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					      Expectations of Professionals in Turkish Stock Market:
                       A Study of Monthly Reuters Survey




                                        Numan ÜLKÜ*




                                              ABSTRACT


Reuters-Turkey News Service conducts a monthly survey of forecasts of the senior analysts,
portfolio managers, or strategists of major brokerage houses in Istanbul Stock Exchange on the
next month’s closing level of the ISE-100 index. This survey data is analysed in this study, in order
to assess forecasting performance of market professionals, to test previously documented
behavioral biases of analyst expectations and some specific hypotheses of the noisy rational
expectations literature. Results indicate that: i) survey participants’ average forecasts have no
predictive power, ii) they are too optimistic, iii) they extrapolate from the past despite the fact that
the degree of extrapolation is negatively correlated with forecast performance, iv) extrapolation
becomes insignificant when previous period’s return is negative, v) the forecast performance is not
correlated accross time, vi) participants are heterogenous in terms of their expectations, vii) but no
association between the level dispersion in forecasts and market volume and volatility is observed,
viii) participants seem to place greater weight on market price than their priors.




* Yeditepe University, Istanbul, Turkey; nurolmenkul@superonline.com
The author is thankful to Güzin Övünç and Reuters Turkey News Service for sharing the survey data. The
views expressed here those of the author and cannot be associated with Reuters.
   I.       INTRODUCTION


Reuters-Turkey News Service conducts a monthly survey of forecasts of the senior analysts,
portfolio managers, or strategists of major brokerage houses in ISE (Istanbul Stock Exchange) on
the next month’s closing level of ISE-100 stock index, since January 2002. This unique data set
provides us an opportunity to assess the predictive performance of professional market
participants and test behavioral hypotheses of analyst expectations, as well as to examine some of
the extant hypotheses in the noisy rational expectations literature.




   II.      LITERATURE REVİEW


Efficient markets theory predicts that no forecaster should have the ability to consistently predict
future returns, if the market is efficient. Evidence from early empirical studies tended to support this
basic prediction of efficient markets theory (Fama, 1970). However, more recent literature of the
last two decades contain a great deal of predictability findings. Stock index returns, the focus of
this paper, are found to have predictable components from past returns (Fama and French, 1988),
or from technical analysis signals (Brock, Lakonishok and LeBaron, 1992).
         These evidence, inconsistent with market efficiency, are, however, obtained in ex post
studies. More direct evidence should come from ex ante forecasts of real-life market participants.
So, studies of the performance of market forecasters and portfolio managers have been part of the
literature testing efficient markets theory. The earliest example was Cowles (1934) who found that
most of the forecasting effort was unsuccessful. See especially Hartzmark (1991) who found that
superior performers are less than that would be expected under pure chance and that above-
average performance is not correlated over time. The performance of mutual fund managers turns
out similar results. Available evidence suggests little systematic predictive ability on the part of
market professionals. These studies, taken together, are consistent with market efficiency and
imply that predictability found in ex post tests could not have not been utilized by real-life
practitioners, at least by an average of those sampled in these studies.
         Another area of interest in analysts’ forecasts stems from behavioral hypotheses.
Lakonishok, Shleifer and Vishny (1994) explain the profitability of value strategies by extrapolation.
Dechow and Sloan (1997), on the other hand, find that stock prices appear to reflect analysts’
biased forecasts of future earnings growth rather than naive extrapolation of past trends. These
studies open up the need for further tests of biases in analysts’ expectations, especially the
tendency to extrapolate from the past. While one way of conducting such tests is experimental
settings (see DeBondt, 1993; and on turkish subjects, Muradoğlu, 1996), transferability of findings
from experimental designs to real-life pose some problems. Direct surveys with real-life agents
may overcome some of such shortcomings.
           Another behavioral bias documented in the literature is systematic optimism. Dechow and
Sloan (1997) report that over their 15-year I/B/E/S sample with 70,000 earnings forecasts per
analyst per quarter per company, the mean forecast earnings growth rate is 14% while the mean
rate of realized earnings growth is 8%. This suggests that analysts systematically overestimate
expected earnings. See also a more detailed study by Easterwood and Nutt (1999) who
differentiate between systematic misreaction and systematic optimism, and provide results
consistent with the latter. In the context of company earnings estimates, systematic optimism bias
is often related to agency issues. It will therefore be very interesting to see whether the optimism
bias still holds in the context of market index forecasting where no agency issues are present.
           Finally, noisy rational expectations literature contains several premises and findings that
need to be tested in real-life settings in order to be assessed in terms of validity. Market models
tend to move from simple structures with uniform information (e.g.; Grossman and Stiglitz (1980)
and Kyle (1985)) to complex structures with heterogenous information (e.g.; Foster and
Viswanathan (1996)). The shift towards the latter reflects the need for more realistic modeling of
financial markets, and results obtained do differ. This suggests that what assumption is valid in
contemporaneous financial markets does matter in determining which results will apply. One such
issue is heterogeneity of expectations: MacDonald and Marsh (1996), for example, find that
“currency forecasters have heterogenous expectations” and “such disagreements are the key
variables in determining market trading volume”. More empirical studies are needed to characterize
the valid structure of expectations as well as to test the hypothesized relationships between degree
of conditional heterogeneity and conditional variances and trading volumes. Another area of
interest is the hypothesized role of prices as aggregators of private information (see Cho and
Krishnan, 2000).
           Section III below outlines details of the data and methodology, results are discussed in
Section IV, and main conclusions repeated in Section V.




