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Conflicts of interest in sell-side research and the moderating role of institutional investors
Alexander Ljungqvist, Felicia Marston, Laura Starks, Kelsey Wei, and Hong Yan
Background
• Analysts’ conflicts of interest subject of many recent investigations
• Congress, SEC, NASD, NYSE, NYSAG
• Usual story: 1. Analysts pressured to provide favorable
recommendations for IB clients / prospects2. Analysts pressured to stimulate trading to generate
brokerage commissions3. Analysts need to keep access to management
Problem:Individual investors may lose out – even though the market may not be fooled (Chen ’04).
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Evidence of investment banking and brokerage pressure
• Recommendations and forecasts of ‘affiliated analysts’ are too optimistic Dugar and Nathan (’95), Lin and McNichols (’98), Michaely and
Womack (’99), …
• Affiliated analysts respond more slowly to negative newsO’Brien, McNichols, and Lin (’05)
• Research bullish in order to stimulate tradingIrvine (2003), Jackson (2003), Agrawal and Chen (2004), Cowen,
Groysberg, and Healy (2003)
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Countervailing influences 1
• Reputation and career concerns • Hong and Kubik (‘03), Hong, Kubik, and
Solomon (‘00): career concerns moderate analyst behavior
• Irvine (‘03), Jackson (‘03): differing from consensus and high reputation generate additional brokerage business
• Mikhail, Walther, and Willis (’99): relatively less accurate analysts generate less brokerage business, have higher job turnover
Motivation Objective The model Predictions Conclusions• • • • • • • • • • • •
Countervailing influences 2
• Presence of institutional investorsinstitutional investors desire useful (and
unbiased) research
• Green (’04): early access to recommendations produces annualized returns of >30%
• Malmendier and Shanthikumar (’03): institutions are wary of affiliated analysts’ recommendations
Motivation Objective The model Predictions Conclusions• • • • • • • • • • • •
Institutional equity investment in U.S. 1952-2004 (in millions of USD)
Source: Federal Reserve
0
2000000
4000000
6000000
8000000
10000000
12000000
1990’s
% of U.S. corporate equities owned by institutional investors 1952-2004
Source: Federal Reserve
0%
10%
20%
30%
40%
50%
60%
70%
% of U.S. corporate equities owned by individual investors 1952-2004
Source: Federal Reserve
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Growth in institutional & individual investor equity investment in U.S. 1952-2004
Source: Federal Reserve
0
2000000
4000000
6000000
8000000
10000000
12000000
Institutional
Individual
in Millions of USD
Implications of institutional presence
• Influence on stock markets through their trading
• Influence on corporate governance
Direct intervention Indirect supply-demand intervention
Consistent with previous research on institutional presence
• Influences executive compensation structures
• Hartzell and Starks (2003), Almazan, Hartzell, and Starks (2005)
• Influences market for corporate control• Pinkowitz (2003),Gaspar, Massa, Matos
(2005), Qiu (2005), Chen, Li, Harford (2005)
• Influences CEO turnover• Parrino, Sias and Starks (2003)
Motivation Objective The model Predictions Conclusions• • • • • • • • • • • •
Presence of institutions
• How does the presence of institutional investors encourage useful research?• They evaluate individual analysts, e.g. in
the ‘All-star’ polls basis for career concerns literature
• They ‘pay’ for research by allocating brokerage commissions (presumably on the basis of quality)
Motivation Objective The model Predictions Conclusions• • • • • • • • • • • •
Three key dimensions to analysts’ research output
• Primary analyst activities• Investment recommendations • Earnings forecasts • Timeliness of updates
• Differences in costs of introducing bias• Verifiability• Importance in compensation and rankings
Objective
• Claim: Analysts trade off • generating revenue for the investment banking
and brokerage operations• while maintaining or building reputation capital in
the eyes of their institutional investor audience
• Thus, expect that the presence of institutional investors • leads to less conflicted analyst behavior• and so moderates conflicts of interest in sell-side
research
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Research design
Angle 1: Recommendations• Controlling for investment banking and brokerage
pressure, expect less aggressive recommendations, the greater is institutional ownership in the stock
Angle 2: Analyst forecast accuracy• Expect analysts to strive for greater accuracy (lower
abs. forecast errors) in stocks predominantly held by institutional investors
Angle 3: Reaction to bad news• Expect analysts to revise opinions faster, the greater is
institutional ownership in the stock
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Preview of primary results: Recommendations
• Recommendations more aggressive among affiliated analysts and at large brokerages
• Ceteris paribus, less aggressive…• the greater is institutional ownership• the fewer institutions are shareholders • the larger is mean size of instl. holdings• if concentrated in the hands of the largest
institutional investors
Preview of primary results: Forecast errors
Analysis of accuracy of forecasts
• Affiliated analysts appear to be more accurate in their forecasts.
