Competition and Bias Paper by Harrison Hong and Marcin Kacperczyk Presentation by Michael-Paul James Treatment effect: a decrease in analyst covering increases optimism bias one year after the merger relative to control. -Evidence for competition reduction bias -Larger bias impact for stocks with less coverage

1. Competition and Bias
Paper by Harrison Hong and Marcin Kacperczyk
Presentation by Michael-Paul James
1

2. Table of contents
Introduction OLS Results
Story, Questions, Context, Issues,
Literature, Data
Results
Analyst Coverage and Optimism Bias, Validity of The
Natural Experiment, Robustness, Additional Analysis
Empirical Design
01 02
04 05
Mergers
Background on Mergers
Conclusion
Summary, Critiques, Thoughts,
Appendix
03
06
2
Michael-Paul James

3. Introduction
01
Story, Questions, Context, Issues, Literature, Data
3
Michael-Paul James

4. Reporting Tradeoffs
4
Michael-Paul James
● Competing interests in Media
○ Profits from information consumption base
■ Command more advertising fees through increased readership
○ Profits from advertisers; firms and political clients
■ Attract advertisers through positive press and good will
■ Bad client press not only hurts the client but also demonstrates
disloyalty to advertiser base.
● Competing interests in rating agencies
○ Paid by firms they evaluate

5. Competing Theories
5
Michael-Paul James
● Competition from suppliers (newspapers, rating agencies, etc) increases
the firm's (politician, bank, etc) difficulty to suppress unfavorable news.
○ More suppliers of information, the greater the cost to suppress.
○ Competition decreases bias
● Consumer preferences motivate information suppliers to confirm
personal biases and not challenge existing beliefs.
○ Competition increases reporting bias to gain user base.

6. Choosing a Target
6
Michael-Paul James
● Bias is more easily measured in security analyst earnings forecasts
○ Similar tradeoffs as other information suppliers
■ Investor’s trust gained through accurate forecasts
■ Prospective payoff from covered firms wanting positive reports
○ Channels for competition to discipline supply-side analyst forecasts
■ Independence rational
● Competition increases preference diversity
● Increasing the likelihood one who cannot be bribed
● Once information is public, other analyst forced to cover
■ Cost of influence increases with # of information suppliers
● Bad forecasts deviator receives monopolist’s payoff
■ Detailed information readily available

7. Natural Experiment
7
Michael-Paul James
● Merging firms: exogenous variation on analysts coverage
○ Merging firms drop redundant analysts, lowering the total number
of security analysts covering a stock
○ Mergers should not impact the behavior of other security analysts
covering the stock
● Events
○ From 1980 to 2005, 15 mergers documented between brokerage
firms affecting 948 stocks
○ Mean analyst coverage of stocks is 21 analyst (10 standard deviation)
● Bias Measured (BIASit
)
○ Difference between the analyst’s forecast and the actual earnings
per share (EPS) divided by previous year’s stock price.

8. Data
8
Michael-Paul James
● Years analyzed: 1980-2005
● Institutional Brokers Estimates System (IBES) database
○ Security analysts reports and data
● Center for Research in Security Prices (CRSP)
○ Monthly closing stock prices
○ Monthly shares outstanding
○ Daily & monthly stock returns for NYSE, AMEX, and NASDAQ stocks
● COMPUSTAT
○ annual information on corporate earnings
○ book value of equity
○ book value of assets during the same period
● Sample of stocks in the top 25% of the market capitalization distribution

9. Findings
9
Michael-Paul James
● Treatment effect: a decrease in analyst covering increases optimism
bias one year after the merger relative to control.
○ Evidence for competition reduction bias
○ Larger bias impact for stocks with less coverage
○ Decrease in one analyst, increases optimism bias by 2 basis points.
(5.7 t-stat)
○ Mean optimism bias increases by 15 basis points due to loss of one
analyst

10. OLS Results
02
10
Michael-Paul James

11. Variables
11
Michael-Paul James
BIASit
Forecast biases and consider the consensus bias expressed as a mean or
median bias among all analysts covering a particular stock,
COVERAGEit
Number of analysts covering stock i in year t
LNSIZEit
Natural logarithm of firm i’s market capitalization (price times shares
outstanding) at the end of year t.
SIGMAit
Variance of daily (simple, raw) returns of stock i during year t
RETANNit
Average monthly return on stock i in year t
LNBMit
Natural logarithm of firm i’s book value divided by its market cap at yeart
end
ROEit
Firm i’s return on equity in year t (ratio of earnings during year t to the book
value of equity.)
VOLROEit
Volatility of ROE
PROFITit
Firm profitability, defined as operating income over book value of assets
SPit
Indicator variable equaling one if included in the S&P 500 index, 0 otherwise
FERRORjt
Absolute difference between forecast of analyst j in year t and actual EPS
FDISPit
analyst forecast dispersion, defined as the standard deviation of all analyst
forecasts covering firm i in year t.

