leaders and followers among security analysts prepared by li wang dept. of statistics at mcmaster...
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Leaders and Followers
among Security Analysts
Prepared by Li WangDept. of Statistics at McMaster University
Supervised by Dr. Veall and Dr. Kanagaretnam
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Outline
Background and Data Description Performance of Security Analysts Logistic Regression Analysis on Security Analysts Dataset Timeliness Analysts and Stock Price Conclusion and Discussion
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Background on Security Analysts
Analyst
Brokerage Firm Institutional
clients
Individual Investor
Institution Investor
Wall Street Journal
Third Party
Buy, sell or hold
rank
rank
Background……
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Continue Background…….
Firm A
Forecast 2
Forecast 4
Forecast 3
Forecast 5
Forecast 6
Forecast 7
Forecast 9
Forecast 11
Forecast 8
Forecast 10
Analyst 1
Analyst 3
Analyst 2
Analyst 1
Analyst 4
Firm B
Forecast 1
Leader
FollowerFollower
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Institution Brokers Estimate System (I/B/E/S)
Adjustment file
Identifier File
Exchange Rate
Stopped Estimate
Report Currency
Excluded Estimates
S/I/G CodesBroker
Translations
Detail File
Actual File
Ticker
Data Description and Background……
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Timeliness Leaders (Cooper, Day and Lewis, 2001)
Superior access to information Differential ability to process information Release forecast before competing analysts Herd behavior in finance market
Performance of Analysts
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Leader and Follow Ratio (LFR)
F N N( , )2 2~
N : the number of earning forecastsToj : lead-timeT1j : follow-time
L F RT
To j
j
2
20
1 1
/
/
( Cooper et al, 2001)
T tj ii
N
0 01
T tj i
i
N
1 11
;
Hypothesis 1: Testing the forecast arrival times for leaders in pre-
release periods are greater than those in post-release periods.
Under null hypothesis θ0 = θ1, L F RT
To j
j
1
( Lawless, 1982 )
Performance of Analysts
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A1
A2
A3
A4
A5
B5
B4
B3
B2
B1
-12 -10 -8 -7 -5 0 1 3 4 5 7 days
L F RT
Tj
j
0
1
1 2 1 0 8 7 5
1 3 4 5 7
5 2
2 02 6.
LFR > 1 The selected analyst is a leader.
LFR For Leaders
Forecast revision dates surrounding the forecast revision of a lead analyst
Performance of Analysts
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LFR For Followers
Forecast revision dates surrounding the forecast revision of a follower analyst
A1
A2
A3
A4
A5
B5
B4
B3
B2
B1
-5 -4 -3 -2 -1 0 3 5 7 9 11 days
L F R
5 4 3 2 1
3 5 7 9 11
1 5
3 50 4 2 8 6.
LFR < 1 The selected analyst is a follower.
Performance of Analysts
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Results of classification
Lead Analysts Percentage
Each firm over sample period (1994-2003) 13.68%
Each firm in a given year 28.10%
At least one firm leader in a given year 31.73%
Leaders in subsequent year 10.89%
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Forecast Accuracy and Bias Percentage Forecast Error (Butler, Lang and Larry)
Forecast Bias: Signed Forecast Error
Standardize the Ranks to Scores (Hong et al, 2000)
Performance of Analysts
P F E F E A E A Eijt ijt j ( ) /
B F E A E F E A Eijt j ijt j ( ) /
ijtijt
ijt
R
N
1
1( Rijt = 1,…, Nit)
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Hypothesis 2: the earning forecasts of followers are more accurate than those of leaders over the estimation year.
Performance of Analysts
Relative forecast accuracy of leaders and followers
Panel A : Half-year ahead earnings forecast errors and forecast bias:
Overall Leader follower T test M-W test
forecast accuracy:N
MeanMedian
7,1550.4930.500
1998 5157 0.486 0.512 -3.12*** -3.627***0.511 0.500
Bias:Mean
Median0.4940.500
0.490 0.494 0.52 -1.3350.500 0.500
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Panel B: One- Quarter ahead of earnings forecast errors and forecast bias
Overall Leader follower T test M-W test
forecast accuracy:N
MeanMedian
3,9920.4740.500
1239 27530.470 0.476 0.65 -2.5670.500 0.500
% Bias:Mean
Median
0.485 0.500
0.471 0.491 1.87* -1.2350.500 0.500
Performance of Analysts
Relative forecast accuracy of leaders and followers
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Boldness
Boldness: Absolute value of the difference between a particular forecast and the mean of outstanding consensus forecast.
■ Standardize to Scores: large deviation with higher scores and small deviation with lower scores.
Performance of Analysts
Forecast Issuance Timeline
Jun. Jul. Aug. Sept. Oct. Nov. Dec.
Pre-release period
Boldness
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Hypothesis 3: A higher percentage of forecasts of leaders derivate from the consensus forecast compared to those of follower analysis.
