher. As a consequence,we use the distance to the historical high to summarize the degree of bad news that the market has overreacted to in the past.Another possible justiﬁcation comes from the experi-mental research on‘‘adjustment and anchoring bias’’. Kahneman,Slovic,and Tversky(1982)report on experi-ments in which subjects are asked to estimate a quantity as an increment to a randomly generated number that the subject observes.Estimates are higher(lower)for subjects that start with higher(lower)random numbers.Based upon this idea,George and Hwang(2004)suggest that traders might use the52-week high as an anchor against which they evaluate the potential impact of news.When good news in the past year pushes a stock’s price near a new52-week high,traders are reluctant to bid the price of the stock higher even if the information warrants it,that is,they underreact to the news.However,the information eventually prevails and the price moves up,leading to a continuation.Similarly,when bad news in the past year pushes a stock’s price far from its52-week high,traders are reluctant to sell the stock at prices that are as low as the information implies,that is,they underreact to the news.The information eventually prevails,however,and the price falls.As a consequence,nearness to the52-week high summarizes the degree of good news that the market has underreacted to in the past year.On the other hand,we further conjecture that traders may use the historical high as another anchor against which they evaluate information.However,this anchor tends to generate overreaction.Representativeness pro-vides a natural account for this overreaction.When prolonged bad news pushes a stock’s price far below its historical high,traders may sell the stock at prices that are lower than the news would imply,that is,traders may overreact.However,the information eventually prevails and the price moves up,resulting in higher subsequent returns when the current price is far below its distantJ.Li,J.Yu/Journal of Financial Economics](]]]])]]]–]]]3historical high.Hence,the distance to the historical high can summarize the degree of bad news that the market has overreacted to in the past.Therefore,psychological anchoring provides an additional possible way to justify our proxies to under-and overreaction.Peng and Xiong (2006)show that investors with limited attention tend to process more market-and sector-wide information than ﬁrm-speciﬁc information.Because the Dow index is arguably the most widely available informa-tion about the market,investors are likely to use the Dow index as a benchmark when evaluating new market-wide information.Taken together,nearness to the Dow 52-week high and nearness to the Dow historical high should both forecast future aggregate market returns,but with a differ-ent sign.Furthermore,nearness to the Dow index should be better at capturing information than nearness to the total market capitalization index (e.g.,NYSE/Amex market cap).Of course,there could be a common component in these two proxies.For example,nearness to the 52-week high may also include information on overreaction since there might be some salient information in past news,especially when the stock is very close to or very far from its 52-week high.However,by controlling for nearness to the historical high,nearness to the 52-week high should be a more pure proxy for underreaction.Therefore,by putting both proxies on the right side of the regression,they should pick up more information on expected returns resulting from under-and overreaction.Furthermore,in unreported analysis,we simulate a variant of the model by Barberis,Shleifer,and Vishny (1998)where investors underreact to sporadic news and overreact to a prolonged record of extreme performance.The simulation results are consistent with our predictions.Speciﬁcally,in the model simulation,nearness to the historical high is a proxy for overreaction and negatively predicts future returns,while nearness to the 52-week high is a proxy for underreaction and positively associated with future returns.For the cross-section of stocks,we consider the special case in which the historical high equals the 52-week high.In this case,investors only have one anchor against which to evaluate information.We conjecture that,in this case,investors tend to ignore the historical anchor because the 52-week high is psychologically more recent.More impor-tantly,when the 52-week high equals the historical high,ﬁrms are unlikely to have experienced a series of bad news in the past,and hence are less likely to have experienced an overreaction.3Thus,compared with other stocks,there should be less overreaction in the past among these stocks.We therefore argue that for ﬁrms with the same 52-week high and historical high,nearness to the 52-week high captures the underreaction effect better.That is,nearness to the 52-week high should predict