12. Table
I:
Summary
Statistics
on
the
IBES
Sample
12
Notes: We consider a sample of stocks covered by IBES during the period 1980–2005 with valid annual earnings forecast records. COVERAGEit
is a measure of analyst coverage, defined as the number of analysts
covering firm i at the end of year t. Analyst forecast bias (BIASjt
) is the difference between the forecast analyst j in year t and the actual EPS, expressed as a percentage of the previous year’s stock price. The consensus
bias is expressed as a mean or median bias among all analysts covering a particular stock. Analyst forecast error (FERRORjt
) is the absolute difference between the forecast of analyst j in year t and the actual EPS,
expressed as a percentage of the previous year’s stock price. The forecast error is expressed as a mean or median bias among all analysts covering a particular stock. FDISPit
is analyst forecast dispersion, defined as the
standard deviation of all analyst forecasts covering firm i in year t. LNSIZEit
is the natural logarithm of firm i’s market capitalization (price times shares outstanding) at the end of year t. SIGMAit
is the variance of daily
(simple, raw) returns of stock i in year t. RETANNit
is the average monthly return on stock i in year t. LNBMit
is the natural logarithm of firm i’s book value divided by its market cap at the end of year t. To measure the
volatility of ROE (VOLROE), we estimate an AR(1) model for each stock’s ROE using a 10-year series of the company’s valid annual ROEs. ROEit is firm i’s return on equity in year t. ROE is calculated as the ratio of
earnings in year t over the book value of equity. We calculate VOLROE as the variance of the residuals from this regression. PROFITit
is the profitability of company i at the end of year t, defined as operating income
over book value of assets. We exclude observations that fall to the left of the 25th percentile of the size distribution, observations with stock prices lower than $5, & those for which the absolute difference between
forecast value and the true earnings exceeds $10.
Table I: Summary Statistics on the IBES Sample
Cross-sectional mean Cross-sectional median Cross-sectional st. dev.
Variable (1) (2) (3)
COVERAGEi,t
21.45 21 9.57
Mean BIASi,t
(%) 2.70 2.10 3.10
Median BIASi,t
(%) 2.64 2.01 3.17
Mean FERRORi,t
(%) 3.31 2.39 2.93
Med. FERRORi,t
(%) 3.24 2.26 3.00
FDISPi,t
(%) 0.75 0.41 1.02
LNSIZEi,t
8.38 8.38 1.62
SIGMAi,t
(%) 40.72 35.04 21.03
RETANNi,t
(%) 1.73 1.49 4.04
LNBMi,t
−1.02 −0.92 0.88
VOLROEi,t
(%) 26.53 10.43 19.79
PROFITi,t
(%) 15.48 15.29 9.38

13. Table
II:
Regression
of
Consensus
Forecast
Bias
on
Company
Characteristics
13
Notes. The dependent variable is BIAS, defined as a consensus forecast bias of all analysts tracking stock i in year t. Forecast bias is the difference between the forecast of analyst j in year t and the actual EPS,
expressed as a percentage of the previous year’s stock price. The consensus is obtained either as a mean or median bias. COVERAGEi,t
is a measure of analyst coverage, defined as the number of analysts covering firm
i at the end of year t. LNSIZEi,t
is the natural logarithm of firm i’s market capitalization (price times shares outstanding) at the end of year t. SIGMAit is the variance of daily (simple, raw) returns of stock i during year t.
RETANNi,t
is the average monthly return on stock i in year t. LNBMi,t
is the natural logarithm of firm i’s book value divided by its market cap at the end of year t. To measure the volatility of ROE (VOLROE), we estimate
an AR(1) model for each stock’s ROE using a 10-year series of the company’s valid annual ROEs. ROEi,t
is firm i’s return on equity in year t. ROE is calculated as the ratio of earnings in year t over the book value of equity.
VOLROE is the variance of the residuals from this regression. PROFITi,t
is the profitability of company i at the end of year t, defined as operating income over book value of assets. SP500i,t
is an indicator variable equal
to one if stock i is included in the S&P500 index in year t. We exclude all observations that fall to the left of the 25th percentile of the size distribution, observations with stock prices lower than $5, and those for which
the absolute difference between forecast value and the true earnings exceeds $10. All regressions include three-digit SIC industry fixed effects and year fixed effects. Some specifications also include brokerage house
and firm fixed effects. Standard errors (in parentheses) are clustered at the industry level. ∗∗∗, ∗∗, ∗ 1%, 5%, and 10% statistical significance.
Table II: Regression of Consensus Forecast Bias on Company Characteristics
Mean BIAS Median BIAS
Variables/model (1) (2) (3) (4) (5) (6)
COVERAGEi,t−1
−0.0002∗∗ −0.0002∗∗ 0.0001 −0.0002∗∗∗ −0.0002∗∗ 0.0001
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)
LNSIZEi,t−1
0.0028∗∗∗ 0.0026∗∗∗ 0.0044∗∗∗ 0.0028∗∗∗ 0.0026∗∗∗ 0.0042∗∗∗
(0.0009) (0.0009) (0.0014) (0.0008) (0.0008) (0.0014)
SIGMAi,t−1
−0.0093 −0.0031 0.0108∗∗∗ −0.0095 −0.0061 0.0108∗∗∗
(0.0062) (0.0058) (0.0039) (0.0061) (0.0059) (0.0041)
RETANNi,t−1
−0.1000∗∗∗ −0.0986∗∗∗ −0.0368∗∗∗ −0.0986∗∗∗ −0.0995∗∗∗ −0.0367∗∗∗
(0.0199) (0.0199) (0.0133) (0.0192) (0.0198) (0.0135)
LNBMi,t−1
0.0121∗∗∗ 0.0115∗∗∗ 0.0053∗∗∗ 0.0118∗∗∗ 0.0116∗∗∗ 0.0049∗∗∗
(0.0016) (0.0015) (0.0013) (0.0016) (0.0015) (0.0013)
VOLROEi,t−1
0.0062∗∗∗ 0.0061∗∗∗ 0.0000 0.0060∗∗∗ 0.0059∗∗∗ 0.0000
(0.0019) (0.0019) (0.0000) (0.0019) (0.0019) (0.0000)
PROFITi,t−1
0.0579∗∗∗ 0.0571∗∗∗ 0.0629∗∗∗ 0.0578∗∗∗ 0.0574∗∗∗ 0.0619∗∗∗
(0.0095) (0.0092) (0.0115) (0.0098) (0.0095) (0.0115)
SP500i,t−1
−0.0110∗∗∗ −0.0110∗∗∗ −0.0017 −0.0110∗∗∗ −0.0110∗∗∗ −0.0026
(0.0024) (0.0024) (0.0072) (0.0025) (0.0024) (0.0075)
Year fixed effects Yes Yes Yes Yes Yes Yes
Industry fixed effects Yes Yes Yes Yes Yes Yes
Brokerage fixed effects No Yes Yes No Yes Yes
Firm fixed effects No No Yes No No Yes
Observations 9,313 9,313 9,313 9,313 9,313 9,313

