Market Response to Investor Sentiment
Journal of Business Finance & Accounting Journal of Business Finance&Accounting,40(7)&(8),901–917,September/October2013,0306-686X
doi:10.1111/jbfa.12039
Market Response to Investor Sentiment J¨O RDIS H ENGELBROCK,E RIK T HEISSEN AND C HRISTIAN W ESTHEIDE?
Abstract:This paper reconsiders the effect of investor sentiment on stock prices.Our main contribution is that,in addition to the intermediate term return predictability,we also analyze the immediate price reaction to the publication of survey-based investor sentiment indicators. We find that the sign of the immediate market response is the same as that of the predictability at intermediate time horizons.This is consistent with underreaction to cash flow news or with investor sentiment being related to mispricing.It is inconsistent with the alternative explanations of a rational response to cash flow news or sentiment indicators providing information about future expected returns.
Keywords:investor sentiment,event study,return predictability
1.INTRODUCTION
Recent empirical research suggests that survey measures of investor sentiment have the ability to predict future stock returns over the intermediate and long term.The usual econometric approach is to regress future stock index returns on a sentiment indicator and appropriate control variables.The aim of using the controls is to account for variables(such as the term and yield spread)that are already known to predict future returns.A significant coefficient for the sentiment indicator is interpreted as evidence that sentiment predicts future returns.
In a rational setting,stock prices will only change in response to measures of investor sentiment when these measures contain new information.This information, in turn,can relate to future cash flows or to expected returns.If sentiment measures contain information on future cash flows and the market reacts rationally to this
?The first author is at the Bonn Graduate School of Economics,University of Bonn.The second author is at the University of Mannheim,the Centre for Financial Research,Cologne,and the Center for Financial Studies,Frankfurt.The third author is at the University of Mannheim and the Center for Financial Studies, Frankfurt.Most of the research was conducted while the last author was at Bonn Graduate School of Economics,University of Bonn.Hengelbrock and Westheide gratefully acknowledge financial support from the German Research Association(DFG).The authors are grateful to J¨o rg Breitung,Greg Brown,Mike Cliff,Daniel Dorn and Markus Glaser for helpful conversations,and to Norman C.Strong(the associate Editor),an anonymous referee,and participants of the Northern Finance Association2009Conference,the Financial Management Association European Meeting2009,the Annual Meeting of the Spanish Finance Association2009,the Economics,Management and Finance Doctoral Meeting of Montpellier2009,the Augustin Cournot Doctoral Days2009,the Macro/Finance Workshop at the University of Bonn2009,and the Campus for Finance Research Conference2010for valuable suggestions that helped to improve the paper.(Paper received December,2010;revised version accepted April,2013).
Address for correspondence:Erik Theissen,Lehrstuhl f¨ur ABWL und Finanzierung,Universit¨a t Mannheim, D-68131Mannheim,Germany.
e-mail:theissen@uni-mannheim.de
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information we should observe an immediate stock price reaction but no intermediate or long-term effect.1Alternatively,sentiment measures may contain information about future expected returns.In this case we expect that the sentiment measure predicts returns at longer horizons and triggers an immediate price reaction in the opposite direction.To see this,assume that an increase in the sentiment measure is informative of lower risk aversion and thus lower discount rates.We should then expect to see lower returns in the future(and thus a negative relationship between the sentiment measure and returns in the intermediate or long term).At the same time we should see an immediate increase in prices because current stock prices are inversely related to expected returns.Thus,in a rational setting the publication of sentiment measures can have a)an immediate price effect but no long-term effect(“cash flow news scenario”), or b)a long-term effect and an immediate price reaction in the opposite direction (“expected return news scenario”).In the latter case the predictive ability of sentiment indicators does not imply a violation of market efficiency.
If market participants are not fully rational they may underreact and/or overreact to news(as in the model of Hong and Stein,1999),or they may extrapolate too much from previous returns(as proposed by Brown and Cliff,2005).In these cases different return patterns can emerge.For example,the sentiment measure may contain information about future cash flows,but there may be an initial underreaction which is later corrected.We would then observe an immediate effect and a long-term effect in the same direction.
In the present paper we want to analyze whether the market reaction to investor sentiment can be explained rationally(i.e.,by the future cash flow scenario or the expected return news scenario introduced above)or whether one has to resort to explanations based on underreaction,overreaction or mispricing.This is an obviously important an as yet unanswered question(e.g.,Brown and Cliff(2005,p.437).Our approach is to simultaneously consider intermediate and long-horizon predictability on the one hand,and the immediate market reaction to the publication of sentiment indicators on the other.As outlined above,in a rational setting the publication of sentiment measures can either have an immediate price effect but no long-term effect, or a long-term effect and an immediate price reaction in opposite directions.If we observe a different pattern we can discard the rational explanations.
To the best of our knowledge,our paper is the first to empirically analyze the immediate response of stock returns to the publication of survey-based sentiment measures.We use data from Germany and the US.In the first part of our analysis we rely on the methodology proposed by Brown and Cliff(2005).We replicate their tests for medium and long-term predictability.Consistent with previous results in the literature(to be briefly reviewed in section2),we find a significant negative relationship between the sentiment indicator and subsequent medium term(up to 3months)index returns in the US for the earlier parts of our sample period(1987–94and,to a much lesser extent,1994–2001).This relationship disappears towards the end of our sample period.In the final sub-period(2001–08),the coefficients of the predictive regressions are predominantly positive but only weakly significant.The sentiment indicator for the German market is correlated positively with future returns. 1The intermediate and long-term predictability reported in previous research is thus inconsistent with the “cash flow news”scenario.
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MARKET RESPONSE TO INVESTOR SENTIMENT903 This is consistent with the results from the US,because the German sample covers the years2001–08,which is precisely the period for which we also find positive coefficients in the US sample.
In the second step of our analysis,we use event study methodology to test whether daily index returns respond to the publication of the sentiment indicator.We do find a significant positive announcement day effect in Germany.Thus,the immediate price reaction is in the same direction as the long-term effect.This is inconsistent with the rational explanations described above.For the US market there is evidence of a negative publication day effect in the sub-period1987–94.
As in the case of Germany this result is inconsistent with rational explanations of the market response to investor sentiment.In later sub-periods there is no announcement day effect in the US market.This should come as no surprise,because the intermediate-to long-term predictability also largely disappears towards the end of the sample period.
The results for both markets lead us to discard the rational explanations described above.There are several alternative explanations which are consistent with our findings.2One of them is underreaction to information contained in the sentiment measure.A second one is a scenario of mispricing and limited arbitrage.If the sentiment measure is an indicator of mispricing,smart investors who are aware of the predictive power of the sentiment indicator will trade accordingly(and cause an immediate price reaction).If they do not fully arbitrage the predictability away (possibly because of increased noise trader risk)we will observe an immediate price reaction and a long-term effect in the same direction.
The remainder of this paper is structured as follows.Section2summarizes related literature and places our paper in the literature.Section3describes our dataset. In section4,we present the methodology and results of our tests for predictability. Section5describes our tests for the existence of announcement day effects.Section6 concludes.
2.PRIOR LITERATURE
Previous papers have used a large variety of measures to capture the concept of investor sentiment.These measures can broadly be classified into two categories,market-based measures and survey-based measures.3Market-based measures include,but are not limited to,mutual fund flows(Brown et al.,2003;Ben-Rephael et al.,2012),the closed-end fund discount(Elton et al.,1998;Lee et al.,1991;Neal and Wheatley,1998),put-call ratios(Dennis and Mayhew,2002),various measures of trading activity(Barber and Odean,2008;Kumar and Lee,2006)and liquidity(Baker and Stein,2004).Baker and Wurgler(2006)construct a composite sentiment measure based on six underlying proxies.This index has also been used by Stambaugh et al.(2012)and Chung et al. 2Besides those explanations discussed in the text one might also think of explanations based on market frictions such as transaction costs.While this might be a relevant argument at the individual stock level we
do not believe that it is a valid explanation at the level of the liquid indices(S&P500and DAX)that we consider in our paper.
3Several authors have analyzed sentiment indicators extracted from postings on internet message boards. In a recent paper,Sabherwal et al.(2011)find evidence of very short-term(up to2days)predictability. Note,though,that the message-board sentiment indicator is a stock-specific measure while the papers to be reviewed below measure market-wide sentiment.
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904HENGELBROCK,THEISSEN AND WESTHEIDE
(2012).The general result from the studies using market-based sentiment proxies is that high levels of sentiment predict low future returns.Baker et al.(2012)confirm this result in an international study that covers data from six major stock markets. Chung et al.(2012)demonstrate that the predictive ability of sentiment is confined to states of economic expansion.
