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How Much Does Your Banker’s Target-Specific Experience Matter? Evidence from Target IPO Underwriters that Advise Acquirers

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Abstract

In a sample of 1,507 US all-public acquisitions from 1985–2014, 5% of acquirers use the same advisor that underwrote the target’s initial public offering. Acquirers who use these informed advisors have acquisition announcement three-day cumulative abnormal returns (CARs) that are 2.048 percentage points higher, all else equal. Same-advisor acquisition announcements have higher combined CARs but not lower target CARs, suggesting higher synergies instead of lower deal premia. Same-advisor acquisition announcement outperformance decays as the target ages and grows. These findings show the value and timeliness of investment bank information production, retention, and transfer.

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Notes

  1. In untabulated results, there are seven common-advisor acquisitions in my sample. Of these seven common-advisor acquisitions, one is also a same-advisor acquisition. Either dropping this acquisition or adding a common-advisor indicator variable does not change my results.

  2. The reservation price includes the stand-alone firm value plus the value of synergies that accrue to the target.

  3. The lead target advisor is necessary to identify common advisors (Agrawal et al. 2013). See Footnote 1 on Page 5.

  4. Two sets of untabulated test results reduce concerns about SDC Platinum database errors. First, of the 1,507 sample acquisitions, 1,308 have only 1 acquirer advisor. My results are qualitatively similar if I drop acquisitions with more than one acquirer advisor. Also, Fisher’s exact test fails to reject that acquirer advisor type and the number of acquirer advisors are independent. Second, of the 1,507 sample acquisitions, 160 have overlap between the target IPO underwriters and acquirer advisors. The same advisor coefficient estimates retain their economic and statistical significance in a horse race between the same advisor and an indicator variable for other overlapping acquisitions. Further, the other overlapping acquisitions coefficient estimates are economically small and not statistically significant.

  5. If I ignore investment bank consolidation, there are 65 same-advisor acquisitions, and my results are qualitatively similar.

  6. My sample differs from Golubov et al. (2012). Still, our eight top tier banks are the same: Goldman Sachs, Merrill Lynch (now Bank of America Merrill Lynch), Morgan Stanley, JP Morgan, Citi/Salomon Smith Barney, Credit Suisse First Boston, Lehman Brothers (now Barclays Capital), and Lazard.

  7. This restriction drops 1 same-advisor acquisition and 30 different-advisor acquisitions. My results are qualitatively similar without this restriction.

  8. These screens are consistent with Schwert (2000), and I do not impose additional screens on acquirer, target, or deal characteristics. Cai and Sevilir (2012) and others use additional filters, such as deal values greater than $5 million, deal values greater than 1% of acquirer market equity, or acquisitions that push acquirer ownership above 50%. Given that the choice and number of screens differ throughout the M&A literature and that my analysis focuses on all-public acquisitions, I do not impose additional screens. Untabulated results with these screens are qualitatively similar, but these filters reduce my sample by up to 8%.

  9. My results are qualitatively similar throughout if I count only acquisitions or only acquisitions and IPOs.

  10. Where computationally feasible, I report the results of Fisher’s exact tests instead of chi-squared tests. Throughout, chi-squared test results are qualitatively similar to Fisher’s exact test results.

  11. However, in untabulated results, I find that same advisors advise about three times larger follow-on deals for their sample acquirers—on average and in total—than different advisors.

  12. I follow Correia (2019, provides Stata command reghdfe) to estimate multiple regressions. By default, reghdfe removes singleton acquirer advisors (i.e., acquirer advisors that appear in the sample once). However, for comparability with my other results, I retain singletons. My results are similar if I remove singleton acquirer advisors, which reduces my sample from 1,507 to 1,429 observations.

  13. I thank an anonymous reviewer for this suggestion.

  14. Bruner (2004, Chapter 3) surveys the M&A literature and provides a meta-analysis of announcement returns.

  15. My results are similar if I remove singleton acquirer industry observations, which reduces my sample from 1,507 to 1,497 observations. See Footnote 12 on Page 17.

  16. My results for target CARs are qualitatively similar if I replace acquirer advisor fixed effects with target advisor fixed effects and cluster standard errors by target advisor.