    III.      DATA and METHODOLOGY


Forecast of each participant is obtained on the last trading day of the preceding month. The
deadline is market closing time, so that informational symmetry among participants is ensured.
Survey results are announced on the same day shortly after market closing as a news headline on
Reuters screens1.
           The survey consisted of a single number representing participants’ point forecast of the
next month’s closing level of the ISE-100 index. The survey was first applied at the end of
December, 2001, so the first survey data available is for January 2002. (As of June 2004, 28
1
 The raw data of participants and their forecasts can be obtained both from the author and from Reuters Turkey News
Service (contact Ms. Güzin Övünç, who conducted the surveys and reported as a news item, at
guzin.ovunc@reuters.com.tr).
months of data were accumulated, the study is planned to be finalized when 36 months of data will
be accumulated, even though basic results seem to have already appeared.) 2
           While participants had been kept anonymous in the first 9 months of the survey, they have
been publicized in the name of the brokerage house since October 2002 3. This change of
procedure was proposed by the author of this paper to prevent strategic behavior, as participants
representing their institutions would be expected to do their best. Though, it has had little impact on
mean forecast errors: under anonymity, the average absolute error was slightly larger than median
absolute error (12.94% vs. 12.70%), under publicity the average has been slightly smaller than the
median (11.77% vs. 11.96%). The difference between the behavior of the mean and median is
consistent with some degree of strategic behavior, but having only a small impact on overall
results.
           Average number of participants per survey is around 17-18. There is some degree of
discontinuity: Only two out of 26 regular participants has 100% attendance, 15 out of them has an
attendance rate of 65% or higher. The remaining 11 with lower attendance are excluded in
performance analysis, but included in the computation of average expectations as they are
unbiased observations. A few participants have transferred from one institution to another; in such
cases we treated the analyst rather than institution as the unit of observation. The average
response rate per survey is aoround 80% (i.e.; around 22 participants were asked to reply on
average each time), which is high compared to similar surveys done with financial market
professionals. This is probably because the survey is conducted with volunteers willing to exhibit
or at least challenge forecast ability, their primary job task.
           Forecast closing levels are first transformed into forecast returns. For each month, the
market’s forecast is computed both by taking the simple average and also the median (to avoid the
impact of outliers) of forecasts of individual participants.
           Strategic behavior (i.e.; submitting a forecast, which, if actualized, would benefit the
participant) has always been an issue in surveys whose results are made publicly available. Even if
one can assume that participants are rational enough to consider that their forecasts would have
little impact on the equilibrium price in an efficient market and the loss of prestige as a result of bad
forecasts would be far more costly, there is another reason to believe that the forecasts are biased
toward the trading positions of the participants: Wishful thinking (i.e.; assigning a higher probability
to a desirable event than its true probability). While the publicity of the his/her institution and the
consequent competition is an incentive for a participant to do his/her best, it cannot perfectly
ensure this. Yet, the bias toward already held trading positions is not something to be avoided from
the researcher’s point of view, rather it enhances the link between what people say and what