Survey-based measures assess investor sentiment by asking a set of respondents about their opinion on the direction the stock market will take.This can be done di-rectly as in the American Association of Individual Investors’(AAII)survey(described in detail in Section3(ii)or indirectly as in the Investor’s Intelligence(II)survey which tracks the expectations expressed in market newsletters(see Brown and Cliff,2005). Several authors have analyzed whether survey-based sentiment indicators can predict future returns in the US market.At weekly or monthly forecasting horizons the AAII index is unrelated or moderately positively related to future stock returns(Brown and Cliff,2004;Verma et al.,2008).At longer horizons the relationship becomes negative (Fisher and Statman,2000;Verma et al.,2008).This pattern is consistent with our own results for the period from1987to2001.Results of recent studies using the II survey data arrive at similar conclusions(Fisher and Statman,2000;Brown and Cliff, 2004,2005;Verma et al.,2008).Two older studies(Solt and Statman,1988;and Clarke and Statman,1998),using datasets ending in1985and1995,respectively,do not find predictive ability of the II survey.Schmeling(2007)uses a survey-based sentiment indicator from Germany and also reports evidence of predictability.Schmeling(2009) uses survey-based measures of consumer confidence for18countries and confirms the results that high sentiment predicts low future returns.Chan and Fong(2004)analyze a survey-based sentiment indicator published by a Hong Kong newspaper.They find that the publication of the index has a short-term impact on the prices of small and medium-sized stocks,but not on the prices of large stocks.Predictive regressions are fraught with econometric problems.Most importantly,persistent regressors can cause biased coefficient estimates(see Stambaugh,1986,1999).Brown and Cliff(2005) proposed a bootstrap-based estimation procedure to correct for this bias.We adopt their methodology.
Our paper contributes to the literature in at least two ways.First,our paper is the first to analyze the immediate(i.e.,announcement day)effect and the long-run effect of the publication of sentiment indices on stock returns.As laid out in the introduction,simultaneously analyzing the immediate and long-term effect allows us to discriminate between rational and non-rational explanations for the stock market response to investor sentiment.This is an important but as yet unresolved issue. Brown and Cliff(2005),who document that the Investor’s Intelligence survey data predicts future stock market returns over long horizons,offer two explanations for their results(p.437).The first explanation is that sentiment is related to a priced factor(similar to our expected return news scenario)and the second explanation is that sentiment is irrational.Their results do not allow discrimination between these explanations.Our approach can help to answer this important question. Second,we analyze a longer(1987–2008)and broader(US and Germany)sample than previous papers.We document that the predictive ability of the AAII survey has weakened and,in the sub-period2001-08,even has changed signs.This is difficult to reconcile with risk-based explanations of the market response to investor sentiment.
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MARKET RESPONSE TO INVESTOR SENTIMENT905
3.DATA
(i)German Data
The analysis of intermediate and long-term predictability is based on weekly data. We use survey data from Sentix as our measure of investor sentiment.We prefer to use survey-based sentiment indicators over market-based ones because the publication of the survey results constitutes new information,while market-based indicators offer only aggregate information that were already available.Sentix conducts weekly surveys of institutional and private investors,and currently reaches over2,700registered participants,about800of whom take part in the survey each week.Individual investors constitute on average about76%of respondents,with this percentage generally varying between70%and80%.Voting is possible between Thursday afternoon and Saturday.Participants are asked whether they are bullish,bearish,neutral,or have no opinion with regard to the future trend of the DAX30stock index over the following 1and6months,respectively.
In our analysis we only use data for the6month horizon because the AAII survey that we use in our US sample is also based on a6months forecasting horizon.4 From the individual opinions obtained,Sentix computes the so-called value index, also known as the bull-bear spread.This is defined as
S t=#bullish?#bearish
#total
.
The Sentix index is published every Sunday evening or Monday morning prior to the opening of the market.It is available to all participants,and additionally,since January2004,it has been available through Thomson DataStream and Bloomberg. Furthermore,sub-indices that cover individual and institutional investors,respectively, are made available exclusively to participants.The Sentix data start on February26, 2001and end on June30,2008.For our predictive regressions,we use forecasting horizons of1,4,8,13and26weeks.To this end,we combine the Sentix data with data on the DAX index for the period February26,2001to December31,2008.5 The aim of the predictive regressions is to test whether the sentiment indicator contains information about future returns beyond the information inferable from other publicly observable variables.We therefore control for variables that are known to predict future market returns.We include the return on the DAX30for the previous week,the exchange rate EUR/USD,the interest rate term spread between10year German government bonds and the Euribor3month rate,the credit spread(defined as the spread between yields on A rated corporate bonds of maturities between3 and5years and the mean of3and5year German government bond yields,6the 4The correlation between the1-month forecasts and the6-month forecasts is0.083in levels and–0.157in first differences.The1-month forecasts do not have predictive ability for future stock returns.This finding is consistent with Schmeling(2007).
5We repeated the analysis with the CDAX,a much broader index which comprises all stocks listed on the Frankfurt Stock Exchange.The results were similar to those obtained for the DAX and are thus omitted from the paper.
6The number of corporate bonds issued by German firms and rated Aaa and Baa is too small to reliably estimate the credit spread as the difference between the yields on Baa-rated and Aaa-rated corporate bonds (as we do in our US sample).Therefore,we use the yield difference between A-rated corporate bonds and government bonds instead.
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906HENGELBROCK,THEISSEN AND WESTHEIDE
Table1
Summary Statistics of German Data
Mean Std.Dev.ρiρs,i Se nti x t0.1210.1130.773 1.000
Se nti x t?0.0000.067?0.3010.335 I nnoSe nti x t0.0020.0580.0700.726
r DAX
t?2,t?1
0.0000.0320.0160.048
r S&P500 t?2,t?10.0000.022?0.0540.007
EUR/USD t?1 1.1850.1870.9880.029 Term Spread t?10.0110.0080.986?0.107 Credit Spread t?10.0110.0030.946?0.243 Liquidity Spread t?10.0010.0010.9370.112 Euribor1m t?10.0310.0090.990?0.025 Note:
The table presents summary statistics for the German data.All returns are from Friday close to the next Friday close.Other control variables(the EUR/USD exchange rate,the term,credit and liquidity spread and the Euribor1-month rate)are from Friday.The Sentix index is published on Sunday evenings or Monday mornings.Sentix t denotes the index level, Sentix t denotes its weekly change,and InnoSentix t the unexpected component of the index(the residual of a linear regression of the index on its lagged value and
the lagged DAX return).r DAX
t?2,t?1is the return on the DAX30from week t?2to week t?1,r S&P500
t?2,t?1
is the
return on the Standard&Poors500during the same week.ρi denotes the first-order serial correlation of variable i,ρs,i denotes the correlation between the Sentix index and variable i.
liquidity spread(defined as the spread between the Euribor3-month and1-month rates),and the Euribor1-month rate.For the analysis of announcement day effects of the Sentix index,i.e.,the test whether the publication of the sentiment indicator has an immediate price effect,we use daily data.As the Sentix index is published at the weekend,we consider the return of the DAX30between its closing value on Friday and that on Monday.To this end,we regress daily DAX returns on a variable which is equal to the sentiment indicator on Mondays and zero on all other days.The regression includes lagged DAX returns,lagged S&P500returns(to account for the fact that respondents may participate in the survey until Saturday and may therefore base their opinion on the US stock market return from the previous week)and a Monday dummy (to control for a weekend effect)as control variables.
Table1provides summary statistics of all the variables.The mean of the Sentix index is0.12,indicating that the respondents are,on average,slightly bullish.The mean daily DAX return is very close to zero.The serial and cross correlations(shown in the last two columns of the table)indicate that the Sentix index is highly autocorrelated and depends on the previous values of the DAX index.Both these observations are consistent with the findings of previous research.
(ii)US Data
We use data obtained from the American Association of Individual Investors(AAII). The AAII conducts weekly surveys of its members,the results of which are published every Thursday7morning,before the stock market opens.Participants are asked 7This applies to the period from November1993onwards.Before,the day of publication had been Friday. In case of public holidays,the index is published on the last trading day before that holiday.In our analysis, we take account of the exact publication days.
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Table2
Summary Statistics of US Data
Mean Std.Dev.ρiρs,i AAI I t0.0990.1880.670 1.000
AAI I t t?0.0010.152?0.3430.400 I nnoAAI I t0.0080.135?0.1440.738
r S&P500 t?2,t?10.0010.021?0.0530.134
USD/EUR t?1 1.1680.1490.990?0.198 Term Spreadt?10.0170.0120.9920.026 Credit Spreadt?10.0090.0020.979?0.202 Liquidity Spreadt?10.0270.0120.989?0.132 Treasury bill1m t?10.0170.0080.988?0.152 Note:
The table presents summary statistics for the US data.All returns are for the week prior to the publication of the AAII index.Other control variables(the USD/EUR exchange rate,the term,credit and liquidity spread and the1-month T-bill rate)are fromWednesdays.The AAII index is published on Thursday morning.AAI I t denotes the index level, AAI I t t denotes its weekly change,and InnoAAII t the unexpected component of
the index(the residual of a regression of the index on its lagged value and the lagged S&P return).r S&P500
t?2,t?1 is the return on the Standard&Poors500from week t?2to week t?1.ρi denotes the first-order serial correlation of variable i,ρs,i denotes the correlation between the AAII index and variable i.
whether they expect the direction of the stock market over the following6months to be‘up’,‘no change’,or‘down’,and can participate once during every weekly period ranging from Thursday to Wednesday.We use a value index(bull-bear spread)that is calculated using these data.Our sample covers more than20years.It starts on July24, 1987and extends until June26,2008.
