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 4 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50% to 100% of ownership) –Includes all pending and completed deals (not rumored or withdrawn deals) –Most advisory roles get full credit for the deal 5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 7

8 Do LT Rankings Affect Market Share? Dependent variable: quarterly market share (1)(2)(3) LYE_Rank0.0026***0.0030*** (3.91)(3.49)(3.55) Deviation_q ***0.0048*** (4.55)(4.92) LYE_Rank x Deviation_q *** (3.87) LY_mkt_share0.5866***0.5985***0.6094*** (8.46)(6.96)(7.16) mkt_share_q ***0.2669***0.2456*** (3.61)(4.13)(3.87) Constant0.0806***0.0930***0.0947*** (4.32)(3.97)(4.15) Year dummiesYes Quarter dummiesYes R²73.73%74.71%74.93% N One-rank increase  0.3% market share increase (i.e., 6% of the within-bank std. dev. of this variable)

9 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

10 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 *** (3.26) LT_contribution0.0308** (2.55) Year dummiesYes Pseudo R²23.58%22.63% N

11 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 *** (3.21) R_LT_contribution0.0430*** (6.48) LYE_rank *** *** (6.08)(3.17) Non_US_bank (1.32) Year dummiesYes Pseudo R²2.67%3.40% N

12 Determinants of Fees OLS(1)(2)(3)(4) Deviation0.0001***0.0001* (3.01)(1.68) LT_contribution *** * (3.07)(1.85) LYE_rank0.0002*** (3.61)(3.74) Last_rank * (0.16)(1.74) Co ***0.0000*** ***0.0000** (3.58)(3.79)(2.86)(2.40) Fo (0.41)(0.84)(1.42)(1.38) Fo_only ***0.0000** ***0.0000*** (4.57)(2.46)(3.68)(2.83) Sell_mandate0.0012**0.0000***0.0013**0.0000*** (2.62)(2.67)(2.60)(2.85) Year dummiesYes Bank dummiesNoYesNoYes R²48.90%54.46%52.02%56.37% N One std. dev. decrease in Deviation  drop of 5bp in fees (about $250k for average deal)

13 Consequences of LT Management For banks −Is league table management effective? Dependent variableEOQ_rank OLS(1)(2)(3) Pct_fo_co3.3226**3.3594*** (2.60)(2.63) Pct_co_late1.5938**1.6333** (2.25)(2.30) LQE_rank0.8626***0.8639***0.8615*** (30.16)(30.30)(30.16) LQ_mkt_share8.0777***8.0603***8.0776*** (3.86) (3.89) Constant *** *** *** (4.14)(4.03)(4.26) Year dummiesYes Quarter dummiesYes Adj. R²89.82%89.75%89.87% N1 885 One within-bank std. dev. increase in these two variables  gain of 0.5 ranks

14 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

15 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?