Download Are Universal Banks Better Intermediaries? ∗ Daniel Neuhann Farzad Saidi

Are Universal Banks Better Intermediaries?∗
Daniel Neuhann†
University of Pennsylvania
Farzad Saidi‡
University of Cambridge
October 30, 2014
Abstract
Are banks of wide scope better intermediaries? Using the variation in bank scope generated by the stepwise repeal of the Glass-Steagall Act in the U.S. and the subsequent
rise of universal banking, we provide evidence that economies of scope in concurrent
lending and underwriting improve the access to finance for risky ventures of publicly
traded companies. Exploiting a bank-level deregulatory shock, as well as detailed
data on bank-firm interactions, we identify increases in sales-growth, stock-return, and
option-implied volatilities for universal-bank-financed firms. These firms also exhibit
lasting increases in total factor productivity of 3 to 4%, echoed by similar findings for
increases of 6 to 7% in capital expenditure and 5 to 9% in market capitalization. Our
findings suggest that the facilitation of cross-selling of loans and non-loan products
through bank-scope deregulation may have led to an increase in the supply of credit
for firms making risky, productivity-increasing investments.
JEL classification: E20, G20, G21
Keywords: universal banking, financial deregulation, bank scope, firewalls, cross-selling
∗
We thank Hal Cole, Alexandra Effenberger, Xavier Gabaix, Itay Goldstein (discussant), Dirk Krueger,
Alessandro Lizzeri, Ulrike Malmendier, Hamid Mehran, Anthony Saunders, Philipp Schnabl, Sophie Shive
(discussant), Per Strömberg, Vikrant Vig, Alexander Wagner, Jeffrey Wurgler and Alminas Zaldokas, as well
as seminar participants at NYU Stern, NYU (Department of Economics), Federal Reserve Bank of Boston,
HEC Paris, London Business School, Cambridge Judge Business School, University of Cambridge (Faculty
of Economics), Stockholm School of Economics, Brown University, Federal Reserve Bank of New York,
University of Illinois at Urbana-Champaign, Federal Reserve Board of Governors, Federal Reserve Bank
of Philadelphia, EIEF Rome, Collegio Carlo Alberto, University of Amsterdam, University of WisconsinMadison, Brandeis University, the 14th FDIC/JFSR Annual Bank Research Conference, the 10th Annual
Cambridge-Princeton Conference, and the 6th Bocconi-CAREFIN International Banking Conference for their
comments and suggestions.
†
University of Pennsylvania, Department of Economics, 160 McNeil Building, 3718 Locust Walk, Philadelphia, PA 19104. E-mail: [email protected]
‡
University of Cambridge, Judge Business School, Trumpington Street, Cambridge CB2 1AG, United
Kingdom. E-mail: [email protected]
1
Introduction
In this paper, we exploit the stepwise repeal of the Glass-Steagall Act in the U.S. to empirically evaluate the effects of bank-scope deregulation on the performance of bank-dependent
firms. In doing so, we take a step towards measuring the value added of large universal
banks as suppliers of financing to the real economy.
The Glass-Steagall Act of 1933 imposed a strict separation between commercial banking,
such as borrowing and lending, and investment banking, such as securities underwriting. The
repeal of the Glass-Steagall Act proceeded in a sequence of distinct deregulatory events that
successively relaxed constraints on bank size and bank scope. A first step, in 1989, allowed
commercial banks to increase in size and become universal banks that were allowed to engage
in lending and underwriting, with strict firewalls in place separating the two activities.
While banks were, thus, free to diversify their business interests, they remained limited
in their ability to bring increased bank scope to bear on bank-firm relationships. These
restrictions were lifted in 1996, when regulators removed the firewalls, which previously
limited information and financial flows, between securities-underwriting and commercialbank divisions within universal banks. We argue that this move to full-fledged universal
banking transformed the nature of bank-firm relationships, and had important consequences
for real outcomes at the firm level for externally financed firms.
Why might this be the case? Many firms require a large number of differentiated financial
services over their life cycle, ranging from pure loan contracts and initial public offerings
to more complex transactions involving third-party investors. If banks and firms benefit
from repeated interaction, for example through reduced information asymmetries or broader
contracting opportunities, economies of scope across financial products are a salient feature of
financial intermediation. The advent of universal banking – and the associated opportunities
to concurrently offer multiple financial services – allows financial intermediaries and their
clients to fully realize these economies of scope. As a consequence, extant financial constraints
in the provision of external finance for firms and investment projects subject to strong moralhazard or asymmetric-information concerns may no longer bind under universal banking.
Since risky projects tend to be particularly sensitive to these concerns (see, e.g., Stiglitz and
Weiss (1981) and Greenwood, Sanchez, and Wang (2010)), we map this channel to the data
by asking whether the deregulation of universal banks led to an increase in the supply of
credit for firms making risky investments.
To delineate the effect of bank scope on firm-level outcomes, we focus on the 1996 deregulatory event, and use data on lending and underwriting relationships between banks and
publicly listed firms. This allows us to directly identify repeated interactions between banks
and firms after the expansion of bank scope and the associated opportunities for the crossselling of financial products in 1996. By cross-selling, or cross-marketing, we understand
the offer and concurrent provision of loans and non-loan products, most notably corporatesecurities underwriting, to firms by universal banks. We treat these incidences as a measure
1
Figure 1: Loan-weighted Average Six-year [t,t+5] Sales-growth Volatility associated with Loans granted to Public Firms by Commercial and Universal Banks
(1987-2005). Post-1996 loans by universal banks are split into cross-sold and non-cross-sold
loans, where cross-sold loans are defined as loans whose debtor firms also received an underwriting product from the same universal bank anytime within the last five years. Source:
own analysis based on CRSP/Compustat, DealScan loan data, and SDC underwriting data.
of the extent to which banks and firms were able to realize economies of scope across financial products through closer relationships. We compare a firm’s riskiness before and after
receiving a universal-bank loan after 1996 in order to estimate a potential treatment effect
of universal-bank cross-selling.
In Figure 1, we plot the loan-weighted average six-year sales-growth volatility of public firms in the U.S. that received loans from commercial and universal banks.1 Among
universal-bank loans, we differentiate between cross-sold and non-cross-sold ones starting in
1996, where we label loans as cross-sold when the respective debtor firms also received an
underwriting product from the same universal bank. Until 1996, commercial- and universalbank loans are associated with similar levels of firm risk, but after 1996 the firm-level risk
associated with cross-sold universal-bank loans exceeds that of all other kinds of loans.
To solidify this finding, in the empirical analysis, our identification strategy is to compare
firms with cross-sold universal-bank loans in or after 1996 to firms with universal-bank loans
that were not cross-sold. That is, the treatment group consists of firms that received at least
1
In Figure A.1 of the Appendix, we also provide this graph for a second measure of volatility, namely
stock-return volatility.
2
one loan and underwriting service concurrently from the same universal bank in or after
1996. The control group consists of firms that in or after 1996 received at least one loan
from a universal bank but trusted another bank with an underwriting mandate. To pick a
control group of firms with universal-bank loans in or after 1996 that were not cross-sold for
plausibly exogenous reasons, we limit the control group to firms that were locked into an
underwriting relationship with an investment bank just before the 1996 deregulation.
In this manner, we provide empirical evidence that the increased scope of universal banking boosted lending to riskier firms, as measured by higher sales-growth, stock-return, and
option-implied volatilities of the treatment vis-à-vis the control group. The estimated treatment effects are sizable, and their order of magnitude corresponds to within-firm increases
of at least 5% across all outcome variables (and up to 14% for sales-growth volatility). We
also show that these increases in firm-level risk were not associated with higher default risk.
We then turn to the question as to whether the realization of economies of scope through
cross-selling has not just enabled universal banks to finance riskier projects, but whether
the risk-increasing developments were accompanied by higher productivity and investment
by universal-bank-financed firms. In doing so, we aim to provide tentative evidence on
whether universal banking may be efficiency-enhancing at the firm level. Using the same
identification strategy as before, we find that treatment effects for cross-sold vs. a control
group of non-cross-sold universal-bank loans led to long-lasting within-firm increases of 3 to
4% in total factor productivity (TFP), 6 to 7% in capital expenditure, and 5 to 9% in market
capitalization. Our findings attest to a potentially efficiency-increasing effect of deregulating
bank scope: when universal banks receive the ability to realize economies of scope across
loans and underwriting services through cross-selling, then this leads to an increase in the
supply of credit for firms making risky, productivity-increasing investments.
Last, we complement our analysis based on loans issued by mature, public firms with
evidence on firms early in their life cycle. Namely, we examine whether universal banks
extended their risk-taking behavior to their role as underwriters by serving as bookrunners
for IPOs of younger and, thus, potentially riskier firms. In this setting, the focus of analysis
shifts to the bank level by comparing the age of firms in IPOs run by universal banks
compared to investment banks, whose scope of banking activities was unaffected by the
deregulation, before and after 1996. We find that, as a response to the deregulation, universal
banks took firms public that were at least 5 years younger than those serviced by investment
banks. Our evidence on IPO age supports the idea that the increase in bank scope relaxed
universal banks’ constraints that previously kept them from contracting with riskier firms.
In summary, our results establish that the financial deregulation of universal banks has
led to the financing of riskier projects with higher-return prospects. From this we infer that
increasing bank scope from pure commercial banking (i.e., lending) to combined lending and
corporate-securities underwriting has not just changed the landscape of U.S. banks, but –
through a transformation of the nature of bank-firm relationships – also left its mark on
publicly listed firms that interacted with, and borrowed from, universal banks.
3
Related Literature
Our paper is related to the literature that attempts to link credit-supply shocks to real
effects, such as investment and employment, of financially constrained firms (see, for example,
Campello, Graham, and Harvey (2010) and Chodorow-Reich (2014)), most notably in the
aftermath of financial deregulation (e.g., Bertrand, Schoar, and Thesmar (2007) using the
French Banking Act of 1985). Since the 1970s, multiple episodes of financial deregulation
have significantly changed the architecture of the U.S. banking sector. Two deregulations
have received notable attention in this literature, namely the staggered passage of intra- and
interstate-banking (i.e., branching) deregulations in the U.S. between 1970 and 1994.
A number of papers from this literature have considered the impact of branch deregulation on some of the real effects discussed in this paper, most notably risk. However, it
appears that this episode of deregulation is not associated with an increase in risk: Morgan,
Rime, and Strahan (2004) find a stabilizing effect on state-level growth, which is confirmed
by Correa and Suarez (2009) at the firm level.2 Other than risk as a relevant outcome
variable in the real economy, Amore, Schneider, and Zaldokas (2013), Chava, Oettl, Subramanian, and Subramanian (2013), and Cornaggia, Mao, Tian, and Wolfe (2013) analyze
the impact of branch deregulation and, thus, banking competition on different dimensions
of innovation. Correa and Suarez (2009) is one of the few papers scrutinizing the impact of
financial deregulation on the volatility of large, publicly listed firms in the U.S. Unlike us,
they find a stabilizing effect on firm volatility.
Furthermore, our identification strategy differs from those in the literature on branch
deregulation in important ways. To establish the causal effects of branch deregulation on
firm-level outcomes, such as innovation, identification strategies in the literature generally exploit the staggered timing of branch deregulation across states, and then distinguish between
bank-dependent and non-bank-dependent firms in treated states (see Amore, Schneider, and
Zaldokas (2013) and, using Italian data, Benfratello, Schiantarelli, and Sembenelli (2008)).
In contrast, we use data on firms’ lending relationships with universal banks in an attempt to directly identify the impact of financial deregulation on firm-level outcomes.3 Our
treatment is defined at the bank-firm level, and affects the scope of activities engaged in by
universal banks, rather than an expansion of the geographical scope, as is the case under
branch deregulation. On the other hand, intrastate- and interstate-banking deregulations
impact firms’ financing decisions through an increase in credit supply, while leaving the nature of financial intermediation unaltered (see, among others, Jayaratne and Strahan (1996)).
2
3
The finding in Kerr and Nanda (2009) as well as Kerr and Nanda (2010) that there has been large-scale
entry at the extensive margin following branch deregulation appears to be instrumental in explaining the
negative effects on the median firm volatility of publicly listed firms (unlike small entering firms) and on
state-level volatility (namely, through diversification).
While this idea is similar in spirit to that pursued by Herrera and Minetti (2007), the authors, using data
from Italy, do not make use of any regulatory quasi-experiment to identify the impact of informed lending
on firm outcomes.
4
This renders it difficult to use loan data to assign any firm-level effects to increased credit
supply due to branching. Furthermore, by presenting evidence on higher risk taking by universal banks not just in the loan market but also in the IPO market, we vary control groups
to include investment banks. In this way, we minimize the possibility that our results may
be confounded by any long-run consequences of branch deregulation in the late 1990s, as
investment banks were not affected by the latter.
Besides the literature on the real effects of financial deregulation, our paper naturally
connects with a large literature on universal and relationship banking, which is surveyed
by Drucker and Puri (2007). One strand of this literature looks at the bank-level, rather
than the firm-level, effects, such as risk indicators (Saunders, Strock, and Travlos (1990) and
Cornett, Ors, and Tehranian (2002)), of the repeal of the Glass-Steagall Act. More than that,
Ang and Richardson (1994), Kroszner and Rajan (1994), and Puri (1996) provide evidence
that, in the pre-Glass-Steagall era, investors were willing to pay higher prices for securities
underwritten by universal rather than investment banks. Also, the price differential between
universal-bank and investment-bank underwritings, both pre-Glass-Steagall and post-1989,
is found to be greater for securities with high information costs, such as non-investment-grade
securities (see, for instance, Gande, Puri, Saunders, and Walter (1997)). Our approach differs
from these papers in that we focus on the firm-level real effects of universal banking.
Very few empirical papers use the stepwise nature of the repeal of the Glass-Steagall
Act. Among those that do, Bhargava and Fraser (1998) use an event-study approach to
analyze wealth and risk effects at the bank-holding-company level around different stages
of the repeal.4 Interestingly, while the authors do find increases in risk, alongside positive
abnormal stock returns, for the early stages of the repeal, they find no such effects for the
August 1, 1996 event that we use in this paper. Furthermore, the bank-level risk measures
employed may also capture diversification effects across commercial- and investment-bank
divisions.
Another strand of the literature on universal banking focuses on concurrent lending and
underwriting.5 With the notable exception of Ljungqvist, Marston, and Wilhelm (2006),
who scrutinize the role of prior relationships on banks’ likelihood of winning underwriting
mandates, most studies document pricing effects for firms contracting with universal banks.
For example, Drucker and Puri (2005) present evidence that issuers derive benefits from
concurrent lending and underwriting. Their view is that both universal banks and investment banks compete for such deals, but through different channels: while universal banks
are more likely to offer discounted yield spreads on concurrent loans, which is also confirmed
by Calomiris and Pornrojnangkool (2009), investment banks are more likely to discount
the underwriter spread for seasoned equity offerings. Regarding the former, the authors
4
5
A similar exercise, albeit not an event study, with different time frames is conducted by Geyfman and
Yeager (2009).
Note that our paper does not focus on universal banks’ holding equity stakes in companies and their
representation on the latter’s boards (see Ferreira and Matos (2012)), as is the case under the classical
model of universal banking in Germany.
5
report that, unlike investment-grade borrowers, non-investment-grade borrowers receive significantly lower yield spreads on concurrent loans relative to matched non-concurrent loans.
Furthermore, Schenone (2004) finds significantly less IPO underpricing for firms that have
pre-IPO lending relationships with prospective underwriters (i.e., universal banks).