As Table2shows,the mean,standard deviation and first order autocorrelation of the AAII indicator are comparable to those of the German Sentix index.8 The AAII survey does not specify which stock index it refers to.We therefore use the Dow Jones Industrial Average,the Standard&Poors500,the NASDAQ100,and the Russell3000indices.We estimate predictive regressions for forecasting horizons of1, 4,8,13and26weeks.As for the German case,we include other variables known to have predictive power for market returns as control variables.We include the same variables as for the German sample but replace the Euribor rates with Treasury bill rates.Thus, we control for the past week’s return of the stock index in question,the exchange rate EUR/USD(DM/USD prior to the introduction of the Euro),the interest rate term spread between10year US Treasury bonds and the Treasury bill3month rate, the credit spread(defined as the yield spread between Baa and Aaa rated corporate bonds),the liquidity spread(defined as the spread between the US Treasury bill3 month and1month rates)and the US Treasury bill1month rate.
In the analysis of announcement day returns,we again use daily data.We regress daily index returns on a variable which is equal to the sentiment indicator on
8Note that while the AAII index published on Thursday morning is more strongly related to the S&P return over the previous week(ending on the Wednesday prior to publication)in comparison to the German data, the relation is significant only for the later part of our sample.This is most likely due to the fact that, until2000,the AAII survey was conducted by regular mail.This procedure obviously introduces a lag of several days.We find strong support for this conjecture when we estimate the correlation between the AAII index and the S&P return over the previous week separately for the period before and after the change in procedure.Prior to2000the correlation is0.010whereas after2000it is0.287.
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908HENGELBROCK,THEISSEN AND WESTHEIDE
Thursdays,and zero on all other days.The regression includes lagged index returns and a Monday dummy(to control for a weekend effect)as control variables.
(iii)Comparison of US and German Data
Although the AAII and Sentix indices share many similarities there are also important differences.First,the Sentix survey specifies the index on which the survey participants base their forecasts while the AAII survey does not.Second,the population of participants is different.Only retail investors participate in the AAII survey whereas a substantial fraction of institutional investors participate in the Sentix survey.The latter is younger than the AAII survey and is less well known to the general public. This suggests that participants in the Sentix survey are likely to be active traders with a strong interest in financial markets.This may not be generally true for respondents to the AAII survey.There may thus be differences in the degree of sophistication of the survey respondents.
The descriptive statistics shown in Tables1and2reveal that the AAII index is more closely related to lagged stock returns than the Sentix index.The correlation between the AAII index and past S&P500returns is0.134while the correlation between the Sentix index and past DAX returns is only0.048.This is consistent with AAII respondents extrapolating more from past returns when making their forecasts than Sentix respondents.To shed more light on the differences between the Sentix and AAII indices we related them to the time series of flows into mutual funds.The results (not shown)indicate that the AAII index is highly positively correlated with net flows into equity funds while there is no significant correlation for the Sentix index.
The AAII index is published on Thursday while the Sentix is published on Sunday or Monday morning.Thus,the AAII index is already available when the respondents of the Sentix index make their forecasts.It is thus not surprising that the two indices are correlated.The correlation is0.24in levels and0.10in first differences.9
4.PREDICTIVE REGRESSIONS
(i)Results for Germany
In this section we analyze whether investor sentiment,measured using the Sentix survey,is able to predict asset returns for horizons from1to26weeks.As proposed by Brown and Cliff(2005),we use a bootstrap simulation to account for problems caused by overlapping observations and persistent regressors.10We estimate:
(r t+1+···+r t+k)=α(k)+ (k)z t+β(k)S t+ε(k)t,(1) where r t+k denotes the k week-ahead future DAX long return.α(k)is the constant for a forecasting horizon of k weeks,and z t is a vector of the control variables listed
9In unreported results we also find that the AAII index has some predictive ability for the future returns of the DAX index.
10Compare also Brown and Cliff(2005),p.418.Britten-Jones et al.(2011)have proposed an alternative way of dealing with the problems caused by overlapping observations.This procedure,however,does not correct for the bias caused by persistent regressors.We therefore prefer the bootstrap procedure described below.
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Table3
Sentiment Coefficient in k-Week Regressions for Aggregate6Month DAX
Sentiment
OLS Bootstrap
Reg.Horizon?βOLS Sig.level?βSI M Sig.level 1week0.0395**0.0110.0403**0.036 4weeks0.1101***0.0000.1156**0.049 8weeks0.1783***0.0000.1887**0.041 13weeks0.1337***0.0000.15190.194 26weeks?0.04550.712?0.01790.958
Note:
The table presents theβcoefficients of equation(r t+1+···+r t+k)=α(k)+ (k)z t+β(k)S t+ε(k)t obtained from OLS estimation(columns1and2)and bootstrap simulations as explained in the Appendix (columns3and4).Results are presented for forecasting horizons of k=1;4;8;13;26weeks.The control variables are listed in Section3(i).
***,**and*denote statistical significance at the1%,5%and10%levels,respectively.
Table4
Sentiment Coefficient in k-Week Regressions for AAII Sentiment and S&P500
OLS Bootstrap
Reg.Horizon?βOLS Sig.level?βSI M Sig.level 1week0.00240.8100.00290.371 4weeks?0.0159***0.002?0.01420.264 8weeks?0.0252***0.000?0.02230.273 13weeks?0.0433***0.000?0.03890.129 26weeks?0.0729***0.000?0.0651*0.076 Note:
The table presents the coefficients of equation(r t+1+···+r t+k)=α(k)+ (k)z t+β(k)S t+ε(k)t ob-tained from OLS estimation(columns1and2)and bootstrap simulations as explained in the Appendix (columns3and4).Results are presented for forecasting horizons of k=1;4;8;13;26weeks.The control variables are listed in section3(ii).
***,**and*denote statistical significance at the1%,5%and10%levels,respectively.
in Section3(i).S t is the value of the long-term Sentix survey Using the bootstrap procedure,we obtain coefficient estimates and associated p-values based on the distribution of the estimated coefficients.All tests reported in the paper are two-sided. Details of the procedure are explained in the Appendix.
Table3shows the results obtained using the procedure described above.It shows that the aggregate Sentix index,which,on average,consists of roughly three-quarters individual and one-quarter institutional respondents,has predictive power for future DAX30returns for periods from1to8weeks.The bootstrap coefficient estimates are always larger than the OLS estimates,although the differences are small.The results shown in Tables3,4and5reveal that the bootstrapped coefficients are always higher than the OLS coefficients.Stambaugh(1986,1999)has shown that the OLS coefficients are biased,and that the sign of the bias is the opposite of the sign of the correlation between the innovations in the stochastic regressor(the sentiment index in our application)and the lagged innovations of the dependent variable(the
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910HENGELBROCK,THEISSEN AND WESTHEIDE
Table5
Sentiment Coefficient in k-Week Regressions for AAII Sentiment and S&P500-
Sub-periods
OLS Bootstrap
Reg.Horizon?βOLS Sig.level?βSI M Sig.level
07/1987to06/1994
1week?0.0054*0.090?0.00420.662 4weeks?0.0526***0.000?0.0470**0.045 8weeks?0.0699***0.000?0.05860.145 13weeks?0.1158***0.000?0.0974*0.058 26weeks?0.1746***0.000?0.1439*0.052
07/1994to01/2001
1week?0.00150.827?0.00040.926 4weeks?0.0520***0.000?0.0475**0.047 8weeks?0.0550***0.009?0.04660.241 13weeks?0.0618**0.022?0.04810.408 26weeks?0.1118***0.000?0.08800.252
02/2001to06/2008
1week0.01000.3380.0111**0.049 4weeks0.02190.4580.0252*0.078 8weeks0.02000.8970.02570.189 13weeks0.01680.3080.02500.267 26weeks?0.0182***0.000?0.00470.804
Note:
The table presents the coefficients of equation(r t+1+···+r t+k)=α(k)+ (k)z t+β(k)S t+ε(k)t ob-tained from OLS estimation(columns1and2)and bootstrap simulations as explained in the Appendix (columns3and4).Results are presented for forecasting horizons of k=1;4;8;13;26weeks.The control variables are listed in section3(ii).
***,**and*denote statistical significance at the1%,5%and10%levels,respectively.
stock index returns).This correlation is positive(about0.05)in our US and German samples.Thus,the OLS coefficients are downward biased and the bootstrapped coefficients are thus larger than the OLS estimates.In spite of their larger numerical values,the bootstrap coefficients have higher p-values.Our interpretation of the results will be based on the more conservative bootstrap procedure.
The sign of the relationship between the sentiment indicator and future DAX returns is positive.From the standard deviation of the Sentix index shown in Table1 and the coefficient of the predictive regression shown in Table3,it follows that a change of one standard deviation in the Sentix index is associated with a change in the DAX of almost2%over an8-week horizon.This is not only statistically,but also economically significant.
These results could indicate that the sentiment index foreshadows future misvalua-tion.Interestingly,the coefficient in the26-week predictive regression is the smallest of all the five predictive regressions.This pattern is consistent with the sentiment index indicating a future misvaluation which is subsequently corrected in the second half of the26-week prediction period.Alternatively,the sentiment indicator may contain
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MARKET RESPONSE TO INVESTOR SENTIMENT911 information on future expected returns.The analysis of the announcement day effects in Section5(i)will allow us to discriminate between these interpretations.As previously noted,if the sentiment indicator contains information about future expected returns, the announcement day effect should have a sign opposite to that in the predictive regressions.
