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Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
Conflicts of interest in sell-side research and the ...
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  • 1. 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
  • 2. Background
    • Analysts’ conflicts of interest subject of many recent investigations
      • Congress, SEC, NASD, NYSE, NYSAG
    • Usual story:
      • Analysts pressured to provide favorable recommendations for IB clients / prospects
      • Analysts pressured to stimulate trading to generate brokerage commissions
      • 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 • • • • • • • • • • • •
  • 3. 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 news
      • O’Brien, McNichols, and Lin (’05)
    • Research bullish in order to stimulate trading
      • Irvine (2003), Jackson (2003), Agrawal and Chen (2004), Cowen, Groysberg, and Healy (2003)
    Motivation Objective The model Results Conclusions • • • • • • • • • • • •
  • 4. 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 • • • • • • • • • • • •
  • 5. Countervailing influences 2
    • Presence of institutional investors
      • institutional 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 • • • • • • • • • • • •
  • 6. Institutional equity investment in U.S. 1952-2004 (in millions of USD) Source: Federal Reserve 1990’s
  • 7. % of U.S. corporate equities owned by institutional investors 1952-2004 Source: Federal Reserve
  • 8. % of U.S. corporate equities owned by individual investors 1952-2004 Source: Federal Reserve
  • 9. Growth in institutional & individual investor equity investment in U.S. 1952-2004 Source: Federal Reserve Institutional Individual in Millions of USD
  • 10. Implications of institutional presence
    • Influence on stock markets through their trading
    • Influence on corporate governance
    Direct intervention
      • Indirect supply-demand intervention
  • 11. 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)
  • 12. 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)
  • 13. 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
  • 14. 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 • • • • • • • • • • • •
  • 15. 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 • • • • • • • • • • • •
  • 16. 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
  • 17. 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.
  • 18. 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.)
  • 19. 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
  • 20. Angle 1: Research design
    • where
    • A i,k,t = analyst i ’s recommendation for company k at time t
    • C k,t = company k ’s characteristics
    • I i,t = analyst i ’s characteristics
    • R j i,k,t = strength of company k ’s relationship with i ’s bank
    • B j i,t = bank j ’s characteristics
    Motivation Objective The model Results Conclusions • • • • • • • • • • • • time company analyst
  • 21. 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 • • • • • • • • • • • •
  • 22. 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 • • • • • • • • • • • •
  • 23. 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 • • • • • • • • • • • •
  • 24. Complication
    • We only observe A i,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 • • • • • • • • • • • •
  • 25. 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 • • • • • • • • • • • •
  • 26. Sample banks Motivation Objective The model Results Conclusions • • • • • • • • • • • •
  • 27. 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
  • 28. 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 • • • • • • • • • • • •
  • 29. Descriptive statistics 1 Motivation Objective The model Results Conclusions • • • • • • • • • • • •
  • 30. Descriptive statistics 2 Motivation Objective The model Results Conclusions • • • • • • • • • • • •
  • 31. 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 • • • • • • • • • • • •
  • 32. Brokerage and IB pressure Motivation Objective The model Results Conclusions • • • • • • • • • • • • From Table 2:
  • 33. Countervailing influences Motivation Objective The model Results Conclusions • • • • • • • • • • • • From Table 2:
  • 34. 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 • • • • • • • • • • • •
  • 35. 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).
  • 36. Endogenous coverage (T3) Motivation Objective The model Results Conclusions • • • • • • • • • • • •
  • 37. Composition of ownership Motivation Objective The model Results Conclusions • • • • • • • • • • • • From Table 5:
  • 38. Angle 2: Forecast accuracy Motivation Objective The model Results Conclusions • • • • • • • • • • • • From Table 6:
  • 39. 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 • • • • • • • • • • • •
  • 40. Angle 3: Research design
    • where
    • T i,k = time to analyst i ’s recommendation revision for k
    •  P k = one-day (event) percentage change in share price
    • C k = company characteristics
    • I i = analyst characteristics
    • R j i,k = prior relationships
    • B j i = bank characteristics
    Motivation Objective The model Results Conclusions • • • • • • • • • • • •
  • 41. Timeliness Motivation Objective The model Results Conclusions • • • • • • • • • • • •
  • 42. 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 • • • • • • • • • • • •
  • 43. 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 • • • • • • • • • • • •

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