These papers have in common the notion that realized economies of scope, as reflected
by pre-existing lending relationships, affect universal banks’ underwriting performance (see
also Kanatas and Qi (1998) and Kanatas and Qi (2003)). We deviate from this literature
in two ways. First, we characterize economies of scope by means of firm-level real effects
rather than effects on the pricing of financial products. Our findings attest to the idea that
the realization of economies of scope across financial products through cross-selling enabled
universal banks to finance riskier projects, and that this kind of risk taking is rewarded by
higher productivity and returns. Second, using the 1996 deregulatory shock, we are among
the first to combine the two-staged structure of the repeal of the Glass-Steagall Act with
the incidence of cross-selling. This is central to the causal interpretation of the effects of
increased bank scope and cross-selling on firm outcomes. To the best of our knowledge, we
provide the most comprehensive effort to relate variation in bank scope to firm-level risk and
productivity.
Lastly, insofar as we can address policymakers’ challenges in regulating the scope of
banking, our paper is also generally related to recent work on the regulation of the financial
sector (see Opp, Opp, and Harris (2013), Harris, Opp, and Opp (2014), and Hoffmann,
Inderst, and Opp (2014)).
2
2.1
Empirical Methodology and Identification
Institutional Background
In this paper, we consider the gradual dismantling of the Glass-Steagall Act, which separated
commercial and investment banking, and the rise of universal banking, culminating in the
Gramm-Leach-Bliley Act in November 1999. Under Section 20 of the Glass-Steagall Act,
commercial banks were prohibited from engaging in any kind of underwriting or securities
business, which was subsequently entirely in the hands of investment banks and other investment houses. The Glass-Steagall Act characterized the financial-architectural landscape
in the U.S. until 1987. Starting April 30, 1987, commercial banks were allowed to open socalled Section 20 subsidiaries and generate up to 5% of gross revenues from underwriting and
dealing in certain securities, namely municipal revenue bonds, mortgage-related securities,
consumer-receivable-related securities, and commercial paper. Two years later – on January
18, 1989 – banks were allowed to engage in veritable investment-banking activities, most notably corporate debt and equity underwriting, and on September 13, 1989, the revenue limit
was raised to 10%. This gave rise to another possibility for commercial banks to become
6
universal banks, other than by opening Section 20 subsidiaries, namely by purchasing or
merging with investment banks. These measures summarize what we understand as the first
stage of the repeal of the Glass-Steagall Act, followed by seven years of no further activity.
A major expansion of universal-banking deregulation took place on August 1, 1996, when
the Federal Reserve Board eliminated firewalls within bank-holding companies, while relaxing
the revenue limit on underwriting securities from 10 to 25%. These measures enhanced the
ability of universal banks to engage in cross-selling, which was previously prohibited, or at
least severely restricted, under the Federal Reserve Act (Sections 23A and B). We argue
that a major driver of universal banks’ capacity to finance riskier firms comes from their
cross-selling and thereby realizing economies of scope across financial products. Loans are
granted upon approval by a credit committee, often on the basis of high expected depth of
cross-selling. This phenomenon has also been discussed in the academic literature: Bharath,
Dahiya, Saunders, and Srinivasan (2007) provide ample evidence of cross-selling of loans and
non-loan products (fee-generating services) such as debt and equity underwriting.6
We use transaction-level data to determine whether a bank was a universal bank at the
time of a given loan transaction. We delineate this by comparing the completion date of a
bank-scope-expanding (from commercial to investment banking) acquisition or the opening
date of the respective bank’s first Section 20 subsidiary to the transaction date.
As an example, consider the historical anatomy of J.P. Morgan. Before acquiring Bank
One on July 1, 2004, J.P. Morgan already became a universal bank by opening a Section 20
subsidiary on April 1, 1987, followed by a merger with Chase Manhattan, which had a Section
20 subsidiary since December 30, 1988 (and later merged with Chemical Bank). Similarly,
Bank One, J.P. Morgan’s acquisition target in 2004, maintained a Section 20 subsidiary
which it had opened on February 2, 1989. Thus, despite a series of mergers, J.P. Morgan
became a universal bank through opening a Section 20 subsidiary in 1987, and any loan
granted by J.P. Morgan before April 1, 1987 is labeled as a loan provided by a commercial
bank that eventually became a universal bank, but was not a universal bank at the time of
the transaction. Similarly, any loan after this date is labeled as a loan granted by a universal
bank.7 In Table 1, we provide an overview of all universal banks in our loan data.
We next face the challenge of identifying cross-sold universal-bank loans. The 1996 deregulation enabled universal banks to coordinate their offerings of loans and non-loan products.
Whereas it is still possible to observe concurrent lending and underwriting before 1996, when
6
7
Furthermore, Drucker and Puri (2005) and Yasuda (2005) examine the relationship between past lending
relationships and seasoned equity offerings and debt underwriting, respectively.
Note that we also have U.S. banks of international origin in our sample. These banks are special cases in
that, before the International Banking Act of 1978, they were not subject to the Glass-Steagall Act. As
a consequence, international banks that were active in the U.S. before 1978 and established as universal
banks outside the U.S. were allowed to continue their business model in the U.S. (as long as they would
not expand their activities further). None of the banks in our sample were subject to the International
Banking Act. For instance, Deutsche Bank became a universal bank only after acquiring Morgan Grenfall,
a London-based investment bank, in 1990. Similarly, Crédit Suisse acquired a controlling stake in the
American investment bank First Boston Corporation in December 1988.
7
Figure 2: Proportion of Cross-sold Loans granted to Public Firms by Universal
Banks (1987-2010). Source: own analysis based on DealScan loan data as well as SDC
underwriting and M&A data.
the firewalls were still in place, because firms could interact separately with a universal bank’s
commercial-bank and securities divisions, the coordinated marketing of loans and non-loan
products across the two divisions took off only in 1996. Besides increased informational exchange between commercial-bank and securities divisions, this should naturally contribute to
more frequent occurrences of concurrent lending and underwriting due to cross-selling, and
generally tighter bank-firm relationships. The role of information – the free flow of which
was brought about by the elimination of firewalls in 1996 – is paramount: the exchange
of information supports the cross-selling efforts of commercial-bank and securities divisions
within universal banks, and the very process of cross-selling generates further information
about the client through closer intermediation relationships.
While we do not observe these cross-marketing efforts directly, we can observe the increased incidence of concurrent lending and corporate-securities underwriting by universal
banks in or after 1996. In Figure 2, we plot separately two time series for the proportions
of universal-bank loans that are associated with (i) cross-sold underwriting services and (ii)
cross-sold M&A-advisory services.8 The figure shows that while firms did occasionally receive
loans and underwriting services together from universal banks before 1996, these incidences
8
We define a pair of loans and non-loan products to be cross-sold if they are issued to a firm by the same
universal bank within five years (from year t − 4 to t). To avoid double-counting, for each cross-selling
incidence, we use only the first year t in which it holds that a firm received a loan and a non-loan product
from the same universal bank anytime from t − 4 to t.
8
have become more prominent starting in 1996, with the respective proportion increasing
sharply from 38% in 1995 to 54% in 1996. From this we conclude that the 1996 deregulation
indeed boosted the cross-selling of loans and corporate-securities underwriting.
We use the cross-selling of loans and M&A-advisory services as a benchmark to demonstrate that the 1996 deregulation affected solely the cross-marketing of underwriting, rather
than any other, services. In particular, unlike corporate-securities underwriting, M&Aadvisory services were not forbidden under Glass-Steagall. What we can see from the figure
is that the cross-selling of loans and M&A-advisory services hardly responded to the 1996
deregulation. Instead, the corresponding time series displays a surge only after 1999, potentially stemming from the Gramm-Leach-Bliley Act and the subsequent merger waves among
universal banks and their consolidation.
2.2
Hypothesis Development
The goal of this paper is to empirically evaluate the impact of increases in bank scope on
real outcomes of borrower firms. As in previous theoretical work, such as Kanatas and Qi
(1998) and Kanatas and Qi (2003), we start from the premise that cross-selling represents
a positive shock to the quality of banks’ information about borrower firms. Increased scope
allows banks and firms to interact more frequently and across a wider array of financial
products through cross-selling. Our basic hypothesis is that this deepening of relationships
improved universal banks’ ability to efficiently provide external finance to firms through
reduced intermediation frictions. To derive testable implications from this channel, we use
economic theory to map increased lender informedness to observable firm outcomes. We
do not, however, take a stand on the precise nature of lender information. Indeed, we
view reductions in ex-ante asymmetric information, improvements in ex-post monitoring
efficiency, or a closer understanding of firm-level moral hazard as a-priori equally plausible
determinants of increased efficiency in financial contracting. To account for this richness,
we look for theoretical predictions that hold true across a wide variety of frameworks used
to study financial intermediation. We find that that a robust conclusion is that lender
informedness is particularly valuable for risky firms.
In a costly-state-verification framework, one of the canonical environments in which to
study financial intermediation, Greenwood, Sanchez, and Wang (2010) show that risky firms
are particularly difficult to monitor. This is the case because large spreads in potential
outcomes make concealing good outcomes very attractive to borrowers. As such, only lenders
for whom it is easy to detect borrower malfeasance, such as those that are well-informed
about their borrowers, are able to deter borrowers from misrepresenting returns. Greenwood,
Sanchez, and Wang (2010) thus show that lender informedness is a crucial determinant of
the access to external finance for risky enterprises.
In a second canonical environment, ex-ante asymmetric information, Stiglitz and Weiss
9
(1981) show that risky borrowers may be credit rationed due to two channels: the adverseselection channel and the incentive channel. In the adverse-selection channel, banks may
refuse funding to risky firms in order to screen firms that are unlikely to repay loans, even
if the risky firm is highly productive. Due to the incentive channel, this problem cannot be
solved by raising interest rates on loans. In particular, firms may react to higher interest
rates by taking on projects with lower chances of success, but higher payoffs if they succeed.
Therefore, again, increased lender informedness proves particularly valuable for risky firms.
In Neuhann and Saidi (2014), we complement this analysis by examining a third canonical
environment, namely borrower moral hazard. To delineate the role of lender informedness
in this context, we assume that lenders are ex-ante uninformed about the precise nature of
the borrower’s moral-hazard problem, but can acquire information about this problem at
a cost. We formally represent this uncertainty by assuming that the lender is uninformed
about which actions the borrower can take in a given state of the world. If the lender acquires information, he learns which actions the borrower can take, which enables him to
tailor intermediation contracts to the moral-hazard problem at hand. In the absence of this
information, the lender must design a contract that reckons with a multitude of potential
actions. Lender informedness therefore allows for more efficient financial contracting, and
reduces frictions in financial intermediation. We find that the benefits of lender informedness are especially large for risky investment projects, because imprecisely tailored financial
contracts are particularly costly when potential actions and outcomes vary substantially.
Hence, in all three frameworks, lender informedness is particularly valuable for risky
projects. We therefore hypothesize universal-bank-financed firms to exhibit higher risk after
an increase in the scope of the respective universal banks’ activities. We will show that this
holds across various risk measures, not just limited to corporate lending, but also in terms
of risk associated with younger firms taken public by universal banks.
2.3
Identifying Economies of Scope from Concurrent Lending and
Underwriting
Our identification strategy is based on a deregulatory shock in 1996 that affected the scope
of banking activities engaged in by universal banks. Most notably, the 1996 deregulatory
shock boosted cross-selling by universal banks by enabling them to freely exchange information between their commercial-bank and securities divisions, and thereby coordinate their
cross-marketing efforts. We focus on the 1996 deregulation because it represents a direct
shock to bank-firm relationships, and allowed for unconstrained, or at least less constrained,
contracting across multiple financial products.
In our analysis, we use the distinction between loans that were cross-sold and those that
were not to identify the role of economies of scope across financial products for the capacity
of universal banks to finance riskier firms. That is, we compare two groups of loans granted
10
by universal banks in or after 1996: loans to firms that received a loan and underwriting
services from the same universal bank (i.e., cross-sold loans) vs. loans to firms that received
a loan from a universal bank, but issued a corporate security through a separate bank (i.e.,
non-cross-sold loans). For the latter group to be a legitimate control group, we require
the non-incidence of cross-selling to be for exogenous reasons. We construct this control
group by looking at loans that could not be cross-sold to firms after 1996 because these
firms had already received underwriting services from an investment bank just before the
1996 deregulation and were, thus, already locked into an underwriting relationship with a
non-universal bank when the 1996 deregulation took place.
In constructing this control group, our identification argument is as follows. Prior to the
1996 deregulation, universal banks and investment banks were functionally equivalent in their
ability to offer underwriting services to firms and, therefore, competed on equal footing in the
underwriting market. In particular, given that no bank was able to offer cross-sold financial
products prior to 1996, firms did not sort into universal banking or investment banking
according to the potential value of cross-selling to the firm. Once the 1996 deregulation
occurred, however, firms that had previously built an underwriting relationship with an
investment bank were at a disadvantage in realizing economies of scope across multiple
financial products relative to firms that had initially built a relationship with a universal
bank.
The validity of our identification argument rests on two key assumptions. First, in order
for firms not to sort into universal vs. investment banking based on the value of cross-selling
to the firm, the timing of the 1996 deregulation must have been unexpected. This assumption
is affirmed by the fact that the banking industry had already proposed the elimination of
firewalls in 1991, but had been rejected by the United States House Committee on Financial
Services. Hence, it is unlikely that banks and firms were anticipating the deregulatory policy
before 1996. Second, in order for pre-existing underwriting relationships to have an impact
on future cross-selling opportunities, there must be substantial switching costs when moving
from one (type of) underwriter to another. This assumption is verified in the literature
on lock-in in underwriting relationships (see, for example, James (1992) and Ljungqvist,
Marston, and Wilhelm (2006)).
We verify the presence of lock-in effects in our sample by showing that the incidence of
pre-1996 underwriting relationships indeed had a lasting (negative) impact on the incidence
of cross-selling after 1996, giving us the required variation. In Figure 3, we plot the proportion
of cross-sold universal-bank loans anytime in or after 1996, conditional on the borrower firm
also receiving an underwriting product in or after 1996, for two groups of firms: those that
in a given pre-deregulation year received an underwriting product from an investment bank
and those that received an underwriting product from a universal bank, which can be, but
need not be, the same as the one granting the loan in or after 1996. We vary the years
prior to the deregulation during which the latter firms could be locked into an underwriting
relationship with an investment bank. The post-1996 cross-selling probabilities are similar
11
Figure 3: Determination of the Control Group. The graph plots the proportion of
cross-sold universal-bank loans in or after 1996, conditional on the borrower also receiving
an underwriting product from any bank in or after 1996, and compares firms that received
an underwriting product from an investment bank to firms that received an underwriting
product from a universal bank in year t before 1996 (varying t from 1991 to 1995). Source:
own analysis based on DealScan loan data and SDC underwriting data.
for the two groups that received an underwriting product either from an investment bank
(our potential control group) or from a universal bank in the pre-deregulation period from
1991 to 1993. This changes abruptly in 1994 and 1995, and the cross-selling probabilities for
our (actual) control group diverge sharply from those for firms that received an underwriting
product from a universal bank, implying a lasting difference in the post-1996 cross-selling
probability of 10.5 to 16.2 percentage points.
We lend further support to our identification strategy by showing that firms in the treatment and control group are comparable along numerous dimensions. Both groups received
universal-bank loans after 1996, and both also received underwriting products around the
time of their universal-bank loans. In addition, in Table 2, we provide evidence that firms
in the treatment and control group are similar along observable characteristics.
