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Federal Reserve Bank of Chicago

Why Do Firms Go Public?
Evidence from the Banking Industry
Richard J. Rosen, Scott B. Smart and
Chad J. Zutter

WP 2005-17

Why Do Firms Go Public?
Evidence from the Banking Industry
November 2005
Richard J. Rosen
Federal Reserve Bank of Chicago
230 S. LaSalle St.
Chicago, IL 60604
Scott B. Smart
Kelley School of Business
Indiana University
1309 E. 10th St.
Bloomington, IN 47405-1701
Chad J. Zutter
Katz Graduate School of Business
University of Pittsburgh
Pittsburgh, PA 15260
Abstract: The lack of data on private firms has made it difficult to empirically examine
theories of why firms go public. However, both public and private banks must disclose
financial information to regulators. We exploit this requirement to explore the goingpublic decision. Our results indicate that banks that convert to public ownership are more
likely to become targets than control banks that remain private. Banks that go public are
also more likely to become acquirers than control banks. IPO banks grow faster than
control banks after going public, although there is some evidence that their performance
deteriorates.
JEL classifications: G32, G21, G34
The authors wish to thank Andy Meyer and Greg Sierra of the Federal Reserve Bank of
St. Louis, as well as Mark Vaughan of the Federal Reserve Bank of Richmond, for
assistance in assembling the data for this paper. We also thank seminar participants at
Indiana University and the Federal Reserve Bank of Chicago. The opinions expressed in
this paper are those of the authors and do not necessarily reflect the opinions of the
Federal Reserve Bank of Chicago or the Federal Reserve System.

Why Do Firms Go Public? Evidence from the Banking Industry
1. Introduction
Why some firms go public and others remain private remains something of a
mystery. Though textbooks describe the conversion from private to public ownership as
an inevitable consequence of (or necessity for) growth, IPO firms display substantial
cross-sectional variation in terms of size, age, profitability, and numerous other
characteristics.1 Though private firms are undoubtedly smaller on average than their
public counterparts, examples of very large private companies abound. The familyowned SC Johnson, for example, estimates 2004 sales in excess of $5.5 billion. SC
Johnson competes with public rivals such as Procter & Gamble and Unilever in more
than 110 countries.2
In the last ten years, a flurry of theoretical papers explore the IPO decision and
produce many interesting hypotheses, however few of these predictions have been tested.
The paucity of empirical evidence, especially for IPOs in the U.S., is primarily due to the
difficulty of obtaining data on private firms. Without data on the ex ante and ex post
characteristics of both public and private firms, drawing conclusions about the factors
that influence the going-public decision is treacherous. In this paper, we take advantage
of the requirement that both public and private depository institutions must disclose
financial data to regulators to learn about the causes and consequences of going public.

1

Ritter and Welch (2002) describe the primary motivation for most IPOs as, “the desire to raise equity
capital for the firm and to create a public market in which the founders and other shareholders can convert
some of their wealth into cash at a future date.” Kim and Weisbach (2005) study almost 17,000 IPOs from
38 countries and find that 79% of IPO firms raise capital by selling primary shares.
2
www.scjohnson.com

2

We use a sample of banking organizations to examine the decision to go public.
With this sample, we are able to compare firms that did IPOs to otherwise similar firms
that did not. Because all banks must file financial reports, the available data on private
banks is not skewed toward large private banks, unusually profitable banks, distressed
banks, or any other attribute. The recent history of banking industry also makes it an
attractive laboratory to examine IPOs. The active corporate control market in banking
makes it possible for us to test theories of IPOs that view control issues as central to the
decision to go public. Related to this, many smaller banks – such as the ones that
typically do an IPO – believe they must grow to survive, given the consolidation and the
economies of scale in the industry.3
We use the data on private and public banks to create a matched sample of banks
that go public and otherwise similar banks that stay private. We match on asset size,
geographic location, and organizational structure since these are all factors that can affect
strategy and returns. Using this sample we are able to provide support for several
theories of why firms go public.
First, banks choosing to go public differ from matched institutions that remain
private on several dimensions. Prior to the IPO, banks that ultimately go public grow
faster, earn higher profits, employ more leverage, and invest more of their assets in loans.
Together, the results suggest that banks going public are riskier than those that remain
private. These findings do not appear to be driven by banks going public to meet
minimum regulatory capital standards.