future returns more strongly among those stocks.On the other hand,if book-to-market captures overreaction as suggested by Lakonishok,Shleifer,and Vishny (1994),then it may be better at capturing information on overreaction among the rest ofthe ﬁrms.Hence,we conjecture that the value premium should be stronger among the ﬁrms with two anchors.Our paper is related to George and Hwang (2004)which shows that nearness to the 52-week high is positively associated with future stock returns in the cross-section.However,we focus on the predictability of the aggregate market.Moreover,we highlight the importance of the historical high anchor and the visibility of the Dow index.The Dow index and the historical high anchor are two key ingredients for our predictability results.In addition to George and Hwang (2004),other notable papers which study the effect of anchoring include Odean (1998)and Grinblatt and Keloharju (2001)on individual trading,Ljungqvist and Wilhelm (2005)on IPO underpricing,Hart and Moore (2008)on contracting,and Baker,Pan,and Wurgler (2009)on mergers and acquisitions,among others.Our paper is also related to the price barrier literature.The concept of psychological barriers was introduced into ﬁnance by Donaldson and Kim (1993),who show that price barriers are often associated with levels that are multiples of 100or especially 1,000.In particular,the Dow index appears to close,on average,less frequently around multiples of 100and more frequently away from these levels.Subsequent studies demonstrate that price barriers also exist in other asset classes,such as Aggarwal and Lucey (2007)in gold prices;Koedijk and Stork (1994)and Cyree,Domian,Louton,and Yobaccio (1999)in inter-national stock indexes;De Grauwe and Decupere (1992)and Westerhoff (2003)in currency markets;and Burke (2001)on bond markets.Ley and Varian (1994)and De Ceuster,Dhaene,and Schatteman (1998)criticize some testing methods on the hypothesis of uniformity of digital distribution in the earlier studies.Overall,evidence of price barriers in various asset classes is fairly robust.One key difference between our anchors and the price barriers is that the price barriers are typically pre-ﬁxed,whereas our anchors move along with the rising index.Hence,nearness to the 52-week high and nearness to the historical high are stationary.More importantly,despite strong ex post evidence of psychological barriers in the Dow index,there is generally no strong evidence of ex ante predictability of stock returns induced by the presence of pre-ﬁxed psychological barriers (see,e.g.,Donaldson and Kim,1993;Koedijk and Stork,1994;Ley and Varian,1994).For example,there is no signiﬁcant relationship between returns in period t and the last two digits of the closing price in period t À1.In contrast,the focus of this paper is the return predictability induced by anchoring on the 52-week high and the historical high,and the evidence suggests that these two anchors induce strong return predictability in the aggregate stock market.Finally,we have used several simulation methods originally developed in the barrier literature to address many potential statistical concerns for our empirical results.3.Anchors and stock market behavior 3.1.Data and notationIn this section,we describe the data used in this paper,and introduce our predictive variables.We obtain monthly3Because there is an upward trend in prices,even if the 52-week high equals the historical high,there is probably only limited good news,rather than prolonged good news in the past.Hence,the probability of overreaction to a series of good news is still very low.J.Li,J.Yu /Journal of Financial Economics ](]]]])]]]–]]]4value-weighted NYSE/Amex returns from the Center for Research in Security Prices(CRSP).Excess returns are formed by subtracting the return on the30-day T-bill from the actual stock return.Daily Dow Jones30-stock Industrial Average Index data for1928–2009come from Dow Jones.Several macroeconomic variables shown by the literature to predict stock returns are used as control variables in this paper.Speciﬁcally,we use monthly default premiumðdef tÞ,monthly term premiumðterm tÞ, monthly real interest rateðr tÞ,monthly inﬂationðp tÞ, Lettau and Ludvigson’s(2001a)quarterly consumption–wealth ratio,cay t,Campbell and Cochrane’s(1999)surplus ratioðs tÞ,and dividend yieldðdp tÞ.Previous studies have shown that each of the above variables has predictive power for the stock market.def t is deﬁned as the yield spread between BAA and AAA bonds obtained from the St.Louis Federal Reserve. term is deﬁned as the difference between the20-year Treasury bond yield and the1-year yield,obtained from the St.Louis Federal Reserve.The inﬂation rate(p t)is calculated from the monthly Consumer Price Index(CPI), obtained from CRSP.The real interest rate(r f t)is deﬁned as the difference between the30-day T-bill rate and inﬂation.cay t is deﬁned