A Hard Knock Life: Why Analyst Accuracy Falls Short
Wednesday, April 08, 2015
QWAFAFEW
May 25, 2010
Carson Boneck CFA, David Pope CFA
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
1
Abstract
It is a commonly held belief among investors that there is persistence in
security analyst's EPS forecast accuracy. Studies by Clement and Brown
examine persistence directly and show that, at least in the pre Reg-FD
period, there is a measurable amount of analyst accuracy persistence. We
confirm this work and extend it to the Post-FD period, to the international
markets, and then focus on the market profitability associated with
persistence.
Perhaps to many practitioners' (non quants) surprise, we find that
persistence is not all that it is believed to be. We show that while
persistence is statistically measurable, it has very limited usefulness in
predicting the future accurate analysts. Our work shows that using past
accurate analysts forecasts leads to somewhat better results than the naive
mean, but falls well short of alternative methods. Our work explores the
metrics that make forecasts accurate rather than the individuals that make
the forecasts themselves.
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
2
Agenda
• Validate Clement / Brown Work
› Update for Post Reg FD
• Isolate importance of Past Skill
• Past skill as indicator of future accuracy
• Compare alternative metrics of forecasting:
› Rock Stars, Time Weighted, Naïve Mean, BEST
• Q&A
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3
How Good are the “Experts”, really?
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4
We want to BELIEVE – “Lead Analyst” Syndrome
• The reality is forecasting is hard
• Part of an Analyst’s role is to get noticed or get “shelf space”
• To be the most accurate, a forecast by definition has to be an outlier or bold
forecast
• Analysts typically form their reputation by making one BIG call rather than
being consistently right – Is Abby Joseph Cohen Accurate?
• Surprisingly clients we spoke with had never actually looked at Analysts’
accuracy themselves
• People like to believe in persistence of skill, or performance when it counts,
often citing sports case studies
› Hot Hand in Basketball - Gilovich, Vallone & Tversky (1985)
› Clutch Hitting in Baseball – Cramer (1977)
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5
Perfect Foresight is Very Profitable
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6
Clement / Brown – Accuracy Persistence
Regress( abs(estimate error )- abs(avg estimate error)) / abs(avg estimate error) = estimate age demaned + Last Years Accuracy
Pre RegFD
Post Reg FD
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
expected sign
Estimate
Age
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.01
Last Years
Accuracy
-0.02
-0.03
-0.02
-0.03
-0.02
-0.03
-0.03
-0.01
-0.01
-0.01
0.00
-0.02
-0.01
-0.02
-0.02
-0.01
-0.01
-0.02
Age
t
147.8
176.8
183.8
210.0
234.6
207.5
263.3
258.4
291.3
384.2
309.4
268.5
282.0
283.9
294.0
283.0
279.6
189.7
Accuracy
t
-18.0
-20.5
-16.1
-21.2
-14.4
-20.7
-22.2
-10.6
-6.2
-10.3
1.0
-13.4
-11.0
-13.4
-17.4
-10.3
-9.3
-8.5
+
-
+
-
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
Adj
R^2
2.3%
3.0%
3.2%
4.0%
4.6%
3.5%
5.2%
5.0%
6.5%
11.5%
8.0%
6.1%
5.8%
5.7%
5.9%
5.2%
5.3%
10.5%
Last Year of Larry Brown Study
7
Clement / Brown – Accuracy Persistence (Top Decile)
Top Decile of Last Years Analysts
Regress( abs(estimate error )- abs(avg estimate error)) / abs(avg estimate error) = estimate age demaned + Last Years Accuracy
Pre RegFD
Post Reg FD
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
expected sign
Estimate
Age
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.01
Last Years
Accuracy
-0.04
-0.02
-0.02
-0.03
0.01
-0.02
-0.04
-0.01