14. Mergers
03
Background on Mergers
14
Michael-Paul James

15. Table
III:
Descriptive
Statistics
For
Mergers
15
Notes. Panel A includes the
names of brokerage houses
involved in mergers, the date of
the merger, and the number of
stocks covered by either
brokerage house or both of
them prior to the merger.
Panel B breaks down the
merger information at the
analyst level. We include
number of analysts employed
in the merging brokerage
houses prior to merger and
after the merger as well as the
detailed information on the
career outcomes of the
analysts after the merger.
Panel C calculates the
percentage of analysts from
the merging houses that cover
the same stock after the
merger. We restrict our sample
of stocks to those which were
covered by bother the bidder
and the target house.
Panel A: Mergers used in the analysis and stocks covered
Brokerage house Merger number Merger date # stocks (bidder) # stocks (target) # stocks (b&t)
Merrill Lynch 1 9/10/1984 762 173
Becker Paribas 288
Paine Webber 2 12/31/1994 659 234
Kidder Peabody 545
Morgan Stanley 3 05/31/1997 739 251
Dean Witter Reynolds 470
Smith Barney (Travelers) 4 11/28/1997 914 327
Salomon Brothers 721
Credit Suisse First Boston 5 10/15/2000 856 307
Donaldson Lufkin & Jenrette 595
UBS Warburg Dillon Read 6 12/10/2000 596 213
Paine Webber 487
Chase Manhattan 7 12/31/2000 487 80
JP Morgan 415
Wheat First Securities 8 10/31/1988 178 8
Butcher & Co., Inc. 66
EVEREN Capital 9 1/9/1998 178 17
Principal Financial Securities 142
DA Davidson & Co. 10 2/17/1998 76 8
Jensen Securities 53
Dain Rauscher 11 4/6/1998 360 26
Wessels Arnold & Henderson 135
First Union 12 10/1/1999 274 21
EVEREN Capital 204
Paine Webber 13 6/12/2000 516 28
JC Bradford 182
Fahnestock 14 9/18/2001 117 5
Josephthal Lyon & Ross 91
Janney Montgomery Scott 15 3/22/2005 116 10
Parker/Hunter 54