Because we define our treatment and control groups at the loan level, and some firms
received multiple universal-bank loans in or after 1996, we can compare firms in these two
groups only if we first move from the loan level to the firm level. As such, we must account
12
for the fact that some firms are part of both the treatment and the control group.9
To this end, we classify as control firms those firms with at least one year in which they
received a non-cross-sold universal-bank loan, in line with our definition of the control group,
but no cross-sold universal-bank loan.10 In reverse, this implies that all treatment firms with
cross-sold universal-bank loans either never received a non-cross-sold universal-bank loan, in
line with our definition of the control group, or, if they did, only when they also received a
cross-sold universal-bank loan in the same year.
In the first panel of Table 2, we focus on differences in our outcome variables for firmlevel risk, productivity, and investment in 1993. Treatment firms exhibit lower stock-return
volatility, but the difference, albeit statistically significant, is rather small. We also note
a higher market capitalization among treatment firms, although the difference is not statistically significant.11 Otherwise, treatment and control firms are very similar in terms of
sales-growth volatility, TFP, and capital expenditure. In the next panel, we see that the
difference in market capitalization holds qualitatively for alternative measures of firm size,
namely sales and the number of employees, but the respective differences are again insignificant. In the last panel, we also fail to find any differences in the total number of loan and
underwriting transactions until 1993.
Overall, our treatment and control firms appear to be very similar before 1994. To show
that our insights from Table 2 are not specific to the year 1993, we plot the ratio between
the mean of the respective variable for treatment firms and the mean for control firms –
i.e., a ratio of one indicates that the two means are identical – for all variables from 1989
to 1993 in Figure A.2. Except for market capitalization (and, to some degree, sales and the
number of employees), all ratios are close to unity. The dashed lines indicate the respective
ratios for market capitalization and stock-return volatility, the only significant difference in
Table 2. For stock-return volatility we note that the difference in means, which is always
statistically significant, remains quantitatively small throughout the entire period. While the
difference in market capitalization is highest in the years 1989 to 1991, it is never statistically
significant, neither then nor thereafter. Instead, the difference in TFP becomes significant
at the 6% level in 1989 and 1990, but remains quantitatively small.
9
10
11
To see why this might be the case, consider the following example. A firm received an underwriting
product from an investment bank in 1995. The firm then received another underwriting product from the
same investment bank in, say, 1998. If the firm also received a non-cross-sold loan from a universal bank in
1998, then the 1998 loan transaction becomes part of the control group. Assume next that the investment
bank was later acquired by said universal bank, from which the firm received a cross-sold universal-bank
loan in 2000. Thus, the firm’s 2000 loan transaction is part of our treatment group.
For these firms, 65% of all loans in either the treatment or the control group belong to the control group.
The difference in market capitalization would be a concern for the validity of our identification strategy
if it represented a difference in the amount of equity raised through cross-sold vis-à-vis non-cross-sold
equity underwriting. To control for this possibility, in regressions unreported in this paper, we include the
borrowing firm’s market capitalization in all specifications without market capitalization as dependent
variable, and find that our treatment effects are robust throughout. Finally, note that our cross-selling
variable includes both equity and debt underwriting, further buttressing the robustness of our results to
the concerns above.
13
These findings attest to the idea that a key factor in accounting for post-1996 differences
in cross-selling between treatment and control are to be found in the lock-in to pre-1996
underwriting relationships. Our bank-firm-level identification has key advantages over a
bank-level identification, where one would estimate the average risk associated with loans
granted by universal banks compared to pure commercial banks before and after 1996. In
particular, we do not rely on the establishment dates of universal banks – i.e., the conversion
of commercial into universal banks – as the main variation in bank scope of the lender, as
commercial banks endogenously chose to become universal banks.12 Conversely, our identification strategy based on the 1996 deregulation focuses exclusively on universal banks, so
that we do not use commercial banks that did not become universal banks prior to 1996 and,
thus, did not experience a shift in the scope of their activities in 1996 as a control group. Our
identification strategy also tackles the concern that post-1996 risk taking by universal banks
may be due to the sorting of new firms with different risk profiles seeking financing from
universal banks after the deregulation was implemented. This would render it problematic
to compare universal-bank loans before and after 1996. Instead, we focus on loans granted
by universal banks only after the deregulation.
Empirical Implementation
To test our claim that universal banks financed riskier firms, we use data on loans issued
by publicly listed firms in the DealScan database. For our analysis, we collapse our loans
sample to the firm-loan-year level, i.e., we summarize all loans of a firm in a given year.
A loan for which a universal bank was a lead arranger is labeled as cross-sold if the same
universal bank also served as a bookrunner in at least one underwriting mandate anytime
from two years before to two years after the respective loan issue (implying a five-year
circle).13 Per firm-loan year (summarizing all loans in a given year, including those given
out by banks that never become universal banks, as will be described in greater detail in
Section 2.4) we record two observations, namely one pre-loan(s)-year and one post-loan(s)year observation. This enables us to compare a firm’s riskiness before and after receiving a
(cross-sold or non-cross-sold) universal-bank loan.
For each loan granted to firm i at time t, we determine whether bank j is a universal bank.
If so, we set U Bijt equal to one. Starting in 1996, conditional on U Bijt × Af ter(1996)t = 1,
there are three possibilities: the firm did not receive any underwriting product around the
time of the loan issue, it received an underwriting product from the same universal bank as
the one granting the loan (which is when we set Cross − sellingijt = 1), or it received an
12
13
As noted by, among others, Bhargava and Fraser (1998), the initiation of universal-banking deregulation
from 1987 to 1989 was based on the Federal Reserve’s responses to specific requests from large banks
(Bankers Trust, Citicorp, and J.P. Morgan).
Note that for all estimations using universal-bank loans in or after 1996, our cross-selling definition is
always censored at the year 1996. Our results are robust to variations of this circle (in addition to those
presented in the paper), and are available upon request.
14
underwriting product from another bank (as indicated by N o CSijt = 1). Firms with crosssold universal-bank loans in or after 1996 constitute our treatment group, so we have for those
firms U Bijt × Af ter(1996)t × Cross − sellingijt = 1. For our control group, we pick firms
that entered into an underwriting relationship with an investment bank in 1994 or 1995; we
indicate these firms using the dummy variable U nderwriting(1994/95)ijt , so for our control
group we have U Bijt × Af ter(1996)t × N o CSijt × U nderwriting(1994/95)ijt = 1. Finally,
note that we define U nderwriting(1994/95)ijt only for firms without cross-sold universalbank loans – i.e., Cross − sellingijt = 0 – for which N o CSijt is not necessarily one.
As we have one pre- and one post-observation per firm-loan year, we always have multiple
observations per firm.14 This enables us to include firm fixed effects and estimate withinfirm effects of universal-bank loans on firm-level risk by estimating the following regression
specification:
outcomeijt = β1 U Bijt + β2 U Bijt × Af ter(1996)t
+β3 U Bijt × Af ter(1996)t × Cross − sellingijt
+β4 U Bijt × Af ter(1996)t × N o CSijt × U nderwriting(1994/95)ijt
+β5 U Bijt × Af ter(1996)t × N o CSijt
+β6 U Bijt × Af ter(1996)t × U nderwriting(1994/95)ijt
+β7 Xijt + ψi + µt + λj + ijt ,
(1)
where outcomeijt is the natural log of firm i’s outcome variable (e.g., sales-growth volatility)
associated with the pre-loan(s) year and post-loan(s) year in which firm i received one or
multiple loans from one or multiple banks j, U Bijt is an indicator variable for whether,
given any loan transactions in a year, at the time of any loan transaction any one of the
lead arrangers was a universal bank formed through a merger or through opening a Section
20 subsidiary, Af ter(1996)t is an indicator for whether the firm’s loan year in question
was in 1996 or later, and Cross − sellingijt is an indicator for whether any loan in year
t was associated with a cross-sold underwriting product by the same bank from t − 2 to
t + 2. Conversely, N o CSijt indicates whether a firm that received a loan in year t also
received an underwriting product from t − 2 to t + 2 which was not issued by the same
bank. U nderwriting(1994/95)ijt is an indicator for whether the firm in question did not
receive a cross-sold loan but, instead, an underwriting product from an investment bank in
1994 or 1995. Xijt denotes time-varying characteristics of the borrowing entity i and of the
originated loans j, ψi and µt are firm and year fixed effects, respectively, and λj denotes
bank fixed effects, which we include for all lead arrangers of all loans j in year t that are
or eventually become universal banks (whereas all remaining commercial banks are grouped
together). For the dependent variable and all firm-year and loan(s)-year controls, the first,
pre-loan(s)-year observation uses information from the last trading day of year t − 3, and
14
Even if a firm received only one loan throughout our sample period, through our method of generating
one pre- and one post-observation per firm-loan year, we would yield two observations for that firm.
15
the second, post-loan(s)-year observation uses information from the last trading day of year
t + 2. In case of multiple loans, loan-year(s)-level control variables reflect the respective
year average over all loans in year t. All loan(s)-year-level (indexed by j) variables and
fixed effects are equal to zero in the pre-loan(s) year. Standard errors are clustered at the
lead-arranger level for both observations of each firm’s loan year, treating each (eventual)
universal bank individually and pooling all pure commercial banks.
Note that as the omitted category consists of firm-loan years with only commercialbank (and no universal-bank) loans, and commercial banks cannot cross-sell by definition
(because they offer only loans), we have that whenever Cross−sellingijt = 1, it must be that
U Bijt × Af ter(1996)t = 1, as universal banks could actively cross-sell starting in 1996. This
is why the stand-alone variable Cross − sellingijt drops out from (1). Similarly, N o CSijt
can only be meaningfully defined if bank j could cross-sell in principle, requiring again that
U Bijt × Af ter(1996)t = 1. Conversely, all commercial-bank loans are by definition not crosssold and, as the omitted category, captured by our bank fixed effects. It is worth noting,
though, that we do not distinguish whether firms with commercial-bank loans received an
underwriting product from another bank. This is because our comparison of interest concerns
solely universal-bank loans in our treatment vs. control group.
The treatment effect – i.e., the difference between treatment and control group – is given
by the difference between β3 and the sum of β4 , β5 , and β6 .
2.4
Data Description
The focus of our analysis will be on estimating the impact of universal banking on different
firm-level outcomes, most notably risk and productivity. To this end, we use as our main
data sources Compustat accounting data, CRSP stock prices, DealScan loan data,15 and
SDC debt- and equity-underwriting data. As is customary, we drop public-service, energy,
and financial-services firms from our analysis. On the transaction level, we focus on loans
granted to public firms in the U.S. in the DealScan database since 1984, as well as on U.S.
IPOs listed in the SDC database since 1976. While for IPOs we consider the bookrunners
(i.e., lead underwriters), the loans in the DealScan database typically constitute syndicated
loans. Furthermore, we focus on the package level, comprising multiple facilities, for the
definition of loan-level variables, except for identifying lead arrangers.
When we consider cross-selling of loans and corporate-securities-underwriting services by
universal banks, we first collapse the loan level to the firm-loan-year level, i.e., we record
all loans of a firm in a given year in a single row. By merging the firm-loan-year data with
the SDC underwriting data, we can determine whether one of the loans in a given year was
accompanied by debt or equity issued through the same universal bank that provided the
loan. In general, we fix the relevant time window to five years.
15
We match DealScan with Compustat data using the link provided by Chava and Roberts (2008).
16
Outcome Variables
The most important outcome variables considered in this paper are firm-level risk measures.
We focus primarily on the six-year volatility of sales-growth rates γit of firm i in year t.16
For sales-growth volatility, we follow Davis, Haltiwanger, Jarmin, and Miranda (2007) in
constructing annual growth rates that accommodate entry and exit:
γit =
1
2
xit − xi,t−1
,
(xit + xi,t−1 )
(2)
where xit denotes sales from Compustat.
As alternative measures of firm-level risk associated with loans, we also consider (sixyear) stock-return volatilities, which are calculated using monthly CRSP data, as well as
five-year implied volatilities calculated using the volatility surface from option prices. The
latter data are obtained from Option Metrics, and are available starting in 1996.
t−7
t−2
t
(pre-loan 6-year volatility)
t+2
t+7
(post-loan 6-year volatility)
Special care needs to be taken with respect to the time horizon of the borrower firm’s sixyear sales-growth and stock-return volatilities to avoid overlapping observations for the preand post-universal-bank-loan(s)-year periods. The above figure summarizes our procedure.
Given any loan in year t, for the first, pre-loan(s)-year observation we use the six-year
volatility from t − 7 to t − 2, where t − 2 indicates the very beginning of the year t − 2 or
the last trading day of the year t − 3 (similarly, t − 7 denotes the last trading day of the
year t − 7). Accordingly, for the second, post-loan(s)-year observation we use the six-year
volatility from year t + 2 to t + 7.
Given that public firms in DealScan are typically mature, we use another outcome measure to capture firm risk earlier in the firm’s life cycle: the firm’s age at the time of its IPO.
To calculate the latter, we use the founding dates of firms with IPOs recorded in SDC until
2006, collected by Loughran and Ritter (2004).
Besides the above-mentioned risk measures, we also analyze effects on firm-level TFP, for
which we use data estimated by Imrohoroglu and Tuzel (2014), who employ the semiparametric estimation procedure by Olley and Pakes (1996), for the panel of Compustat firms.17
As alternative outcome variables, we will also use capital expenditure (from Compustat) to
show that changes in TFP translate into actual investment, as well as market capitalization
(i.e., market value of equity) from CRSP.
16
17
We use six-year volatilities to limit the number of firms dropping out of our sample due to survival reasons.
We thank the authors for sharing their data with us.
17
Summary Statistics
In Table 3, we present summary statistics of firm-specific and transaction-level variables for
all major regression samples used in the paper. In doing so, we roughly follow the chronology
of the tables: our loans sample is the foundation for Table 7 as well as for generating the
firm-loan-years sample used in Tables 4 to 6. We add observations from Compustat, not
just in terms of outcome variables but also in terms of firms that never received loans in
DealScan, in Tables 8 to 10, Finally, we use SDC IPO data for Table 11. For all samples,
we use the actual regression sample, i.e., the sample that comprises all variables used in any
of the specifications of the respective tables.
Our loans sample is based on DealScan data from 1984 to 2010. In Table 7 only, the
sample is limited to transactions with at most one universal-bank lead arranger in order to tie
the universal-bank treatment closer to individual firms.18 The respective regression sample
comprises 15,650 loans in general, and 9,090 and 8,808 observations when using six-year
\i )t,t+5 and six-year stock-return volatilities σ(returni )t,t+5 ,
sales-growth volatilities σ(sales
respectively. The sample drops to 5,147 observations (because of availability starting in
1996) when using five-year implied volatilities calculated using the volatility surface from
option prices, σ(impliedi )5y
t . Independently from the availability of these risk measures, the
“total” regression sample, including deals with more than one universal-bank lead arranger,
has 17,147 observations.
Moving to the firm-loan-years sample, we consider all loans in 1996 or later, including
those with more than one universal-bank lead arranger. The regression sample is conditioned
on the availability of the dependent variable before and after loan issuance. Here we limit
the sample to loans for which we have firm i’s six-year sales-growth volatility from t − 7 to
t − 2 for the first, pre-loan(s)-year observation, and from t + 2 to t + 7 for the second, postloan(s)-year observation. Furthermore, in the case of multiple loans per firm in consecutive
years, the pre-loan(s) year is chosen to be the last year without any loans for the respective
firm prior to the sequence of years with loans, and the post-loan(s) year is chosen to be
the last year in the sequence. This sample corresponds to the first, second, and fourth
columns of Table 4. That is, we have 2,528 observations divided by two, because we use two
observations per firm-loan-year, and summarize here only variables that are non-zero for the
second observation.
