Conflicts of interest in sell-side research and the

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)


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


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


0%

10%

20%

30%

40%

50%

60%

70%

% of U.S. corporate equities owned by individual investors 1952-2004


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Growth in institutional & individual investor equity investment in U.S. 1952-2004


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)


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)


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


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


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


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.


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.


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


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


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


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


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”


Descriptive statistics 1


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


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



Brokerage and IB pressure


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



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


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)


Composition of ownership


From Table 5:


Random analyst effects

Random firm

effects


Random firm

effects


% 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


From Table 6:

Dependent variable: Analyst forecast accuracy


(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 -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


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


kij

ijkiikkki BRICPT ,,,ln

Timeliness


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


• Forecast errors are smaller in stocks predominantly held by institutional investors

• Analysts react more quickly to bad news, the greater institutional ownership


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.


Documents

Conflicts of interest in sell-side research and the