16. Table
III:
Descriptive
Statistics
For
Mergers
16
Panel B: Career outcomes of analysts after mergers
#analysts # analysts after merger
Brokerage house Merger number Prior After
Retained in the
house
Left to another
house
Exited sample
(fired) New analysts
Merrill Lynch 1 90 98 84 0 5 13
Becker Paribas 27 — 1 11 15 —
Paine Webber 2 50 57 42 1 7 6
Kidder Peabody 51 — 9 28 14 —
Morgan Stanley 3 70 92 61 2 7 26
Dean Witter Reynolds 35 — 5 16 14 —
Smith Barney (Travelers) 4 91 140 70 6 15 27
Salomon Brothers 76 — 43 20 13 —
Credit Suisse First Boston 5 120 146 93 5 22 35
Donaldson Lufkin and Jenrette 77 — 18 17 42 —
UBS Warburg Dillon Read 6 94 118 80 5 9 0
Paine Webber 64 — 38 8 17 —
Chase Manhattan 7 64 106 48 5 11 24
JP Morgan 50 — 34 1 15 —
Wheat First Securities 8 13 21 13 0 0 8
Butcher & Co., Inc. 13 — 3 3 7 —
EVEREN Capital 9 27 31 21 4 2 8
Principal Financial Securities 18 — 2 6 10 —
DA Davidson & Co. 10 6 8 4 1 1 0
Jensen Securities 4 — 4 0 0 —
Dain Rauscher 11 39 36 19 9 11 6
Wessels Arnold & Henderson 15 — 11 0 4 —
First Union 12 35 54 26 2 7 16
EVEREN Capital 32 — 12 10 10 —
Paine Webber 13 54 55 37 9 8 18
JC Bradford 22 — 0 14 8 —
Fahnestock 14 14 16 7 1 6 9
Josephthal Lyon & Ross 14 — 0 5 9 —
Janney Montgomery Scott 15 13 15 11 1 1 3
Parker/Hunter 5 — 1 0 4 —

17. Table
III:
Descriptive
Statistics
For
Mergers
17
Panel C: Percentage of stocks covered by analysts from bidder
and target houses after mergers
Percentage of stocks (bidder) Percentage of stocks (target)
Merger (1) (2)
1 85.7 1.1
2 73.7 15.8
3 66.3 5.4
4 50.0 30.7
5 63.7 12.3
6 67.8 32.3
7 45.3 32.1
8 61.9 14.3
9 67.7 6.5
10 50.0 50.0
11 52.8 30.6
12 48.1 22.2
13 67.3 0
14 43.8 0
15 73.3 6.7
Notes. Panel A includes the names of brokerage houses involved in mergers, the date of the merger, and the number of stocks covered by
either brokerage house or both of them prior to the merger. Panel B breaks down the merger information at the analyst level. We include
number of analysts employed in the merging brokerage houses prior to merger and after the merger as well as the detailed information on the
career outcomes of the analysts after the merger. Panel C calculates the percentage of analysts from the merging houses that cover the same
stock after the merger. We restrict our sample of stocks to those which were covered by bother the bidder and the target house.

18. Empirical Design
04
18
Michael-Paul James

19. Table
IV:
Summary
Statistics
For
The
Treatment
Sample
19
Notes: We consider all stocks covered by two merging brokerage houses around the one-year merger event window. COVERAGEit
is a measure of analyst
coverage, defined as the number of analysts covering firm i at the end of year t. Analyst forecast bias (BIASjt
) is the difference between the forecast analyst j at
time t and the actual EPS, expressed as a percentage of the previous year’s stock price. The consensus bias is expressed as a mean or median bias among all
analysts covering a particular stock. Analyst forecast error (FERRORjt
) is the absolute difference between the forecast analyst j at time t and the actual EPS,
expressed as a percentage of the previous year’s stock price. The forecast error is expressed as a mean or median bias among all analysts covering a particular
stock. FDISPit
is analyst forecast dispersion, defined as the standard deviation of all analyst forecasts covering firm i at time t. LNSIZEit
is the natural logarithm of
firm i’s market capitalization (price times shares outstanding) at the end of year t. SIGMAit
is the variance of daily (simple, raw) returns of stock i during year t.
RETANNit
is the average monthly return on stock i during year t. LNBMit
is the natural logarithm of firm i’s book value divided by its market cap at the end of year t.
To measure the volatility of ROE (VOLROE), we estimate an AR(1) model for each stock’s ROE using a 10-year series of the company’s valid annual ROEs. ROEit
is
firm i’s return on equity in year t. ROE is calculated as the ratio of earnings during year t over the book value of equity. We calculate VOLROE as the variance of the
residuals from this regression. PROFITit
is the profitability of company i at the end of year t, defined as operating income over book value of assets. We exclude
observations with stock prices lower than $5 and those for which the absolute difference between the forecast value and the true earnings exceeds $10.
Table IV: Summary Statistics For The Treatment Sample
Cross-sectional
mean
Cross-sectional
median
Cross-sectional
st. dev.
Variable (1) (2) (3)
COVERAGEi,t
21.12 20 9.45
Mean BIASi,t
(%) 2.79 2.24 3.10
Median BIASi,t
(%) 2.74 2.21 3.19
Mean FERRORi,t
(%) 3.40 2.52 2.90
Med. FERRORi,t
(%) 3.33 2.43 2.99
FDISPi,t
(%) 0.75 0.40 0.94
LNSIZEi,t
8.39 8.37 1.60
SIGMAi,t
(%) 41.00 35.86 21.02
RETANNi,t
(%) 1.74 1.52 4.13
LNBMi,t
−1.03 −0.92 0.91
VOLROEi,t
(%) 25.32 9.89 43.40
PROFITi,t
(%) 15.52 15.25 9.22