as in Lettau and Ludvigson (2001a),obtained from Martin Lettau’s Web site.cay t spans from1951Q2–2009Q3.Campbell and Cochrane’s (1999)surplus ratio is approximated by a smoothed average of the past40-quarter consumption growth as in Wachter(2006).As a result,the surplus ratio spans from1957Q2to2009Q4.Finally,the monthly dividend yield is calculated as the difference between the log of the last12-month dividend and the log of the current level of the CRSP valued-weighted index.Since cay t and surplus ratio are available at a quarterly frequency,we convert them into monthly frequency by assigning the most recent quarterly value to each month.We use these variables as our control variables for nearness to the52-week high and the historical high in the predictive regressions.Our aggregate analysis uses the sample period1958–2009and our cross-sectional analysis starts with1963.Our aggregate sample starts with1958for several reasons.First,and most importantly,while the Dow Jones30-stock Industrial Average index starts in late1928, its visibility was not large in its early days.Hence,we discard theﬁrst three decades of the Dow index.Second,there was a great depression and two world wars in the early part of the Dow sample,and the Dow index did not return to its pre-depression level until November of1954.As such,the historical high probably would not mean much to investors, and this would not be a good anchor in the Dow’s early years.Third,one of our macro control variables starts with 1957Q2,which also makes1958a natural choice.Let p t denote the level of the Dow Jones Industrial Average index at the end of day t.p max,t and p52,t denote its historical high and52-week high at the end of day t. We can now deﬁne our main predictive variables.Near-ness to the52-week high is computed as the ratio of the current Dow index and its52-week high,x52,t¼p t52,tð1Þand nearness to the historical high is calculated as theratio of the current Dow index and its historical high,x max,t¼p tp max,t:ð2ÞWe also deﬁne two indicators D t and I t.The Dow historicalhigh indicator D t equals one when the Dow reaches arecord high at day t,and zero otherwise.Similarly,I t isdeﬁned to equal one when the historical high at day tequals its52-week high at day t,and zero otherwise.Yuan(2008)uses D t as a proxy for attention-grabbing events,andﬁnds that D t negatively predicts next-day returnsbecause of the selling pressure in the next day,afterinvestors realize their gains following the attention-grab-bing event.Instead of using daily data,for the majority ofour analysis,we use monthly observations to reducestatistical concerns from overlapping observations.Thevalue at month t is just deﬁned as the value at the lasttrading day of month t.The Dow Jones Industrial Average index is the oldestcontinuing U.S.market index.It represents the average of30stocks from various important American industries andis arguably the most widely used and visible index.Thereason that we use the Dow index is that it is more visiblethan the total market value of NYSE/Amex stocks or otherindexes.Hence,it should have stronger predictive powerresulting from anchoring and limited attention.Panel A of Table1reports summary statistics of ourproposed predictors,along with those of other predictorssuggested by previous literature.Because the Dow indexis increasing over time,the average value of x52and x max ishigh and close to1.0.As expected,nearness to the52-weekhigh,x52,and nearness to the historical high,x max,are quitepersistent,but less persistent than the traditional macropredictors,such as the consumption–wealth ratio or dividendyield.Our predictors are quite negatively skewed becausethey are bounded from above by one.Panel B of Table1shows that nearness to the52-weekhigh and nearness to the historical high are not stronglycorrelated with traditional macro predictors.Among allthe macro variables,dividend yield is most correlatedwith nearness to the historical high,with a correlation ofÀ0.32.As expected,the correlation between x52and x maxis as high as86%.However,as discussed in Section2,thepredictive ability of these variables with respect to futuremarket returns runs in opposite directions.Hence,it isimportant to include both variables into our predictiveregressions.Furthermore,when the historical high isequal to the52-week high,traders would have only oneanchor in mind,and hence,we shall take special care inthe case in which I t¼1.Weﬁrst examine the predictive ability of nearness tothe52-week high and the historical high at an aggregatelevel.In Section3.8,we then explore their implications forthe cross-section of expected returns,especially withrespect to momentum and the value premium.3.2.Main time-series regressionWe now explore the link between market returns andnearness to the52-week high and nearness to the historical J.Li,J.Yu/Journal of Financial Economics](]]]])]]]–]]]5。