(ii)Results for the US
We conduct the same analysis as for the Sentix data for the American Association of Individual Investors sentiment index.We use the Standard&Poors500index as the index whose return is to be predicted.However,the results are qualitatively identical for the Dow Jones Industrial Average,the NASDAQ100,and the Russell3000indices. First,we apply our procedure to the whole period from1987to2008.The results, shown in Table4,indicate that US individual investor sentiment is inversely related to future S&https://www.sodocs.net/doc/49495791.html,ing the bootstrap results,this relationship is significant only for the26-week ahead forecast.These findings are consistent with those of Fisher and Statman(2000)and Brown and Cliff(2005).These authors also find an inverse relationship between sentiment and future returns for samples covering the periods 1987–98and1963–2000,respectively.
The record of the AAII sentiment index is much longer than that of the Sentix index.In order to check whether the results are stable over time we split the AAII data into three sub-periods of approximately equal length and apply our bootstrap procedure to each of these sub-samples.The third sub-sample coincides with the period of our German sample.Table5shows that the negative relationship between the AAII index and subsequent returns disappears over time.11It is pronounced and significant in three out of five cases in the1987–94sample.In the1994–2001sample the coefficients retain their sign but are smaller in magnitude and(when considering the bootstrap results)mostly insignificant.In the final sub-period,most coefficient estimates are positive,and the coefficients for the one-and four-week horizons are significant at the10percent level.In this sub-period,then,the results for the US are qualitatively similar to those obtained for the German case documented in Table3. We also found coefficients that were unanimously positive and significant for short forecasting horizons in that case.We can only speculate about the reasons for the change in the predictive ability of the AAII index over time.One possible explanation is the change in the way the AAII survey is conducted.Originally,the votes were collected by post which resulted in a lag of some days.This lag ceased when AAII began to collect the votes via the internet in2000.The change in the procedure may also have affected the composition of the sub-group of AAII members that respond to the survey. Finally,it is conceivable that the characteristics of the AAII members themselves have changed over time.As noted above,for the period2001–08we find positive coefficients in the predictive regressions both for Germany and the US.Although the signs of the coefficients are similar for the two countries,their magnitude is not.Consider the 8-week forecasting period as an example.As noted in the previous section a change of one standard deviation in the Sentix index is associated with a2%change in 11Similar findings are reported by Neuhierl and Schlusche(2011).They analyze the predictive ability of a large set of market timing rules in the US market.Applying a test procedure that corrects for data snooping bias they find that the best rules have predictive power in the period1981to1994,but lose their predictive power in the later period1995–2007.
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912HENGELBROCK,THEISSEN AND WESTHEIDE
the DAX over an8-week horizon.The corresponding figure for the US is less than 0.5%.It thus appears that the predictive power of the Sentix index is stronger than that of the AAII index.This may be due to the differences in the populations of the respective participants and their degree of sophistication as discussed in Section3(iii).
5.ANNOUNCEMENT DAY EFFECTS
(i)Results for Germany
Having established that the German investor sentiment survey Sentix is indeed able to predict the future movements in the DAX index,we now test whether the market reacts to the publication of the sentiment indicator.To this end,we regress daily DAX
log returns r DAX
t?1,t on their first lag12and on the variable Sentiment t which captures the
information content of the sentiment indicator.Because the Sentix index is published on Sunday evenings or on Monday mornings prior to the start of trading,the variable Sentiment t is non-zero on Mondays and zero from Tuesdays to Fridays.
Respondents to the German survey can submit their statement after observing the closing prices on the US stock market.We therefore include the lagged log returns
of the S&P500index,r S&P500
t?2,t?1in our regression.Finally,we include a Monday dummy
1Monday
t in order to capture possible day-of-the-week effects.13
For daily returns,problems induced by serial correlation are not an issue.However, the pattern of OLS residuals indicates strong ARCH effects.We therefore estimate a GARCH model.Likelihood ratio tests suggest that a GARCH(1,1)specification is appropriate.We estimate the following equations:14
r DAX t?1,t =a0+a1Sentiment t+a2r DAX
t?2,t?1
+a3r S&P500
t?2,t?1
+a41Monday
t
+e t.
σ2
t
=b0+b1e2
t?1
+b2σ2
t?1
.
We estimate three specifications.In the first,sentiment is measured as the level of the Sentix value index.The second specification includes the change in the value index rather than its level.The third specification only uses the unexpected change in the value index.We obtain the unexpected change by first regressing the sentiment index on its own lagged values and lagged DAX and S&P500returns and then using the residuals from this regression.15This procedure is implemented using expanding 12The DAX index is calculated from the prices in Xetra,the by far most liquid market for German stocks.
Until November2003trading in Xetra closed at8pm.Since then,however,trading in Xetra closes at5.30 pm,while trading on the floor of the Frankfurt Stock Exchange(which coexists with Xetra)continues until 8pm.When survey respondents submit their opinion during the week end they know the prices from floor trading.Therefore,from November2003onwards,the lagged DAX return included on the right-hand side is the return of an index called Late DAX.It is based on the same formula and weighting scheme as the DAX but uses the prices from the floor of the Frankfurt Stock Exchange.
13If we omitted the lagged S&P500returns,the sentiment indicator could be significant merely due to the possibility that it serves as a proxy for the US stock returns after the close of trading in Germany.
14As mentioned previously the Sentix index is published on Sunday evening or Monday morning prior to the opening of the market(time index t).We analyze whether the publication of the Sentix index affects the DAX return from Friday’s close(time t–1)to Monday’s close(t).
15We also estimate a version where we included,as additional explanatory variables,the short-term interest rate,liquidity spread,credit spread,term spread as well as the EUR/USD exchange rate in the first-pass regression.The results are similar and are thus omitted from the paper.
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MARKET RESPONSE TO INVESTOR SENTIMENT 913
Table 6
Estimation Results for Daily DAX Log Returns of Closing Prices
Specification
(1)(2)(3)Coef.Coef.Coef.Variable
(|τ–στατ.|)(|τ–στατ.|)(|τ–στατ.|)Sentix t
0.012**(2.12) Sentix t
0.025**(2.52)InnoSentix t
0.025**(2.30)r DAX t ?2,t ?1
?0.166***?0.166***?0.182***(5.98)(5.83)(5.99)r S &P 500t ?2,t ?10.285***0.287***
0.289***(8.91)(8.51)(8.05)1Monday t ?0.0017e-043e-04(1.27)(1.14)
(0.51)Const .6e-04**
6e-04**
7e-04**
(2.33)(2.32)(2.52)
Note:The table shows the results of a GARCH(1,1)with mean equation r DAX t ?1,t =a 0+a 1Sentiment t +a 2r DAX t ?2,t ?1+
a 3r S &P 500t ?2,t ?1+a 41Monday t +e t .r DAX t ?1,t is the return on the DAX index,r S &P 500t ?2,t ?1is the return on the S&P 500index,Se nti x t is equal to our sentiment measure on Mondays and zero else,and 1Monday t is a dummy variable that is set to one on Mondays.We use three sentiment measures,the level of the Sentix index (column 1),the first difference (column 2)and the residual from a regression of the Sentix index on its lagged value and the lagged DAX and S&P 500returns (column 3).***,**and *denote significance at the 1%,5%,and 10%level,respectively.
windows.Thus,the first-pass regression used to identify the unexpected component of the sentiment index only uses information available at time (t ?1).16Results are presented in Table 6.
We find a positive and significant announcement day effect irrespective of the specification used.Thus,all three sentiment variables are significantly positively correlated to daily closing log returns.Hence,the market appears to react to the publication of the investor sentiment index.The DAX increases after a rise and decreases after a fall in the sentiment https://www.sodocs.net/doc/49495791.html,gged index returns are also significant,while we find no clear evidence in favour of a Monday effect on the German stock market.
The announcement day effect is positive and thus has the same sign as the inter-mediate term predictability documented in Section 4(i).This finding is inconsistent with the idea that the sentiment indicator provides information about future expected returns.If it did,we would expect the announcement day effect to have the opposite sign to that found in the predictive regressions for the intermediate term.Our results thus support interpretations of the predictive power of sentiment indicators that are related to under-or over-reaction or misevaluation.
16We use the data for 2001to initialize the procedure.The first observations included in the second-pass regression are those for January 2002.Therefore,there are fewer observations in model 3than in models 1and 2.