U B is an indicator variable for whether, given any loan transactions in year t, at the time
of any loan transaction any one of the lead arrangers was a universal bank formed through
a merger or through opening a Section 20 subsidiary. Cross − selling is an indicator for
whether any loan in year t was associated with a cross-sold underwriting product by the
same bank from t − 2 to t + 2, which corresponds to our treatment group. Note that
because we use underwriting data until 2012, we can – depending on the time horizon of the
dependent variable in question – detect cross-sold loans for our entire loans sample, which
18
All results in that table are robust to including the remaining loans.
18
runs until 2010. Conversely, N o CS indicates whether a firm that received a loan in year
t also received an underwriting product from t − 2 to t + 2 which was not issued by the
same bank. U nderwriting(1994/95) is an indicator for whether the firm in question did not
receive a cross-sold loan but, instead, an underwriting product from an investment bank in
1994 or 1995. The interaction between the two variables, N o CS × U nderwriting(1994/95),
marks our control group of firms that received at least one loan from a universal bank in
year t that was not cross-sold due to the firm entering into an underwriting relationship with
an investment bank just before the 1996 deregulation.
Judging from the second panel in Table 3, our sample of post-1996 firm-loan years comprises 72% universal-bank loans roughly half of which (38%) we identify as cross-sold.19 We
thus have a large group of treated firms. Regarding our control, we also have a sufficiently
high proportion of non-cross-sold universal-bank loans issued by firms that had entered into
underwriting relationships with investment banks prior to the 1996 deregulation, namely a
bit less than one-sixth of the universal-bank loans (or 11% of the total sample). In absolute
numbers, these figures correspond to 474 firm-loan years in the treatment and 113 firm-loan
years in the control group. The drop in numbers is due to the data availability for the
dependent variable, which requires six years of data starting two years before and after the
loan-issue year.
Then, in Tables 8, 9, and 10, we merge our DealScan data with Compustat data starting
in 1984, including firms that never received loans recorded in DealScan. The smaller sample
size for our TFP measure is due to data availability in our TFP-data source (Imrohoroglu
and Tuzel (2014)), which covers the period from 1984 to 2009.
Finally, our SDC IPO sample is limited to IPOs with no more than one bookrunner,
leaving us with a regression sample of 3,827 initial public offerings. This sample is conditional
on the availability of IPO age (based on Loughran and Ritter (2004)), which we use as a
measure of firm-level risk early in the life cycle.
In Table 11, we explicitly distinguish by whether a universal bank was established through
M&A or through opening a Section 20 subsidiary. As we will use the 1996 deregulation as a
shock to the scope of pre-existing universal banks, we report the number of universal banks
established prior to that date.20 Besides our SDC IPO data set, we also present the general
breakdown for DealScan. In DealScan, 6 out of 8 universal banks established through M&A
existed before August 1, 1996. In the SDC IPO data, 4 out of 7 universal banks established
through M&A existed before that date. Among Section 20 subsidiaries, 28 out of 37 and 10
out of 14 were founded before August 1, 1996 in DealScan and the IPO data, respectively.
19
20
Note that while 72% of the investigated loans (firm-loan years) in or after 1996 came from universal banks,
this proportion is not entirely made up of universal-bank loans for which Cross − selling or N o CS is
equal to one, indicating that 72% − 38% − 28% = 6% of the universal-bank loans were issued by firms
that did not receive any underwriting services from t − 2 to t + 2.
A detailed overview is provided in Table 1.
19
3
Results
We now turn to the estimation results using the loan data, and investigate whether universal
banks financed riskier firms. In doing so, we explicitly account for cross-selling and economies
of scope potentially realized by universal banks across loans and underwriting services. We
compare universal-bank loans issued after the 1996 deregulation, and build treatment and
control groups corresponding to whether universal-bank loans were cross-sold (treatment),
or whether they could not be cross-sold for plausibly exogenous reasons (control). In this
manner, we show that cross-sold loans led to substantial within-firm increases in firm-level
risk, and this finding is robust to using a wide range of risk measures (based on book and
market data alike). We then investigate whether these risk-increasing developments were
accompanied by within-firm increases in total factor productivity. Indeed, we find that firms
that received cross-sold universal-bank loans exhibit increases in TFP, lasting up to six years.
This is echoed by similar effects for the firms’ capital expenditure and market capitalization.
3.1
Impact of Universal Banking on Firm Risk
In this section, we test the impact of changes in bank scope on firm-level risk. For this
purpose, we move to our firm-loan-years sample. In Table 4, we estimate specification (1)
for the sample of post-1996 loans (so that U Bijt = U Bijt × Af ter(1996)t ) without and with
firm-year and loan(s)-year controls, and use as dependent variable the natural logarithm of
\i )6y . As we include firm fixed effects, we
the firm’s six-year sales-growth volatility σ(sales
can interpret our universal-bank coefficients as percentage changes. Note that the omitted
category corresponds to pure-commercial-bank-loan years.
Here and in the following tables, in the first panel we focus on the difference between
our two groups of universal-bank loans depending on their cross-selling status, and highlight
the estimates for our treatment and control groups. In doing so, we provide p-values from
two (two-sided) test statistics, most importantly the difference between the treatment group
(captured by U Bijt ×Af ter(1996)t ×Cross−sellingijt ) and the control group (as captured by
the sum of the coefficients on U Bijt × Af ter(1996)t × N o CSijt × U nderwriting(1994/95)ijt ,
U Bijt × Af ter(1996)t × N o CSijt , and U Bijt × Af ter(1996)t × U nderwriting(1994/95)ijt ).
For completeness, we include all estimated coefficients in the second panel.
We also test whether the sum of the coefficients on U Bijt , U Bijt × Af ter(1996)t , and
U Bijt × Af ter(1996)t × Cross − sellingijt is different from zero, i.e., whether firms with
cross-sold universal-bank loans became riskier than those with commercial-bank loans. This
is, however, not our comparison of interest, as commercial banks are systematically different from universal banks, for example, in that they have chosen not to become universal banks in the first place. In similar spirit, our statistical test focuses on the difference between treatment and control, rather than on the significance of the coefficient on
U Bijt × Af ter(1996)t × Cross − sellingijt . The latter coefficient captures the difference be20
tween cross-sold universal-bank loans and any other kind of universal-bank loan, including
those granted to firms that did not receive any underwriting product in the period around
the universal-bank loan. The specification of our treatment and control groups safeguards
that the respective firms received both at least one universal-bank loan and one underwriting
product, either from the same or another bank.
We find that cross-sold universal-bank loans (treatment) led to greater within-firm increases in sales-growth volatility, namely 13.6% and even 20.0% after including controls, than
plausibly exogenously non-cross-sold ones (control), namely 1.9% and 6.6% after including
controls. The differences are significant at the 6% and 3% level, respectively. As mentioned
above, we also test whether the sum of the coefficients on U Bijt and U Bijt ×Cross−sellingijt
is different from zero. This is generally the case (p-values are 0.08 and 0.01 after including
firm-year and loan(s)-year controls). In light of the fact that we include firm fixed effects
and, thus, estimate within-firm effects, our results stem from universal-bank-financed firms
embarking on riskier projects, rather than a reallocation of funds towards generally riskier
firms. That is, firms actually became riskier after universal-bank financing, regardless of how
risky they were at the outset, and especially so when the respective loans were cross-sold.
While comparing universal-bank loans issued only after the 1996 deregulation makes our
treatment and control groups more comparable, we may still face potential endogeneity issues
if (i) the respective firms existed before 1996 and were unable to attain loans from universal
banks before 1996, and (ii) this selection affected cross-sold and non-cross-sold loans after
1996 differentially. To alleviate this concern, in the third column of Table 4, we re-run the
regression from the second column, and limit our sample to firms that did not enter into
loan agreements with universal banks only in or after 1996.21 The resulting increase in salesgrowth volatility due to cross-sold universal-bank loans amounts to 12.9%, which is 13.7%
higher than the corresponding increase experienced by the control group. This difference is
significant at the 4% level.
Finally, we consider the possibility that cross-selling universal banks are different from
non-cross-selling universal banks along time-varying characteristics that are correlated with
borrower risk. One such explanation would be that cross-selling universal banks could finance riskier projects not because of the economies of scope in information acquisition, but
simply because of the higher revenues generated from the bank-firm relationship. Since
our treatment and control groups both comprise universal-bank loans, bank size and related
too-big-to-fail considerations are unlikely to drive our estimates. In order to account for competing explanations above and beyond bank size, such as bank-level revenue fluctuations,
including those that are potentially unrelated to the bank-firm relationship under consideration, we include bank-year fixed effects in the last column of Table 4. The estimates suggest
that the within-firm increase in risk amounts to 19.1% for treated firms and 4.9% for the
control group, with the difference being significant at the 4% level. The estimated treat21
To make the subsample of commercial-bank clients comparable, we also drop all those firms that entered
into loan agreements with commercial banks only in or after 1996.
21
ment effects, i.e., the difference between treatment and control, across the second to fourth
columns are remarkably stable around 14%. This implies that our results for cross-sold
universal-bank loans are not driven simply by universal banks’ generating higher revenues
from cross-selling.
To further buttress these results, in Table 5 we repeat the same estimations as in Table
4, and consider a firm-level risk measure that is based on market, rather than book, values,
namely the firm’s six-year stock-return volatility. From an a-priori perspective, it is unclear
whether increases in real measures of volatility should be accompanied by increases in marketbased volatility: to the extent that market participants efficiently incorporate news into
prices, stock-return volatility should move in lockstep with sales-growth volatility only insofar
as the latter measure of real volatility is associated with news-releasing events.
The results suggest that treated firms experienced significantly greater within-firm increases in their stock-return volatility, ranging from 8.1% (fourth column) to 12.1% (second
column), than the control group, namely 2.4% (third column) to 5.0% (second column). The
respective p-values generally imply significance at the 10% level after including firm-year and
loan(s)-year controls, except in the last column after including bank-year fixed effects. The
difference between treatment and control is positive throughout all four columns.
We consider one more alternative risk measure, namely option-implied volatility. Thus
far, we have used six-year sales-growth and stock-return volatilities to demonstrate the longlasting nature of the impact of universal banking on firm risk. However, this came at the
cost of requiring firms to be publicly listed for a sufficiently long time (in our previous
analysis, up to 6 + 5 + 6 = 17 years). Using option-implied volatilities relaxes this data
requirement (but, instead, imposes an option-trading requirement). In doing so, we rely on
the empirical options literature, most notably the finding that option-implied volatility does
not just subsume information from past-realized volatility, but is also forward looking in the
sense that it helps forecast future volatility (Christensen and Prabhala (1998)).
In Table 6, we use as dependent variable the natural logarithm of the five-year implied
volatility calculated using the volatility surface from option prices, σ(impliedi )5y . We use for
the first, pre-loan(s) year the implied volatility at the end of year t − 3 (or the very beginning
of year t − 2) and for the second, post-loan(s) year the implied volatility measured at the end
of year t + 2. As one can infer from the second column of Table 6, after including firm-year
and loan(s)-year controls, treated firms exhibit significantly higher within-firm increases in
their implied volatility (of 2.9%) upon receiving cross-sold universal-bank loans than our
control group (-4.0%).22 The difference between treatment and control groups is significant
throughout all four columns, suggesting treatment effects of 5.1 to 7.9%. However, we do
acknowledge that overall, the within-firm increases in option-implied volatility due to crosssold universal-bank loans, although they are significantly different from those for our control
group, are not significantly higher, and sometimes even negative, than those associated with
22
We also notice that the inclusion of firm-year and loan(s)-year controls appears to be especially crucial
for our estimates of the coefficient on U Bijt in the first compared to the second column of Table 6.
22
commercial-bank loans.
Altogether, in this section, we have used the 1996 deregulation to build a control group
of firms with universal-bank loans that were not cross-sold. We find that firms that received
cross-sold universal-bank loans experienced significantly higher within-firm increases in risk.
Our confidence in these estimates is affirmed primarily by the robustness of our findings
across three different risk measures based on sales, stock prices, and option prices.
3.2
Impact of Universal Banking on Default Risk and Loan Spreads
In this section, we scrutinize two loan-level outcome variables that are related to the riskiness
of the borrower. First, we assess whether cross-sold universal-bank loans were associated
with higher credit risk. This is to examine whether universal banks financed riskier firms,
or excessively risky ones that were on the verge of defaulting. In doing so, we also exclude
the possibility that our analysis of universal-bank loans may be systematically excluding (or
prematurely dropping) firms that did not survive 6 + 5 + 6 = 17 years into the future, which
was necessary for constructing our outcome variable in the previous section, because they
were riskier.
For this purpose, in the first two columns of Table 7, we estimate specification (1) without
and with firm-level and loan-level controls, and use as dependent variable an indicator for
whether the borrowing company went bankrupt in the ten years (our results are robust
to variations in the horizon) following the (potential) cross-selling period.23 Note that the
respective loans sample is not conditional on the availability of six-year-volatility data before
and after the cross-selling period.
As before, our focal point is the test between treatment and control groups among
universal-bank loans. As can be seen in the first two columns of Table 7, cross-sold universalbank loans did not lead to greater default risk among borrower firms, i.e., the coefficient on
U Bijt ×Af ter(1996)t ×Cross−sellingijt is not significantly different from – and, if anything,
is more negative than – the sum of the coefficients on U Bijt × Af ter(1996)t × N o CSijt ×
U nderwriting(1994/95)ijt , U Bijt × Af ter(1996)t × N o CSijt , and U Bijt × Af ter(1996)t ×
U nderwriting(1994/95)ijt . Additionally, in the second column, after including controls, we
find that cross-sold universal-bank loans were no more or less likely to be associated with
bankruptcy than commercial-bank loans.
Next, we consider the possibility that cross-selling universal banks extended loans at
favorable terms, as measured by the so-called all-in-drawn spread, which is the sum of the
23
We use the following CRSP delisting codes to identify bankruptcy: any type of liquidation (400-490); price
fell below acceptable level; insufficient capital, surplus, and/or equity; insufficient (or non-compliance
with rules of) float or assets; company request, liquidation; bankruptcy, declared insolvent; delinquent in
filing; non-payment of fees; does not meet exchange’s financial guidelines for continued listing; protection
of investors and the public interest; corporate governance violation; and delist required by Securities
Exchange Commission (SEC).
23
spread over LIBOR and any annual fees paid to the lender syndicate. We test this in the
last two columns of Table 7 with the natural logarithm of the all-in-drawn spread (in bps)
as dependent variable. In the third and fourth column, respectively, we find that cross-sold
universal-bank loans were associated with 7.8 − (6.2 + 0.6 − 5.8) = 6.8 (fourth column) to
32.2 − (12.9 + 13.3 − 6.8) = 12.8 (third column) percent lower spreads (significant at the
7% and 1% level, respectively) than the control group after the 1996 deregulation. This
shows that universal banks’ realization of economies of scope across financial products led to
benefits that were (partially) passed on to their clients. Our estimates confirm the (abovementioned) findings reported by Drucker and Puri (2005) in an empirical setting that uses
variation in the incidence of cross-selling generated by the 1996 deregulation.
3.3
Impact of Universal Banking on Productivity and Investment
Thus far, we have considered only measures related to firm-level risk as outcomes. We now
turn to the question as to whether the additional risk of universal-bank-financed firms was
rewarded by higher productivity. We find that cross-selling has enabled universal banks not
just to finance riskier projects, but that cross-sold universal-bank loans also led to lasting
increases in the borrowing firms’ TFP, capital expenditure, and market capitalization.