  17. In Columns 2 and 5, the number of bidders coefficient estimates are significantly negative at the 0.10 level. This result is unexpected, but the number of bidders is correlated with hostile and tender offer in this sample. If I remove hostile and tender offer from these regressions, then the number of bidders coefficient estimates are small and not statistically significant.

  18. Sekhon (2011) shows that propensity score matching models can be sensitive to model specification and that matching directly on observable characteristics is less susceptible to model specification errors. I also match directly on these independent variables using a genetic algorithm and find qualitatively similar results.

  19. I estimate these endogenous treatment effects models by full maximum likelihood (i.e., simultaneous estimation) with Stata’s teffects command. Cameron and Trivedi (2005, §16.7 and §25.3.4) and Wooldridge (2010, §21.4.1) discuss endogenous treatment effects models.

  20. My results are qualitatively similar if I measure the information age as the years between acquisition and the most recent target equity issue underwritten by the target IPO underwriter, either IPO or SEO.

  21. Dependent sorts balance target public age terciles within acquirer advisor types but are unnecessary to identify trends. The correlation between same advisor and target public age is low (− 0.0369), and Table 2 shows that target public ages are not statistically different between acquirer advisor types. The same is true for the target post-IPO growth analysis in Section 7.2. The correlations between same advisor and target post-IPO growth are low (− 0.0303 and − 0.0200 for assets and sales, respectively). Finally, neither post-IPO growth measures are statistically different between acquirer advisor types in untabulated results.

  22. Adverse selection may also limit the same-advisor information advantage shortly after IPO. Target IPO underwriters that are available to advise an acquirer may be available for a reason and have a limited understanding of target operations, leadership, and growth options.

  23. I choose ± 2 standard deviations because the standard deviation of target public age is small. My conclusions are similar if I estimate the same advisor marginal effects at the 5th, 50th, and 95th percentiles of target public age.

  24. These results are qualitatively similar at 2 standard deviations above the mean target public age and the mean number of target equity issues and ± 2 standard deviations.

  25. I omit target public age for the multiple regressions with target post-IPO assets and sales growth to reduce collinearity concerns. However, my results are qualitatively similar if I do not omit target public age.

  26. My conclusions are qualitatively similar if I estimate same advisor marginal effects at either the mean and ± 2 standard deviations or the 5th, 50th, and 95th percentiles.

  27. Testing the same advisor marginal effects at different target post-IPO asset growth values is equivalent to testing coefficient estimates on the interaction between same advisor and target post-IPO asset growth. Panel C of Table 10 in the Internet Appendix shows that these coefficient estimates are negative but not statistically significant for three- and five-day acquirer CARs. However, if I replace acquirer advisor fixed effects with acquirer industry fixed effects and top tier, the interaction coefficient estimate is statistically significant at the 0.10 level for three-day acquirer CARs.

  28. Complete sales data are not available for eight targets.

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Acknowledgments

Thank you for helpful comments and discussions to an anonymous reviewer, Haluk Ünal (editor), Jennifer Bethel, Utpal Bhattacharya, Donal Byard, Jay Dahya, Michael Goldstein, Andrey Golubov, Kateryna Holland, Jian Hua, Laurie Krigman, Harold Mulherin, Rajarishi Nahata, Louis Nguyen, Jérôme Taillard, Joseph Weintrop, and seminar participants at Babson College, Baruch College, Cornerstone Research New York, Elon University, the Federal Reserve Bank of Richmond, the 2016 Financial Management Association Annual Meeting, the 2016 Financial Management Association European Conference, Florida State University, Ohio University, Sacred Heart University, and the University of Minnesota Duluth. Thank you to Alua Askarbek and Jack Cahill for excellent research assistance. All remaining errors are my own.

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  1. D’Amore-McKim School of Business, Northeastern University, 413 Hayden Hall, 360 Huntington Avenue, Boston, MA, 02115, USA

    Richard Herron

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Appendices

A Appendix

1.1 A.1 Interviews with investment bankers

To provide qualitative evidence of the same-advisor information advantage, I spoke with experienced M&A investment bankers, several of whom also had IPO experience. These investment bankers all agreed that same-advisor acquisitions would have an information advantage relative to different-advisor acquisitions, but the details of their responses differed based on their seniority. Junior bankers focused on the importance of hard information. Several quickly highlighted the importance of non-disclosure agreements, which would prevent the transfer of private information (e.g., IPO valuation models and projections). However, one managing director suggested that non-disclosure agreements are heavily negotiated at IPO and often waived. One vice president offered evidence where a client waived a managing director’s previous non-disclosure agreement and used a “clean team” of analysts to execute the deal.

