The Perverse Effects of Investment Bank Rankings:Evidence from M&A League Tables

François Derrien, HEC ParisOlivier Dessaint, HEC Paris

November 8, 2012Corporate Governance of Financial Institutions Conference

What is a League Table?

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Questions

• Do league tables matter?• Do banks respond to incentives created by

league table rankings?– With what consequences?

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Some Evidence that League Tables Matter

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Weekly frequency of reporting to Thomson by banks (« Date advisor added » item in SDC)

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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– Includes all pending and completed deals (not rumored or withdrawn deals)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

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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

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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

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dealcred2log(utionLT_contrib

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Do LT Rankings Affect Market Share?

• Dependent variable: quarterly market share

(1) (2) (3)

LYE_Rank 0.0026*** 0.0030*** 0.0030***(3.91) (3.49) (3.55)

Deviation_q-2 0.0017*** 0.0048***(4.55) (4.92)

LYE_Rank x Deviation_q-2 0.0002***(3.87)

LY_mkt_share 0.5866*** 0.5985*** 0.6094***(8.46) (6.96) (7.16)

mkt_share_q-2 0.2766*** 0.2669*** 0.2456***(3.61) (4.13) (3.87)

Constant 0.0806*** 0.0930*** 0.0947***(4.32) (3.97) (4.15)

       Year dummies Yes Yes YesQuarter dummies Yes Yes YesR² 73.73% 74.71% 74.93%N 1768 1186 1186

One-rank increase 0.3% market share increase (i.e., 6% of the within-bank std. dev. of this variable)

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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

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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 (1) (2)     

Deviation -0.0092***(3.26)

LT_contribution 0.0308**(2.55)

     

Year dummies Yes YesPseudo R² 23.58% 22.63%N 2 851 1 859     

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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 (1) (2)     R_deviation -0.0057***

(3.21)

R_LT_contribution 0.0430***(6.48)

LYE_rank -0.0092*** -0.0078***(6.08) (3.17)

Non_US_bank 0.0361 0.0549(1.32) (1.32)

     

Year dummies Yes Yes

Pseudo R² 2.67% 3.40%

N 2 685 1 394

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Determinants of Fees

OLS (1) (2) (3) (4)         Deviation 0.0001*** 0.0001*

(3.01) (1.68)LT_contribution -0.0002*** -0.0002*

(3.07) (1.85)LYE_rank 0.0002*** 0.0002***

(3.61) (3.74)Last_rank 0.0000 0.0000*

(0.16) (1.74)Co -0.0010*** 0.0000*** -0.0009*** 0.0000**

(3.58) (3.79) (2.86) (2.40)Fo -0.0003 0.0000 -0.0009 0.0000

(0.41) (0.84) (1.42) (1.38)Fo_only -0.0037*** 0.0000** -0.0035*** 0.0000***

(4.57) (2.46) (3.68) (2.83)Sell_mandate 0.0012** 0.0000*** 0.0013** 0.0000***

(2.62) (2.67) (2.60) (2.85)

Year dummies Yes Yes Yes YesBank dummies No Yes No YesR² 48.90% 54.46% 52.02% 56.37%N 1 267 1 267 1 126 1 126

One std. dev. decrease in Deviation drop of 5bp in fees (about $250k for average deal)

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Consequences of LT Management• For banks

− Is league table management effective?

Dependent variable EOQ_rank EOQ_rank EOQ_rankOLS (1) (2) (3)       

Pct_fo_co 3.3226** 3.3594***(2.60) (2.63)

Pct_co_late 1.5938** 1.6333**(2.25) (2.30)

LQE_rank 0.8626*** 0.8639*** 0.8615***(30.16) (30.30) (30.16)

LQ_mkt_share 8.0777*** 8.0603*** 8.0776***(3.86) (3.86) (3.89)

Constant -3.3158*** -3.2733*** -3.4325***(4.14) (4.03) (4.26)

       Year dummies Yes Yes YesQuarter dummies Yes Yes YesAdj. R² 89.82% 89.75% 89.87%N 1 885 1 885 1 885

One within-bank std. dev. increase in these two variables gain of 0.5 ranks

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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

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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?