Our analysis proceeds much like that in the previous section. In addition, we generalize
the definition of our cross-selling variable with respect to the time lag between loans and
underwriting services. In our previous analysis, we were constrained by the data requirements of our sales-growth-volatility and stock-return-volatility measures, in that both were
calculated based on six years of data. To safeguard that the two pre- and post-loan volatility
measures do not overlap, we had to preserve equal distances between the time window for
calculating these risk measures and the actual cross-selling window. For this purpose, we
built our cross-selling variable around the loan-issue year, comprising two years before and
after. In this section, we consider outcome variables that are based on a single annual observation. As such, we do not face any trade-offs regarding the distance of the time horizon
of our outcome variables from the cross-selling window, allowing us to cover any distance
between loans and underwriting services from 0 to 5 years.
We now define a pair of loans and non-loan products to be cross-sold if they are issued
to the same firm by the same universal bank within five years (from year t − 4 to t) and,
most importantly, in any order.24 We modify our loans-related variables along the same
lines, in that U Bijt now indicates whether, given any loan transactions from (and including)
year t − 4 to (and including) year t, at the time of any loan transaction any one of the lead
24
For example, imagine a firm i interacted only with a universal bank j, and the firm received a loan in year
t − 1 and an equity-underwriting service in t, then Cross − sellingijt is zero in year t − 1, but equal to
one from years t to t + 3. Thereafter, Cross − sellingijt is zero again. Next, imagine firm i still received
a loan in t − 1, but the equity-underwriting service only in year t + 2. Then, Cross − sellingijt is zero
from years t − 1 to t + 1, but becomes equal to one from t + 2 to t + 3, and is zero thereafter.
24
arrangers was a universal bank.25
As the time window of our cross-selling variable is no longer defined solely by the firmloan year, we can also include all firm-years without any loan transactions or security issues.
The resulting sample comprises all publicly listed firms for which all our non-banking-related
variables are available. This corresponds to what we label as our “Compustat sample” in
the third panel of Table 3. We then run regression specification (1) on this sample, including
all firm-year observations from 1984, and cluster the standard errors at the firm-year level.
Note that we now also include firm-year observations for which all loans-related variables
are zero, so that firms with no loan in a given year become the omitted category.
In Table 8, we use the natural logarithm of firm-level total factor productivity (TFP) in
year t + 1 as dependent variable. We use TFP in t + 1 because our TFP measure is the result
of an estimation, conducted by Imrohoroglu and Tuzel (2014), that uses as input variables
capital and labor in t, which are potentially correlated with our right-hand-side variables.
In the first column, we find that universal-bank loans are, in general, not associated with
higher TFP, but the universal-bank-loan coefficient becomes significantly more positive after
1996. In the remaining columns, we adopt our tests for the treatment and control group,
as in the first three columns of Tables 4 to 6. Note that as we include data from before
1996, we always include U Bijt × Af ter(1996)t in our regressions, the coefficient on which
is, however, extremely close to zero, suggesting that incidences of post-1996 cross-selling
explain that coefficient away. As before, we also provide p-values from two (two-sided)
test statistics. Besides testing for the difference between the treatment and control group,
which is our primary interest, we also test whether the sum of the coefficients on U Bijt ,
U Bijt ×Af ter(1996)t , and U Bijt ×Af ter(1996)t ×Cross−sellingijt is different from zero, i.e.,
whether cross-sold universal-bank loans led to higher within-firm increases in TFP compared
to years in which firms received no loans. The respective difference is significant at the 1%
level across all outcome variables and specifications discussed hereafter.
In the second column and the third column after including firm-year and loan(s)-year
controls, we see that firms with cross-sold universal-bank loans experienced TFP increases
compared to our control group of firms with non-cross-sold universal-bank loans after 1996.
While the effect for the control group is -0.6% and -0.8%, the effect for cross-sold universalbank loans amounts to 2.3% and 2.4% in the second and third column, respectively, and the
differences are both significant at the 1% level. Note that we can preclude the possibility
that cross-selling to the treated group of borrowers leads to higher TFP simply because
firms that demand multiple financial products are more productive overall. This is due to
two reasons. First, if firms making use of multiple financial products are more productive
in a time-invariant sense, then this should be absorbed by the firm fixed effects. Second,
and more importantly, firms in both the treatment and the control group received at least
one loan and one underwriting service within five years. Hence, they do not differ in the
25
The bank fixed effects for all (eventual) universal banks, as well as the commercial-bank fixed effect (for
all commercial banks that never become universal banks pooled together), are defined accordingly.
25
number of financial products used, just in whether they received both products from the
same source.
After limiting our sample in the last column of Table 8, as we have already done for
our risk measures, to firms that had universal-bank lending relationships before and after
1996, the treatment effect – i.e., the difference between treatment and control – becomes
starker, amounting to 3.9% (significant at the 1% level). To sum up, we have presented
evidence that the risk-increasing impact of cross-sold universal-bank loans was accompanied
by within-firm increases in TFP. The difference between treatment and control group ranges
from 2.9% to 3.9%. What is more, our estimated treatment effects are relatively long-lived,
up to six years, due to the definition of the five-year cross-selling window and an additional
lag due to measurement of TFP in year t + 1.
To show that these increases in productivity also translate into increases in actual investment and higher market capitalization, we re-run the regressions from Table 8, and use as
dependent variable the natural logarithm of the firm’s capital expenditure in year t as well
as the natural logarithm of the firm’s market value of equity in year t. The results are in
Tables 9 and 10, respectively, and demonstrate that our previous findings for TFP are also
valid for these measures. After including firm-year and loan(s)-year controls, our treatment
group yields a within-firm increase in capital expenditure of 10.1%, which is 6.9% more –
and significantly so (at the 1% level) – than the increase for the control group (cf. third
column of Table 9). In the last column, the treatment effect barely changes for our limited
firm sample: the treatment group’s effect amounts to 12.4%, which is 5.7% higher than for
the control group, and this difference is again significant at the 1% level.
We yield a very similar picture with regard to changes in firms’ market capitalization, i.e.,
their market values of equity, in Table 10. Across all three specifications, we find that the
treatment group yields a significantly higher within-firm increase in market capitalization
upon receiving a cross-sold universal-bank loan than the control group, implying treatment
effects ranging from 4.6 to 9.0%. The respective differences between treatment and control
are significant at least at the 4% level. Note that these treatment effects are unlikely to be due
to equity-raising activities, which would be associated with equity-underwriting products, as
firms in both the treatment and the control group received underwriting services concurrently
with their universal-bank loans; the only difference lies in whether these products were crosssold by the same universal bank.
Finally, as we used as outcome variables TFP in year t + 1 for the above-mentioned
reasons, but capital expenditure and market capitalization in year t, we also show in Tables
B.2 and B.3 that our results for the latter two dependent variables are robust to using their
realizations in year t + 1.
To conclude, we have found that after the 1996 deregulation, cross-sold universal-bank
loans were associated with significantly higher TFP, capital expenditure, and market capitalization, as compared to a control group of firms with universal-bank loans that were not
26
cross-sold for plausibly exogenous reasons. The treatment effects for capital expenditure and
market capitalization are larger in size than those we have found for TFP, and measure up
with the previously discussed treatment effects for our risk measures. This set of results
complements our findings for firm-level risk in a meaningful way, and guides the economic
interpretation. Our evidence is consistent with universal-bank relationships resulting in firms
making risky, productivity-increasing investments, which implies that there is a real component to the increase in risk that we document in this paper. Still, this leaves open the
question of whether the increases in productivity and capital expenditure are commensurate
with the increase in risk, which would be necessary to assess efficiency gains. To the extent
that we find risk and productivity to move in the same direction, our evidence at the very
least does not contradict the possibility of firm-level efficiency gains from universal banking.
3.4
Impact of Universal Banking on IPO Age
The evidence from the loan data suggests that universal-bank-financed firms were riskier, but
the analysis is confined to publicly listed and, therefore, mature firms. We now complement
our loans-based analysis with evidence on firms earlier in their life cycle, and scrutinize
the impact of universal banking on the age of firms when they go public. In our previous
analysis, by looking at firms that were already public, we were able to explore the depth
of bank-firm relationships in the form of cross-selling by universal banks. For the IPO-level
analysis, we are confined to a bank-level identification, where we compare the average age of
IPOs with universal banks as bookrunners to the average age of IPOs with investment-bank
bookrunners before and after 1996.
In this section, we consider whether the risk-taking behavior of universal banks documented in the loan data extends to their role as underwriters, and we use as an alternative
risk measure the age of firms at the time of their IPOs, following the logic that younger firms
are typically riskier (Pastor and Veronesi (2003)). We find that after the 1996 deregulation,
universal banks took significantly younger firms public than investment banks. Looking
at the effect of universal banking on IPO age may also be a fruitful exercise in the sense
that previous research by Brown and Kapadia (2007) and Fink, Fink, Grullon, and Weston
(2010) has found that higher idiosyncratic risk in the U.S. stock market could be driven
by increased entry into the stock market of younger and riskier firms. Brown and Kapadia
(2007) hypothesize that increasing financial-market development may have been a decisive
catalyst. In order to evaluate this possibility, we use SDC IPO data in conjunction with
additional data on firm age at the time of the IPO to test whether universal banks took
younger firms public than other underwriters, most notably investment banks.
In Figure 4, we plot the market-value-weighted average age of firms at the time of their
IPOs and the proportion of IPOs accompanied by universal banks. We observe a negative
correlation that is fairly strong after 1996. Note, also, that the IPO market share of universal
banks soars around 1996 as well.
27
Figure 4: Market-value-weighted Average Age of Firm at IPO vs. Fraction of
IPOs run by Universal Banks (1976-2006). Source: own analysis based on SDC IPOs
and data based on Loughran and Ritter (2004).
Given that commercial banks that are not yet universal banks cannot be bookrunners,
our sample is limited to universal banks and investment banks. That is, investment banks
are the control group, a subset of which was eventually acquired by commercial or already
existing universal banks. In a difference-in-differences setup, we test whether following the
1996 deregulation, universal banks took younger firms public than investment banks whose
scope of banking activities was unaffected by the deregulation. For a universal bank to
be treated under the 1996 deregulation, it needs to be established before the deregulation.
Against this background, we run the following regression specification:
IP O ageijt = β1 U Bj + β2 U Bj × Est.(1996)j × Af ter(1996)t
+β3 U Bj × Est.(1996)j + β4 Af ter(1996)t
+β5 Eventually U B through M &Aj
+β6 Xijt + β7 industryi + µt + ijt ,
(3)
where IP O ageijt is firm i’s age in years at the time of the IPO, U Bj is an indicator variable for whether the bookrunner was a universal bank (formed through a merger or through
opening a Section 20 subsidiary), Af ter(1996)t is an indicator variable for whether the IPO
was on or after August 1, 1996, Est.(1996)j is an indicator variable for whether a universal
bank (through M&A or Section 20) was established prior to August 1, 1996, Eventually U B
through M &Aj is an indicator variable for whether the bookrunner, which was still an invest28
ment bank, eventually becomes a universal bank through M&A, Xijt denotes firm and IPO
characteristics, and industryi and µt are industry and IPO-year fixed effects, respectively.
Standard errors are clustered at the bookrunner level.
We use the 1996 deregulation as a shock to the scope of banking activities engaged in
by universal banks to examine whether universal banks established before that date took
younger firms public following the deregulation. In the first column of Table 11, we estimate
(3) without any firm or IPO-specific controls. The difference-in-differences estimate for
treated universal banks compared to the control group of pure investment banks, which is
captured by the coefficient on U Bj × Est.(1996)j × Af ter(1996)t , is significantly negative
(at the 1% level), reflecting 8.4 years younger and, thus, riskier IPOs.
This difference-in-differences estimate drops to 5.3 and 5.2 years, but remains statistically
significant, after including firm-level and IPO-level controls in the second and third column,
respectively. Focusing on the estimates in the third column, we make two observations.
First, universal banks established before and after August 1, 1996 took 2.8 (= the sum of
all three universal-bank coefficients) and 6.9 years younger firms public, respectively (both
effects are significant at the 1% level). Second, the difference-in-differences estimate for
treated universal banks implies 5.2 years younger firms, which corresponds to one-quarter of
a standard deviation of IPO age (cf. summary statistics in the last panel of Table 3). The
economic significance of these effects renders it likely that the deregulation of bank scope
constitutes an important channel by which financial development led to riskier IPOs.
The 1996 deregulation carries particular significance for the underwriting activities of
universal banks. Besides the increased scope for cross-selling, interaffiliate loans could be
used to cross-finance riskier investment-banking operations.26 An alternative explanation
may be that commercial banks inherited the risk-taking properties of the smaller investment
banks that they acquired or merged with. To control for this possibility, we include an
indicator for whether the bookrunner in question was an investment bank that eventually
merged with a commercial or an already existing universal bank, Eventually U B M &Aj , on
the right-hand side. However, the respective coefficient is significantly larger (at the 1% and
10% level, respectively, implying that these investment banks took older firms public) than
the coefficient on U Bj and the sum of all three coefficients for universal banks established
before 1996. Therefore, this alternative explanation seems unlikely.
In the fourth column, we delineate our treatment effects by the universal banks’ mode
of establishment, namely whether the universal bank in question was established through
M&A or through opening a Section 20 subsidiary. The difference-in-differences estimates are
both negative, but only significantly so for universal banks established through M&A. This
chimes with our findings in the loan data insofar as given that universal banks established
through M&A engage in a wider range of banking activities, they also have more possibilities
for realizing economies of scope. This, in turn, enables them to take on more risk, here in
26
The Federal Reserve Act limits such loans to any single securities affiliate to 10% of a bank’s capital.
29
the form of taking younger firms public.
In order to evaluate whether these results may be driven by any other characteristics
that differ between universal banks established through M&A and Section 20 subsidiaries,
we collected key summary statistics for the bank-holding companies in our sample a year
before to a year after becoming universal banks. As Table B.1 shows, universal banks
established through M&A are typically larger than Section 20 subsidiaries. Such mergers
constitute one-time increases in total assets, net income, cash flow (approximated by EBIT),
and the number of employees, whereas Section 20 subsidiaries grow more continuously over
time.27 Most importantly, both types of universal banks are strikingly similar in their equityto-assets and cash-to-assets ratios. That is, higher risk taking by universal banks established
through M&A cannot be readily explained by a different leverage position or excess cash.
Loan-to-assets ratios are somewhat higher for universal banks formed through Section 20
subsidiaries, as investment-banking operations are a smaller portion of their business model.
Finally, we consider another, market-structure-based competing explanation for the younger
age of firms that were taken public by universal banks. Commercial banks entering the underwriting business as newly formed universal banks naturally lack a track record for IPOs.
This may, in turn, force them to take younger firms public. That is, in an effort to build
a track record, universal banks potentially took young and particularly risky firms public –
something that incumbent investment banks would not be likely to do.
To test for this possibility, we include interactions of U B through M &Aj and U B through
Section 20j with IP O countjt , which is equal to the number of IPOs accompanied by the
respective universal banks, up to and including the IPO in question (of firm i with bookrunner
j at time t). If inexperience and lack of a track record were responsible for our findings, then
one would expect the respective interaction effects to be positive, indicating that universal
banks with an established track record of IPOs took older firms public. While the interaction
effect for Section 20 subsidiaries is positive and significant at the 8% level in the last column of
Table 11, the interaction effect for universal banks established through M&A is insignificant
and fairly close to zero. At a coefficient of 0.119 for Section 20 subsidiaries, however, it
would take at least twice as many IPOs run by Section 20 subsidiaries than the average IPO
count for that group (see summary statistics in the last panel of Table 3) to eliminate the
IPO-age effect compared to regular investment banks. Still, it remains plausible that the
age of firms taken public by universal banks is, in general, governed by the degree to which
universal banks have less experience in the IPO market than incumbent investment banks.