-0.01
0.01
0.00
-0.01
-0.01
0.00
-0.01
-0.01
0.00
0.02
Age
t
30.29
35.136
32.925
32.79
44.911
34.10
52.49
40.70
63.60
83.00
60.72
50.14
56.18
50.20
57.50
59.52
65.24
42.00
Accuracy
t
-11.52
-6.012
-6.991
-8.371
2.47
-4.97
-12.12
-3.93
-2.45
1.82
1.03
-2.54
-4.70
-1.45
-4.56
-5.15
0.47
2.86
+
-
+
-
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
Adj
R^2
2%
2%
2%
3%
4%
3%
6%
4%
7%
15%
9%
6%
6%
5%
5%
5%
6%
12%
Last Year of Larry Brown Study
8
Isolate Age – Regress on Accuracy
First Regress Out Age then Regress Resids on Last Years Skill
Year
2001
2002
2003
2004
2005
2006
2007
2008
2009
Average
r-sq
0.01%
0.00%
0.03%
0.02%
0.01%
0.01%
0.01%
0.01%
0.02%
0.014%
Top Decile Top Decile
All
All
Bottom Decile
Avg DError Avg Dage Avg DError Avg Dage
Avg DError
-0.03
0.12
0.00
0.41
0.01
-0.01
-0.20
0.00
0.44
0.00
-0.02
-1.10
-0.01
0.21
0.01
-0.05
-1.38
0.00
0.37
0.02
-0.04
-0.91
0.00
0.28
0.01
-0.04
-0.36
0.00
0.36
0.01
-0.04
-1.36
0.00
0.31
0.01
-0.04
-1.53
0.00
0.17
0.02
-0.05
0.10
0.00
0.18
0.05
-0.04
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
-0.74
0.00
0.30
0.02
Bottom Decile
Avg Dage
0.84
-0.57
1.25
0.21
0.52
0.36
0.28
0.98
0.91
0.53
9
Isolate Accuracy – Regress on Age
First Regress Out Last Year kKill then Regress Resids on Age
Year
2001
2002
2003
2004
2005
2006
2007
2008
2009
Average
r-sq
11.5%
8.0%
6.1%
5.8%
5.7%
5.9%
5.2%
5.2%
10.4%
7.098%
Top Decile Top Decile
All
All
Bottom Decile
Avg DError Avg Dage Avg DError Avg Dage
Avg DError
-0.16
-41.19
0.00
0.41
0.27
-0.12
-41.24
0.00
0.44
0.20
-0.10
-40.80
-0.01
0.21
0.16
-0.08
-39.20
0.00
0.37
0.15
-0.10
-39.31
0.00
0.28
0.15
-0.10
-41.17
0.00
0.36
0.16
-0.09
-40.30
0.00
0.31
0.14
-0.08
-40.23
0.00
0.17
0.14
-0.14
-45.88
0.00
0.18
0.31
-0.108
-41.035
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
-0.001
0.303
0.187
Bottom Decile
Avg Dage
31.06
28.84
28.52
27.52
28.06
29.43
28.41
29.29
37.93
29.897
10
Skill Looks to Mean Revert (1990 – 2009)
This years Accuracy Decile
BEST
WORST
BEST
Last Year Accuracy Decile
1
2
3
1 6%
9%
10%
2 3%
8%
10%
3 1%
4%
10%
4 0%
2%
6%
5 0%
1%
4%
6 0%
1%
3%
7 0%
1%
3%
8 0%
1%
3%
9 0%
1%
2%
10 0%
1%
2%
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WORST
4
10%
14%
23%
24%
18%
13%
9%
8%
6%
5%
5
16%
17%
25%
33%
36%
31%
24%
19%
15%
10%
6
14%
13%
15%
19%
24%
29%
29%
25%
22%
14%
7
11%
13%
10%
8%
10%
14%
19%
22%
23%
20%
8
12%
10%
6%
4%
4%
6%
9%
13%
17%
21%
9
9%
7%
4%
2%
2%
2%
4%
6%
9%
16%
10
4%
5%
3%
2%
2%
1%
2%
4%
5%
11%
11
Post FD – Story is the Same (2001-2009)
Post FD
This years Accuracy Decile
BEST
WORST
Last Year Accuracy Decile
1
2
3
1 7%
10%
10%
2 3%
9%
11%
3 1%
4%
10%
4 0%
2%
6%
5 0%
1%
4%
6 0%
1%
3%
7 0%
1%
3%
8 1%
2%
3%
9 1%
2%
3%
10 1%
2%
2%
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
4
9%
15%
24%
24%
18%
14%
11%
9%
7%
5%
5
17%
16%
25%
33%
35%
31%
25%
20%
16%
11%
6
14%
13%
14%
18%
23%
27%
28%
25%
22%
15%
7
11%
12%
10%
8%
10%
13%
17%
20%
21%
20%
8
12%
9%
6%
4%
4%
6%
8%
12%
15%
20%
9
9%
7%
3%
2%
2%
2%
4%
6%
9%
15%
10
3%
5%
3%
2%
2%
1%
2%
4%
4%
11%
12
In the (not so) Long-Run All Analysts Are Average
Average Quintile Accuracy Rank
Avg. Accuracy Rank Sorted on Lagged 1 Year Accuracy
Quintile 1
Quintile 2
Quintile 3
Quintile 4
Quintile 5
0
Year +1
Year +2
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Year +3
Year +4
Year +5
13
High T-Stat, Low R^2 – Explaining a lot of nothing
• Quantitative Slight of Hand
› Neither Clement or Brown ever show R^2’s on past accuracy, only multiple
regressions
• Analogy:
Think about medicine as an example. Maybe they can find out
that coffee drinking has a statistically significant and even
important effect on the probability of heart disease, but still not
be able to predict whether or not you will suffer from heart
disease, regardless of your coffee-drinking habits.