20. Results
05
Analyst Coverage and Optimism Bias, Validity of The Natural Experiment,
Robustness, Additional Analysis
20
Michael-Paul James

21. Table
V:
Change
in
Stock-Level
Coverage
and
Bias:
DiD
Estimator
21
Notes. N = 1,656 in Panels A and B. We measure analyst coverage as the number of analysts covering firm i at the end of year t. For all mergers, we split the sample
of stocks into those covered by both merging brokerage houses (treatment sample) and those not covered by both houses (control sample). We also divide stocks
into premerger period and postmerger period (one-year window for each period). For each period we further construct benchmark portfolios using the control
sample based on stocks’ size (SIZE), book-to-market ratio (BM), and average past year’s returns (RET). Our benchmark assignment involves three portfolios in each
category. Each stock in the treatment sample is then assigned to its own benchmark SIZE/BM/RET/NOANmatched). Next, for each period, we calculate the
cross-sectional average of the differences in analyst stock coverage across all stocks in the treatment sample and their respective benchmarks. Finally, we
calculate the difference in differences between postevent period and pre-event period (DID estimator). Our sample excludes observations with stock prices lower
than $5 and those for which the absolute difference between forecast value and the true earnings exceeds $10. In Panel B, we exclude from our sample all
analysts employed in the merging houses. Panel C presents our results by cuts on initial coverage. There are three groups: lowest coverage (≤5), medium coverage
(>5 and ≤20) and highest coverage (>20). Standard errors (in parentheses) are clustered at the merger groupings. ∗∗∗, ∗∗, ∗ 1%, 5%, and 10% statistical significance.
Table V: Change in Stock-Level Coverage and Bias: DiD Estimator
Panel A: Coverage and bias
Coverage Mean BIAS Median BIAS
(1) (2) (3)
SIZE/BM/RET/NOAN-matched −1.021∗∗∗ 0.0013∗ 0.0016∗∗
(0.179) (0.0007) (0.0008)
Panel B: Change in forecast bias: DID estimator without analysts from merging houses
Mean BIAS Median BIAS
(1) (2)
SIZE/BM/RET/NOAN-matched 0.0011∗∗ 0.0012∗∗
(0.0005) (0.0005)
Panel C: Change in forecast bias: Conditioning on initial coverage
Mean BIAS Median BIAS
(1) (2)
SIZE/BM/RET/NOAN-matched 0.0078∗∗ 0.0096∗∗
(coverage≤5) (0.0036) (0.0044)
SIZE/BM/RET/NOAN-matched 0.0017∗ 0.0020∗
(coverage>5 and ≤20) (0.0011) (0.0011)
SIZE/BM/RET/NOAN-matched 0.0003 0.0007
(coverage>20) (0.0013) (0.0013)
Drop one analysts
per merger; 16 basis
points in median
Large effect for firms
with few analyst

22. Figure
I
(1
0f
2):
Trend
of
Analyst
Coverage
and
Bias
in
the
Treatment
Sample
(Net
of
Control)
22
We show the trend of average analyst coverage and forecast bias in the treatment sample net of the control group (in a given year) up to three years before and
after the merger event. Panel A is for stocks with low analyst coverage (less than six analysts); Panel B is for stocks with medium analyst coverage (six to twenty
analysts); Panel C is for stocks with high analyst coverage (above twenty analysts). Panel D documents aggregate results. Dotted lines illustrate 95% confidence
intervals.
~1 analyst decrease
~.80% increase in bias
~.5 to -.6 analyst decrease
~.20% increase in bias

23. Figure
I
(2
0f
2):
Trend
of
Analyst
Coverage
and
Bias
in
the
Treatment
Sample
(Net
of
Control)
23
Regression toward
the mean?
Big differences
between T&C