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914HENGELBROCK,THEISSEN AND WESTHEIDE
Table 7
Estimation Results for Daily S&P 500Log Returns of Closing Prices
Specification
(1)(2)(3)(1)(2)(3)Coef Coef.Coef.Coef (|t ?stat.|)Coef Variable (|t ?stat.|)(|t ?stat.|)(|t ?stat.|)(|t ?stat.|).(|t ?stat.|)
07/1987to 06/2008
07/1987to 06/1994AAI I t
0.000?0.005**(0.17)(2.43) AAIIt t
?0.001?0.003(0.77)(1.23)InnoAAI I t
?0.000?0.004*(0.06)(1.71)r S &P 500t ?2,t ?1?0.000?0.007?0.0070.0060.0030.011(0.43)(0.48)
(0.47)(0.20)(0.10)(0.46)1Monday t 0.0000.0000.0000.0010.001?0.001**(1.15)(1.17)
(1.40)(1.22)(1.33)(2.18)Const .0.000**
0.000***
0.000***0.0000.0000.000(3.50)
(3.36)(3.06)(1.58)(1.28)(0.42)07/1994to 01/200102/2001to 06/2008AAI I t
?0.0010.002(0.31)(0.86) AAII t
0.002?0.001(0.54)(0.49)InnoAAI I t
0.0010.001(0.24)(0.30)r S &P 500t ?2,t ?10.0410.041
0.041?0.061**?0.061**?0.061**(1.53)(1.54)(1.54)(2.40)(2.40)(2.39)1Monday t ?0.000?0.000?0.0000.0000.0000.000(0.62)(0.60)
(0.58)(0.58)(0.44)(0.44)Const .0.001***
0.001***
0.001***
0.0000.0000.000(4.04)(4.32)(4.23)(0.98)(1.28)(1.28)
Note:The table shows the results of a GARCH(1,1)with mean equation r S &P 500t ?1,t =a 0+a 1Sentiment t +a 2r S &P 500t ?2,t ?1+a 31Monday t +e t .The top left panel shows the results for the whole period,the other panels those for the sub-
period indicated at the top of each panel.r S &P 500t ?1,t is the return on the S&P 500index,Se nti x t is equal to our sentiment measure on Thursdays and zero else,and 1Monday t is a dummy variable that is set to one on Mondays.We use three sentiment measures,the level of the AAII index (column 1),the first difference (column 2)and the residual from a regression of the AAII index on its lagged value and the lagged S&P 500return (column 3).***,**and *denote significance at the 1%,5%,and 10%level,respectively.
(ii)Results for the US
We conduct a similar analysis to that described above for the AAII sentiment survey.17Remember from Section 4(ii)that we found negative,but mostly insignificant coef-ficients in the predictive regressions over the full sample period.Consistent with this result,the first panel of Table 7shows that,for the whole period,there is no significant announcement effect on the day the AAII sentiment is published.By considering the 17Model 3again uses an expanding-window procedure.The first year of data (July 1987–June 1988)is used to initialize the procedure,the analysis of the announcement day effects starts in July 1988.
C 2013John Wiley &Sons Ltd
MARKET RESPONSE TO INVESTOR SENTIMENT915 three sub-samples,we find results that mirror those of the predictive regressions shown in Table5.The publication of the sentiment index triggers a negative announcement day effect in the first sub-sample.The respective coefficient is significant(at the10% level or better)in two out of the three specifications.We do not find a significant announcement day effect for the later sub-samples.This is not surprising because the predictive regressions presented earlier led to the conclusion that the AAII index is largely unrelated to futurereturns in these sub-periods.The announcement day effect in the first sub-period has the same sign as that of the coefficients in the predictive regressions.The results for the US,like those for Germany,are thus inconsistent with the expected return news scenario.Rather,they support the interpretation that investor sentiment is related to under-or over-reaction or misvaluation.
6.CONCLUSION
Previous research has shown that sentiment indicators predict future returns.This predictive ability can be rationally explained when the sentiment indicator contains news about expected returns.In this case,however,there should be an immediate price reaction to the publication of the sentiment indicator that is in the opposite direction as the medium-to long-term predictability.Note that the intermediate and long-term predictability cannot be a rational response to news about future cash flows. Information about future cash flows would result in an immediate share price reaction but no long-run effect.If,however,there is underreaction to cash flow news we would expect to see an immediate share price reaction and a long-term effect in the same direction.
The present paper is the first to empirically analyze whether an immediate market reaction can be identified in the data,and whether the sign of such a reaction corresponds to the sign of the intermediate and long-term predictive ability.In order to investigate these matters,we use survey-based sentiment indicators from the US(the AAII sentiment index)and for Germany(the Sentix index).In a first step,we replicate earlier results showing that the sentiment indicators do indeed have predictive power for future stock market returns over the intermediate term.We further document that the predictive power of the AAII index has largely disappeared in recent years.
In the second step of our analysis,we use event study methodology to test whether the daily index returns respond to the publication of the sentiment indicator.We do find a significant positive announcement day effect in Germany.This pattern is inconsistent with the rational explanation based on expected return news.Rather,it is consistent with underreaction to new information or mispricing and limited arbitrage. Smart investors are aware of the predictive power of the sentiment indicator and trade accordingly.However,they do not fully arbitrage the predictability away,possibly because of increased noise trader risk.For the US market,there is evidence of a negative publication day effect in the sub-period1987–94.In later sub-periods,there is no significant publication day effect.This is unsurprising,because the intermediate to long-term predictability also disappears towards the end of the sample period.
Notwithstanding the differences between the results for Germany and the US,the results for the two countries share one characteristic.They are both consistent with underreaction to new information or a mispricing interpretation of the predictive C 2013John Wiley&Sons Ltd
916HENGELBROCK,THEISSEN AND WESTHEIDE
power of sentiment,and inconsistent with the hypothesis that the sentiment indicator contains information about future expected returns.
APPENDIX
Similar to Brown and Cliff(2005),we regress future k-week returns on the current value of the sentiment index and control variables
(r t+1+···+r t+k)=α(k)+ (k)z t+β(k)S t+ε(k)t(1) where the variables are defined as in Section4(i).The fact that we use overlapping observations for the regressand induces MA(k?1)tructure in the error terms under the null hypothesis thatε(1)is serially uncorrelated.Since robust standard errors, suggested by Hansen and Hodrick(1980),are known to perform poorly in small samples and the existence of persistent regressors leads to a bias in the coefficient estimates,we opt for a simulation approach to account for the bias and to obtain appropriate critical values for inference.
We replicate the bootstrap simulation of Brown and Cliff(2005,p.437),and start by estimating a VAR(1)model for y t=r t S t z t.After the estimation,we impose the null hypothesis that the Sentix sentiment survey does not predict1-week returns, by setting the appropriate element in the coefficient vector of the return equation equal to zero.We then adjust the constant in the constrained model by adding the contribution of average sentiment to the returns obtained by multiplying the original slope value of the sentiment by the average sentiment level to the constant of the return equation.We bootstrap the residuals from the calibration estimates to account for heteroscedasticity,and generate and discard100additional observations to delete possible starting effects.In each of the replications,a number equal to our original sample of simulated observations is used to estimate our equation of interest for horizons from1to26weeks.Analogous to Brown and Cliff,we repeat the procedure 10,000times in order to obtain a distribution of the values of?β(k).
In order to gauge the statistical significance of the coefficient estimates we com-pare the sentiment coefficient of the original model with the simulated probability distribution in order to obtain p-values.Because these p-values are based on the actual distribution of the residuals,they are robust to deviations from the normal distribution.