3

See Gorton, Kahl, and Rosen (2005) on the need to grow to survive and Saunders and Cornett (2003) on
economies of scale in banking.

3

Second, we find that banks that have recently gone public are more likely to be
acquired than otherwise similar private banks. This finding emerges from a multivariate
logit model with controls for bank size, profitability, and leverage, and the result is robust
to changes in our control sample. These results are consistent with the models in
Zingales (1995) and Mello and Parsons (1998) which view the IPO as the first stage in a
subsequent sale of the firm.
Third, banks going public are also more likely to become acquirers than their
peers who remain private, again controlling for factors such as the size, age, and
profitability of the bank. This suggests that the motivation for going public may vary
dramatically across institutions, with some banks viewing the IPO as the first step in an
exit strategy and others using the capital raised to acquire additional assets.
Fourth, IPO banks exhibit weakly deteriorating performance after going public as
measured by either return on equity, return on assets, or the ratio of chargeoffs to total
loans. This is consistent with Hogue and Loughran’s (1999) growth fixation hypothesis.
However, we find no evidence that, after going public, IPO banks differ from their
privately held counterparts in terms of asset growth.
Fifth, we find some evidence that banks go public following a period of high
stock market returns (broadly and for bank stocks particularly), though this result is
sensitive to the index used to measure market returns and the pre-IPO event window over
which returns are calculated.
In the next section we provide some context for these results by reviewing the
theoretical and empirical literature on the decision to convert to public ownership. In the

4

following section, we describe our data sources and the matching process we use to pair
public and private banks. The last two sections of the paper present our results and
conclusions.

2. Why Do Firms Go Public?
In this section, we discuss the theoretical and empirical IPO literature with a focus on
whether we can use bank IPOs to shed any additional light on the theories of the decision
to take a firm public.4
2.1. Theory
Any theory of the decision to go public must describe the costs and benefits of
public versus private ownership that confront private firms. A simple observation that
conducting an IPO involves significant fixed costs, as documented in Ritter (1987), leads
to the prediction that bigger firms or firms seeking larger capital infusions will go public.
A recent and growing body of literature models a wide range of costs and benefits that
influence the IPO decision. One of the earliest papers to examine this question is
Zingales (1995). In Zingales’ model, an original owner sells shares in a competitive
market to dispersed shareholders, thereby capturing the surplus associated with an
increase in the value of cash flow rights associated with a future change in control. The
owner retains enough shares to retain voting control which subsequently allows the
4

Some studies in corporate finance drop banks from their samples. This is appropriate if the technique
involves looking across industries, since banks have different capital structures than other firms. However,
the theories that we test do not put restrictions on the capital structure of a firm. Moreover, regulation does
not play much of a role in the day-to-day operations of most adequately-capitalized banks. Bank regulators
are primarily concerned with the safety and soundness of banks, and focus much of their attention on

