Acquiring bank networks

Ross Levine, Chen Lin, Zigan Wang 26 June 2017



Mergers and acquisitions have reduced the number of US banks by 60% since the mid-1980s, spurring research into the causes and consequences of bank mergers. Researchers have examined whether bank mergers create value (James and Weir 1987, Houston and Ryngaert 1994, 1997, DeLong 2001, Houston et al. 2001), enhance operating performance (Cornett and Tehranian 1992, Boyd and Runkle 1993), reduce competition (Focarelli and Panetta 2003, Erel 2011), or satisfy the empire-building incentives of executives at the expense of shareholders (Brook et al. 1998, Bliss and Rosen 2001, Laeven and Levine 2007, Goetz et al. 2013).

What has received surprisingly little attention is how the geographic overlap of the subsidiaries and branches of two bank holding companies (BHCs) influences (1) the likelihood that they merge, and (2) post-merger value creation and synergies. This is surprising both because bank executives overseeing a merger typically advertise the synergistic opportunities created by combining the geographic networks of the BHCs involved in the merger, and because existing research offers differing perspectives on the impact of network overlap on the likelihood and effects of mergers. In terms of differing predictions, several strands of research suggest that more overlap will provide greater opportunities for a merger to lower costs through the elimination of redundant operations, and boost revenues through the exploitation of enhanced market power. On the other hand, research also suggests that more overlap limits risk diversification opportunities, boosting the cost of capital. In terms of past work, Houston and Ryngaert (1994, 1997) and DeLong (2001) examine how pre-acquisition network overlap affects stock price reactions to BHC mergers. They find that returns are positively associated with the degree of pre-deal network overlap.

Analysing the effect of geographical overlap

In a recent paper, we push this examination forward in several ways (Levine et al. 2017). First, we evaluate whether and how the degree of geographic overlap between the subsidiaries and branches of two BHCs influence the likelihood that they merge. We believe ours is the first study of this ‘extensive margin’. Second, we contribute to research concerning the ‘intensive margin’ – given that banks merge, how does the geographic overlap between the subsidiaries and branches of the acquiring and target BHCs influence the cumulative abnormal returns of the acquirer, target, and combined BHC? We not only quadruple the sample of bank acquisitions relative to past studies, we also develop and implement a new instrumental variable strategy to assess the impact of network overlap on cumulative abnormal returns. Third, we explore potential mechanisms linking pre-acquisition network overlap and post-deal stock returns, such as post-deal labour costs, interest margins, the replacement of directors and executives, and loan quality. We believe that ours is the first study of the mechanisms through which pre-acquisition networks shape post-acquisition synergies.

To conduct these examinations, we compile a comprehensive dataset on BHC mergers and acquisitions over the period from January 1986 through December 2014, the geographic location of bank subsidiaries and branches, stock prices, and other BHC and deal traits. We have data on 716 deals in which the acquiring BHC is publicly traded and 429 deals in which the target is publicly traded. We construct several measures of the degree of overlap between the networks of the acquirer and target BHCs. These overlap measures focus on the degree to which the BHCs have subsidiaries (and branches) in the same or different states prior to the acquisition. To measure the CARs of the acquiring, target, and merged BHC, we use the five-day event window around the announcement of the acquisition – that is, the window from two days before until two days after the announcement. To evaluate how the merged BHC responds to the deal in terms of other performance criteria, we examine changes in the target firm’s:

  • number of board members, executives, employees;
  • total salary expenditures;
  • insider loans;
  • net loan charge-offs; and
  • net interest margins.

Network overlap and likelihood of merging

We turn first to the question of whether more network overlap between two BHCs increases, decreases, or has no effect on the likelihood that they merge. To identify this relationship, we construct pseudo-matching deals as in Gompers et al. (2016). The goal is to form pseudo acquire-target pairs that are the same as those in the actual deal except that the pseudo-pairs have different degrees of pre-deal network overlap. We use two matching criteria. First, for each actual deal, we match the actual acquiring BHC with five pseudo-target BHCs that are closest in total assets to the actual target. We create an additional five pseudo-pairs by matching the actual target with five pseudo-acquirers that are closest in total assets to the actual acquiring BHC. Thus, for each completed deal, we create ten pseudo-deals. For these pseudo-deals, we also create network overlap measures. We then run a probit regression in which the dependent equals one for actual deals, and zero for pseudo-deals. The main explanatory variable is a measure of network overlap between the acquirer and target BHCs in the actual or pseudo deal. For the second matching criterion, we use the market-to-book ratio rather than total assets to create pseudo-pairs and repeat the analyses.