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经典大学毕业演讲致辞经典大学毕业演讲稿
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1、实体、对象与类的概念 2、类的定义 3、对象声明与引用 4、私有、公有与保护 5、日期类的设计 6、两种程序设计思想 7、汽车类的设计 8、几何图形圆类的设计 9、构造函数的定义10、重载构造函数11、析构函数的定义12、整数翻译函数13、实际意义的析构函数14、Person类的设计15、对象与指针16、this指针
?实体：指客观世界存在的某个事物?一所大学，例如：西安交通大学 ?某动物，例如：一只羊 ?一本图书，例如：《C++程序设计教程》?一篇文章，例如：“羊年趣赏羊联” ?一个专业班级，例如：材料21班 ?一名学生，例如：材料21班的蒋贵川?……
?可以拍摄视频描述实体 ?也可以写一篇文章描述实体 ?我们设计的程序都是为了求解某个(些)问题 ?如果求解的问题中涉及到某个实体，那么在程序中如何描述实体呢？ ?通过对实体进行抽象，来描述实体
?每个实体都有其特征和功能，特征和功能通称为属性?实体与实体的不同在于属性的不同 ?所谓抽象描述实体是指： ?从实体中抽取出若干特征和功能，来表示实体 ?特征指实体的静态属性，功能指实体的动态属性 ?对实体加以抽象要注意下面两点： ?移出细节看主干 ?不是借助具体形象反映现实，而是以抽象表达科学的真实
毕加索画《牛》 1945年12月5日 1946年1月17日 ?形体逐渐概括 ?线条逐步简练 ?别人认为的终点，他 作为起点 ?每幅画不重复 ?精炼地表现了公牛的
?电视机的特征：?型号?尺寸?液晶?价格? ……?电视机的功能： ?播放影视?选频道?调颜色?调音量?……
大学毕业生演讲稿
大学毕业生演讲稿 四年时间，不知不觉已到尽头，记忆中留下了太多的片段，有太多的不舍、太多的留恋。下面是小编为你整理的几篇大学毕业生演讲稿，希望能帮到你哟。 大学毕业生演讲稿篇一同学们： 今天的典礼标志着你们圆满结束了学业，即将开启崭新的人生旅程。首先，我代表学院，向你们致以热烈的祝贺和深情的祝福!同时，向所有教育、帮助、关心你们的老师、亲人和朋友们，致以诚挚的敬意和亲切的问候! 在中青院不大却精致的校园里，你们度过了人生中最宝贵的一段时光。你们志存高远、胸怀祖国、关注社会，你们风华正茂、勤勉苦读、砥砺思想，你们脚踏实地、热心公益、投身实践。这里留下了你们的成长足迹，镌刻了你们的青春乐章。我相信，你们必将永远铭记这段洋溢激情与希望的校园岁月，必将永远传承这种追求光荣与梦想的奋斗精神。 毕业典礼后，你们将奔赴五湖四海，拼搏奋斗在祖国各地各条战线上。正如毛泽东同志所讲，“同学们毕业出去，好像撒入河水里去一样，可能有若干人会被潮水卷去”。如何做到“战胜潮水，朝着总的方向，达到预定的目的”，是你们必须面对、必须思考的人生重大课题。 今年五月四日，参加“实现中国梦、青春勇担当”主题团日活动
时，深刻指出“中国梦是我们的，更是你们青年一代的”，希望青年一代坚定理想信念，练就过硬本领，勇于创新创造，矢志艰苦奋斗，锤炼高尚品格。团xx大闭幕后，在同团中央新一届领导班子集体谈话时强调，青年时代树立正确的理想、坚定的信念十分紧要，不仅要树立，而且要在心中扎根，一辈子都能坚持为之奋斗。我想，高举理想信念的旗帜，认真践行提出的五条希望，正是回答如何“战胜潮水”、“实现梦想”这一人生课题的满意答案。希望你们用心学习、真正践行的重要讲话，把未来的人生道路越走越宽广。 作为院长，在大家即将毕业之际，与大家分享些什么?最近两部热映的青春题材电影给了我一些启发。一个是《中国合伙人》。三位追梦人的成功，有发现机会的敏锐嗅觉，有精诚合作的团队精神，有经受挫折的坚韧意志，但最不可忽视的还有改革开放这一支撑他们干事创业的宏观时代背景。另一个是《致我们终将逝去的青春》。将来你们回忆青春，当然希望看到诚挚的友情，看到青涩的爱情，但更重要的应该还有奋斗的激情。因为，任何一代青年追梦圆梦、有所作为，都离不开时代主题，都缺不得奋斗精神。你们这一代青年尤其如此。 希望你们在追梦圆梦、干事创业中，识大势、知大局、行大道。深刻指出，全党全国各族人民正在为实现党的提出的奋斗目标而奋发努力，正在朝着实现中华民族伟大复兴的中国梦而奋勇迈进。这是党和国家工作大局，也是中国青年运动的时代主题。我想，这更是你们这一代青年练本领、展身手、有作为的广阔舞台。你们的人生，注定与实现中国梦的征程同行。再过7年，正当你们积累人生阅历、步入
类与对象答案
类与对象 知识要点 1．掌握类与对象的概念和定义方法，掌握类成员的访问属性。 2．掌握构造函数的概念、定义方法和使用方法。 3．掌握析构函数的概念、定义方法和使用方法。 4．掌握拷贝构造函数的概念、定义方法和使用方法。 5．掌握包含对象成员的类构造函数的定义方法。 6．掌握静态成员的概念、定义方法、作用和特点。 7．掌握友元的概念、定义方法、作用和特点。 8．掌握类模板的定义格式与使用方法。 典型例题分析与解答 例题1：下列有关类的说法不正确的是（）。 A．对象是类的一个实例 B．任何一个对象只能属于一个具体的类 C．一个类只能有一个对象 D．类与对象的关系和数据类型与变量的关系相似 答案：C 分析：对象是类的一个实例，类与对象的关系和数据与变量的关系相似，所以一个类可以有多个对象。 例题2：下面（）项是对构造函数和析构函数的正确定义。 A．void X::X(), void X::~X() B．X::X(参数), X::~X() C．X::X(参数), X::~X(参数) D．void X::X(参数), void X::~X(参数) 答案：B 分析构造函数无返回类型、可带参数、可重载；析构函数无返回类型、不可带参数、不可重载。 例题3：（）的功能是对象进行初始化。 A．析构函数 B. 数据成员 C.构造函数 D.静态成员函数 答案：C 分析：当一个对象定义时，C++编译系统自动调用构造函数建立该对象并进行初始化；当一个对象的生命周期结束时，C++编译系统自动调用析构函数注销该对象并进行善后工作； 例题4：下列表达方式正确的是（）。 A．class P{ B． class P{ public： public： int x=15； int x； void show(){cout< 大学毕业生代表演讲稿 尊敬的领导,老师,各位同学: 大家好! 今天,我站在这里,代表06电气j4.5班的毕业生向我们的母校道别,向学院的师长道别,向朝夕相处的同窗们道别,也向这段不能忘怀的岁月道别! 岁月匆匆,四年半转瞬即逝.我们将离开我的学生生活.走过楼兰,走过荒滩,只是为了那句"路在脚下,明天会更好". 这四年半的路,我们走的辛苦而快乐,四年半的生活,我们过的充实而美丽,我们流过眼泪,却伴着欢笑.四年半的岁月,多少个日日夜夜,听起来似乎是那么的漫长,而当我们今天面对离别,又觉得它是那么的短暂.四年半的时光,弹指一挥间,但很多记忆将成为我们生命中最为珍重的收藏:宽阔的操场,明亮的教室,甜蜜的欢笑......我们一定还记得刚入校时你我所立的雄心壮志,一定还记得在教室,图书馆和实验室中你我孜孜不倦学习的身影,一定还记得老师的谆谆教诲,一定还记得在运动场上你我生龙活虎的锻炼场景.....太多太多的情景值得我们去回忆. 在四年半里,我们更进一步学会了分析与思考,学会了丰富与凝练,学会了合作与竞争,学会了继承与创新,也进一步学会了如何不断超越,突破自己的极限而成长.如今我们就要毕业了,所有这些温暖的记忆都将铭刻在我们内心深处,那是我们生命中最难忘的日子.喜欢好友常说的一句话:"我们都是只有一只翅膀的天使,只有互相拥抱才能飞翔."四年半的同窗友谊,让我们学会了彼此相信并依赖.四年半的生活,我们都有过低谷,但我们相互扶持,鼓励,朋友温馨的笑容,班级温暖的气氛,让我们都走了过来,让我们学会去爱,去坚持,去相信"阳光总在风雨后".我敬爱的老师,您用您辛勤的汗水,无私的奉献,无数夜的伏案耕耘,给了我们一个清醒的头脑,一双洞察的眼睛和一颗热忱的心灵,再华丽的辞藻也无法表达我们对您——既是老师,又是朋友,更是亲人的尊敬和爱戴.学生即将远行,请允许我们深情地道一声:"老师,您辛苦了!谢谢你们的关怀和教育" 毕业是一首久唱不衰的老歌,是散场之后的余音绕耳,所有甜美或者苦涩的故事,定格为热泪盈眶的欣悦,依然真诚直率的目光,依然奔流激荡的热血,正牵引着我们再一次传唱, 实验4 类和对象（一） 实验目的及要求 1．掌握C++类的概念和基本组成、类的使用方法以及静态数据成员的使用； 2．熟悉各种成员函数包括构造函数、析构函数、内嵌函数的定义与使用。 实验内容 1．设计一个Birthday类，包含数据成员year、month、day，成员函数Print()和Age()，计算年龄并输出。