However, as our remaining estimates are close to those in the fourth column of Table 11, the
explanatory power of this alternative explanation for the effects of increased bank scope on
IPO age seems limited.
27
Note that we could not include universal banks for which the data do not cover all three time periods;
i.e., we had to drop universal banks that were established right when the data became available (1987) or
that were eventually acquired by other banks.
30
4
Conclusion
In this paper, we focus on a narrowly defined set of deregulatory events that expanded
the scope of banking in the U.S. to evaluate bank scope as a determinant of firm-level real
outcomes. Our empirical strategy exploits a deregulatory shock to the scope of banking
activities in 1996. We use detailed data on bank-firm interactions to identify plausibly
exogenous incidences of cross-selling following the 1996 deregulation. In this manner, we
provide evidence that the advent of universal banking improved the access to finance for
risky enterprises through economies of scope in the provision of concurrent lending and
underwriting.
Our results indicate that universal-bank-financed firms in cross-selling relationships exhibit significantly higher volatility than a control group of firms contracting with non-crossselling universal banks, but are not any more likely to default in the long run. We also find
that cross-sold universal-bank loans are associated with long-lasting increases in TFP, capital expenditure, and market capitalization. We then investigate the implications of universal
banking for the cross-section of publicly traded firms with respect to their riskiness early
in their life cycle. In particular, we show that following the 1996 deregulation, universal
banks have contributed to increased entry into the stock market of risky firms by taking
significantly younger firms public than their investment-bank competitors. Universal banks
are, thus, better intermediaries in the sense that they relax financial constraints for volatile
but productive firms.
Our paper highlights two avenues for future research. In light of recent proposals to limit
the scope of banking, we have taken a first step towards providing an empirical backdrop
against which to evaluate the set of activities banks should be allowed to engage in. Namely,
we provide evidence that there may be firm-level efficiency gains from allowing banks to
engage in concurrent lending and underwriting of corporate securities, which runs counter
to recent proposals of re-establishing the Glass-Steagall Act. These benefits would have to
be balanced against costs such as risks associated with banks becoming too big to fail and
other concerns of macroeconomic fragility.
Besides our potentially policy-relevant contribution, our findings are in accordance with
previous research on the evolution of firm-level volatility in the U.S. Based on earlier observations by Campbell, Lettau, Malkiel, and Xu (2001), Comin and Philippon (2006) document
empirically that idiosyncratic firm risk has been rising over the past thirty years. Our results
suggest that bank-scope deregulation may have contributed to this phenomenon. Therefore,
another direction for future research could be to quantify the explanatory power of increased
bank scope for the observed run-up in firm-level fluctuations.
The channels through which universal banking impacts firm risk in our paper chime
with previous evidence in the literature. First, Brown and Kapadia (2007) and Fink, Fink,
Grullon, and Weston (2010) find that higher idiosyncratic risk in the U.S. stock market is
31
associated with younger firms that went public. This is in line with our documented effect
of increased bank scope allowing universal banks to act as bookrunners for IPOs of younger
firms. Second, any attempt to explain the observed increases in firm risk must contend
with the finding of Davis, Haltiwanger, Jarmin, and Miranda (2007) that volatility has
been increasing for publicly listed but not for private firms. Our proposed explanation can
accommodate this dichotomy, because equity underwriting is a major cross-selling product,
so universal banking affects primarily firms that eventually go, or already are, public.
Placing our findings into a broader macroeconomic context, we end by emphasizing that
the strongest surge in firm risk is well known to have taken place in the 1990s (Brandt, Brav,
Graham, and Kumar (2010)), and was accompanied by a simultaneous boom in measured
TFP of public firms. While our findings suggest that bank-scope deregulation could have
been an important driver of these effects, this period also saw many other innovations in
financial markets and in the real economy. We therefore view our paper as potentially
motivating further studies evaluating the importance of universal banking as a contributing
factor in the comovement of risk and productivity among publicly listed firms in the U.S.
32
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5
Tables
Table 1: Timeline of Universal Banks
Section 20
M&A
Established before August 1, 1996
BankBoston (later acquired by Fleet)
Credit Suisse (First Boston)
Bankers Trust (later acquired by Bank of America)
Deutsche Bank USA
Bank of America
Equitable (later acquired by SunTrust)
Bank of New England (defunct since 1991)
HSBC Bank USA
Bank One (later acquired by J.P. Morgan)
Sovran Bank (later acquired
BankSouth
by NationsBank)
Barnett Bank (later acquired by NationsBank)
Travelers Group∗
Chase Manhattan (later acquired by J.P. Morgan)
Chemical Bank (later acquired by Chase Manhattan)
Citicorp∗
Dauphin Deposit Corp.
First Chicago NBD
First Union
Fleet (later acquired by Bank of America)
Huntington Bancshares
J.P. Morgan
Liberty National Bank
Marine Midland Bank (later acquired
by HSBC Bank USA)
Mellon (later acquired by BNY)
National City (later acquired by PNC)
National Westminster Bank USA (later acquired
by Fleet)
NationsBank (later acquired by Bank of America)
Norstar (later acquired by Fleet)
Norwest (later acquired by Wells Fargo)
PNC
Security Pacific Bank (later acquired
by Bank of America)
SouthTrust (later acquired
by Wachovia/First Union)
SunTrust
Established on or after August 1, 1996
BB&T
Citigroup∗
BNY
Wells Fargo
Commerce Bancshares
CoreStates/Philadelphia National Bank
(later acquired by First Union)
Crestar Bank
First Tennessee
KeyBank
U.S. Bancorp
Wachovia (first acquired by First Union
and later by Wells Fargo
∗
Citigroup emerged as a result of the merger of Travelers Group and Citicorp on October 8, 1998. Before,
Travelers Group became a universal bank by our definition through a series of mergers, most notably with
investment banks Smith Barney and Salomon Brothers, and Citicorp had registered a Section 20 subsidiary.
Given the size of this merger of equals, we do not treat either one as the surviving entity and, instead, label
Citigroup as a separate universal bank established through M&A in 1998.
37
Table 2: Summary Statistics for Treatment and Control Group in 1993
Variable
\i )1988,1993
σ(sales
σ(returni )1988,1993
TFP in 1993
Capital expenditure in 1993 $bn
Market capitalization in 1993 $bn
Sales in 1993 $bn
No. of employees in thousands in 1993
No. of loans until 1993
No. of underwriting mandates until 1993
Treatment
Mean
N
(Std. dev.)
0.15
666
(0.14)
0.38
659
(0.19)
0.66
693
(0.26)
0.14
944
(0.45)
2.09
955
(5.84)
2.16
960
(6.23)
13.39
954
(32.59)
1.12
977
(1.44)
3.12
977
(3.55)
Control
Mean
(Std. dev.)
0.17
(0.16)
0.41
(0.20)
0.70
(0.36)
0.12
(0.51)
1.63
(5.71)
1.77
(7.69)
10.24
(36.44)
1.16
(1.38)
3.15
(3.48)
N
p-value
153
0.12
150
0.05
177
0.16
270
0.61
271
0.25
272
0.38
268
0.17
282
0.68
282
0.89
\i )1988,1993 and σ(returni )1988,1993 are the six-year standard deviations of firm i’s sales growth
Notes: σ(sales
and stock return, respectively, from 1988 to 1993. In the last panel, the number of loans and underwriting
mandates is calculated as a given firm’s total number of all transactions from 1984 to 1993.
38
Table 3: Summary Statistics
Loans sample (starting in 1984)
\i )t,t+5
σ(sales
σ(returni )t,t+5
σ(impliedi )5y
t
UB through M&A ∈ {0, 1}
UB through Section 20 ∈ {0, 1}
Sales at close in 2010 $bn
No. of employees in thousands
Deal size/Assets in %
Refinancing ∈ {0, 1}
No. of lead arrangers
Bankruptcy ∈ {0, 1}
All-in-drawn spread in bps
No. of UBs M&A
No. of UBs M&A with Est.(1996) = 1
No. of UBs Section 20
No. of UBs Section 20 with Est.(1996) = 1
Firm-loan-years sample (starting in 1996)
UB ∈ {0, 1}
Cross-selling (CS) ∈ {0, 1}
No CS ∈ {0, 1}
Underwriting(1994/95) ∈ {0, 1}
No CS × Underwriting(1994/95) ∈ {0, 1}
Compustat sample (starting in 1984)
T F Pit+1
CapExti (in 2010 $bn)
M arketCapti (in 2010 $bn)
IPO sample (starting in 1976)
IPO age in years
UB through M&A ∈ {0, 1}
UB through Section 20 ∈ {0, 1}
Eventually UB M&A ∈ {0, 1}
Sales in 2010 $bn
No. of employees in thousands
Book-value leverage
Gross spread in %
IPO count if UB through M&A = 1
IPO count if UB through Section 20 = 1
No. of UBs M&A
No. of UBs M&A with Est.(1996) = 1
No. of UBs Section 20
No. of UBs Section 20 with Est.(1996) = 1
Mean
0.19
0.48
0.50
0.04
0.56
3.34
14.25
27.52
0.48
1.13
0.18
189.85
Std. dev.
0.15
0.23
0.22
0.20
0.50
13.52
56.61
49.03
0.50
0.35
0.39
138.71
Min
0.01
0.12
0.12
0
0
0.00
0.00
0.01
0
1
0
0.70
Max
1.90
3.04
1.88
1
1
485.15
2,100.00
3,960.40
1
6
1
1,490.02
Mean
0.72
0.38
0.28
0.11
0.09
Mean
0.66
0.24
2.65
Mean
14.43
0.11
0.06
0.26
0.31
1.47
0.19
7.48
85.96
23.87
Std. dev.
0.45
0.48
0.45
0.32
0.29
Std. dev.
0.35
1.35
13.67
Std. dev.
20.28
0.31
0.24
0.44
1.40
6.21
0.21
1.33
59.37
18.96
Min
0
0
0
0
0
Min
0.01
0.00
0.00
Min
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.70
1
1
Max
1
1
1
1
1
Max
15.00
59.28
780.50
Max
165.00
1.00
1.00
1.00
41.70
203.00
0.89
20.25
229
74
N
9,090
8,808
5,147
15,650
15,650
15,650
15,650
15,650
15,650
17,147
8,627
13,859
8
6
37
28
N
1,264
1,264
1,264
1,264
1,264
N
66,986
121,377
122,760
N
3,827
3,827
3,827
3,827
3,827
3,827
3,827
3,827
405
231
7
4
14
10
\i )t,t+5 and σ(returni )t,t+5 are the six-year standard deviations of firm i’s sales growth and
Notes: σ(sales
stock return, respectively, from t to t+5. σ(impliedi )5y
t is firm i’s five-year implied volatility calculated using
the volatility surface from option prices (source: Option Metrics) in t. Est.(1996) is an indicator variable for
whether the universal bank in question was established before August 1, 1996.
39
Table 4: Impact of Universal-bank Financing on Sales-growth Volatility – Firmloan-years Sample, Within-firm Effects
Treatment ([1]+[2])
Control ([1]+[3]+[4]+[5])
Test of Treatment = Control (p-value)
Test of Treatment = 0 (p-value)
[1] UB
[2] UB × Cross-selling
[3] UB × No CS × Underwriting(1994/95)
[4] UB × No CS
[5] UB × Underwriting(1994/95)
Controls
Bank FE
Bank-year FE
Firm FE
Year FE
Sample
N
13.6%
1.9%
0.06
0.08
0.072
(0.07)
0.064
(0.06)
0.092
(0.17)
-0.059
(0.08)
-0.086
(0.13)
N
Y
N
Y
Y
Post 1996
2,528
\i )6y )
ln(σ(sales
20.0%
12.9%
6.6%
-0.8%
0.03
0.04
0.01
0.14
0.124*
0.089
(0.07)
(0.09)
0.076
0.040
(0.06)
(0.07)
0.095
0.101
(0.17)
(0.20)
-0.058
-0.084
(0.07)
(0.10)
-0.095
-0.114
(0.12)
(0.15)
Y
Y
Y
Y
N
N
Y
Y
Y
Y
Post 1996 Post 1996,
rel. w. UB
before 1996
2,528
1,936
19.1%
4.9%
0.04
0.04
0.116
(0.09)
0.075
(0.07)
0.202
(0.19)
-0.075
(0.08)
-0.194
(0.13)
Y
N
Y
Y
N
Post 1996
2,528
Notes: All regressions include firm fixed effects. In general, the sample consists of two observations per year
during which a firm received at least one loan, where the loans sample consists of all completed syndicated
loans (package level) of publicly listed firms, subject to availability of the dependent variable. Furthermore,
\i )6y is the six-year standard deviation
we limit the sample to the years including and after 1996. σ(sales
of firm i’s sales growth from t − 7 to t − 2 for the first, pre-loan(s)-year observation, and from t + 2 to
t + 7 for the second, post-loan(s)-year observation. U B is an indicator variable for whether, given any loan
transactions in a year, at the time of any loan transaction any one of the lead arrangers was a universal
bank formed through a merger or through opening a Section 20 subsidiary. Af ter(1996) is an indicator for
whether the firm’s loan year in question was in 1996 or later. Cross − selling is an indicator for whether any
loan in year t was associated with a cross-sold underwriting product by the same bank from t − 2 to t + 2.
Conversely, N o CS indicates whether a firm that received a loan in year t also received an underwriting
product from t − 2 to t + 2 which was not issued by the same bank. U nderwriting(1994/95) is an indicator
for whether the firm in question did not receive a cross-sold loan but, instead, an underwriting product from
an investment bank in 1994 or 1995. The first, pre-loan(s)-year observation uses information from the last
trading day of year t−3, and the second, post-loan(s)-year observation uses information from the last trading
day of year t + 2. Control variables include the log of the firm’s sales, the log of its number of employees,
the log of the ratio of the average deal size across all loans in a given year over the firm’s assets, and the
average value of the refinancing indicator (the latter two loans-related variables are always zero for the first,
pre-loan(s)-year observation). The sample in the third column comprises only firms that did not enter into
loan agreements with universal banks only in or after 1996. Bank fixed effects are included for all lead
arrangers of all loans in a given year that are or eventually become universal banks, whereas all remaining
commercial banks are grouped together (omitted category). Public-service, energy, and financial-services
firms are dropped. Robust standard errors (clustered at the lead-arranger level for both observations of each
firm’s loan year, treating each (eventual) universal bank individually and pooling all pure commercial banks)
are in parentheses.