Senior bankers focused on the importance of soft information. The recurring theme was that IPO underwriters understand management and could better identify synergies between acquirers and targets. One managing director highlighted that IPO underwriting gave him exposure to decision-makers, as well as an understanding of “what makes them tick” and “how to approach and pitch them.” One vice president felt confident that he understood the growth sources and potential synergies for IPOs that he underwrote. He thought this would help him better pair acquirers and targets, if only by quickly ruling out bad matches.

Several bankers highlighted that Goldman Sachs organizes teams by industry and client instead of by product. Organization by clients would strengthen any information advantage because there are close connections between IPO underwriters and acquirer advisors. Consistent with this anecdote, Goldman Sachs is the lead advisor for 9.62% of all acquisitions but is the lead advisor for 24.3% of same-advisor acquisitions. However, one managing director cautioned that most investment banks have a fluid organization and a “coverage banker” to connect product teams.

My interviews with investment bankers suggest that soft information (i.e., knowledge of management, growth sources, and potential synergies) is useful and could be a source of same-advisor acquisition outperformance.

1.2 A.2 Mini-Case Study: NetApp and Data Domain

SEC filings are often vague about how acquirers choose advisors and targets, but these filings add color on same-advisor acquisitions. For example, on May 20, 2009, NetApp announced its plan to acquire Data Domain. Goldman Sachs managed Data Domain’s June 2007 IPO and advised NetApp’s acquisition attempt, which began in November 2008. The NetApp and Data Domain CEOs were familiar, but Goldman Sachs formally pitched NetApp and Data Domain on the acquisition. NetApp’s S-4 filing provides the following details.

Since 2006, Frank Slootman, President and Chief Executive Officer of Data Domain, and Daniel J. Warmenhoven, Chairman and Chief Executive Officer of NetApp, have from time to time had informal discussions regarding their respective businesses and the data storage industry in general.

Goldman Sachs & Co., or Goldman Sachs, had served as co-managing underwriters in Data Domain’s initial public offering in June 2007. In November 2008, prior to the engagement of Goldman Sachs by NetApp, a representative of Goldman Sachs arranged for a meeting between Messrs. Slootman and Warmenhoven to discuss the possibility of a business combination involving NetApp and Data Domain.

NetApp’s acquisition announcement press release emphasizes portfolio synergy between NetApp and Data Domain.

The Data Domain portfolio brings a complementary offering to NetApp, expanding NetApp’s reach in the market for heterogeneous disk-based backup. Today, NetApp’s heterogeneous backup offering (with its VTL product line) provides installations with disk-based solutions to augment their tape backup infrastructure. Data Domain’s portfolio will extend NetApp’s ability to compete in the increasing number of installations wanting to minimize their reliance on tape. The Data Domain acquisition increases NetApp’s ability to capitalize on the growth of disk-based backup adoption, a trend accelerated by the economics of deduplication.

NetApp’s three- and five-day CARs were 2.88% and 3.04%. Consistent with the conjecture of high-quality advice in same-advisor acquisitions, NetApp later lost a bidding war to EMC, who paid a 20% premium over NetApp’s bid. The proximity of this IPO and acquisition attempt suggests that non-disclosure agreements do not preclude IPO underwriters from using their information advantage in same-advisor acquisitions.

NetApp’s S-4 filing provides further evidence that non-disclosure agreements do not preclude same-advisor acquisitions.

On March 26, 2009, the Data Domain board of directors held a meeting to discuss a potential business combination with NetApp. Mr. Slootman reviewed the conversation he and Mr. Scarpelli had with Messrs. Georgens and Gomo regarding a potential proposal from NetApp to acquire Data Domain. Mr. Slootman proposed hiring Qatalyst Partners LP, or Qatalyst, as Data Domain’s financial advisor to advise the Data Domain board of directors regarding the evaluation of a potential NetApp proposal and other strategic alternatives for Data Domain. Mr. Slootman noted that Goldman Sachs had been previously engaged by NetApp to serve as its financial advisor. The Data Domain board of directors approved the engagement of Qatalyst as Data Domain’s financial advisor.