24. Table
VI:
Validity
of
the
Natural
Experiment
24
Notes. N = 1,656 in Panels A, B, and D. In Panel A, we measure analyst
earnings (EARNjt ) as the actual EPS expressed as a percentage of the
previous year’s stock price. For all mergers, we split the sample of stocks
into those covered by both merging brokerage houses (treatment sample)
and those not covered by both houses (control sample). We also divide
stocks into premerger period and postmerger period (one-year window for
each period). For each period we further construct benchmark portfolios
using the control sample based on stocks’ size (SIZE), book-to-market ratio
(BM), average past year’s returns (RET), and analyst coverage (NOAN). Our
benchmark assignment involves three portfolios in each category. Each
stock in the treatment sample is then assigned to its own benchmark
portfolio (SIZE/BM/RET/NOAN-matched). Next, for each period, we
calculate the cross-sectional mean and median of the differences in
earnings across all stocks in the treatment sample and their respective
benchmarks. Finally, we calculate the difference in differences between
postevent period and pre-event period (DID estimator). In Panel B, we
provide the DID estimator for various corporate characteristics, including
Tobin’s Q, asset size, stock returns, volatility, profitability, and log sales. In
Panel C, the treatment sample is constructed based on the stocks that are
covered by one but not both merging houses. In Panel D, the control
sample is constructed using the stocks that are covered by one but not
both merging houses. Our sample excludes observations with stock prices
lower than $5 and those for which the absolute difference between
forecast value and the true earnings exceeds $10. Standard errors (in
parentheses) are clustered at the merger groupings. ∗∗∗, ∗∗, ∗ 1%, 5%, and
10% statistical significance.
Table VI: Validity of the Natural Experiment
Panel A: Change in earnings
MeanEARN (1) MedianEARN (2)
DID estimator (SIZE/BM/RET/NOAN-matched) −0.0002 −0.0002
(0.0005) (0.0005)
Panel B: Change in firm characteristics
Stock characteristic SIZE/BM/RET/NOAN-matched
Tobin’s Q 0.0397
(0.1138)
Size 30.52
(20.63)
Returns 0.0015
(0.0012)
Volatility −0.0069
(0.0054)
Profitability −0.0593
(0.0597)
Log(sales) 0.0046
(0.0090)
Panel C: Change in forecast bias for non-overlapping stocks
Coverage Mean BIAS (2) Median BIAS (3)
SIZE/BM/RET/NOAN-matched 0.080 0.0001 0.0001
(0.106) (0.0004) (0.0004)
Panel D: Change in forecast bias for non-overlapping stocks as a control
Mean BIAS (2) Median BIAS (3)
SIZE/BM/RET/NOAN-matched 0.0017∗∗∗ 0.0018∗∗∗
(0.0006) (0.0006)
C. Little change to
average on event dates
D. Using stocks used by
one firm but not both
A. Competition does not
impact earnings
B. Observables immune
to merger event

25. Figure
II:
Kernel
Densities
of
Differences
between
Treatment
and
Control
before
and
after
Merger
25
We show Epanechnikov kernel
densities of differences in forecast bias
between treatment and control
groups for the period before and after
the merger. The bandwidth for the
density estimation is selected using
the plug-in formula of Sheather and
Jones (1991). The rightward shift of the
distribution after the merger is
significant because the hypothesis of
equality of distributions is rejected at
1% level using the
Kolmogorov–Smirnov test.
Hypothesis of equality of
distributions is rejected at the 1% level
using the Kolmogorov Smirnov test

26. Table
VII:
Change
in
Forecast
Bias:
Regression
Evidence
26
Notes. The dependent variable is forecast bias
(BIAS), defined as the difference between
forecasted earnings and actual earnings,
adjusted for the past year’s stock price. For
each merger, we consider a one-year window
prior to merger (pre-event window) and a
one-year window after the merger (postevent
window). We construct an indicator variable
(MERGE) equal to one for the postevent period
and zero for the pre-event period. For each
merger window, we assign an indicator variable
(AFFECTED) equal to one for each stock
covered by both merging brokerage houses
(treatment sample) and zero otherwise. LNSIZE
is a natural logarithm of the market cap of the
stock; SIGMAit is the variance of daily (simple,
raw) returns of stock i during year t; RETANN is
annual return on the stock; LNBM is a natural
logarithm of the book to market ratio;
COVERAGE denotes the number of analysts
tracking the stock. To measure the volatility of
ROE (VOLROE), we estimate an AR(1) model for
each stock’s ROE using a ten-year series of the
company’s valid annual ROEs. ROEit is firm i’s
return on equity in year t. ROE is calculated as
the ratio of earnings in year t over the book
value of equity. VOLROE is the variance of the
residuals from this regression. PROFITit is the
profitability of company i at the end of year t,
defined as operating income over book value of
assets. SP500 is an indicator variable equal to
one if a stock is included in the S&P500 index.
RECit is the recency measure of the forecast,
measured as an average distance between the
analyst forecast and the earnings’ report. All
regressions include three-digit SIC industry
fixed effects, merger fixed effects, brokerage
fixed effects, and firm fixed effects. We report
results based on both mean and median bias.
Standard errors (in parentheses) are clustered
at the merger groupings. ∗∗∗, ∗∗, ∗ 1%, 5%, and
10% statistical significance.
Mean BIAS (1) Median BIAS (2) Mean BIAS (3) Median BIAS (4)
MERGEi
0.0005 0.0005 0.0005 0.0006
(0.0008) (0.0008) (0.0008) (0.0008)
AFFECTEDi
−0.0019∗∗ −0.0019∗∗ −0.0019∗∗∗ −0.0019∗∗∗
(0.0006) (0.0007) (0.0006) (0.0006)
MERGEi
×AFFECTEDi
0.0021∗ 0.0024∗∗ 0.0021∗ 0.0024∗∗
(0.0012) (0.0012) (0.0012) (0.0012)
LNSIZEi,t−1
0.0038∗∗∗ 0.0037∗∗∗ 0.0038∗∗∗ 0.0037∗∗∗
(0.0010) (0.0010) (0.0009) (0.0010)
RETANNi,t−1
0.0005 0.0004 0.0005 0.0004
(0.0017) (0.0017) (0.0017) (0.0017)
LNBMi,t−1
−0.0037 0.0001 −0.0037 0.0001
(0.0073) (0.0074) (0.0073) (0.0074)
COVERAGEi,t−1
0.0001 0.0001 0.0001 0.0001
(0.0001) (0.0001) (0.0001) (0.0001)
SIGMAi,t−1
0.0001∗∗ 0.0000 0.0001∗∗ 0.0000
(0.0000) (0.0000) (0.0000) (0.0000)
VOLROEi,t−1
0.0000 0.0000 0.0000 0.0000
(0.0000) (0.0000) (0.0000) (0.0000)
PROFITi,t−1
0.0629∗∗∗ 0.0620∗∗∗ 0.0630∗∗∗ 0.0621∗∗∗
(0.0052) (0.0052) (0.0052) (0.0052)
SP500i,t−1
0.0032 0.0039 0.0032 0.0039
(0.0031) (0.0037) (0.0031) (0.0037)
RECi,t−1
−0.0000 −0.0000
(0.0000) (0.0000)
Merger fixed effects Yes Yes Yes Yes
Industry fixed effects Yes Yes Yes Yes
Brokerage fixed effects Yes Yes Yes Yes
Firm fixed effects Yes Yes Yes Yes
Observations 57,005 57,005 57,005 57,005