• Analysts have more accurate forecasts in the presence of institutional investors.
Preview of primary results: Reaction to bad news
• O’Brien, McNichols and Lin (’05): after equity issues, underwriter-affiliated analysts downgrade stock more slowly
• We identify set of ‘bad news’ events, and relate time-to-revision to• bank-firm relationships• presence of institutional investors• (plus analyst and bank reputation etc.)
Contributions of our paper
• We examine analyst opinions on all companies in contrast to earlier studies of investment banking conflicts who restrict samples to recent issuers of securities
• We examine countervailing influence of institutional investors
Angle 1: Research design
where
Ai,k,t = analyst i’s recommendation for company k at time t
Ck,t = company k’s characteristics
Ii,t = analyst i’s characteristics
Rji,k,t = strength of company k’s relationship with i’s bank
Bji,t = bank j’s characteristics
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
tkijti
jtkititktki BRICA ,,,,,,,,,
timecompany analyst
Interpretation 1
Consider stock k covered at time t by several analysts i • k’s institutional ownership does not vary across the
analysts …• yet the trade-off between career concerns and IB and
brokerage considerations differs across analysts i …• … in line with each analyst’s reputation, the
employing bank’s reputation and brokerage needs, and the strength of the relationship between k and each analyst’s bank.
• Thus, holding the stock constant, we expect different analysts to behave differently towards the same company k.
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Interpretation 2
Consider analyst i who at time t covers several stocks k
• The analyst’s reputation and the bank’s reputation and brokerage considerations do not vary across the stocks
• … yet the trade-off between career concerns and IB considerations differs across stocks k …
• … in line with each stock’s institutional ownership and the relationship between k and the analyst’s bank.
• Thus, holding the analyst constant, we expect different behavior across the companies covered, with more aggressive recommendations for relationship clients and less aggressive recommendations for companies predominantly owned by institutions.
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Estimation
• Unbalanced three-way panel with overlapping effects relevant estimator doesn’t (yet) exist
• Follow the literature:• estimate with firm (k) or analyst (i) random
effects, and compare results• estimate Fama-MacBeth regressions• estimate ordered probits
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
tkitkitki uv ,,,,
Complication
• We only observe Ai,k,t conditional on coverage
• Coverage is presumably not random, plausibly related to institutional ownership possible bias
• (Really) hard to correct for in panel data with random effects;
• However, we find no evidence of bias if we• Ignore random effects and make correction
• Focus on largest companies
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Sample and data
• Intersection of Spectrum 13f and I/B/E/S recommendation files• 6,337 unique non-financial companies
• in sample for 1-28 quarters (1994-2000), mean=17
• mean 52.8% institutional ownership
• each usually covered by multiple analysts
• To keep sample size manageable, focus on the 16 most-active underwriting banks as of 2000-2002, and their predecessors• 230,268 firm-analyst quarters
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Sample banks
Market share
(%)
Amount raised ($m,
nominal)
Market share
(%)
Amount raised ($m,
nominal)
Market share
(%)
Amount raised ($m,
nominal)
Goldman Sachs & Co 17.5 227,333 13.7 371,736 14.9 599,069Merrill Lynch & Co Inc 11.5 148,982 13.5 365,412 12.8 514,394Salomon Smith Barney 8.7 113,432 14.4 389,678 12.5 503,110Credit Suisse First Boston 14.0 181,579 10.9 297,165 11.9 478,744Morgan Stanley Dean Witter 12.4 161,265 10.8 293,156 11.3 454,421JP Morgan Chase 4.5 58,730 9.7 264,421 8.1 323,150Lehman Brothers 5.0 65,413 6.5 175,650 6.0 241,063Banc of America Securities LLC 3.0 39,386 5.0 135,634 4.4 175,020UBS Warburg 4.7 60,459 3.9 105,557 4.1 166,015Deutsche Banc Securities 4.2 54,185 2.2 60,744 2.9 114,930Bear Stearns & Co Inc 2.0 26,154 1.6 43,052 1.7 69,207Prudential Volpe Technology Group 0.8 10,340 0.3 8,918 0.5 19,258CIBC World Markets Inc 0.8 10,264 0.3 7,036 0.4 17,299Fleet Boston (Robertson Stephens) 1.0 13,299 0.1 4,069 0.4 17,368SG Cowen Securities Corp 0.6 8,038 0.1 2,211 0.3 10,248Thomas Weisel Partners LLC 0.2 2,119 0.0 25 0.1 2,144