5

owner to extract some of the eventual buyer’s private benefits. Thus, the IPO serves as a
precursor to the firm being acquired.
Mello and Parsons (1998) reach a similar conclusion with a different setup. Their
model argues that a firm’s owner receives valuable information from dispersed investors
in the IPO, and that this information increases the value that the owner can obtain from
the subsequent sale of a controlling block. Whereas in Zingales the optimality of an
initial IPO is conditional on a subsequent buyer’s ability to increase the firm’s cash
flows, the IPO is always best in Mello and Parsons because going public reveals
information that determines whether a sale to a new owner increases firm value and that
allows the original owner to extract a larger fraction of the surplus.5
Banking provides a good opportunity to test theories that center on acquisition
activity. Our sample period, 1981-2002, is a period of rapid consolidation in banking.
Over the period, the number of banks fell by almost half, largely via bank mergers. This
means that merger strategy is likely to be an important factor when considering other
strategic opportunities such as an IPO. Note that there is an extra step in the merger
approval process in banking relative to unregulated industries. Bank mergers must be
approved by bank regulators and the antitrust regulators at the US Justice Department.
For all but the weakest institutions, bank regulators focus on the same antitrust guidelines
as the Justice Department. The extra hurdle, however, may be one reason that hostile
takeovers are rare in banking. To the extent that this affects the decision to go public, it
under-capitalized banks. Most banks that go public are adequately capitalized, and our results are robust to
the exclusion of under-capitalized banks.
5
Stoughton and Zechner (1998) argue that selling to a large blockholder before the IPO maximizes firm
value because of the externality that a large blockholder provides through monitoring. Brennan and Franks

6

gives bank managers less cause to avoid an IPO since they would have more control over
their private benefits than managers of other firms. Thus, it should be easier to pick up
IPOs intended to pave the way for a bank to be acquired.
Two papers that emphasize other informational effects on IPO decisions are
Chemmanur and Fulghiere (1999) and Subrahmanyam and Titman (1999). A significant
cost of public ownership in Chemmanur and Fulghiere’s model arises from small
investors’ (duplicative) costs of learning about a firm, which the firm bears in the form of
a lower offer price if it goes public. Their model predicts that a firm goes public when
information gathering costs are low or when enough information about the firm has
accumulated in the public domain (e.g., as the firm ages).6 Subrahmanyam and Titman
also investigate how information gathering by dispersed investors influences the IPO
decision. Their model allows investors to acquire information about the firm that insiders
lack and this information improves the firm’s investment decisions. When insiders can
uncover this “serendipitous information” at low cost, firms go public otherwise they
remain private.7
Pastor and Veronesi (2005) model the optimal IPO timing decisions of private
firms. Firms in their model decide when to exercise a real option to go public, invest
proceeds, and begin production. The value of this option rises when expected market
returns fall, when aggregate profitability is high, and when uncertainty about future
(1997) predict that insiders who value control and want to limit monitoring by outside investors will
underprice IPOs to create excess demand which permits discrimination against large bidders.
6
Welch and Ritter (2002) report that with the exception of the Internet bubble period, there is little time
series variation in the average age of IPO firms.
7
Benveniste, Busaba, and Wilhelm (2002) argue that there is an informational externality associated with
IPOs because private firms learn from first movers in the IPO market. Investment bankers resolve this

7

aggregate profitability rises. Among the predictions of their model are that IPO waves
caused by declining expected market returns are preceded by high market returns (which
are not a function of mispricing, but rather depend on falling expected returns), and
similarly, IPO waves driven by increased aggregate profitability follow periods of high
market returns. During our sample period, banking went through some very strong and
very weak periods of profitability, making it a good industry to test theories of IPO
timing based on option value.
There are several theories that we are unable to test using our sample of banks.
Boot, Gopalan, and Thakor (2005) envision entrepreneurs trading off the benefits of
greater “elbow room” when running a private firm against the higher cost of capital
associated with greater managerial autonomy. Most of the empirical predictions of this
model are tied to variations in the restrictiveness of corporate governance regimes
(making this a good model to test with international data) or to a parameter ρ, a measure
of agreement between the entrepreneur and investors about whether a particular
investment should or should not be undertaken. Lacking a good empirical proxy for ρ
which we could apply to a cross section of banks, we fail to provide any evidence to
support or refute this model. Similarly, because banks must disclose a great deal of
information whether or not they go public, the banking industry seems an unlikely fit for
the model of Yosha (1995), which envisions a small, innovative private firm facing a cost

externality by using their access to investors to influence going-public firms to share information gathering
costs.