Performance of Analysts
Table 2-6 Boldness of leaders and followers
Absolute Consensus Surprise
Overall Leader Follow T-test M-W test
N
Mean Median
7149 0.4954 0.5000
2021 5128 0.5225 0.4847 -5.44*** -5.758***0.5294 0.4808
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Other AttributesPerformance of Analysts
Other Attributes of Leaders and Followers Overall Leader follower T test M-W test
Brokerage Size Mean Median
16.9414.00
17.47 16.75 -2.16** -2.468** 15.00 14.00
Forecast Frequency Mean Median
1.1320.882
1.119 1.136 1.03** -9.55* 0.879 0.882
Stock Coverage Mean Median
4.4082.000
2.393 2.447 1.16* -1.497* 2.000 2.000
Firm-Specific EXPR Mean Median
4.7154.000
4.79 4.68 1.2** -1.463*4.00 4.00
Business EXPRMean Median
8.0367.000
8.18 7.98 -1.45* -1.437* 7.00 7.00
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Variable Specification of Security Analyst Dataset
Dependent Variable: Leader=1; Follower=0 Explanatory Variable: Forecast Accuracy (ACCUSCORE) Forecast Bias (BIASSCORE) Forecast Boldness (BDSCORE) Larger Brokerage Firm (LARGEBRKR ) Small Brokerage Firm (SMALLBRKR) Stock Coverage (COVER) Relative Forecast Frequency (RFREQ) Experience (EXPR) Following Analyst (NUMANALYST)
Model Fitting……
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Summary Statistics of Analysts DatasetModel Fitting……
Variables
Mean Std. Deviation
Overall Leader Follower Overall Leader Follower
# of analysts 7341 1956 5385
BROKERSIZE 16.9384 17.4392 16.7415 12.9170 12.9315 12.9073
LARGEBRKR .2426 .2521 .2389 .4287 .4344 .4264
SMALLBRKR .2186 .1987 .2264 .4133 .3991 .4185
BUSYEAR 4.7149 4.7927 4.6842 3.6378 3.6503 3.6325
NUMANAYST 9.2213 9.0156 9.3021 5.3753 5.2158 5.4351
ACCUSCORE .4933 .5106 .4865 .3147 .2764 .3284
BIASSCORE .4935 .4903 .4947 .3184 .2824 .3315
RFREQ 1.1311 1.1191 1.1358 .6098 .5980 .6143
BDSCORE .4963 .5244 .4852 .2658 .2626 .2663
EXPR 1.7691 1.7927 1.7598 .6358 .6360 .6355
LFR 1.3037 2.2749 .9220 1.0993 1.4295 .6033
COVER 2.4079 2.3444 2.4329 1.7531 1.6875 1.7778
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Logistic regression model has the form
Linear predictor
Modeling the conditional probability
Logistic Regression Model
Logistic Regression Analysis……
p Y y xx
x x
y y
( | )ex p [ ( )]
ex p [ ( )] ex p [ ( )]
1
1
1
1
( , )y 0 1
lo g( )
( )( ).
p x
p xX
1
( ) ,x x xd d 0 1 1
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Logistic Regression Model for Analysts Dataset
Logistic Regression Analysis……
Coefficient Correlation
䦋
Largebroker
Smallbroker cover
Numanalyst rfreq accu bias bold expr
largebroker 1 -.299(**) -.119(**) -.052(**) -.035(**) .006 .017 -.007 -.073(**)
smallbroker -.299(**) 1 -.040(**) .013 .032(**) -.007 .011 -.023 .032(**)
cover -.098(**) -.071(**) 1 .168(**) .034(**) .011 .008 .020 .209(**)
numanalyst -.045(**) .004 .161(**) 1 -.103(**) .028(*) .027(*) .022 .137(**)
Rfreq -.023(*) .017 .027(*) -.095(**) 1 .018 .003 .000 .049(**)
accuscore -.006 .004 .007 .016 .026(*) 1 .433(**) .052(**) .010
biasscore .010 .024(*) -.004 .013 -.001 -.020 1 -.012 -.002
bdscore -.006 -.025(*) .016 .020 .007 .076(**) -.033(**) 1 .034(**)
expr -.072(**) .032(**) .195(**) .111(**) .038(**) .006 -.014 .033(**) 1
• Pearson coefficients are above the diagonal line and Spearman coefficients are below the line. •***significant at 1% level, ** significant level at 5% level, * significant level at 10% level.
