Transcript Quid Pro Quo in IPOs: Why Bookbuilding is dominating Auctions

The Perverse Effects of Investment Bank Rankings:
Evidence from M&A League Tables
François Derrien, HEC Paris
Olivier Dessaint, HEC Paris
November 8, 2012
Corporate Governance of Financial Institutions Conference
What is a League Table?
2
Questions
• Do league tables matter?
• Do banks respond to incentives created by
league table rankings?
– With what consequences?
3
Some Evidence that League Tables Matter
Weekly frequency of reporting to Thomson by banks (« Date advisor
added » item in SDC)
4
Data and League Table Construction
• M&A data from SDC
− Bank data: All banks that appear at least once in the LT since
2000 and do at least two deals in the year  101 banks
− Deal data: All deals in which the banks above are involved 
38,839 deal-bank observations
• We reconstruct historical M&A league tables since 1999
• We use the same criteria as Thomson
– LT credit = sum of « rank value » (deal value + target’s net debt
if acquirer goes from <50% to 100% of ownership)
– Includes all pending and completed deals (not rumored or
withdrawn deals)
– Most advisory roles get full credit for the deal
5
League Table Management Hypothesis
• Trade-off between current and future fees
– Banks are willing to give up on current fees and focus on
activities that will increase their league table ranking, and their
future fees
– League table management tools
• Fairness opinions
– Assessment of the fairness of a deal price
– Low effort / low fees
– Same league table credit as regular advisory work
• Free-riding on existing mandates
– Low effort / low fees
– Late co-advisors are likely to be free-riders
• Low fees
6
League Table Management Hypothesis
• When do banks engage in league table management?
– When they lost ranks recently
• We use the Deviation variable
• Deviation = Number of LT ranks gained by the bank since the end
of previous year
– At the deal level, when a deal has more impact on the bank’s
rank
• We use the LT_contribution variable, which measures the
deal credit relative to gap with closest competitors
LT _contrib
utioni  log(
2  dealcred
)
valcredi 1  valcredi 1
7
Do LT Rankings Affect Market Share?
• Dependent variable: quarterly market share
LYE_Rank
(1)
(2)
(3)
0.0026***
(3.91)
0.0030***
(3.49)
0.0030***
(3.55)
0.0017***
(4.55)
0.0048***
(4.92)
Deviation_q-2
LYE_Rank x Deviation_q-2
0.0002***
(3.87)
LY_mkt_share
0.5866***
(8.46)
0.5985***
(6.96)
0.6094***
(7.16)
mkt_share_q-2
0.2766***
(3.61)
0.2669***
(4.13)
0.2456***
(3.87)
Constant
0.0806***
(4.32)
0.0930***
(3.97)
0.0947***
(4.15)
Yes
Yes
73.73%
1768
Yes
Yes
74.71%
1186
Yes
Yes
74.93%
1186
Year dummies
Quarter dummies
R²
N
One-rank increase 
0.3% market share
increase (i.e., 6% of
the within-bank std.
dev. of this variable)
8
Banks’ Response
• League table management hypothesis
− Banks should
• do more fairness opinions
• do more late co-mandates
• lower their fees
− When
• they have lost ranks in recent league tables
• the relative impact of the deal on their ranking is large
9
Determinants of Fairness Opinions
• Deal-level tests
− Dependent variable
•
•
1 if the bank does a FO in a co-mandate context
0 if the bank does a FO in a sole-mandate context (no suspicion
of league table management)
− We include standard control variables
Probit - marginal effects
Deviation
(1)
-0.0092***
(3.26)
LT_contribution
Year dummies
Pseudo R²
N
(2)
0.0308**
(2.55)
Yes
23.58%
2 851
Yes
22.63%
1 859
10
Determinants of Late Co-Mandates
• Deal-level tests
− Dependent variable
•
•
1 if the bank reports its role late
0 if the bank reports its role early (first bank to report)
− We include standard control variables
Probit - marginal effects
R_deviation
(1)
-0.0057***
(3.21)
R_LT_contribution
LYE_rank
(2)
0.0430***
(6.48)
-0.0092***
(6.08)
-0.0078***
(3.17)
Non_US_bank
0.0361
(1.32)
0.0549
(1.32)
Year dummies
Pseudo R²
N
Yes
2.67%
2 685
Yes
3.40%
1 394
11
Determinants of Fees
One std. dev. decrease in Deviation  drop of
5bp in fees (about $250k for average deal)
OLS
Deviation
(1)
(2)
0.0001***
(3.01)
0.0001*
(1.68)
LT_contribution
LYE_rank
0.0002***
(3.61)
Last_rank
Co
Fo
Fo_only
Sell_mandate
Year dummies
Bank dummies
R²
N
(3)
(4)
-0.0002***
(3.07)
0.0002***
(3.74)
-0.0002*
(1.85)
-0.0010***
(3.58)
-0.0003
(0.41)
-0.0037***
(4.57)
0.0012**
(2.62)
0.0000
(0.16)
0.0000***
(3.79)
0.0000
(0.84)
0.0000**
(2.46)
0.0000***
(2.67)
-0.0009***
(2.86)
-0.0009
(1.42)
-0.0035***
(3.68)
0.0013**
(2.60)
0.0000*
(1.74)
0.0000**
(2.40)
0.0000
(1.38)
0.0000***
(2.83)
0.0000***
(2.85)
Yes
No
48.90%
1 267
Yes
Yes
54.46%
1 267
Yes
No
52.02%
1 126
Yes
Yes
56.37%
1 126
12
Consequences of LT Management
• For banks
− Is league table management effective?
Dependent variable
OLS
EOQ_rank
(1)
Pct_fo_co
3.3226**
(2.60)
Pct_co_late
EOQ_rank
(2)
EOQ_rank
(3)
3.3594***
(2.63)
1.5938**
(2.25)
1.6333**
(2.30)
LQE_rank
0.8626***
(30.16)
0.8639***
(30.30)
0.8615***
(30.16)
LQ_mkt_share
8.0777***
(3.86)
8.0603***
(3.86)
8.0776***
(3.89)
Constant
-3.3158***
(4.14)
-3.2733***
(4.03)
-3.4325***
(4.26)
Yes
Yes
89.82%
1 885
Yes
Yes
89.75%
1 885
Yes
Yes
89.87%
1 885
Year dummies
Quarter dummies
Adj. R²
N
One within-bank std. dev.
increase in these two
variables  gain of 0.5
ranks
13
Consequences of LT Management
• For M&A clients
− In fairness opinions, higher LT_contribution associated
with
•
•
•
Lower probability of deal completion
Higher valuation range of the FO
Lower combined CAR (-1,+1) around deal announcement
14
Conclusion
• League tables affect banks’ behavior
− Banks are more likely to do FOs, co-mandates, and to cut their
fees when their incentives to manage their position in the
ranking are higher (i.e., when they lost ranks in recent league
tables, or when the relative impact of the deal on their LT
position is bigger)
− Some evidence that league table management hurts the banks’
clients
• Questions
− Why are clients naive about the banks’ incentives to manage
league tables?
− How could we improve the criteria used to construct league
tables?
15