- Leamer
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14
How Do You Define Accuracy
• Two Definitions of Accuracy
› Demeaned Accuracy
- Allows easy comparisons across stocks, time
- [Abs(Estimate – Actual) – Abs(Average Error) ] / Abs( Avg Error)
› Accuracy relative to Actual
- Probably the first thing most investors think about when considering accuracy
- Abs(Estimate – Actual) / Abs(Actual)
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15
Rockstars Mean Revert (Post FD)
Avg Error RockStar = 0, TimeWeighted = -.16
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16
“RockStar” Analysts are BOLD, but not Accurate
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17
The Reality of Analysts’ (Lack of) Accuracy
Accuracy of Various Forecasting Methodologies – FY1
Russell 3000; 1990-2009; Monthly Rebalance
% Absolute Diff. between Forecast and Actual
60.0%
50.0%
All Stocks
48.2%
Positive Earnings Stocks
40.0%
39.4%
30.0%
38.0%
36.3%
26.7%
35.0%
25.6%
24.8%
20.0%
10.0%
0.0%
Overweight Most Accurate
Analysts - 1 Year
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Naïve Mean
Exponential Time Weighted
Estimates
Overweight Best Forecasts With
Perfect Foresight
18
Even “Rockstars” don’t beat Naïve Mean Consensus
“Rockstar” Accuracy compared to Naïve Mean & Time Weighted
Russell 3000; 1990-2009; Monthly Rebalance
% Absolute Diff. between Forecast and Actual
35.0%
Positive Earnings Stocks
32.8%
31.6%
29.7%
30.0%
25.0%
All Stocks
23.3%
22.3%
21.0%
20.0%
15.0%
10.0%
5.0%
0.0%
Rock Star
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Naïve Mean
Exponential Time Weighted Estimates
19
What, then, makes a Forecast more Accurate?