构造一个Birthday的对象进行测试。 2．改正以下程序中的错误，并分析错误原因。提示：该程序中point类的构造函数定义不正确, 在main()中对数据成员的访问不正确。 #include 实验步骤 1．设计一个Birthday类，包含数据成员year、month、day，成员函数Print()和Age()，计算年龄并输出。构造一个Birthday的对象进行测试。 参考程序如下： #include 2020大学生毕业典礼演讲稿5篇 【篇一】2020大学生毕业典礼演讲稿 尊敬的老师、亲爱的同学们： 大家好！ 眨眼间，又一批熟悉的面孔将带着大学四年的回忆离开校园。四年时光匆匆，大一的青涩懵懂，大二的奋起拼搏，大三的勤学苦练似乎都还在眼前，还未好好享受，却要轻轻挥手，告别哭过、笑过、闹过的兄弟姐妹，告别迷惘过、奋斗过的校园青春。 大学里最值得骄傲的事情也许是坚持并实现了自己的梦想，也许是收获了甜蜜的爱情，也许是结识到了知己。有无悔，也有遗憾。比赛的失力，考试的挂科，同学间的小摩擦等等，也在调剂着大学这碗五味杂陈的汤。当然，还会有不少感悟。新闻学院大四的邹佼向记者畅谈道：“我认为大一到大四都要坚持努力学习，劳逸结合，学和玩都不能少。大一做到适应大学生活，学会独立，多阅读、多交朋友，打下优良基础。大二思考一下自己想做什么，确定大体的方向，并做初步准备。大三可以根据之前所学去实践一下，并为自己选择的道路做最后的冲刺。在大四，一方面要淡定地等待努力的结果，另一方面可以去做自己以前没时间做的事情。”这些感悟都为学弟学妹们提供了优良的参考，促进他们主动向上，创造一个多彩无悔的大学生活。 我始终相信，告别是为了下一次更好的相见。步入竞争激烈的社会，才真正的见识到拥挤的招聘大厅，或是切实地 感受到考研的重重压力，遇到了以前从未碰到的难题，拉开了另一个生活场景的帷幕。据了解，这一年全国毕业生数量达到xx万人，被戏称为“最难就业年”，竞争压力可想而知。与此同时，在收入等严峻的实际问题驱使之下，众多大学生竟相信花x万就能进银行、国企或事业单位等骗局，不仅打碎了“铁饭碗”梦，也将自己推入更深的渊谷。这其中虽然有许多社会急待解决的问题，但仔细想想许多大学生的观念更是亟须纠正，走后门、托关系的投机取巧心理是万万不可行的，既危害个人，又败坏社会风气。 要想出人头地，大学毕业生不应该嫌弃职位的高低贵贱，三百六十行，行行出状元，只要脚踏实地，做好手头工作，多积累经验，终有出头日。 又到了要说再见的时候了，可是再见是为了再一次的相见。等到几年、十几年、几十年之后，等到大家都接受了社会和生活的磨炼，等到每个人都变得成熟优秀，等到各自都有了全新的生活，再次相见，微微一笑，大家仍然是朋友，仍然有拥抱的理由，因为友谊地久天长，因为青春值得我们去追忆。 毕业离歌渐响，还未离开，却已开始怀念。青春终究是会逝去的，不必伤感，不必慌张，它将永刻在岁月里，化成一股温暖的力量，让前行的路因回忆而充满无限阳光。 我的演讲完毕，谢谢大家！【篇二】2020大学生毕业典礼演讲稿 尊敬的各位领导、老师、亲爱的同学们： 大学生关于毕业的演讲稿(精选多篇) 第一篇：大学生毕业演讲稿 尊敬的各位领导、各位老师，亲爱的同学们： 大家好!我很荣幸能作为毕业生代表在此发言。首先,请允许我代表所有毕业生同学向长期以来关心、教育和帮助我们的学校各位领导致以衷心的感谢!向辛勤培育我们的各位辅导员、各位任课老师们致以崇高的敬意!向为我们的学习、生活和工作默默奉献的学校各部门的老师们表示诚挚的感谢! 今天，我站在这里，代表毕业生向我们的母校道别，向老师道别，向朝夕相处的同窗们道别，也向这段不能忘怀的岁月道别! 都说时光匆匆，岁月荏苒，一低眉间大家就要背上行李，各自奔赴下一段旅程。我此刻的心情应当和在座的各位一样，掩不住的是回忆与留恋。留恋我走过大学的每一个角落,留恋我相识的老师,留恋我相知的朋友,留恋这美丽的南华。2014年9月，命运让我们聚首南华，回望过往，经历的点点滴滴仍历历在目，我们一定还记得清远校区辽阔的操场上，那段挥洒汗水、灼伤皮肤的军训岁月，炎炎烈日晒去了我们的懒惰，雨水洗去我们的娇气，同时它也凝结了我们的友谊。我们一定还记得在那如火如荼进行着的各样“金鹰杯”大赛，赛场上配合与拼杀身影，呐喊与助威的声音，是那么的激烈，紧张与精彩。一定还记得那些丰富多彩的文艺晚会和社团演出，一定还记得我们一起参与一起奉献的社会志愿者活动，一定还记得我们一起学习，一起看书， 一起考试考证的种种……南华是一个充满激情的舞台，让我们个性飞扬，流光溢彩。同时她也是一个温暖的大家庭，让我们无忧无虑，自然快乐。阳光下，我们笑靥如花。树荫下，我们舒适而自在。淡淡的微风，淡淡的凉意。吹不淡的是我们共同拥有的美好回忆。 如今大学即将结束了，而我们的生活却刚刚拉开序幕。现在的我们正处在一个尴尬的年龄，我们能做什么，会做什么?仿佛我们什么都不懂!离开学校，社会把我们青涩的外衣脱了，却不肯让我们立马换上成熟的那件。妖怪般的大都市鲸吞了多少热血的梦想?却不肯给我们一份合适工作一份可靠爱情。逆着时间，往回推几个岁月，那时的我们会有欢笑，有泪水，有同学，有一起站在阳光下的影子，背后有父母，有老师，生活得无忧无虑;往后穿梭数个岁月，各兄弟姐妹会有自己的家庭，有经历岁月淘沙留下来的亲朋好友。可是现在的我们正在社会上裸奔，且方向迷茫。 初出茅庐，对于社会的了解，凤毛麟角。也许我们说话依然天真幼稚，处事不够圆滑，让我们工作、生活处处碰壁。但请记住大学三年我们并非一无所获，在这里我们学会了从思考中确立自我，从学习中寻求真理，从独立中体验自主，从实践中赢得价值，从追求中获得力量。我们有足够的自信和勇气应对各种磨砺与挑战。阅历不足，但只要有信心，时间会帮助我们挺过去。现在给我们挫折，让我们难堪，我们要心存感激，只有这样才能成长，才能弥补不足。 亲爱的同学们请勇敢起来，现在的你并不是一个人在战斗。有一句话说：“我们都是只有一只翅膀的天使，只有互相拥抱才能飞翔。”三年 2021年大学毕业生演讲稿400字 2021年大学毕业生演讲稿400字1 四年让我们知道什么是痛并快乐着，我们带着这样美好的回忆踏入社会，开始另一段的奋斗。我们朝夕相处，情同手足。这四年的盛宴，让我们更加懂得了珍惜。 时光飞逝,转眼间四年的大学生崖即将结束。在即将毕业离校之计,我心中充满了对母校的不舍以及对学校领导和老师们的感激之情。在这四年经历了许多喜怒哀乐的事,我也交了许多朋友,这些朋友在我伤心的时候，鼓励我、安慰我，也有许多老师教导我做人做事的道理,让我在这四年里成长了许多,我要感谢老师的教导,虽然大学生崖结束了，但不代表学习结束。 现在要离开，还真舍不得，这些美好回忆将永远留在我心里,成为我一生最难忘的事。在上大学的这四年里，我曾经常回忆我的小学，初中，高中生活，那时的人和事，让我觉得那么的美好......而如今，我深深的明白，大学里的一点一滴，也将是我一生的美好回忆......深切地祝福沈阳理工大学，祝福老师们，祝福同学们...... 我是如此平凡，却又如此幸运。真心谢谢大家!四年改变了我们的容颜和那颗曾经年轻的心，而成长的代价就是我们失去纯真的微笑，而多了一份离别的伤感。无论我们有多少不舍，都唤不回逝去的四年。但我们无须失落，我们依然可以一路高歌，让六月的骄阳永远见证我们的无悔青春…… 快毕业了，真的很留恋大学四年的美好时光。我最感谢的是四年 来同甘共苦的好朋友，好姐妹们。虽然还有一个月就要离开了，但是在最后的这段日子里，我相信我们能开心度过，珍惜相处的每一刻。我在大学体会了除离别以外的一切美好!现在让离别画上美好的句号!道一声“思量”，又怎不“思量”!要记住，作为沈阳理工大学的毕业生，不管今后我们身在何方，我们永远都是母校的一员，永远都将代表自己的母校。 2021年大学毕业生演讲稿400字2 亲爱的老师、同学们： 大家好!我今天要演讲的题目是：《我难忘的大学生活》 2018年9月我从地处偏僻的黄金乡来到这旅游名城哈尔滨，走进了旅游学院，圆了我多年梦寐以求的大学梦。有人说：“大学是铸就平凡人的辉煌，造就大师的摇篮。”作为一名普通的大学生，我注定不能成为大师，但平凡的我决不能选择平庸。“非志无以成学，非学无以广才”，这次难得的求学机会将是我人生中一次重要的转折点，所以我会倍加珍惜。带着这份朴素的感情、也带着我的希望和梦想，就这样默默地坚持着三年的大学生活。我虽然柔弱，但我不乏毅力;我虽然基础不扎实，但我用勤奋和汗水弥补着不足。 在两年多姿多彩的大学生活中，学院领导的关心爱护以及老师的谆谆教诲、同学们的和谐快乐相处，给我留下了美好的记忆，我衷心的感谢你们，感谢你们给予我知识和能力，给予我温暖和关怀，在这里我要真诚地说一声：谢谢你们!丰富多彩的大学生活需要自己好好 类和对象的概念 类(Class) 面向对象思想来源于对现实世界的认知。现实世界缤纷复杂、种类繁多，难于认识和理解。但是聪明的人们学会了把这些错综复杂的事物进行分类，从而使世界变得井井有条。