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Analysis of Maximum Likelihood Estimation
Logistic Regression Analysis……
Parameter Estimate StandardError
WalkChiSq
Pr >ChiSq
OR 95% WaldConfid limits
INTECEPT -1.2182 0.1291 89.0212 <.0001
COVER -0.0372 0.0160 5.4334 0.0198 0.963 0.934 0.994
FREQ -0.0645 0.0414 2.4272 0.1192 0.938 0.864 1.017
EXPR 0.1227 0.0423 8.4142 0.0037 1.131 1.041 1.228
BOLDNESS 0.5410 0.0983 30.3107 <.0001 1.718 1.417 2.083
NUMANALYST -0.0119 0.00501 5.6637 0.0173 0.988 0.978 0.998
ACCU 0.3144 0.0995 9.9859 0.0016 1.369 1.127 1.664
BIAS -0.1303 0.0977 1.7792 0.1822 0.878 0.725 1.063
SMALLBRKR -0.1653 0.0677 5.9708 0.0145 0.848 0.742 0.968
LARGEBRKR 0.0218 0.0633 0.1188 0.0730 1.022 0.903 1.157
Goodness-of-fit: Hosmer and Lemeshow χ2=14.137 on 8 d.f., P=0.0483
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Test the Hypothesis: Timeliness leader will have a higher impact on the stock price than followers.
Abnormal Excess Return: security return - value weighted
industry index in I/B/E/S. (EVENTUS)
Forecast Surprise: Forecast Revision:
Predecessor-based Surprise:
Consensus-based Surprise:
Timeliness and Stock Price……
F SC F E P F E
P F Eit
it i t
i t
( )
( )
1
1
F SC F E F E
F Eitit i
i
1
1
F SC F E C F
C Fitit t
t
( );
w here C FF E
ntit
i n
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Analyst Timeliness and Contemporaneous Stock price
Hypothesis 4: The coefficient of regression of excess return (in short window) on forecast surprise for leader analysts is greater than the coefficient for follower analysts.
Where is the cumulative excess return over the two-day released period.
Timeliness and Stock Price……
E X R L eader F S F ollow er F Sijt ijt ijt ijt 1 2 3* *
E X R ijt
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2-day Release Period Forecast Surprise Coefficients for Lead
and Follower Analysts
Timeliness and Stock Price……
Forecast Revision
Predecessor-based surprise
Consensus-based surprise
Coefficient(1)
F-value(2)
Coefficient(3)
F-value(4)
Coefficient(5)
F-value(6)
Intercept 0.0137 <0.0001 0.00038 <0.0001 -0.0046 <0.0001
Leader* FEijt0.0345 0.0345 0.135 <0.0001 0.382 <0.0001
Follow* FEijt-0.0182 0.0267 0.078 0.012 0.227 0.004
Adj. R2
0.03 0.056 0.253
N 7,025 7,350 6,987
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Analyst Timeliness and Past Stock price
Hypothesis 5: Forecast surprises by leader analysts are not significantly correlated with the stock price performance during the period preceding the forecast revisions ( in long window). However, forecast surprises by follower analysts are positively correlated with excess return during the pre-revision period ( in long window).
Timeliness and Stock Price……
E X R L eader F S F ollow er F Sijt ijt ijt ijt 1 2 3* *
Where is the cumulative excess return over the 20 days pre- released period.
E X R ijt
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20-Day Pre-Released Period Forecast Surprise Coefficients
for Leader and Follower
Timeliness and Stock Price……
Forecast RevisionPredecessor-
based surpriseConsensus-
based surpriseCoefficient
(1)P-value
(2)Coefficient
(3)F-value
(4)Coefficient
(5)T-value
(6)
Intercept 0.00034 <0.0001 0.0053 <0.0001 0.0274 <0.0001
Leader* FEijt0.0043 0.0023 0.023 0.134 0.0045 0.345
Follow* FEijt0.0012 0.0215 0.006 0.234 0.0034 0.0013
Adj. R2 0.083 0.067 0.087
N 6,945 6,752 6,894
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Discussion and Conclusion:
There are quality differentials among security analysts in terms of the timeliness of their forecasts.
Lead analysts tend to be employed by larger brokerage firm and follow up fewer stocks than follow analysts.
Leaders are bolder than followers. Followers release more accurate forecasts than leaders since
leaders have to sacrifice the accuracy to be the first movers. Lead analysts identified by timeliness have a greater impact on
stock price than follower analysts. Forecast surprises by leader analysts are not significantly
correlated with the stock price performance during the period preceding the forecast revisions. However, forecast surprises by follower analysts are positively correlated with excess return during the pre-revision period.
Discussion and Conclusion……
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Reference:
Rick A. Cooper, Theodore E. Day, Craig M. Lewis, 2000. Following the Leader: a study of individual analysts’ earnings forecasts. Journal of Finance Economics, 61, 383-416.
Brown, Lawrence D., 2001, How import is past analyst forecast accuracy?. Financial Analysts Journal 57,4-49
Brennan, M., and Subrahmanyam, A. 1995. Investment analysis and Price formation in securities markets. Journal of Financial Economics 38, 361-381.
Gleason, Cristi A., and Charles M. C. Lee, 2003, Analyst forecast revisions and market price discovery. The Accounting Review 78, 227-250.
Leone, A., and Wu, J., 2002. What does it take to become a superstar? Evidence from institutional investor rankings of financial analysts. Working paper, University of Rochester.
Milkhail, M., R. Willis and B. Walther, 1997. Do Security Analysts Improve Their Performance with Experience? Journal Accounting Research 35: 131-157.
Reference…..
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Thank You!