• We know Analysts are not Accurate; they have
more error than a naïve consensus
• Literature proves us a guide into other areas
• Clement, Lee
Importance:
1000
Importance:
100
Importance:
10
Estimate Age
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Broker Size
Tenure
20
Alternative – Focus on Factors that Make Forecast
Accurate
Regress abs(analyst error - average error) / abs(average error) = each of the factors demeaned univariately below
Pre RegFD
Post Reg FD
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
expected sign
Estimate
Age
0.001
0.003
0.003
0.003
0.003
0.003
0.004
0.004
0.003
0.004
0.004
0.005
0.007
0.006
0.005
0.005
0.005
0.005
0.004
0.007
0.004
t
80.32
146.3
152.8
146.1
178.4
184.2
210.1
235.9
207.8
262
260.1
294.2
386.5
320.8
280.8
286.9
287.7
299.9
288.4
283.2
187.9
Adj
R^2
0.67%
2.29%
2.57%
2.25%
3.05%
3.18%
3.89%
4.61%
3.47%
5.10%
4.97%
6.46%
11.39%
8.09%
6.29%
5.83%
5.65%
5.97%
5.28%
5.27%
10.38%
Broker
Size
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
+
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
-
t
-53.92
-56.93
-62.37
-48.99
-48.64
-55.76
-55.19
-64.4
-60.03
-73.11
-60.1
-66.91
-69.3
-31.26
-33.62
-47.88
-48.76
-35.86
-43.72
-22.05
-9.796
Adj
R^2
0.30%
0.35%
0.44%
0.26%
0.23%
0.30%
0.28%
0.36%
0.30%
0.42%
0.28%
0.36%
0.41%
0.08%
0.10%
0.17%
0.17%
0.09%
0.13%
0.03%
0.03%
Analyst
Tenure
-0.014
-0.006
-0.005
-0.004
-0.006
-0.003
-0.002
-0.002
-0.001
-0.001
0.000
0.001
0.001
0.001
-0.001
0.000
0.001
0.000
0.000
-0.002
-0.002
-
t
-42.96
-21.89
-19.21
-16.33
-27.58
-18.72
-12.27
-10.59
-9.754
-4.254
3.629
4.483
6.243
4.342
-4.937
-0.965
7.873
-0.527
-2.175
-16.21
-12.54
Adj
R^2
0.19%
0.05%
0.04%
0.03%
0.08%
0.03%
0.01%
0.01%
0.01%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.02%
0.05%
Estimate
Age
0.001
0.003
0.003
0.003
0.003
0.003
0.004
0.004
0.003
0.004
0.004
0.005
0.007
0.006
0.005
0.005
0.005
0.005
0.004
0.004
0.007
Dummy
Broker Size
-0.066
-0.059
-0.044
-0.022
0.001
-0.011
-0.012
-0.007
0.000
-0.069
-0.062
-0.039
-0.039
-0.023
-0.041
-0.021
-0.020
-0.007
-0.016
-0.006
0.007
Age
t
80.48
146.39
152.81
146.214
178.349
184.183
210.112
235.93
207.802
261.670
259.800
293.660
385.700
320.900
280.640
286.720
287.610
299.861
288.160
283.092
187.912
Size
t
-29.16
-30.61
-22.38
-9.976
0.304
-5.753
-6.608
-3.77
-0.239
-40.150
-37.000
-24.110
-21.800
-13.520
-22.480
-12.510
-12.240
-4.499
-10.690
-4.288
1.782
+
-
+
-
21
Similar Results Internationally
Regress abs(analyst error - average error) / abs(average error) = each of the factors demeaned univariately below
Pre RegFD
Post Reg FD
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
expected sign
Estimate
Age
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.0
0.0
0.0
+
t
87.8
111.6
154.7
153.1
172.4
133.5
184.6
207.9
174.4
210.7
171.9
184.5
222.5
220.5
200.4
215.1
227.8
213.3
202.8
234.4
293.6
Adj
R^2
2%
3%
4%
3%
3%
1%
2%
2%
2%
2%
1%
2%
3%
3%
2%
2%
2%
2%
2%
2%
3%
Broker
Size
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
-
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
t
-4.671
-15.47
-8.648
-10.59
-10.73
-6.459
-9.024
-12.31
-27.31
-42.4
-29.98
-13.38
-9.758
18.37
-4.609
-11.97
-24.46
-26.08
-33.12
-47.04
-27.39
Adj
R^2
0.01%
0.06%
0.01%
0.01%
0.01%
0.00%
0.01%
0.01%
0.04%
0.09%
0.05%
0.01%
0.01%
0.02%
0.00%
0.01%
0.03%
0.03%
0.04%
0.08%
0.03%
Analyst
Tenure
-0.01
-0.02
0.00
-0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
-
t
-8.587
-17.58
-2.904
-30.18
1.706
6.176
2.321
3.812
22.19
6.05
1.406
4.334
15.5
9.282
-5.02
-2.011
-5.331
-2.28
-5.275
-11.05
-16.54
Adj
R^2
0.02%
0.08%
0.00%
0.10%
0.00%
0.00%
0.00%
0.00%
0.02%
0.00%
0.00%
0.00%
0.01%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.01%
Estimate
Age
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Dummy
Broker Size
0.00
-0.01
-0.03
-0.02
-0.01
-0.01
-0.02
-0.01
0.00
-0.03
-0.03
-0.02
0.00
0.04
0.00
0.00
-0.01
-0.01
-0.02
-0.02
-0.01
Age
t
88.36
111.66
154.78
153.80
171.98
132.93
183.60
207.18
174.30
209.48
171.50
183.38
221.21
219.68
199.94
214.38
227.10
211.86
201.83
233.26
293.205
Size
t
1.19
-4.70
-12.40
-12.19
-6.81
-5.86
-12.91
-7.65
-16.29
-27.27
-20.10
-13.70
1.31
25.20
0.52
-0.43
-6.40
-7.42
-16.16
-20.97
-7.956
+
-
+
-
Adj
R^2
2.3%
3.1%
3.6%
2.6%
2.8%
1.4%
2.1%
2.3%
1.5%
2.3%
1.5%
1.9%
2.8%
2.7%
2.1%
2.2%
2.5%
2.0%
1.6%
2.0%
3.72%
22
Beware….