2
  There has been an interruption for two months between Dec.2003 and Jan.2004 due to temporary leave of the Reuters
News Service reporter conducting the survey. This is unlikely to affext results of the study, nor to alter the treatment of
data.
3
  This is done with the consent of the participant, expectedly with the approval of the management of participant’s
affiliated institution. 3 participants refused this and withdrew from the survey.
people do. This link is crucial in making inferences from research results. The chronological order,
however, must be taken into account here: To the extent that current forecasts are biased toward
already held positions, they will reflect action that must have been already taken in the recent past,
rather than to be taken in the future. These ideas will help in interpreting the results.
         To present some descriptive statistics, the mean realized monthly raw return of the ISE-100
index over the 27-months sample period is 0.009, while the sample standard deviation is 0.127.
This suggests a period of negative excess market returns, but remember that the unconditional
estimate of the excess market portfolio returns for the ISE since July 1987, the beginning of the
available return data, is negative. The mean average forecast return is 0.065 and the sample
standard deviation of average forecasts 0.043.




   IV.       RESULTS


   4.1.      The Forecasting Performance:
The forecasting performance of all participants as a group (let’s call “the market”) is assessed by
the relation between median forecast returns and realized returns of the ISE-100 index. The results
with simple average forecast returns are similar and not reported here. Let’s notate realized return
of the ISE-100 index in month t as Rt and median forecast return for month t as FRt . The following
simple linear regression is run (so far t = 27):
                Rt = a + b FRt + et                                                             (1)
Forecast performance of the market is simply assessed by testing the null hypothesis that b = 0 ,
and by the R2 of this regression. The null hypothesis that b = 0 (i.e.; that there is no statistical
relation between forecast and realized returns) cannot be rejected at 10% level; in fact F = 0.13
and sig (F) = 0.72 is far from any significance. R 2 is 0.005. Put in another way, the correlation
between median forecast return and the realized return is insignificant at +0.072. The clear
conclusion is that median forecast does not have any significant predictive ability. The average
absolute forecast error is 12.16% which is comparable to the standard deviation of monthly returns
over the sample period at 12.68%. Additional inspection by subperiods suggests that market’s
absolute forecast error and standard deviation of realized returns tend to move together.
         As a robustness check, the ability of the median forecast to predict the return of the first half
of the month and the average index level over the month (rather than the end of the month) is
investigated in order to account for the possibility that even though median forecast has some
predictive ability under the current information set but unforeseen events later in the month makes
them obsolete. Results, though slightly stronger, are still far from significance. The null hypothesis
“b = 0” cannot be rejected at 10% level under any of these alternative definitions of the dependent
variable (i.e.; forecast month’s return).
        As to the performance of individual participants, the same regression is run seperately for
each of the 15 particiants with attendance rate 65% or higher. Let’s notate the forecast of
participant i for month t as FRit .
              Rt = ai + bi FRti + eti                                                                    (2)
The results are presented in Table 1. Forecast performance is assessed by testing the null
hypothesis “b i = 0”. Results suggest that the null hypothesis cannot be rejected at 10% level for
none of the 15 participants. The coefficient for 6 participants is negative. The mean absolute
forecast error of the “best” participant is 10.5%, and that of other participants range between 12.1%
and 16.5%. The overall result is that none of participants has statistically significant predictive
ability over the 1-month horizon. The distribution of the b i coefficients is close to what would be
expected under pure chance (i.e.; if all participants were forecasting randomly), as Kolmogorov-
Smirnov test cannot reject uniform distribution.