We discover that the degree of network overlap is positively associated with the likelihood of a bank merger, using either matching criterion. The estimates indicate that a one standard deviation increase in overlap is associated with an almost 9% increase in the probability of a merger.

Network overlap and cumulative abnormal returns

We next evaluate whether the degree of network overlap between merging BHCs influences the cumulative abnormal returns of the acquirer, target, and combined BHC. There are material identification concerns. For example, BHCs with weak governance systems might allow empire-building executives to acquire BHCs with geographically dispersed networks, and markets might interpret such acquisitions as a signal that the acquiring BHC is poorly governed. In this case, both the choice of acquiring a target’s network and the post-deal performance might reflect the acquiring BHC’s governance system rather than the independent effect of network overlap on post-deal performance.

To mitigate endogeneity concerns, we design an instrument variable of network overlap. We exploit two plausibly exogenous sources of variation in the likelihood that a BHC acquires a target with subsidiaries in the same states as the acquirer. The first source of variation is interstate bank deregulation, which determined whether and when BHCs headquartered in one state could establish subsidiaries in each other state. For most of the 20th century, BHCs headquartered in one state were prohibited from establishing subsidiaries (or branches) in other states. Starting in 1982, individual US states started removing these restrictions. Not only did states start the process of interstate bank deregulation in different years, they also followed very different dynamic paths, as states signed bilateral and multilateral reciprocal agreements in a fairly chaotic process over time. The Riegle-Neal Act eliminated regulatory restrictions on interstate banking in 1995. The process of interstate bank deregulation yields information on whether BHCs headquartered in two different states can establish subsidiaries in the same states and hence on the potential degree of network overlap between BHCs headquartered in those states. But, interstate bank deregulation does not distinguish among BHCs within the same state.

The second source of variation uses the geographic location of BHCs within a state to identify which BHCs in a given state are more likely to have subsidiaries in other states. In particular, the gravity model of investment predicts that the costs of acquiring and managing target BHCs increase with distance, implying that BHCs are more likely to acquire BHCs in geographically close states. By distinguishing among BHCs within a state, the gravity model provides additional information on the degree of network overlap between each BHC headquartered in a state and potential targets headquartered in other states. By integrating interstate bank deregulation with the gravity model, we create time-varying, BHC-specific instrument of the degree to which a BHC has a subsidiary network that overlaps with potential targets in other states.

We discover that greater network overlap materially boosts the cumulative abnormal returns of the acquirer, target, and merged BHC. The economic magnitudes are material – a one standard deviation increase in the overlap measures is associated with a 5% increase in acquirer cumulative abnormal return, which is large given that the mean acquirer cumulative abnormal return is -0.13%.

Network overlap, synergies, and value creation

We next examine three specific mechanisms through which network overlap might affect synergies and value creation.

  • First, if network overlap boosts cumulative abnormal returns by offering opportunities to review and replace inefficient or redundant executives and board members, then we should observe both an uptick in the rate of c-suite turnover in targets following an acquisition and an improvement in bank governance as, for example, measured by a reduction in insider lending and fewer bad loans.
  • Second, if greater network overlap offers expanded opportunities for the combined BHC to economise on labour costs, then we should observe cuts in staff and total salary expenditures.
  • Third, if greater network overlaps creates a combined bank with more market power, then we should observe an increase in net interest margins following the merger.

We examine each of these predictions by examining changes at target BHCs during the year following the acquisition.

We find that more pre-acquisition network overlap is associated with:

  • acquiring BHCs replacing a higher proportion of directors and executives at target BHCs;
  • greater cuts in the number of employees and the total salary bill at target BHCs;
  • larger reductions in insider lending and net charge-offs at targets; and
  • bigger increases in net interest margins.

These findings are consistent with the views that when merging banks overlap geographically, there are greater opportunities for the merged bank to address managerial inefficiencies, reduce workforce redundancies, and increase revenues through the exercise of greater market power.


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Levine, R, C Lin and Z Wang (2017), “Acquiring banking networks”, NBER, Working Paper 23469.



Topics:  Financial markets Financial regulation and banking

Tags:  banking, mergers, acquisitions, bank mergers, US, geography, network overlap, market power

Willis H. Booth Chair in Banking and Finance, Haas School of Business, University of California, Berkeley

Professor in Finance at Department of Finance, Chinese University of Hong Kong

Assistant Professor of Finance, University of Hong Kong