• Forecast Accuracy is not the same as market impact
›
›
›
›
Take an average analyst at a top brokerage
Compare to a skilled unknown analyst at a brokerage we never heard of…..
Analyst typically establish their reputation on ONE big call
II Analysts (Institutional Investor) Poll
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
23
Bibliography
Bagnoli, Levine, Watts, 2003,”Analyst Estimation Revision Clusters and Corporate Events, Parts I and II”, Working
Paper
Beaver, Cornell, et al, 2008,” The Impact of Analysts’ Forecast Errors and Forecast Revisions on Stock Prices”,
Journal of Business Finance & Accounting, 35(5) & (6), 709–740
Brown, Lawrence, 2001, “How Important is Past Analyst Forecast Accuracy?”, Financial Analysts Journal, Vol 57,
pp 44-49.
Chan, Jegadeesh, Lakonishok, 1996, “Momentum Strategies “, Journal of Finance
Clement, M. B., 1999. “Analyst forecast accuracy: Do ability, resources, and portfolio complexity matter?”, Journal
of Accounting and Economics, 27 (3): 285-303.
Clement, Tse, 2003, “Do Investors Respond to Analysts' Forecast Revisions as If Forecast Accuracy Is All That
Matters?”, The Accounting Review, Vol. 78 (1), 227-249
Doyle, Lundholm, Soliman, 2006, “The Extreme Future Stock Returns Following I/B/E/S Earnings Surprise”, Journal
of Accounting Research, 44 (5)
Elgers, P., M. Lo, and R. Pfeiffer,J r, 2001, “Delayed security price adjustment to financial analysts' forecasts of
annual earnings”, The Accounting Review, 76 (4): 613-632.
Gleason, C., and C. Lee, 2003, “Analyst forecast revisions and market price discovery”, The Accounting Review, 78
(1): 193-225.
Mikhail, Walther, Lewis, 1999, “Does Forecast Accuracy Matter to Security Analysts?”,The Accounting Review, Vol.
74( 2), pp. 185-200
Sinha, Brown, Das, 1997, “A Re-Examination of Financial Analysts’ Differential Earnings Forecast Accuracy”,
Contemporary Accounting Research; 14, 1;
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
24
Interesting Stats – US: FY1
Year
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
# StocksCovered # Analysts # Broker Stock-Analyst Stock-Broker
4078
2705
184
7.6
112.4
3967
2677
191
7.3
101.9
3877
2362
190
7.9
98.8
4157
2235
198
8.5
96.3
4498
2414
222
8.6
93.8
5085
2830
231
7.8
95.4
5639
3099
236
7.5
99.1
6357
3497
263
7.0
93.7
6869
3900
308
6.7
85.3
6898
4348
341
6.3
80.7
6684
4591
342
6.0
80.2
6226
4660
323
5.8
83.5
5350
4641
314
5.3
78.2
4767
4814
263
5.0
91.1
4658
4879
342
4.9
70.2
4928
4638
395
5.8
67.8
5112
4586
405
6.3
70.8
5325
4628
379
6.3
77.4
5433
4709
353
6.6
87.7
5106
4565