基于行为金融学视角下的证券投资研究

基于行为金融学视角下的证券投资研究1. 引言1.1 研究背景研究背景：行为金融学是一门结合心理学、经济学和金融学的跨学科领域，它试图解释投资者在做出决策时所表现出的非理性行为和偏差。

相比于传统的理性投资者假设，行为金融学更加注重投资者的情绪、偏好和认知错误对投资决策的影响。

通过深入研究投资者的行为模式和心理倾向，行为金融学为我们探索了新的投资策略和思维模式。

在这样一个信息爆炸的时代，投资者所面临的选择和决策越来越复杂，市场也越来越不确定。

行为金融学的兴起为我们提供了一种全新的思维方式，帮助我们更好地理解市场中的种种变化。

通过研究行为金融学在证券投资中的应用，我们可以更好地把握市场脉搏，制定更加科学的投资策略。

【研究背景】1.2 研究意义证券投资是金融领域的重要组成部分，对于资本市场的稳定发展和投资者的财富增值起着至关重要的作用。

而行为金融学作为金融学中的一个新兴领域，通过对投资者行为和心理偏差的研究，为证券投资提供了全新的视角和方法。

从行为金融学的角度来研究证券投资具有极其重要的意义。

行为金融学能够揭示投资者的心理偏差和行为特征，帮助投资者更好地理解自身的投资行为和决策过程。

通过了解投资者在投资过程中可能存在的错误认知和情绪偏差，可以帮助投资者避免冲动和盲目的投资决策，提高投资决策的准确性和效率。

行为金融学的研究成果可以为投资者提供更为科学和精准的投资策略和方法。

通过结合行为金融学的理论和实证研究，可以开发出一系列能够利用投资者心理偏差的证券投资策略，帮助投资者实现更好的投资回报。

2. 正文2.1 行为金融学概念介绍行为金融学是一门研究人类行为对金融市场的影响的学科，它将心理学和金融学相结合，探讨了人类在做出金融决策时所存在的种种心理偏差和误判。

行为金融学认为人类的决策往往会受到情绪、认知偏差、群体思维等因素的影响，而这些因素在传统金融理论中并没有被充分考虑。

行为金融学的出现填补了传统金融学的不足，帮助人们更好地理解金融市场的运行规律。

行为金融学基础文献

真正经典的行为金融学基础文献没有包括进啊，我这里有个单子，供大家参考行为金融学基础文献1、Barberis, N., M. Huang, and T. Santos , 2001 , ―Prospect Theory and Asset Prices,‖ Quarterly Journal of Economics, Vol.116, pp. 1-53.2、Barberis, N., A. Shleifer , and R. Vishny, 1998 , ―A Model of Investor Sentiment,‖ Journal of Financial Economics 49 , 307 -343.3、Campbell, J., 1999 ,―Asset Prices, Consumption, and the Business Cycle,‖ in J.B. Taylor and M. Woodford eds. Handbook of Macroeconomics Vol. 1, North–Holland, Amsterdam, 1231–1303.4、Daniel, K., Hirshleifer, D., and Subrahmanyam, A., 1998 , ―Investor Psychology and Security Market under-and Overreactions,‖ Journal of Finance, Vol.53 pp.1839-1886.5、De Long, J.B., Shleifer, A., Summers, L., Waldmann, R., 1990a, ―Positive Feedback Investment Strategies and Destabilising Rational Speculation,‖ Journal of Finance 45, 375–395.6、De Long, J.B., Shleifer, A., Summers, L., Waldmann, R., 1990b, ―Noise Trader Risk in Financial Markets,‖ J ournal of Political Economy 98, 703–738.7、Hong, H., and J., Stein , 1999 , ―A Unified Theory of Underreaction, Momentum Trading and Overreaction in Asset Markets,‖ Journal of Finance, 54: 2143-2184.8、Kahneman, D., and A., Tversky, 1979 , ―Prospect Theory: An Analysis of Decision under Risk,‖ Econometrica, Vol. 47, No. 2., pp. 263-292.9、Le Roy, S., and R., Porter, 1981 , ―The Present-Value Relation: Tests Based on Implied Variance Bounds,‖ Econometrica, Vol. 49, No. 3, pp. 555-574.10、Mehra, R. and E. Pre scott, 1985, ―The Equity Premium: A Puzzle,‖ Journal of Monetary Economics, Vol15, pp.145-161.11、Shiller, R., 1981 , ―Do Stock Prices Move too much to be justified by Subsequent Changes in Dividends?‖ American Economic Review, Vol.71, pp. 421-436.12、Tver sky, A., D., Kahneman, 1974 , ―Judgment under Uncertainty: Heuristics and Biases,‖ Science, New Series, Vol. 185, No. 4157, pp. 1124-1131.13、Jegadeesh and Titman，―momentum‖.相关书籍：1、金融异象与投资者心理胡昌生2、投资心理学（The Psychology of Investing）Nofsinger3、金融心理学拉斯.特维德4、并非有效的市场——行为金融学导论Shleifer5、乌合之众勒庞。

行为金融学文献综述

行为金融学文献综述行为金融学，就是将心理学尤其是行为科学的理论融入到金融学中，从微观个体行为以及产生这种行为的更深层次的心理、社会等动因来解释、研究和预测资本市场的现象和问题。