40
Table 5: Impact of Universal-bank Financing on Stock-return Volatility – Firmloan-years Sample, Within-firm Effects
Treatment ([1]+[2])
Control ([1]+[3]+[4]+[5])
Test of Treatment = Control (p-value)
Test of Treatment = 0 (p-value)
[1] UB
[2] UB × Cross-selling
[3] UB × No CS × Underwriting(1994/95)
[4] UB × No CS
[5] UB × Underwriting(1994/95)
Controls
Bank FE
Bank-year FE
Firm FE
Year FE
Sample
N
9.4%
3.1%
0.12
0.01
0.067*
(0.04)
0.027
(0.04)
0.120
(0.09)
-0.020
(0.04)
-0.136
(0.09)
N
Y
N
Y
Y
Post 1996
2,404
ln(σ(returni )6y )
12.1%
11.0%
5.0%
2.4%
0.07
0.03
0.00
0.01
0.089**
0.104**
(0.04)
(0.04)
0.032
0.006
(0.04)
(0.04)
0.115
0.092
(0.09)
(0.11)
-0.020
-0.050
(0.04)
(0.05)
-0.134
-0.122
(0.09)
(0.10)
Y
Y
Y
Y
N
N
Y
Y
Y
Y
Post 1996 Post 1996,
rel. w. UB
before 1996
2,404
1,846
8.1%
2.8%
0.40
0.10
0.055
(0.05)
0.026
(0.04)
0.140
(0.09)
0.015
(0.05)
-0.182**
(0.09)
Y
N
Y
Y
N
Post 1996
2,404
Notes: All regressions include firm fixed effects. In general, the sample consists of two observations per year
during which a firm received at least one loan, where the loans sample consists of all completed syndicated
loans (package level) of publicly listed firms, subject to availability of the dependent variable. Furthermore,
we limit the sample to the years including and after 1996. σ(returni )6y is the six-year standard deviation
of firm i’s stock return from t − 7 to t − 2 for the first, pre-loan(s)-year observation, and from t + 2 to
t + 7 for the second, post-loan(s)-year observation. U B is an indicator variable for whether, given any loan
transactions in a year, at the time of any loan transaction any one of the lead arrangers was a universal
bank formed through a merger or through opening a Section 20 subsidiary. Af ter(1996) is an indicator for
whether the firm’s loan year in question was in 1996 or later. Cross − selling is an indicator for whether any
loan in year t was associated with a cross-sold underwriting product by the same bank from t − 2 to t + 2.
Conversely, N o CS indicates whether a firm that received a loan in year t also received an underwriting
product from t − 2 to t + 2 which was not issued by the same bank. U nderwriting(1994/95) is an indicator
for whether the firm in question did not receive a cross-sold loan but, instead, an underwriting product from
an investment bank in 1994 or 1995. The first, pre-loan(s)-year observation uses information from the last
trading day of year t−3, and the second, post-loan(s)-year observation uses information from the last trading
day of year t + 2. Control variables include the log of the firm’s sales, the log of its number of employees,
the log of the ratio of the average deal size across all loans in a given year over the firm’s assets, and the
average value of the refinancing indicator (the latter two loans-related variables are always zero for the first,
pre-loan(s)-year observation). The sample in the third column comprises only firms that did not enter into
loan agreements with universal banks only in or after 1996. Bank fixed effects are included for all lead
arrangers of all loans in a given year that are or eventually become universal banks, whereas all remaining
commercial banks are grouped together (omitted category). Public-service, energy, and financial-services
firms are dropped. Robust standard errors (clustered at the lead-arranger level for both observations of each
firm’s loan year, treating each (eventual) universal bank individually and pooling all pure commercial banks)
are in parentheses.
41
Table 6: Impact of Universal-bank Financing on Option-implied Volatility – Firmloan-years Sample, Within-firm Effects
Treatment ([1]+[2])
Control ([1]+[3]+[4]+[5])
Test of Treatment = Control (p-value)
Test of Treatment = 0 (p-value)
[1] UB
[2] UB × Cross-selling
[3] UB × No CS × Underwriting(1994/95)
[4] UB × No CS
[5] UB × Underwriting(1994/95)
Controls
Bank FE
Bank-year FE
Firm FE
Year FE
Sample
N
-6.0%
-11.6%
0.05
0.05
-0.123***
(0.03)
0.063***
(0.02)
-0.117
(0.11)
-0.000
(0.04)
0.124
(0.12)
N
Y
N
Y
Y
Post 1996
2,614
ln(σ(impliedi )5y )
2.9%
2.1%
-4.0%
-3.0%
0.01
0.06
0.35
0.55
-0.030
-0.031
(0.03)
(0.04)
0.059***
0.052**
(0.02)
(0.02)
-0.148
-0.281**
(0.12)
(0.12)
0.002
0.021
(0.03)
(0.03)
0.136
0.261**
(0.12)
(0.12)
Y
Y
Y
Y
N
N
Y
Y
Y
Y
Post 1996 Post 1996,
rel. w. UB
before 1996
2,614
1,644
-3.2%
-11.1%
0.00
0.44
-0.114**
(0.04)
0.082***
(0.03)
-0.089
(0.11)
0.004
(0.03)
0.088
(0.11)
Y
N
Y
Y
N
Post 1996
2,614
Notes: All regressions include firm fixed effects. In general, the sample consists of two observations per year
during which a firm received at least one loan, where the loans sample consists of all completed syndicated
loans (package level) of publicly listed firms, subject to availability of the dependent variable. The sample is
limited to the years including and after 1996 due to the availability of the dependent variable. σ(impliedi )5y
is firm i’s five-year implied volatility calculated using the volatility surface from option prices (source: Option
Metrics) in t − 2 for the first, pre-loan(s)-year observation, and in t + 2 for the second, post-loan(s)-year
observation. U B is an indicator variable for whether, given any loan transactions in a year, at the time
of any loan transaction any one of the lead arrangers was a universal bank formed through a merger or
through opening a Section 20 subsidiary. Af ter(1996) is an indicator for whether the firm’s loan year in
question was in 1996 or later. Cross − selling is an indicator for whether any loan in year t was associated
with a cross-sold underwriting product by the same bank from t − 2 to t + 2. Conversely, N o CS indicates
whether a firm that received a loan in year t also received an underwriting product from t − 2 to t + 2 which
was not issued by the same bank. U nderwriting(1994/95) is an indicator for whether the firm in question
did not receive a cross-sold loan but, instead, an underwriting product from an investment bank in 1994 or
1995. The first, pre-loan(s)-year observation uses information from the last trading day of year t − 3, and
the second, post-loan(s)-year observation uses information from the last trading day of year t + 2. Control
variables include the log of the firm’s sales, the log of its number of employees, the log of the ratio of the
average deal size across all loans in a given year over the firm’s assets, and the average value of the refinancing
indicator (the latter two loans-related variables are always zero for the first, pre-loan(s)-year observation).
The sample in the third column comprises only firms that did not enter into loan agreements with universal
banks only in or after 1996. Bank fixed effects are included for all lead arrangers of all loans in a given year
that are or eventually become universal banks, whereas all remaining commercial banks are grouped together
(omitted category). Public-service, energy, and financial-services firms are dropped. Robust standard errors
(clustered at the lead-arranger level for both observations of each firm’s loan year, treating each (eventual)
universal bank individually and pooling all pure commercial banks) are in parentheses.
42
Table 7: Impact of Universal-bank Financing on Loan Characteristics – Loans
Sample
Treatment ([1]+[2]+[3])
Control ([1]+[2]+[4]+[5]+[6])
Test of Treatment = Control (p-value)
Test of Treatment = 0 (p-value)
[1] UB
[2] UB × A.(1996)
[3] UB × A.(1996) × Cross-selling
[4] UB × A.(1996) × No CS × Underwriting(1994/95)
[5] UB × A.(1996) × No CS
[6] UB × A.(1996) × Underwriting(1994/95)
Log of sales at close in 2010 $
Log of no. employees
Log of deal size/assets
Refinancing indicator
Bank FE
Industry FE
Year FE
Sample
N
Bankruptcy
-4.6%
-3.1%
-2.1%
-0.9%
0.37
0.44
0.09
0.27
-0.020
-0.020
(0.03)
(0.03)
0.092**
0.087**
(0.04)
(0.04)
-0.118*** -0.098**
(0.04)
(0.04)
-0.148*
-0.142*
(0.08)
(0.08)
-0.045
-0.036
(0.03)
(0.04)
0.100
0.102
(0.09)
(0.09)
-0.023***
(0.00)
0.000
(0.00)
-0.006*
(0.00)
0.000
(0.01)
Y
Y
Y
Y
Y
Y
All
All
8,627
8,627
ln(All-in-drawn spread)
-24.0%
-9.0%
-11.2%
-2.2%
0.01
0.07
0.00
0.09
0.070
0.016
(0.09)
(0.06)
0.012
-0.028
(0.06)
(0.04)
-0.322***
-0.078*
(0.05)
(0.05)
-0.129**
-0.062
(0.06)
(0.07)
-0.133***
-0.006
(0.04)
(0.03)
0.068
0.058
(0.06)
(0.07)
-0.199***
(0.01)
-0.083***
(0.01)
0.032*
(0.02)
0.047***
(0.01)
Y
Y
Y
Y
Y
Y
All
All
13,859
13,859
Notes: The sample consists of all completed syndicated loans (package level) of publicly listed firms, subject
to availability of the dependent variable. Furthermore, we limit the sample to loans with at most one lead
arranger that was a universal bank. The dependent variable in the first two columns is an indicator variable
for whether the borrowing company went bankrupt (according to CRSP delisting codes) in the ten years
following the cross-selling period (i.e., t + 3 to t + 12), and the dependent variable in the last two columns is
the natural logarithm of the all-in-drawn spread (in bps), which is the sum of the spread over LIBOR and any
annual fees paid to the lender syndicate. U B is an indicator variable for whether, given any loan transactions
in a year, at the time of any loan transaction any one of the lead arrangers was a universal bank formed
through a merger or through opening a Section 20 subsidiary. Af ter(1996) is an indicator for whether the
firm’s loan year in question was in 1996 or later. Cross − selling is an indicator for whether any loan in year
t was associated with a cross-sold underwriting product by the same bank from t − 2 to t + 2. Conversely,
N o CS indicates whether a firm that received a loan in year t also received an underwriting product from
t − 2 to t + 2 which was not issued by the same bank. U nderwriting(1994/95) is an indicator for whether the
firm in question did not receive a cross-sold loan but, instead, an underwriting product from an investment
bank in 1994 or 1995. Bank fixed effects are included for all lead arrangers of the respective loan that
are or eventually become universal banks, whereas all remaining commercial banks are grouped together
(omitted category). Industry fixed effects are based on two-digit SIC codes. Public-service, energy, and
financial-services firms are dropped. Robust standard errors (clustered at the lead-arranger level, treating
each (eventual) universal bank individually and pooling all pure commercial banks) are in parentheses.
43
Table 8: Impact of Universal-bank Financing on Total Factor Productivity – Compustat Sample, Long-run Within-firm Effects
Treatment ([1]+[2]+[3])
Control ([1]+[2]+[4]+[5]+[6])
Test of Treatment = Control (p-value)
Test of Treatment = 0 (p-value)
[1] UB
-0.015**
(0.01)
0.019**
(0.01)
[2] UB × A.(1996)
[3] UB × A.(1996) × Cross-selling
[4] UB × A.(1996) × No CS × Underwriting(1994/95)
[5] UB × A.(1996) × No CS
[6] UB × A.(1996) × Underwriting(1994/95)
Controls
Bank FE
Firm FE
Year FE
Sample
N
N
Y
Y
All
N
66,986
ln(T F Pit+1 )
2.3%
2.4%
-0.6%
-0.8%
0.01
0.00
0.00
0.00
-0.010
-0.010
(0.01)
(0.01)
-0.000
-0.003
(0.01)
(0.01)
0.033*** 0.037***
(0.01)
(0.01)
-0.052** -0.050**
(0.02)
(0.02)
0.021*** 0.022***
(0.01)
(0.01)
0.035*
0.033
(0.02)
(0.02)
N
Y
Y
Y
Y
Y
Y
Y
All
All
66,986
66,986
3.8%
-0.1%
0.00
0.00
-0.006
(0.01)
-0.003
(0.01)
0.047***
(0.01)
-0.081***
(0.03)
0.030***
(0.01)
0.059**
(0.02)
Y
Y
Y
Y
Rel. w. UB
before 1996
50,242
Notes: All regressions include firm fixed effects. In general, the sample consists of all available firm-year
observations from Compustat, the unit of observation is the firm-year level. T F Pit+1 is firm i’s average total
factor productivity in year t+1 from Imrohoroglu and Tuzel (2014). U B is an indicator variable for whether,
given any loan transactions from (and including) year t − 4 to (and including) year t, at the time of any
loan transaction any one of the lead arrangers was a universal bank formed through a merger or through
opening a Section 20 subsidiary. Af ter(1996) is an indicator for whether the year in question was in 1996
or later. Cross − selling is an indicator for whether any loan from year t − 4 to t was associated with a
cross-sold underwriting product by the same bank anytime from t − 4 to t. Conversely, N o CS indicates
whether a firm that received a loan in year t also received an underwriting product anytime from t − 4 to
t which was not issued by the same bank. U nderwriting(1994/95) is an indicator for whether the firm in
question did not receive a cross-sold loan but, instead, an underwriting product from an investment bank
in 1994 or 1995. Control variables are measured in year t, and include the log of the firm’s sales, the log
of its number of employees, the log of the average ratio of deal size across all loans over the firm’s assets
from t − 4 to t, and the average value of the refinancing indicator from t − 4 to t. The sample in the last
column comprises only firms that did not enter into loan agreements with universal banks only in or after
1996. Bank fixed effects are included for all lead arrangers of all loans in a given year that are or eventually
become universal banks, whereas all remaining commercial banks are grouped together (omitted category).
Public-service, energy, and financial-services firms are dropped. Robust standard errors (clustered at the
firm-year level) are in parentheses.
44
Table 9: Impact of Universal-bank Financing on Capital Expenditure – Compustat Sample, Long-run Within-firm Effects
Treatment ([1]+[2]+[3])
Control ([1]+[2]+[4]+[5]+[6])
Test of Treatment = Control (p-value)
Test of Treatment = 0 (p-value)
[1] UB
0.159***
(0.02)
0.002
(0.02)
[2] UB × A.(1996)
[3] UB × A.(1996) × Cross-selling
[4] UB × A.(1996) × No CS × Underwriting(1994/95)
[5] UB × A.(1996) × No CS
[6] UB × A.(1996) × Underwriting(1994/95)
Controls
Bank FE
Firm FE
Year FE
Sample
N
N
Y
Y
All
N
121,377
ln(CapExti )
28.4%
10.1%
18.6%
3.2%
0.00
0.00
0.00
0.00
0.189***
0.061***
(0.02)
(0.01)
-0.094***
-0.022
(0.02)
(0.02)
0.189***
0.062***
(0.02)
(0.01)
-0.024
-0.138***
(0.06)
(0.04)
0.055***
0.025**
(0.02)
(0.01)
0.060
0.106**
(0.05)
(0.04)
N
Y
Y
Y
Y
Y
Y
Y
All
All
121,377
121,377
12.4%
6.7%
0.01
0.00
0.075***
(0.02)
-0.039**
(0.02)
0.088***
(0.01)
-0.186***
(0.05)
0.035**
(0.02)
0.182***
(0.05)
Y
Y
Y
Y
Rel. w. UB
before 1996
93,513
Notes: All regressions include firm fixed effects. In general, the sample consists of all available firm-year
observations from Compustat, the unit of observation is the firm-year level. CapExti is firm i’s capital
expenditure in year t. U B is an indicator variable for whether, given any loan transactions from (and
including) year t−4 to (and including) year t, at the time of any loan transaction any one of the lead arrangers
was a universal bank formed through a merger or through opening a Section 20 subsidiary. Af ter(1996)
is an indicator for whether the year in question was in 1996 or later. Cross − selling is an indicator for
whether any loan from year t − 4 to t was associated with a cross-sold underwriting product by the same
bank anytime from t − 4 to t. Conversely, N o CS indicates whether a firm that received a loan in year
t also received an underwriting product anytime from t − 4 to t which was not issued by the same bank.