All 16 sample banks (and predecessors) 91.0 1,180,977 93.0 2,524,463 92.3 3,705,440
Equity deals Debt deals All deals
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Prior underwriting relationships
• Defined as bank j’s share of company k’s proceeds raised over prior T years, T=1…5
• e.g. ABC raised 500m in 5 years to quarter t, GS underwrote 150m 30%ML underwrote 100m 20%BoA underwrote 25m 5%
• Estimated separately for debt versus equity deals
• Banks “inherit” relationships post-merger• e.g. post 5/97, MSDW has relationships with MS’s and
DW’s former clients
Measuring bias in recommendations
• Focus on analyst recommendations, normalized by subtracting “consensus”• analyst i’s relative recommendation for company k in quarter
t = (i’s rec. level) – (median rec. level)e.g. “strong buy” – “buy” = 5–4 = 1
• Ensures comparability across companies and provides natural measure of analyst optimism
• Recommendations arrive infrequently and irregularly so measured over prior four quarters (t-3,t)
• Robust to binary or three-level specification, and alternative definitions of “consensus”
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Descriptive statistics 1
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Mean Min Median Max
Firm characteristics
no. of analysts covering stock 5.6 1 5 36
company’s institutional ownership (%) 52.8 0 55.5 100
company’s equity market cap. ($m) 6,646 0 1,218 602,000
Bank characteristics
bank’s eq. mkt share prior calendar year (%) 5.2 0 3.3 21.5
Investment banking pressure
bank’s share of issuer’s eq. deals (5 yrs, %) 10.7 0 0 100
bank’s share of issuer’s debt deals (5 yrs, %) 4.1 0 0 100
Brokerage pressure
# registered representatives 3,798 20 948 19,000
Descriptive statistics 2
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Mean Min Median Max
Analyst characteristics and behavior
relative recommendations 0.015 -4 0 4
Institutional Investor "all-stars" 29.2
analyst’s seniority (in years) 6.9 0 6 19
analyst’s relative forecast accuracy 51.8 0 52.4 100
no. of quarters since analyst initiated coverage (seasoning) 9.9 0 5 75
no. of stocks covered by analyst 13.2 1 11 115
Angle 1: Preview of results
• Controlling for IB and brokerage pressure, and for analyst and company characteristics…
• … recommendations less aggressive…• the greater institutional ownership• the larger the mean size of inst. holdings• if concentrated in the hands of the largest
institutional investors
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Brokerage and IB pressure
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Dependent variable: Relative recommendations
Randomanalyst effects
Fama-MacBeth
Random firm
effectsOrdered probit
IB pressure (bank-firm relationships)
bank’s share of company’s equity deals prior 5 yrs 0.094*** 0.094*** 0.091*** 0.135***
0.006 0.005 0.006 0.016
bank’s share of company’s debt deals prior 5 yrs 0.106*** 0.102*** 0.141*** 0.201***
0.009 0.009 0.009 0.026
Brokerage pressure (size of brokerage)
log no. of registered representatives 0.012*** 0.024*** 0.037***
0.002 0.001 0.005
From Table 2:
Countervailing influences
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Dependent variable: Relative recommendations
Randomanalyst effects
Fama-MacBeth
Random firm
effectsOrdered probit
Bank reputation
bank’s loyalty index -0.109*** -0.174*** -0.273***
0.013 0.011 0.048
bank’s equity market share prior calendar yr -0.448*** 0.01 0.0590.059 0.034 0.168
Institutional ownership
% institutional ownership -0.072*** -0.066*** -0.087*** -0.089***
0.008 0.01 0.01 0.027
From Table 2:
Controls
From Table 2:• More accurate and senior analysts are bolder;
mixed evidence that all-stars are less bold• Relative recommendations
• increase with seasoning• are lower the more stocks the analyst covers• increase in # of analysts covering the stock
• Mixed evidence on issuance history; no effect from company size
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Endogenous coverage
• Two approaches:• Run model for subsample of large firms, defined as the five
largest firms in each three-digit SIC code, ranked quarterly by sales. • Analysts arguably have less discretion with respect to covering the
largest companies.