自1980年代以来，随着金融市场的发展和研究的深入，人们发现了金融市场中存在很多不能被传统金融学所解释的现象，比如股权滋价之谜、波动率之谜、封闭式基金之谜、股利之谜、小公司现象、一月份效应、价格反转、反应过度和羊群行为等等。

学者们将这些违背有效市场假说，传统金融学理论无法给出合理解释的现象称之为“异象”或“未解之谜”。

金融市场中存在的大“异象”对传统金融学产生了巨大冲击，尤其向有效市场假说提出严峻挑战。

因此，人们开始重新审视“完美的”传统金融学理论。

传统金融学理论把人看作是理性人，即人们在从事经济活动时总是理性的，追求收益最大化和成本最小化人们的估计是无偏的，满足贝叶斯过程。

因为人的假设与现实中人的决策行为有一定差异，所以人们开始关注人类行为及心理在决策中的作用，运用心理学的研究方法来研究金融问题，行为金融学应运而生。

从而金融学的研究焦点开始从“市场”研究转向“人类行为”研究。

心理因素在投资决策中的作用方面的研究可以追溯至1936年凯恩斯的“空中楼阁理论”，该理论认为投资者是非理性的，证券的价格取决于投资者共同的心理预期。

然而，真正意义上的行为金融学是由美国奥瑞格大学教授Burrel和Bauman（1951年）提出来的。

他们认为在对投资者的决策研究仅仅依赖于化的模型是不够的，还应该考虑投资者的某些相对固定的行为模式对决策的影响。

心理学Slovic（1972）教授从行为学角度研究了投资者的投资决策过程。

随后，Tversky 和Kahneman在1974年和1979年分别对投资者的决策行为进行了行为金融学研究，分别讨论了直觉驱动偏差和框架依赖的问题，从而奠定了行为金融学研究的基础。

20世纪80年代，金融市场中的大量“异象”被发现，推动了行为金融学的发展。

行为金融学经典文献(英文版)(pdf 47页)

IN RECENT YEARS A BODY OF evidence on security returns has presented a sharp challenge to the traditional view that securities are rationally priced to ref lect all publicly available information. Some of the more pervasive anomalies can be classified as follows ~Appendix A cites the relevant literature!:
THE JOURNAL OF FINANCE • VOL. Байду номын сангаасIII, NO. 6 • DECEMBER 1998
Investor Psychology and Security Market Under- and Overreactions
KENT DANIEL, DAVID HIRSHLEIFER, and AVANIDHAR SUBRAHMANYAM*
*Daniel is at Northwestern University and NBER, Hirshleifer is at the University of Michigan, Ann Arbor, and Subrahmanyam is at the University of California at Los Angeles. We thank two anonymous referees, the editor ~René Stulz!, Michael Brennan, Steve Buser, Werner DeBondt, Eugene Fama, Simon Gervais, Robert Jones, Blake LeBaron, Tim Opler, Canice Prendergast, Andrei Shleifer, Matt Spiegel, Siew Hong Teoh, and Sheridan Titman for helpful comments and discussions, Robert Noah for excellent research assistance, and participants in the National Bureau of Economic Research 1996 Asset Pricing Meeting, and 1997 Behavioral Finance Meeting, the 1997 Western Finance Association Meetings, the 1997 University of Chicago Economics of Uncertainty Workshop, and finance workshops at the Securities and Exchange Commission and the following universities: University of California at Berkeley, University of California at Los Angeles, Columbia University, University of Florida, University of Houston, University of Michigan, London Business School, London School of Economics, Northwestern University, Ohio State University, Stanford University, and Washington University at St. Louis for helpful comments. Hirshleifer thanks the Nippon Telephone and Telegraph Program of Asian Finance and Economics for financial support.

行为金融学实验报告(A股H股溢价分析,心理账户,过度自信)

行为金融学实验报告(A股H股溢价分析,心理账户,过度自信)行为金融学实习报告摘要行为金融学就是将心理学尤其是行为科学的理论融入到金融学之中，是一门新兴边缘学科，它和演化证券学一道，是当前金融投资理论最引人注目的两大重点研究领域。

行为金融学从微观个体行为以及产生这种行为的心理等动因来解释、研究和预测金融市场的发展。

这一研究视角通过分析金融市场主体在市场行为中的偏差和反常，来寻求不同市场主体在不同环境下的经营理念及决策行为特征，力求建立一种能正确反映市场主体实际决策行为和市场运行状况的描述性模型。