27. Table
VIII:
Change
in
Alternative
Forecast
Bias
Measures:
DiD
Estimator
27
Notes. We measure analyst forecast bias (BIASjt
) using two different measures: the forecast of longterm growth of analyst j at time t (Panel A), and the analyst’s j stock
recommendation at time t (Panel B). For each analyst, the recommendation variable is ranked from 1 to 5, where 1 is strong sell, 2 is sell, 3 is hold, 4 is buy, and 5 is
strong buy. The consensus bias is expressed as a mean or median bias among all analysts covering a particular stock. For all mergers, we split the sample of stocks
into those covered by both merging brokerage houses (treatment sample) and those not covered by both houses (control sample). We also divide stocks into
premerger period and postmerger period (one-year window for each period). For each period we further construct benchmark portfolios using the control sample
based on stocks’ size (SIZE), book-to-market ratio (BM), average past year’s returns (RET), and analyst coverage (NOAN). Our benchmark assignment involves three
portfolios in each category. Each stock in the treatment sample is then assigned to its own benchmark portfolio (SIZE/BM/RET/NOAN-matched). Next, for each period,
we calculate the crosssectional average of the differences in analyst forecast bias across all stocks in the treatment sample and their respective benchmarks. Finally,
we calculate the difference in differences between postevent period and pre-event period (DID estimator). Our sample excludes observations with stock prices lower
than $5 and those for which the absolute difference between forecast value and the true earnings exceeds $10. Standard errors (in parentheses) are clustered at the
merger groupings. ∗∗∗, ∗∗, ∗ 1%, 5%, and 10% statistical significance.
Table VIII: Change in Alternative Forecast Bias Measures: DiD Estimator
Mean BIAS (1) Median BIAS (2)
Panel A: Long-term growth
DID estimator (SIZE/BM/RET/NOAN-matched) 0.553∗∗∗ 0.352∗
(0.202) (0.212)
Panel B: Analyst recommendations
DID estimator (SIZE/BM/RET/NOAN-matched) 0.0501 0.0902∗
(0.0412) (0.0556)
Long term forecasts increase
mean by 55 basis points
Long term forecasts increase
median by 35 basis points
1 for strong sell
2 for sell
3 for hold
4 for buy
5 for strong buy
Analyst recommendations
increase mean by 5%
Analyst recommendations
increase median by 9%