• Run Heckman (1979) selection model on full sample• Step 1: Model whether a given analyst i covers a given stock k. To
instrument the choice, we include the fraction of firms in company k’s Fama-French (1997) industry that analysts at i’s bank cover at time t. The broader the bank’s existing coverage of an industry, the lower the cost of covering company k’s stock. This variable is uncorrelated with the second-step residuals.
• Step 2: Estimate using the MLE version of Heckman (1979).
Endogenous coverage (T3)
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Composition of ownership
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
From Table 5:
Dependent variable: Relative recommendations
Random analyst effects
Random firm
effects
Random analyst effects
Random firm
effects
Institutional ownership
% institutional ownership: Top 100 investors -0.095*** -0.094***
0.013 0.016
% institutional ownership: Other investors -0.047*** -0.080***
0.013 0.016
mean size of inst. holdings: Top 100 investors -0.414** -0.477**
0.138 0.154
mean size of inst. holdings: Other investors -0.134 0.0900.235 0.257
Angle 2: Forecast accuracy
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
From Table 6:
Dependent variable: Analyst forecast accuracy
Random analyst effects
(1)
Random firm
effects (2)
Investment banking pressure
bank’s share of company’s eqty deals prior 5 yrs -0.034*** -0.037*** 0.007 0.007
bank’s share of company’s debt deals prior 5 yrs 0.000 -0.010 0.010 0.010
Brokerage pressure (size of brokerage)
log no. of registered representatives -0.009*** -0.009*** 0.001 0.001
Institutional ownership
% institutional ownership -0.018* -0.021* 0.009 0.009
Angle 3: Sample and data
• In CRSP, identify all one-day stock price falls in 1994-2000 exceeding X times company’s prior-year st.dev. of daily returns (X=4 or 5)
• For X=4 (X=5), have 27,804 (15,279) events with companies experiencing price drops averaging -17.9% (-21.7%)
• Focus on active coverage (prior report within 365 days), and revisions within 365 days
• Average analyst revises 120 days after event
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Angle 3: Research design
where
Ti,k = time to analyst i’s recommendation revision for k
Pk = one-day (event) percentage change in share price
Ck = company characteristics
Ii = analyst characteristics
Rji,k = prior relationships
Bji = bank characteristics
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
kij
ijkiikkki BRICPT ,,,ln
Timeliness
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Summary of key results
• Recommendations less aggressive…• the greater institutional ownership• the larger the mean size of inst. holdings• if concentrated in the hands of the largest
institutional investors
• Forecast errors are smaller in stocks predominantly held by institutional investors
• Analysts react more quickly to bad news, the greater institutional ownership
Motivation Objective The model Results Conclusions• • • • • • • • • • • •
Conclusions
• Results support hypothesis that institutional investors moderate conflicts of interest in sell-side research, in the context of recommendations, earnings forecasts, and reactions to bad news
• Role of regulation?• Research more likely biased in ‘retail’ stocks, and
‘retail’ investors less likely to adjust for biases • But research also more likely biased for
companies served by lower-tier investment banks, which have largely escaped regulatory attention.
Motivation Objective The model Results Conclusions• • • • • • • • • • • •