341
6.5
87.0
4885
4207
360
6.7
78.7
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
EstTenure EstimateAge Analyst-Broker
4.3
62.3
14.7
4.6
58.0
14.0
5.3
55.5
12.4
6.2
55.1
11.3
6.7
55.9
10.9
6.9
57.2
12.3
7.0
56.0
13.1
7.1
56.8
13.3
6.9
56.5
12.7
6.9
56.5
12.8
6.9
56.8
13.4
6.8
57.6
14.4
6.8
53.6
14.8
6.5
53.3
18.3
6.5
51.8
14.3
6.7
50.9
11.7
6.7
51.7
11.3
6.9
52.9
12.2
7.0
52.7
13.3
7.3
50.7
13.4
7.8
51.1
11.7
#
1,074,981
1,012,435
976,423
1,010,437
1,103,558
1,146,323
1,215,823
1,281,370
1,366,492
1,458,400
1,453,759
1,401,955
1,277,551
1,246,362
1,248,955
1,419,823
1,491,761
1,526,356
1,609,796
1,542,621
1,501,152
Tenure
3.7
4.1
4.9
5.5
5.6
5.5
5.5
5.2
4.9
5.0
5.1
5.2
5.2
4.8
4.7
4.8
5.2
5.4
5.4
5.7
6.2
topQTenure bottomQTenure
7.7
0.23
8.6
0.54
9.4
0.59
10.4
0.64
11.8
0.34
12.4
0.35
12.5
0.38
12.8
0.33
12.8
0.33
13.4
0.38
14.2
0.41
14.0
0.44
14.6
0.37
14.1
0.38
13.7
0.39
14.1
0.44
14.1
0.48
14.5
0.51
14.3
0.47
15.1
0.61
16.7
0.77
25
Interesting Stats – Intl: FY1
Year
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
#StocksCovered #Analysts
4724
1505
4599
2023
5519
2380
6679
3173
7331
4480
8149
5352
9211
7110
9595
8234
10081
9116
9855
9941
9499
10338
9495
10683
8898
10874
8364
10430
8530
10532
9333
9678
10243
9796
11357
10799
12610
12099
13085
12492
13328
12687
#Broker
254
295
328
397
479
537
577
599
623
652
619
571
567
535
560
590
625
660
688
709
800
StockPerAnalyst
7.6
6.3
6.4
6.7
5.2
5.4
4.9
4.9
4.7
4.2
4.1
3.7
3.4
3.5
3.7
4.2
4.2
4.1
4.1
4.3
4.5
SystematIQ Research| Capital IQ, A Standard & Poor’s Business
Stock-Broker EstTenure
45.2
1.8
43.2
2.4
46.6
2.9
53.2
3.5
48.4
3.7
53.4
3.2
60.9
3.2
67.5
3.1
68.8
3.0
63.4
3.0
68.3
3.1
68.7
3.3
64.4
3.3
68.7
3.3
68.7
3.7
68.5
4.1
65.6
4.7
66.8
4.9
72.4
5.0
75.7
5.2
71.0
5.5
EstimateAge
64.5
63.3
61.1
61.1
63.3
65.4
64.7
66.3
65.4
66.6
64.5
67.9
64.0
63.0
61.0
61.0
61.7
61.2
61.6
59.1
58.0
Analyst-Broker
5.9
6.9
7.3
8.0
9.4
10.0
12.3
13.7
14.6
15.2
16.7
18.7
19.2
19.5
18.8
16.4
15.7
16.4
17.6
17.6
15.9
#
596,423
663,098
795,447
1,118,865
1,228,241
1,490,118
1,828,268
2,103,468
2,227,660
2,191,471
2,242,210
2,038,502
1,899,902
1,912,244
2,001,120
2,141,238
2,132,897
2,293,725
2,589,599
2,791,745
3,011,257
Tenure
1.68
1.73
2.05
2.24
2.25
2.26
2.32
2.40
2.44
2.61
2.74
2.88
2.97
3.07
3.20
3.54
3.91
3.97
3.93
4.15
4.47
topQTenure bottomQTenure
2.95
0.35
3.68
0.25
4.53
0.30
5.10
0.29
5.29
0.25
5.56
0.28
5.92
0.25
5.93
0.29
6.25
0.32
6.48
0.34
6.77
0.34
7.14
0.33
7.40
0.36
7.55
0.41
7.93
0.36
8.56
0.41
9.24
0.41
9.83
0.34
10.16
0.33
10.63
0.45
11.55
0.54
26