1.3 A.3 Mini-Case Study: Microchip Technology and TelCom Semiconductor

On October 27, 2000, Microchip Technology announced its plan to acquire TelCom Semiconductor with financial advice from Morgan Stanley, who also managed TelCom’s July 1995 IPO. Microchip’s S-4 filing offers the following.

On September 18, 2000, Microchip entered into an engagement letter with Morgan Stanley & Co., which had been advising Microchip regarding strategic business alliances for several months, as its financial advisor in connection with the merger.

On September 10, 2000, Morgan Stanley & Co. made a preliminary presentation to Microchip’s management regarding a potential acquisition by Microchip of TelCom.

Microchip’s press release also touted synergies.

The purchase is intended to accelerate Microchip’s efforts to offer stand-alone analog ICs and add functionality to embedded-control devices, said Steve Sanghi, president and chief executive officer of the Chandler microcontroller supplier.

“We estimate that there is approximately $1.50 of analog product embedded around each $1.00 of our microcontrollers,” said the CEO. “One-and-a-half years ago, we began exploiting this opportunity by building and attaching our stand-alone analog products to our microcontrollers. TelCom brings to us a highly synergistic portfolio of analog products, which will accelerate our ability to capture this significant revenue opportunity,” he added.

IA Internet Appendix

2.1 IA.1 Who Initiates Same-Advisor Acquisitions?

In this section, I explore who initiates same-advisor acquisitions. I repeat the propensity score matching model in Section 6.1 and limit my analysis to the closest match (with replacement) for each same-advisor acquisition. This matching process identifies 71 matched different-advisor acquisitions. I then follow Boone and Mulherin (2007) and read SEC filings to determine who initiated these 145 deals. Table 11 presents several cross tables of who initiated these same-advisor and matched different-advisor acquisitions.

Table 11 Who Initiates Same-Advisor Acquisitions? This table presents counts of who initiates acquisitions by acquirer advisor type. The sample is 74 same-advisor acquisitions and 71 propensity score matched different-advisor acquisitions. Different-advisor acquisitions are matched with replacement using the propensity score matching model from Section 6.1 and Table 5. Same advisor is one if the lead acquirer advisor is the same investment bank as the lead target IPO underwriter. Classification of who initiates acquisitions is from Security and Exchange filings
Full size table

Panel A shows that it is unclear who initiated 60 of these 145 deals from a close reading of SEC filings. Acquirers initiated another 48 acquisitions, while acquirer advisors, targets, and target advisors initiated the remaining 37 acquisitions. For acquirer-initiated acquisitions, same-advisor acquisitions dominate different-advisor acquisitions by a count of 29 to 19. Among target-initiated acquisitions, different-advisor acquisitions dominate same-advisor acquisitions by a count of 16 to 5. Fisher’s exact test rejects the null of no relation between initiator and advisor with a p-value of 0.03.

Panel B aggregates acquirer-initiated and target-initiated acquisitions. I obtain the same result. Acquirers preferentially initiate same-advisor acquisitions, and targets preferentially initiate different-advisor acquisitions. Again, Fisher’s exact test rejects the null of no relation between initiator and advisor with a p-value of 0.01. Panel C removes the unclear category and again rejects the null of no relation between initiator and advisor classification with a p-value of less than 0.01. Altogether, these results suggest that acquirers preferentially initiate same-advisor acquisitions, consistent with the conjecture that acquirers appreciate the value of the same-advisor information advantage. As well, these results reduce concerns that non-disclosure agreements either preclude same-advisor acquisitions or limit same-advisor acquisitions to a particular style of target-initiated acquisitions.

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Herron, R. How Much Does Your Banker’s Target-Specific Experience Matter? Evidence from Target IPO Underwriters that Advise Acquirers. J Financ Serv Res 61, 217–258 (2022). https://doi.org/10.1007/s10693-020-00346-5

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