8

of going public in the form of an existing competitor who learns from IPO-related
disclosures.8
2.2. Empirical Evidence
Lack of data on private firms creates a significant obstacle for researchers
attempting to test predictions of the models described above. European scholars have
had more success in overcoming this hurdle than their US counterparts. The only
published study with a broad sample of public and private firms is Pagano, Panetta, and
Zingales (1998; henceforth PPZ). Tracking a sample of almost 20,000 private Italian
firms from 1982-1992, they find that the most important driver of the IPO decision is the
market-to-book ratio of existing public firms in an industry. Private firms in a particular
industry go public when public valuations in that industry are high. High market-to-book
ratios can indicate an increase in growth opportunities, or they might simply reflect
temporarily high valuations. Supporting the latter view, Italian IPO firms go public after
a period of rapid growth and high investment, but not before such a period. Not
surprisingly, firm size is the second most important factor in determining which Italian
firms go public, with larger firms being more likely to conduct an IPO. PPZ also find
evidence that Italian IPOs lead to subsequent control changes. Because institutional
features of markets as well as the relative importance of the stock market to the overall
economy differ considerably between Italy and the United States, it is not clear whether
the results from PPZ extend to the US. For example, the typical IPO firm in Italy is eight

8

Black and Gilson (1998) view the IPO primarily as an exit strategy for venture capitalists. Our paper is
silent on the predictions of their model because very few banks in our sample receive venture backing.

9

times as large and six times as old as the average IPO in the United States, despite no
compelling evidence that listing costs are significantly higher in Italy.
Two working papers examine IPO decisions using German data. Fischer (2000)
examines a sample of private German firms, some of which ultimately listed on the shortlived Neuer Market. The data on private firms used in this study come from
Hoppenstedt, a German financial data provider. Fischer does not specify exactly how
Hoppenstedt gains access to private firm data, but he does acknowledge a very large size
bias in their figures. The control group of private firms is, on average, seven times larger
than the IPO group. In addition, firms in this sample use a mix of accounting standards,
with the private companies using the German Company Code and the IPO firms using a
mix of that as well as IAS and GAAP. Factors that appear to prompt private firms to go
public in this study are high capital expenditures, high intangible assets, and growth in
sales. Fischer finds that the holdings of corporate insiders, relative to the holdings of
other blockholders prior to the IPO, actually increases after the IPO, and he finds no
evidence that IPO firms are more likely to be acquired than private firms.9 Thus, he
concludes that the IPO is not a mechanism that facilitates a later sale.
Boehmer and Ljundqvist (2004) study a sample of private German firms that
announced an intention to go public between 1984 and 1995. They use a hazard model to
measure the effects of various factors on IPO timing, conditional on the announcement of
intent. Increasing the likelihood of an IPO are increases in profitability, sales, earnings
growth, or stock market returns. Family-run companies are less likely to complete IPOs.

9

The incidence of M&A activity in Germany is far less than in the U.S., so this finding may be driven by
characteristics of the larger market environment.

10

Comparing their sample firms to a broad sample of public and private German firms
covered by Worldscope, Boehmer and Ljundqvist find that firms announcing their intent
to do an IPO grow faster than other firms both before and after the announcement, though
growth does slow a little after the announcement. The median age of IPO firms in their
sample is 38 years, a little more than 5 times the age of IPO firms in the US. As with
PPZ, it is unclear how these results would transfer to the very different market
environment in the US. In addition, because Boehmer and Ljundqvist lack data on
private firms that did not announce their intent to go public, they cannot address why
some private firms make these announcements while others do not. Boehmer and
Ljundqvist exploit their time series to determine which factors raise or lower the
likelihood of completing an IPO conditional upon an initial announcement, but the thrust
of our paper is on the more primitive initial decision.
Interesting evidence on the motives for going public in the U.S. is provided in a
working paper by Chemmanur, He, and Nandy (2005), who use plant level data from the
U.S. Bureau of Census to study public and private manufacturing concerns. They study
the performance of firms that go public, both in absolute level and compared to the
performance of private firms over the same period. The performance measure they
choose to emphasize is total factor productivity. They find that larger and more rapidly
growing firms are more likely to go public, as are firms with greater productivity and
higher market share. However, Chemmanur, He, and Nandy do not examine whether
IPO firms are more likely to be acquired or to become acquirers themselves relative to
private firms, which is a central focus of this paper.