通过三个板块来说明行为金融学：心理账户、过度自信、A股H股溢价分析让我们能够直观的了解行为金融学的基本状况，通过对理论与实践的结合，有利于我们更好的理解行为金融学的知识。

总之，通过实习，我们对行为金融学有了更深层次地理解。

本次实习报告从实习目的和意义、工作方法、取得的成果及经验、收获及体会来具体说明下实习的过程。

关键词：行为金融学心理账户过度自信A股H股溢价分析1 行为金融学实习报告目录论文总页数：14页 1 2 3 4 实习的目的............................................................... . (3)实习的时间............................................................... . (3)实习的地点............................................................... . (3)实习内容............................................................... .................................................................... 3A、H股溢价问.......................................... 3 A股与H 股的价差能说明内地和中国香港地区市场中有一个市场不是有效的吗？为什么？........................................................... ..................................................................... ... 3 你认为导致A 、H股价差的原因有哪些？........................................................... .. 3心理账户............................................................... (5)概况............................................................... . (5)实验............................................................... . (5)实验一——成本与损失的不等价实验............................................................... ............................ 5 实验二——赌场资金效应实........................................ 6 实验三——沉没成本效应实验............................................................... ........................................ 8 过度自信............................................................... (9)概况............................................................... . (9)实验............................................................... . (9)实验一——打折和邮购返券............................................................... ............................................ 9 实验二——创业............................................................... .. (10)5 实习心得体会............................................................... .......................................................... 12 6教师评语............................................................... ..................................................................13 2 行为金融学实习报告 1 实习的目的通过行为金融学实习，让我们增加了对行为金融学的了解，同时对行为金融学的研究成果有一个初步的认识，并通过不同的心理账户和过度自信的案例分析，熟悉理论发展，感受消费者决策时的自身心理变化。

行为金融学分析范文

行为金融学分析范文行为金融学是一门研究人类在金融决策中所表现出的心理和行为特征的学科。

通过分析人们的行为模式、心理偏差和错误决策，行为金融学试图解释金融市场的非理性行为和价格波动。

本文将从行为金融学的基本原理、应用领域和影响等方面进行分析。

行为金融学的基本原理在于人类的决策行为往往受到情绪、直觉和认知偏差的影响。

与传统的经济学理论假设人们是理性决策者不同，行为金融学认为人们在金融决策中往往被恐惧、贪婪和羊群效应所影响。

例如，人们在面临损失时会更加谨慎，而面临收益时会更加冒险。

这种非理性的反应导致了市场的过度买卖和价格波动，形成了所谓的“牛市”和“熊市”。

行为金融学的应用领域广泛。

首先，行为金融学可以帮助投资者更好地理解市场行为和价格波动，从而做出更明智的投资决策。

其次，行为金融学可以对金融市场的制度设计和监管政策提出建议。

例如，为了减少羊群效应和过度交易，可以采取一些措施，如限制杠杆交易和增加交易费用。

最后，行为金融学还可以用于解释其他领域的决策行为，如消费决策、借贷决策和退休规划等。

行为金融学的研究对金融市场产生了广泛的影响。

首先，行为金融学的理论发现改变了人们对市场行为的认识，使得传统的经济学理论不再被接受为唯一的解释。

其次，行为金融学的研究促进了金融市场的完善和监管政策的改进。

例如，对于投资者的信息不对称问题，可以通过加强监管和信息披露来解决。

此外，行为金融学还为投资者提供了一些实用的建议，如定期投资、分散投资和避免过度交易等。

然而，行为金融学也存在一些争议。

首先，行为金融学往往被指责过度强调了投资者的情感和认知偏差，忽略了理性决策的可能性。

其次，行为金融学的实证研究往往受到样本和数据选择的限制，可能不具有普遍性。

最后，行为金融学的应用也需要谨慎。

虽然行为金融学可以提供一些关于决策行为的见解，但并不意味着每个人都会受到相同的影响，每个时期和市场也会有所不同。

综上所述，行为金融学是一门研究人类在金融决策中所表现出的心理和行为特征的学科。

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Does the Stock Market Overreact?
Werner F.M.De Bondt;Richard Thaler
The Journal of Finance,Vol.40,No.3,Papers and Proceedings of the Forty-Third Annual
Meeting American Finance Association,Dallas,Texas,December28-30,1984.(Jul.,1985),pp.
793-805.
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