比如我们由各式各样的汽车抽象出汽车的概念，由形形色色的猫抽象出猫的概念，由五彩斑斓的鲜花抽象出花的概念等。汽车、猫、鲜花都代表着一类事物。每一类事物都有特定的状态，比如汽车的品牌、时速、马力、耗油量、座椅数，小猫的年龄、体重、毛色，鲜花的颜色、花瓣形状、花瓣数目，都是在描述事物的状态。每类事物也都有一定的行为，比如汽车启动、行驶、加速、减速、刹车、停车，猫捉老鼠，鲜花盛开。这些不同的状态和行为将各类事物区分开来。 面向对象编程也采用了类的概念，把事物编写成一个个“类”。在类中，用数据表示事物的状态，用函数实现事物的行为，这样就使编程方式和人的思维方式保持一致，极大的降低了思维难度。 封装(Encapsulation) 制造汽车的过程中什么人最牛？当然不是焊钢板的，也不是装轮胎的，更不是拧螺丝的，而是设计汽车的工程师，因为他知道汽车的运行原理。但是我们开车时，需要知道汽车的运行原理吗？答案显然不需要。汽车的运行原理已经被伟大的工程师封装在汽车内部，提供给司机的只是一个简单的使用接口，司机操纵方向盘和各种按钮就可以灵活自如的开动汽车了。 与制造汽车相似，面向对象技术把事物的状态和行为的实现细节封装在类中，形成一个可以重复使用的“零件”。类一旦被设计好，就可以像工业零件一样，被成千上万的对其内部原理毫不知情的程序员使用。类的设计者相当于汽车工程师，类的使用者相当于司机。这样程序员就可以充份利用他人已经编写好的“零件”，而将主要精力集中在自己的专署领域。 接口(Interface) 今天你买了一台等离子电视机，你拿出遥控器，操纵按钮，开始欣赏精彩的电视节目。人们通过遥控器这个接口使用电视机，同样，人们通过类的接口使用类。程序员在编写类时精心地为它设计接口，而设计良好的接口不但方便其他程序员使用，还有利于类的升级改造。 假设你是一个环保人士，最近你用节能的荧光灯代替了普通的白炽灯。白炽灯通过加热电阻丝照亮整个屋子；荧光灯则是激活空气中的某些分子的电子能而发光。虽然它们的原理不同，但是它们的功能却是一样的——照明。因为两种灯具有相同的接口，所以你只需拧下白炽灯，然后再拧上荧光灯即可，轻易的实现“零件”的更新换代。同样，在面向对象编程中，只要保持接口不变，你可以任意更改类的实现细节，用一个设计更好的类替换原来的类，实现类的升级换代。 现在软件维护和修改的成本已经占到了整个软件开发的80%，类的这一编成思想极大的方便了程序的维护和修改，降低了软件成本。 大学生毕业典礼发言稿(15篇) 大学生毕业典礼发言稿1 尊敬的亓院长、各位领导，亲爱的老师和同学们： 大家下午好！ 我是电气自动化专业3102班的任俊峰。很荣幸今天能站在这里代表电气系20xx届715名毕业生发言，向辛勤培养我们的母校道别，向亓院长等学院领导道别，向潜心教育我们的老师道别，向三年来朝夕相处的同学道别，更向这段令人无法忘怀的青春时光道别。 三年弹指一挥间。三年前，我们怀揣梦想，背负期望，来到安徽机电职业技术学院，来到电气工程系。还记得刚报到时那一张张稚气未脱的脸庞，还记得军训时那一声声铿锵有力的呼喊，还记得教室里那一个个刻苦学习的背影，还记得运动场上那一阵阵响彻云霄的掌声。感谢你们，我亲爱的老师，你们传道、授业、解惑，让我们成为社会主义合格的建设者和可靠的接班人；感谢你们，我亲爱的同学，三年来，我们相互了解，相互学习，相互倾诉，相互帮助，最终成为奋斗的伙伴，一生的挚友，更成为我们人生路上一笔最珍贵的财富。 进入安机电以来，遵循“修德，练技，立业，报国”的校训，在老师和同学们的帮助下，通过自身的努力，我取得了优异的成绩。先后获得校三好学生和优秀共青团员称号；光荣地加入了中国共产党；多次荣获学院奖学金并获得国家励志奖学金；在安徽省大学生创业大赛中两次获奖；毕业前获得校级和省级优秀毕业生光荣称号。现在顺利地通过了专升本的考试，被铜陵学院自动化专业录取。在此，我特别要向精心培养我的领导及所有老师表示最衷心的感谢和最诚挚的敬意（鞠躬）。 感谢我们的母校，给了我们学习求知的环境，给了我们成长成才的阶梯，给了我们展现自己的舞台，更给了我们拼搏进取的信心！在学院的亲切关怀之下，我们在学习中追求真理，在实践中成就价值，在兴趣中获得快乐，在历练中懂得了感恩。 大学毕业生演讲稿3篇 College graduates' speech 演讲人：JinTai College 大学毕业生演讲稿3篇 前言：演讲是指在公众场合，以有声语言为主要手段，以体态语言为辅助手段，针对某个具体问题，鲜明、完整地发表自己的见解和主张，阐明事理或抒发情感，进行宣传鼓动的一种语言交际活动。本文档根据题材主题演讲内容要求展开说明，具有实践指导意义，便于学习和使用，本文档下载后内容可按需编辑修改及打印。 本文简要目录如下：【下载该文档后使用Word打开，按住键盘Ctrl键且鼠标单击目录内容即可跳转到对应篇章】 1、篇章1：大学毕业生演讲稿 2、篇章2：大学毕业生演讲稿 3、篇章3：大学毕业生演讲稿 篇章1:大学毕业生演讲稿 同学们： 今天的典礼标志着你们圆满结束了学业，即将开启崭新的人生旅程。首先，我代表学院，向你们致以热烈的祝贺和深情的祝福!同时，向所有教育、帮助、关心你们的老师、亲人和朋友们，致以诚挚的敬意和亲切的问候! 在中青院不大却精致的校园里，你们度过了人生中最宝贵的一段时光。你们志存高远、胸怀祖国、关注社会，你们风 华正茂、勤勉苦读、砥砺思想，你们脚踏实地、热心公益、投身实践。这里留下了你们的成长足迹，镌刻了你们的青春乐章。我相信，你们必将永远铭记这段洋溢激情与希望的校园岁月，必将永远传承这种追求光荣与梦想的奋斗精神。 毕业典礼后，你们将奔赴五湖四海，拼搏奋斗在祖国各 地各条战线上。正如毛泽东同志所讲，“同学们毕业出去，好像撒入河水里去一样，可能有若干人会被潮水卷去”。如何做到“战胜潮水，朝着总的方向，达到预定的目的”，是你们必须面对、必须思考的人生重大课题。 今年五月四日，参加“实现中国梦、青春勇担当”主题 团日活动时，深刻指出“中国梦是我们的，更是你们青年一代的”，希望青年一代坚定理想信念，练就过硬本领，勇于创新创造，矢志艰苦奋斗，锤炼高尚品格。团xx大闭幕后，在同 团中央新一届领导班子集体谈话时强调，青年时代树立正确的理想、坚定的信念十分紧要，不仅要树立，而且要在心中扎根，一辈子都能坚持为之奋斗。我想，高举理想信念的旗帜，认真践行提出的五条希望，正是回答如何“战胜潮水”、“实现梦想”这一人生课题的满意答案。希望你们用心学习、真正践行的重要讲话，把未来的人生道路越走越宽广。 大学毕业生回校演讲稿 尊敬的各位领导，敬爱的各位老师，亲爱的同学们： 大家上午好! 我是来自班的。今天我非常荣幸能够作为毕业生代表在这个具有特殊意义的典礼上发言。首先，代表xx届全体毕业生向我们的母校道别，向母校的师长们道别，向朝夕相处的同窗们道别，也向这段不能忘怀的美好岁月道别! 对于我和在座的各位毕业生来说，今天是我们人生旅途中一个特殊的日子。今天，我们拿到了梦寐以求的大学毕业证书，为大学时代画上了一个圆满的句号。三年的大学生活在今天到了临行话别的时候。此刻，我想在座的各位毕业生同学应当和我一样，纵然喜悦，但也掩盖不住对即将过去的大学生活的回忆与留恋。 回首大学三年生活的点点滴滴，那些曾经一同走过的日子始终都是我们心中的一段绚丽的记忆。三年前，我们带着家人的叮咛，带着一颗勇往直前的拼搏之心，走进了邮院的美丽校园。季节流转的美心岗、花开花谢的木棉树、丰富的图书藏馆、先进的实验设备和浓厚的人文内涵都深深地打动着我们的心。当然，我们一定还记得刚入校时你我所立的雄心壮志，还记得在教室和图书馆里你我孜孜不倦的学习和钻研，还记得在我院运动会上你我生龙活虎的比赛场景，还记得母校六十周年校庆时隆重而紧张欢快的时刻……太多太多的情景值得我们去回忆，去回味。 “乘风破浪会有时，直挂云帆济沧海。”让时间作证，承载着我们邮院师长们的殷切期望和深情嘱托，我们一定会做拥有智慧并富有激情的人、做胸怀大志 并脚踏实地的人，做德才兼备并勇于创新的人、做富有责任并敢挑重担的人! 大学毕业既是一个终点又是一个起点，我们即将走上社会，走进新的生活。我相信，无论我们是走上了工作岗位，还是继续深造，我们都将以最饱满的热情去勇敢地面各种挑战。无论走到哪里，我们永远都会记得我们是美心岗人，我们必将以“团结，进取，创新，求实”的校训作为动力，用最豪迈最稳重地步伐朝着美好的未来努力前进。 最后，在这离别的时刻，请允许我代表14届全体毕业生，真心地祝愿我们的同学们、师弟师妹们青春如夏花般灿烂，生命繁华似锦!祝愿我们敬爱的老师们身体健康，桃李满天下!祝愿我们所深爱的母校前程似锦、蒸蒸日上! 谢谢大家! xx年6月22日大学毕业生代表演讲稿
实验4 类和对象(一)
2020大学生毕业典礼演讲稿5篇
大学生关于毕业的演讲稿(精选多篇)
2021年大学毕业生演讲稿400字
类和对象的概念
大学生毕业典礼发言稿(15篇)
大学毕业生演讲稿3篇
大学毕业生回校演讲稿