                                                                Mean Abs.
Participant     n        bi  sig (b)        Cor     sig (cor)    For.Err
     1          26    -0.255 0.54         -0.126      0.27           0.121
     2          22     0.184  0.52         0.146      0.26           0.147
     3          24    -0.345 0.21         -0.265      0.11           0.145
     4          25    -0.162 0.59         -0.113      0.30           0.165
     5          25     0.047  0.87         0.034      0.44           0.141
     6          21     0.270  0.45         0.173      0.23           0.121
     7          25    -0.081 0.69         -0.084      0.35           0.159
     8          25     0.293  0.28         0.130      0.10           0.127
     9          18     0.204  0.64         0.117      0.32           0.122
    10          24     0.133  0.71         0.079      0.36           0.134
    11          17    -0.216 0.63         -0.127      0.31           0.140
    12          19    -0.177 0.59         -0.133      0.29           0.139
    13          25     0.555  0.29         0.218      0.15           0.105
    14          24     0.312  0.44         0.165      0.22           0.125
    15          21     0.173  0.54         0.142      0.27           0.132
  Median        27     0.213  0.72         0.072      0.36           0.122
 Table 1: n is the number of months the participant attended the survey. b i is the coefficient of forecast return
in Equaiton 2. Sig (b) is the signicance level of rejecting the null hypothesis “b i = 0”. Cor is the coefficient of
correlation between forecast and realized returns. Sig (cor) is the the p-value for the one-tailed test that cor =
0. Avg.For.Err. is the simple average of each participant’s absolute forecast error over the months.

        Another way of assessing forecast performance is to compare mean absolute forecast
errors of participants to random walk model’s mean absolute forecast error. Using a simple random
walk model of Rt = μ + et         where μ is the mean return over the sample period and et is i.i.d., the
mean absolute error is 0.105. This equals to the mean absolute forecast error of the “best”
participant. In other words, 14 out of 15 participants have produced larger errors than a simple
random walk model, suggesting that participants would have performed better had they been
forecasting randomly.
        As in Hartzmark (1991), the persistence in performance is assessed by dividing the sample
into two halves. Ranking participants according to average absolute forecast error, presented in
Table 2, suggests that, as in Hartzmark (1991), there is little correlation in average forecast
performance of individual participants accross time. This reiterates Hartzmark’s conclusion that
luck rather than skill seems to drive forecasting performance.


Persistence in Performance

First Half
                         Middle
Best 20%     Second 20% 20%        Fourth 20% Worst 20%
 13    0.092  1    0.129 14 0.155    2 0.158    4 0.180
  9    0.121  6    0.148   3 0.156   5 0.159    7 0.184
 10    0.124  8    0.152 11 0.156 15 0.159     12 0.191
Second Half
                         Middle
Best 20%     Second 20% 20%        Fourth 20% Worst 20%
 15    0.089  8    0.100 13 0.118    7 0.126    2 0.133
  6    0.094 12    0.102   5 0.121   3 0.132    4 0.146
 14    0.100  1    0.111   9 0.125 11 0.132    10 0.149
Table 2: Participants are ranked according to their average absolute forecast errors in the first and second
halves of the sample period. They are divided into 5 quintiles of 20% or 3. The first coloumn of each quintile
provides the participant’s code and the second coloumn his/her average forecast error.



    4.2.     Behavioral Hypotheses:
Extrapolation Bias: To determine whether the participants as a group are extrapolating from the
past, the following regression is run:
               FRt = α + β Rt-1 + et                                                                   (3)
The result indicates that the null hypothesis “β = 0” can be rejected at 10% level (F=3.63 and
sig(F)=0.068). If FR is defined to be the average, rather than median, forecast return, the
relationship turns out to be stronger: The null hypothesis “β = 0” can be rejected at 5% level
(F=6.60 and sig(F)= 0.016). This is quite interesting, because it is difficult to obtain a reliable
estimate of the market return from experts’ forecasts, but the opposite is possible: The forecasts of
the experts can be estimated from previous month’s market return. Given that the first order
autocorrelation in monthly return series over the sample period has been insignificantly negative at
− 0.20, there is strong evidence of unwarrented extrapolation from the past. An R 2 of 0.20 suggests
that around 1/5 of the variation in average forecasts can be explained by the previous period’s
return. 4 These results are consistent with the hypothesis that analysts, as a group, herd on market
price.
         The similar regression ( FRti = α + βi Rt-1 + et, i ) is run for each of the 15 high-attendance-
participants to find out individual tendencies to extrapolate from the past. Results, presented in
Table 3, indicate that the coefficient of the previous month’s return is positive for 14 of 15