U nderwriting(1994/95) is an indicator for whether the firm in question did not receive a cross-sold loan
but, instead, an underwriting product from an investment bank in 1994 or 1995. Control variables are
measured in year t, and include the log of the firm’s sales, the log of its number of employees, the log of the
average ratio of deal size across all loans over the firm’s assets from t − 4 to t, and the average value of the
refinancing indicator from t − 4 to t. The sample in the last column comprises only firms that did not enter
into loan agreements with universal banks only in or after 1996. Bank fixed effects are included for all lead
arrangers of all loans in a given year that are or eventually become universal banks, whereas all remaining
commercial banks are grouped together (omitted category). Public-service, energy, and financial-services
firms are dropped. Robust standard errors (clustered at the firm-year level) are in parentheses.
45
Table 10: Impact of Universal-bank Financing on Market Capitalization – Compustat Sample, Long-run Within-firm Effects
Treatment ([1]+[2]+[3])
Control ([1]+[2]+[4]+[5]+[6])
Test of Treatment = Control (p-value)
Test of Treatment = 0 (p-value)
[1] UB
0.091***
(0.02)
0.053***
(0.02)
[2] UB × A.(1996)
[3] UB × A.(1996) × Cross-selling
[4] UB × A.(1996) × No CS × Underwriting(1994/95)
[5] UB × A.(1996) × No CS
[6] UB × A.(1996) × Underwriting(1994/95)
Controls
Bank FE
Firm FE
Year FE
Sample
N
N
Y
Y
All
N
122,760
ln(M arketCapti )
27.7%
16.0%
21.3%
11.4%
0.01
0.04
0.00
0.00
0.128*** 0.045***
(0.02)
(0.02)
-0.054***
-0.005
(0.02)
(0.02)
0.203*** 0.120***
(0.02)
(0.01)
0.142**
0.065
(0.06)
(0.05)
0.077*** 0.057***
(0.02)
(0.01)
-0.080
-0.048
(0.05)
(0.05)
N
Y
Y
Y
Y
Y
Y
Y
All
All
122,760
122,760
23.0%
14.0%
0.00
0.00
0.061***
(0.02)
0.005
(0.02)
0.164***
(0.02)
-0.007
(0.06)
0.095***
(0.02)
-0.014
(0.06)
Y
Y
Y
Y
Rel. w. UB
before 1996
94,706
Notes: All regressions include firm fixed effects. In general, the sample consists of all available firm-year
observations from Compustat, the unit of observation is the firm-year level. M arketCapti is firm i’s market
value of equity in year t. U B is an indicator variable for whether, given any loan transactions from (and
including) year t−4 to (and including) year t, at the time of any loan transaction any one of the lead arrangers
was a universal bank formed through a merger or through opening a Section 20 subsidiary. Af ter(1996)
is an indicator for whether the year in question was in 1996 or later. Cross − selling is an indicator for
whether any loan from year t − 4 to t was associated with a cross-sold underwriting product by the same
bank anytime from t − 4 to t. Conversely, N o CS indicates whether a firm that received a loan in year
t also received an underwriting product anytime from t − 4 to t which was not issued by the same bank.
U nderwriting(1994/95) is an indicator for whether the firm in question did not receive a cross-sold loan
but, instead, an underwriting product from an investment bank in 1994 or 1995. Control variables are
measured in year t, and include the log of the firm’s sales, the log of its number of employees, the log of the
average ratio of deal size across all loans over the firm’s assets from t − 4 to t, and the average value of the
refinancing indicator from t − 4 to t. The sample in the last column comprises only firms that did not enter
into loan agreements with universal banks only in or after 1996. Bank fixed effects are included for all lead
arrangers of all loans in a given year that are or eventually become universal banks, whereas all remaining
commercial banks are grouped together (omitted category). Public-service, energy, and financial-services
firms are dropped. Robust standard errors (clustered at the firm-year level) are in parentheses.
46
Table 11: Impact of Universal-bank Underwriting on Age of Firms at their IPOs
UB
UB × Est.(1996) × A.(1996)
UB × Est.(1996)
-3.939*
(2.34)
-8.429***
(3.03)
12.291***
(3.50)
UB M&A
UB M&A × Est.(1996) × A.(1996)
UB M&A × Est.(1996)
IPO age in years
-6.938*** -6.862***
(2.19)
(2.27)
-5.335*
-5.213*
(3.03)
(3.00)
9.372***
9.227***
(3.47)
(3.48)
-8.825***
(2.16)
-5.824*
(3.45)
11.983***
(3.61)
UB M&A × IPO count
UB Section 20
-4.215**
(1.71)
-1.052
(3.18)
2.190
(3.52)
UB Section 20 × Est.(1996) × A.(1996)
UB Section 20 × Est.(1996)
UB Section 20 × IPO count
After(1996)
Eventually UB through M&A
1.345
(1.53)
2.345*
(1.38)
0.944
(1.57)
-1.189
(0.83)
2.260***
(0.32)
2.514***
(0.50)
Y
Y
3,827
Y
Y
3,827
Log of sales in 2010 $
Log of no. employees
Book-value leverage
Gross spread in %
Industry FE
IPO-year FE
N
0.804
(1.62)
-0.919
(0.88)
2.189***
(0.31)
2.409***
(0.51)
6.473***
(1.84)
0.117
(0.27)
Y
Y
3,827
0.603
(1.63)
-0.924
(0.88)
2.176***
(0.31)
2.414***
(0.51)
6.440***
(1.85)
0.108
(0.27)
Y
Y
3,827
-9.032***
(1.97)
-7.037**
(3.20)
11.544***
(3.54)
0.017
(0.01)
-5.134***
(1.59)
-3.273
(3.26)
2.219
(3.62)
0.119*
(0.07)
0.783
(1.63)
-0.955
(0.87)
2.170***
(0.32)
2.402***
(0.52)
6.415***
(1.85)
0.080
(0.27)
Y
Y
3,827
Notes: IP O age is firm i’s age in years at the time of its IPO. The unit of observation is a firm’s IPO.
U B (through M &A or U B through Section 20) is an indicator variable for whether the bookrunner was a
universal bank (formed through a merger or through opening a Section 20 subsidiary). Af ter(1996) is an
indicator for whether the IPO date was on or after August 1, 1996. Est.(1996) indicates whether a universal
bank (through M&A or Section 20) was established prior to August 1, 1996. Eventually U B through M &A
is an indicator variable for whether the bookrunner, which was still an investment bank, eventually becomes
a universal bank through M&A. IP O count denotes the number of IPOs accompanied by universal banks,
up to and including the current IPO. Book-value leverage is winsorized at the 1st and 99th percentiles. All
firm-level explanatory variables are measured at the end of the IPO year. Industry fixed effects are based
on two-digit SIC codes. Public-service, energy, and financial-services firms are dropped. Robust standard
errors (clustered at the bookrunner level) are in parentheses.
47
Supplementary Appendix (Not for Publication)
A
Supplementary Figures
Figure A.1: Loan-weighted Average Six-year [t,t+5] Stock-return Volatility associated with Loans granted to Public Firms by Commercial and Universal Banks
(1987-2005). Post-1996 loans by universal banks are split into cross-sold and non-cross-sold
loans, where cross-sold loans are defined as loans whose debtor firms also received an underwriting product from the same universal bank anytime within the last five years. Source:
own analysis based on CRSP/Compustat, DealScan loan data, and SDC underwriting data.
48
Figure A.2: Ratios between the Mean for the Treatment Group and the Mean
for the Control Group in the Pre-deregulation Period. Variables are the same as in
Table 2.
49
B
Supplementary Tables
Table B.1: Summary Statistics for Universal Banks Established through M&A
and Section 20 Subsidiaries
Total assets in 2010 $bn
Total equity/assets in %
Cash balance/assets in %
Total loans/assets in %
Net income in 2010 $bn
EBIT in 2010 $bn
No. of employees in thousands
N
t = −1
513.7
(129.3)
7.547
(1.032)
4.944
(2.571)
65.78
(7.724)
6.675
(2.542)
10.17
(3.248)
132.2
(39.06)
M&A
t=0
1,110.2
(305.3)
6.977
(0.833)
5.177
(2.764)
51.77
(23.02)
5.227
(3.591)
8.415
(6.264)
227.9
(60.74)
5
t=1
1,101.0
(230.4)
7.959
(1.455)
4.776
(2.040)
51.86
(20.76)
12.69
(0.308)
20.15
(1.618)
228.2
(55.33)
t = −1
47.82
(33.11)
8.670
(1.994)
5.484
(1.663)
66.95
(6.654)
0.330
(0.275)
0.525
(0.458)
14.20
(9.815)
Section 20
t=0
51.54
(35.26)
8.648
(1.772)
5.768
(2.320)
67.03
(6.744)
0.333
(0.285)
0.544
(0.482)
15.44
(10.13)
30
t=1
52.33
(36.22)
8.903
(2.659)
5.338
(2.027)
67.96
(6.349)
0.350
(0.375)
0.560
(0.607)
15.34
(10.18)
Notes: This table reports means with standard deviations in parentheses, for universal banks established
through M&A in the first three columns and for Section 20 subsidiaries in the last three columns. The data
are taken from the respective banks’ call reports. t indicates the year of the respective call report, and t = 0
denotes the first call report after the bank becomes a universal bank, and t = −1 and t = 1 correspond to
the call reports one year before and after the call report used for t = 0, respectively. Cash balance is the sum
of non-interest-bearing balances and currency and coin, and interest-bearing balances in U.S. offices. EBIT
is net income before income taxes, extraordinary items, and other adjustments on a fully taxable equivalent
basis.
50
Table B.2: Impact of Universal-bank Financing on Future Capital Expenditure –
Compustat Sample, Long-run Within-firm Effects
Treatment ([1]+[2]+[3])
Control ([1]+[2]+[4]+[5]+[6])
Test of Treatment = Control (p-value)
Test of Treatment = 0 (p-value)
[1] UB
0.111***
(0.02)
-0.002
(0.02)
[2] UB × A.(1996)
[3] UB × A.(1996) × Cross-selling
[4] UB × A.(1996) × No CS × Underwriting(1994/95)
[5] UB × A.(1996) × No CS
[6] UB × A.(1996) × Underwriting(1994/95)
Controls
Bank FE
Firm FE
Year FE
Sample
N
N
Y
Y
All
N
107,572
ln(CapExt+1
)
i
22.0%
6.0%
12.7%
1.2%
0.00
0.02
0.00
0.00
0.136***
0.026*
(0.02)
(0.02)
-0.085***
-0.006
(0.02)
(0.02)
0.169***
0.040***
(0.02)
(0.01)
-0.043
-0.173***
(0.06)
(0.05)
0.035**
0.015
(0.02)
(0.01)
0.084
0.150***
(0.06)
(0.04)
N
Y
Y
Y
Y
Y
Y
Y
All
All
107,572
107,572
7.5%
4.2%
0.15
0.00
0.035**
(0.02)
-0.019
(0.02)
0.059***
(0.02)
-0.249***
(0.05)
0.037**
(0.02)
0.238***
(0.05)
Y
Y
Y
Y
Rel. w. UB
before 1996
82,353
Notes: All regressions include firm fixed effects. In general, the sample consists of all available firm-year
observations from Compustat, the unit of observation is the firm-year level. CapExt+1
is firm i’s capital
i
expenditure in year t + 1. U B is an indicator variable for whether, given any loan transactions from (and
including) year t−4 to (and including) year t, at the time of any loan transaction any one of the lead arrangers
was a universal bank formed through a merger or through opening a Section 20 subsidiary. Af ter(1996)
is an indicator for whether the year in question was in 1996 or later. Cross − selling is an indicator for
whether any loan from year t − 4 to t was associated with a cross-sold underwriting product by the same
bank anytime from t − 4 to t. Conversely, N o CS indicates whether a firm that received a loan in year
t also received an underwriting product anytime from t − 4 to t which was not issued by the same bank.
U nderwriting(1994/95) is an indicator for whether the firm in question did not receive a cross-sold loan but,
instead, an underwriting product from an investment bank in 1994 or 1995. Control variables include the log
of the firm’s sales, the log of its number of employees (both measured at the end of year t + 1), the log of the
average ratio of deal size across all loans over the firm’s assets from t − 4 to t, and the average value of the
refinancing indicator from t − 4 to t. The sample in the last column comprises only firms that did not enter
into loan agreements with universal banks only in or after 1996. Bank fixed effects are included for all lead
arrangers of all loans in a given year that are or eventually become universal banks, whereas all remaining
commercial banks are grouped together (omitted category). Public-service, energy, and financial-services
firms are dropped. Robust standard errors (clustered at the firm-year level) are in parentheses.
51
Table B.3: Impact of Universal-bank Financing on Future Market Capitalization
– Compustat Sample, Long-run Within-firm Effects
Treatment ([1]+[2]+[3])
Control ([1]+[2]+[4]+[5]+[6])
Test of Treatment = Control (p-value)
Test of Treatment = 0 (p-value)
[1] UB
0.049***
(0.02)
0.069***
(0.02)
[2] UB × A.(1996)
[3] UB × A.(1996) × Cross-selling
[4] UB × A.(1996) × No CS × Underwriting(1994/95)
[5] UB × A.(1996) × No CS
[6] UB × A.(1996) × Underwriting(1994/95)
Controls
Bank FE
Firm FE
Year FE
Sample
N
N
Y
Y
All
N
109,064
)
ln(M arketCapt+1
i
22.5%
11.3%
16.6%
15.1%
6.7%
9.4%
0.01
0.05
0.01
0.00
0.00
0.00
0.075***
-0.000
0.013
(0.02)
(0.02)
(0.02)
-0.010
0.042**
0.045**
(0.02)
(0.02)
(0.02)
0.160*** 0.071***
0.108***
(0.02)
(0.01)
(0.02)
0.107*
0.013
-0.046
(0.06)
(0.05)
(0.07)
0.045*** 0.032**
0.064***
(0.02)
(0.01)
(0.02)
-0.066
-0.020
0.018
(0.06)
(0.05)
(0.06)
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
All
All
Rel. w. UB
before 1996
109,064
109,064
83,627
Notes: All regressions include firm fixed effects. In general, the sample consists of all available firm-year
observations from Compustat, the unit of observation is the firm-year level. M arketCapt+1
is firm i’s
i
market value of equity in year t + 1. U B is an indicator variable for whether, given any loan transactions
from (and including) year t − 4 to (and including) year t, at the time of any loan transaction any one of the
lead arrangers was a universal bank formed through a merger or through opening a Section 20 subsidiary.
Af ter(1996) is an indicator for whether the year in question was in 1996 or later. Cross − selling is an
indicator for whether any loan from year t − 4 to t was associated with a cross-sold underwriting product by
the same bank anytime from t − 4 to t. Conversely, N o CS indicates whether a firm that received a loan in
year t also received an underwriting product anytime from t − 4 to t which was not issued by the same bank.
U nderwriting(1994/95) is an indicator for whether the firm in question did not receive a cross-sold loan but,
instead, an underwriting product from an investment bank in 1994 or 1995. Control variables include the log
of the firm’s sales, the log of its number of employees (both measured at the end of year t + 1), the log of the
average ratio of deal size across all loans over the firm’s assets from t − 4 to t, and the average value of the
refinancing indicator from t − 4 to t. The sample in the last column comprises only firms that did not enter
into loan agreements with universal banks only in or after 1996. Bank fixed effects are included for all lead
arrangers of all loans in a given year that are or eventually become universal banks, whereas all remaining
commercial banks are grouped together (omitted category). Public-service, energy, and financial-services
firms are dropped. Robust standard errors (clustered at the firm-year level) are in parentheses.
52