28. Table
IX:
Incentives
and
Career
Outcomes
28
Notes. The dependent variables are promotion
(PROMOTION), defined as an indicator variable
equal to one when an analyst moves to a
larger brokerage house, and zero otherwise;
and demotion (DEMOTION), defined as an
indicator variable equal to one when an
analyst moves to a smaller brokerage house,
and zero otherwise. BIAS is forecast bias,
defined as the difference between forecasted
earnings and actual earnings, adjusted for the
past year’s stock price. COVERAGE denotes
the number of analysts tracking the stock.
HIGH ACCURACY is an indicator variable that
equals one if an analyst’s error falls into the
highest decile of the distribution of accuracy
measure, and zero otherwise; LOW ACCURACY
is an indicator variable that equals one if the
analyst’s error falls into the highest decile of
the distribution of accuracy measure, and zero
otherwise. EXPERIENCE is the natural
logarithm of the number of years that the
analyst is employed in the brokerage house.
BROKERAGE SIZE is the number of analysts
employed by the brokerage house. This table
includes an interaction term between BIAS
and COVERAGE. All regressions include year
fixed effects, brokerage fixed effects, and
analyst fixed effects. Standard errors (in
parentheses) are clustered at the analyst
groupings. ∗∗∗, ∗∗, ∗ 1%, 5%, and 10% statistical
significance.
Table IX: Incentives and Career Outcomes
Promotion Demotion
(1) (2) (3) (4)
BIAS 0.0106∗ 0.0448∗∗∗ −0.0036 −0.0065
(0.0067) (0.0136) (0.0074) (0.0092)
HIGH ACCURACY 0.0082∗∗ 0.0183∗∗
(0.0040) (0.0080)
LOW ACCURACY 0.0262∗∗∗ 0.0233∗∗
(0.0062) (0.0098)
BIAS×COVERAGE −0.0019∗∗∗ 0.0005∗
(0.0006) (0.0003)
HIGH ACCURACY×COVERAGE −0.0006
(0.0004)
LOW ACCURACY×COVERAGE 0.0001
(0.0005)
COVERAGE −0.0004∗ 0.0002 0.0004 0.0002
(0.0002) (0.0003) (0.0003) (0.0003)
EXPERIENCE 0.0262∗∗∗ 0.0265∗∗∗ 0.0067 0.0065
(0.0052) (0.0052) (0.0043) (0.0043)
BROKERAGE SIZE −0.0016∗∗∗ −0.0016∗∗∗ 0.0008∗∗∗ 0.0008∗∗∗
(0.0003) (0.0003) (0.0002) (0.0002)
Year fixed effects Yes Yes Yes Yes
Brokerage fixed effects Yes Yes Yes Yes
Analyst fixed effects Yes Yes Yes Yes
Observations 45,770 45,770 45,770 45,770
Bias
increases
chance of
promotion

29. Conclusion
06
Summary, Critiques, Thoughts, Appendix
29
Michael-Paul James

30. Summary
30
Michael-Paul James
● Research Question: Does competition affect incentives for bias?
● Natural experiment: Brokerage house mergers remove redundant
analysts exogenously reducing the number of reporting analysts on a
stock.
● Treatment effect: a decrease in analyst covering increases optimism
bias one year after the merger relative to control.
○ Evidence for competition reduction bias
○ Larger bias impact for stocks with less coverage

31. 31
Appendix: Mergers Included In The Sample (Sorted By Date)
#
Meger
date Target house IBES Target's industry
Ind.
code Bidder house IBES Bidder's industry
Ind.
code
1 9/10/1984 Becker Paribas 299 Brokerage firm 6211 Merrill Lynch & Co., Inc. 183 PVD investment, financial
advisory services
6211
8 10/31/1988 Butcher & Co., Inc. 44 Securitiesdealer; RE broker 6211 Wheat First Securities
Inc(WF)
282 Investment bank, brokerage
firm
6211
2 12/31/1994 Kidder Peabody & Co. 150 Investment bank 6211 Paine Webber Group, Inc. 189 Investment bank 6211
3 5/31/1997 Dean Witter Discover & Co. 232 PVD SEC brokerage services 6211 Morgan Stanley Group, Inc. 192 Investment bank 6211
4 11/28/1997 Salomon Brothers 242 Investment bank 6211 Smith Barney 254 Investment bank 6211
9 1/9/1998 Principal Financial Securities 495 Investment bank; securities
firm
6211 EVEREN Capital Corp. 829 Securities brokerage firm 6211
10 2/17/1998 Jensen Securities Co. 932 Securities brokerage firm 6211 DA Davidson & Co. 79 Investment company 6799
11 4/6/1998 Wessels Arnold & Henderson
LLC
280 Investment bank 6211 Dain Rauscher Corp. 76 Investment bank 6211
12 10/1/1999 EVEREN Capital Corp. 829 Securities brokerage firm 6211 First Union Corp., Charlotte,
NC
282 Commercial bank; holding
company
6021
13 6/12/2000 JC Bradford & Co. 34 Securities brokerage firm 6211 Paine Webber Group, Inc. 189 Investment bank 6211
5 10/15/2000 Donaldson Lufkin & Jenrette 86 Investment bank 6211 CSFB 100 Investment bank 6211
6 12/10/2000 Paine Webber 189 Investment bank 6211 UBS Warburg Dillon Read 85 Investment bank 6211
7 12/31/2000 JP Morgan 873 Investment bank 6211 Chase Manhattan 125 Investment bank 6211
14 9/18/2001 Josephthal Lyon & Ross 933 Security brokers and dealers 6211 Fahnestock & Co. 98 Securities brokerage firm 6211
15 3/22/2005 Parker/Hunter Inc 860 PVD investment ,investment
banking services
6211 Janney Montgomery Scott
LLC
142 PVD SEC brokerage services 6211
Appendix:
Mergers Included In The Sample (Sorted By Date)

32. You are Amazing
Ask me all the questions you desire. I will do my best to answer honestly
and strive to grasp your intent and creativity.
32
Michael-Paul James