4
  Muradoğlu (1996) documents a hedging tendency for forecasts extrapolating past trends with skewed interval
estimates. Since Reuters data consists of only point forecasts, it is unfortunately impossible to test this hedging tendency
in this study.
participants, significant at 10% for 2 and at 5% for additional 3. Interestingly, for the best performer
(according to minimum average absolute forecast error criterion), the null hypothesis “βi = 0”
cannot be rejected at any significance level, implying that he is not extrapolating from the past.
Moreover, 4 of the 5 participants who has exhibited significant tendency to extrapolate from the
past entered Equation 2 with negative signs. These findings are consistent with the hypothesis that
extrapolation and poor performance coincide. A regression of the b i coefficients on βi coefficients
turned out significant inverse relationship (n=15, F=3.77, sig(F)=0.074, the coefficent on β i = −
0.97, R2= 0.22 implying that 22% of the variation in performance could be attributed to
extrapolation bias).
Participant     n         Βi  sig (F)    R-sqd
     1          27      0.208  0.02       0.20
     2          23      0.206  0.25       0.07
     3          25      0.249  0.09       0.12
     4          26      0.022  0.88       0.00
     5          26      0.083  0.58       0.01
     6          22      0.244  0.10       0.13
     7          26      0.446  0.02       0.22
     8          26      0.074  0.64       0.01
     9          19      0.173  0.23       0.09
    10          25      0.049  0.69       0.01
    11          18      0.186  0.21       0.10
    12          20      0.335  0.04       0.22
    13          26      0.042  0.60       0.01
    14          25     -0.014 0.90        0.00
    15          22      0.152  0.39       0.04
  Median        28      0.213  0.07       0.07
Table 3: Extrapolation bias tested by the significance of rejecting the null hypothesis “βi = 0” in the
regression equation FRti = α + β i Rt-1 + et, i. n is the number of months. β i is participant i’s coefficient on
previous month’s return. Sig(F) is the significance level of rejecting the null hypothesis “β i = 0”. R-sqd is the
ratio of variation in participant i’s forecasts explained by R t-1 to total variation.

        An interesting question arises here: Can these findings of extrapolation be in fact a
reflection of wishful thinking? Most, if not all, of the survey participants are active traders in the
market. It is likely that when the market has moved in a particular direction in the recent past most
active traders have positions in that direction. Hence, wishful thinking may be as well a potential
explanation of these findings as extrapolation. Unfortunately, however, the coverage of Reuters’
survey does not enable us to differentiate between the two alternative explanations. To answer this
question, participants’ transactions data is needed which is unlikely to be available. My opinion
based on available information is that extrapolation and wishful thinking act together hand-in-hand.


Systematic Optimism Bias: The mean forecast error of the market is + 5.6 %, which is significantly
positive at 1% level. This indicates that forecasters participating in the Reuters survey have, as a
group, overestimated the ISE-100 index return, consistent with the systematic optimism bias.
Again, the degree of the optimism bias is negatively correlated with the forecast performance. 32%
of absolute forecast errors of individual participants can be explained by optimism bias.
       This finding is quite important: It suggests that the systematic optimism bias is not confined
to only analysts’ earnings forecasts, but is a more general phenomenon. Moreover, as the
forecasts are made for the market index, which consists of 100 stocks, it is unlikely that agency
issues are involved here. Hence, the agency issues referred to as the likely reason underlying
optimism bias in the extant literature fail to account completely for it. Allthough the fact that all
participants are employed in the investments sector desiring the market to be attractive may
suggest another type of agency issue, this is clearly different and far less direct in effect from that
mentioned in the extant literature. A more appropriate explanation may be wishful thinking.
       Easterwood and Nutt (1999), differentiating between reaction to positive and negative
information, document that analysts underreact to negative information but overreact to positive
information (which they summarize as “systematic optimism”). In a similar manner, our data set
allows us to compare the extent of the extrapolation bias with respect to nature of the information
(previous month’s return). Employing a simple approach, data is grouped into two according to the
sign of the realized return in previous month, and Equation 3 is estimated seperately for
subsamples of positive and negative monthly returns, respectively. Results suggest that
extrapolation bias is significant when previous month’s return is positive (n=13, β=+0.2925,
F=3.18, sig(F)=0.102, R2=0.22; note that reduced significance levels are due to halved sample
size), but insignificant when previous month’s return is negative (n=15, β=+0.1096, F=0.42,
sig(F)=0.527, R2=0.03). Similarly, a comparison of mean forecast errors on subsamples
conditioned by sign of the previous month’s return, indicates significant differences in the optimism
bias: When the ISE-100 index rose in month t-1, the mean forecast error for month t is +0.075,
when it fell in month t-1, the mean forecast error for month t is +0.034 (the difference being
significant at 10% level according to Wilcoxon’s nonparametric test). These findings reiterate some
of the asymmetry, noticed by Easterwood and Nutt (1999), in analysts’ reactions due to the nature
of information and enhance their conclusion of “systematic optimism rather than systematic
misreaction”. More general conclusion is that part of the extrapolation and optimism biases is
confined to extrapolating positive information.


   4.3.    Hypotheses in the Noisy Rational Expectations Literature
Heterogenous Expectations: MacDonald and Marsh (1996) measure the significance of individual
differences as follows:              FRti = Xt + gi + uti           (4)
where Xt is the component based on public information common to all participants, g i is the
individual (idiosyncratic) effect, and u ti is an individual random disturbance term, which could occur
due to measurement errors (such as strategic behavior). The average forecast is:
                                      FRtA = Xt + gA + utA                (5) .
Normalizing such that gA equals zero and subtracting (5) from (4), we obtain:
                              FRti − FRtA = gi + ( uti − utA )                    (6)
The individual effects (biases) gi may then be measured by the significance of a nonzero g i ,
without specifying the common information set X t . It is possible that heterogeneity follows both
from individual effects and idiosyncratic coefficients on information I t (i.e., each forecaster placing
different weights on some element of public information set). Then, FR ti = Xt + gi + βi It + uti where
βi is the weight that participant i places on information I. The equivalent for Equation 6 is then:
                     FRti − FRtA = gi + (βi − βA ) It + ( uti − utA )            (7)
Under this formulation, individual biases (g ≠ 0) and idiosyncratic interpretation of public
                                                        i


information (βi − βA ≠ 0) can be jointly tested.
        Equation 7 is estimated by OLS for each of the 15 high-attendance participants. Lagged
return Rt-1 is used as the information term.
        Results indicate 5 significant individual biases and 2 significant idiosyncratic interpretation
of the common information (Rt-1). Compared to the results reported by MacDonald and Marsh
(1996)5, these imply a slightly lower degree of heterogeneity.


The Relation between Dispersion in Expectations and Volatility and Trading Volume: The
dispersion of expectations is measured by the standard deviation of forecast returns on a month
accross participants. Two regressions of volatility and trading volume, respectively, are run on
dispersion of expectations, to test hypotheses in the literature that link trading volume and volatility
with disagreement of prior opinions. Volatility in month t is measured in two ways: i) absolute value
of monthly index return ii) standard deviation of daily returns in month t.
        The results suggest that there is no significant relationship between the dispersion of
forecasts and volatility (F=0.02, sig(F): 0.88 with monthly absolute returns; and F=0.93, sig(F)=0.34
with standard deviation of daily returns). When the volatility is measured by the standard deviation
of daily returns, a slightly positive relationship is observed, but it is far from being significant. There
is no significant relationship between dispersion of forecasts and the monthly trading volume,
adjusted both for trend and volatility, either (F=1.29, sig(F)=0.27). These findings contradict with
those of MacDonald and Marsh (1996) on currency forecasters, who concluded that heterogeneity
of forecasts was a factor determining trading volume.
        Although reading too much of this survey data should be avoided as it represents only a
small proportion of market participants, our results support new information arrivals, rather than
heterogeneity of prior expectations, as the factor driving volume and volatility6.


The Role of Market Price as Aggregator of Private Information: A direct test of the hypothesized
role requires the use of multiple forecasts taken at different points in time for the same terminal
value. The mere possible test with the limited coverage of this survey data is to check the
relationship between forecast errors in month (FRt-1 − Rt-1)                  and forecasts (FRt). A positive
5
 See Tables 1 and 2 on pp.670-671.
6
 Note that over the sample period there has been a strong positive relationship between monthly volume and absolute
monthly return, and not between monthly volume and standard deviation of daily returns..
relationship would be inconsistent with the role of market price as aggregator of private
information, as it would suggest that participants insist on their private beliefs (expecting the
market to eventually reverse) rather than revise them in line with the market price. No-relation
would be consistent with an assumption that participants, as a group, conform to the information
delivered by market price, whereas a negative relationship would suggest that analysts employ
some form of an error correction model. The analysis is clouded, however, by the fact that average
forecast erorrs and realized returns, which we already know to be significantly positively associated
with next month’s forecasts, are strongly positively correlated (the coefficient of Pearson correlation
is +0.97 significant at p=0.0000). To avoid multicollinearity, the following regression is estimated:
                               FRt = a + b Res.FEt + et                    (8)
where Res.FEt is the residual of a regression of average forecast error on realized return.
        Result from the estimation of Equation 8 suggests no significant relation between the
forecast error for month t-1 and forecasts for month t (b= 0.053, sig(b)=0.799, F=0.06). This is
consistent with the assertion that analysts place greater weight on the information they derive from
the market price, so that they conform to the information implied by the market price innovation.




   V.      CONCLUSIONS


The main conclusions from the results presented in Section IV are as follows:
        Participants of the survey conducted by Reuters Turkey news service exhibit no significant
ability to predict monthly returns of the ISE-100 index. The distribution of performance is similar to
what would be obtained under pure chance. There is little evidence of performance correlated
accross time. Almost all of the participants forecasts performs worse than a simple random walk
model. These results are more than consistent with the implications of efficient markets theory.
        Participants of the survey analysed in this study, as a group, exhibit significant biases of
optimism and extrapolation, unjustified by the behavior of realized returns. The degree of being
vulnerable to these biases is positively correlated with forecast errors. Individual differences in
forecast performance can partly be explained by the degree of these biases. Optimism bias, which
has been traced to agency issues, is documented in a context of index return forecasting where no
agency issue is present. A potential explanation could be wishful thinking. The asymmetry
observed in extrapolating positive vs.negative past returns prefers optimism bias over systematic
extrapolation (misreaction) as the valid explanation. The fact that short positions are very costly to
build in ISE, however, supports “wishful thinking” as the most plausible explanation.
        Participants’ forecasts are heterogenous, supporting the use of heterogenous expectation
models in market equilibrium dynamics. However, the degree of the heterogeneity in forecasts did
not seem to drive market volume and volatility. Another possible reason for this finding, however,
could be that survey participants constituted only a small proportion of the market. Finally,
participants seem to place greater weight on the information they derive from market price than
their priors.
        One contribution of this study might be to confirm the significance of previously documented
behavioral biases in analysts’ expectations, in a different real-life context of stock index forecasting
in an emerging market. It seems that little predictive ability of forecasters is more than wiped out by
behavioral biases to turn out worse performance than a simple random walk model.




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