11

Helwege and Packer (2004) exploit the SEC’s requirement that private firms with
either public debt or a large number of shareholders must file public disclosures. The
sample described in their working paper contains very large firms with high leverage.
They track 27 firms that complete an IPO after 1996 and another 15 firms that express an
interest in doing an IPO but did not complete one. Like PPZ, Helwege and Packer find
that industry market-to-book ratios are important drivers of firms’ IPO decisions, and
they interpret this as being consistent with the windows of opportunity hypothesis. They
also find that the presence of an outside equity block raises the odds of a successful IPO
as predicted by Black and Gilson (1998). They find no difference in merger activity
between the firms that remain private and those that go public, so in their data it does not
appear that the IPO is an initial step in the ultimate sale of the firm.
It is natural to worry that sample selection biases could drive some of Helwege
and Packer’s findings. For example, in their sample of large, highly levered firms, they
find that firm age is inversely related to the probability of an IPO, which is at odds with
conventional wisdom and the results in PPZ. Similarly, their proxies for growth
opportunities do not have explanatory power, perhaps because there is little variation in
growth opportunities among mature firms.
Several papers examine ex post characteristics of formerly private firms in an
attempt to test some of the theories mentioned above. Hogue and Loughran (1999)
examine the post-IPO stock returns (as well as some accounting performance measures)
of banks and thrifts that go public from 1983-1991. Comparing these firms to existing
public financial institutions (and in some cases to the aggregate banking sector which is
dominated by public firms), Hogue and Loughran find unusually poor performance after

12

the IPO. They argue that investors fixate on these firms’ pre-IPO growth figures, which
are above average before the IPO.
The insights gained from Hogue and Loughran into the IPO decision are limited
not only because they do not draw on a sample of private firms, but also because their
sample of IPOs includes a large fraction of thrift institutions. For these firms, an IPO is a
joint event in which the thrifts convert from a mutual to a stock form of organization
while simultaneously switching from private to public ownership. Therefore, it is
difficult to disentangle the factors related to each of these organizational changes.
Moreover, Esty (1997) documents that during the 1980s, regulators actively encouraged
thrifts to convert from mutual to stock ownership because they believed that stock-based
thrifts would take fewer risk than mutual thrifts. Esty argues that the stock organizational
structure provides managers of thrifts with greater risk-taking incentives because of the
call-option characteristics of levered equity. Consistent with this view, Esty finds that
after a conversion from mutual to stock ownership, thrifts invest more in risky assets and
exhibit greater profit variability. Therefore, to the extent that Hogue and Loughran’s
post-IPO performance results are influenced by the large number of thrifts in their
sample, the simultaneous shift from mutual to stock ownership encouraged by regulators
and from private to public ownership clouds the interpretation of their findings. Clearly
most private firms that do IPOs do not begin as mutual organizations.
Two other studies that examine ex post characteristics of IPO firms are Brau,
Francis, and Kohers (2003, henceforth BFK) and Ciccotello, Field, and Bennett (2001,
henceforth CFB). BFK gather data on two types of private firms – those that go public
via an IPO and those that are acquired by a public company. The sample construction

13

allows them to examine, conditional on a desire to sell stock, the factors that influence
one method of selling versus another. Focusing on broad external influences rather than
firm-specific factors, BFK find that firms are more likely to conduct an IPO when the
private firm’s industry is more concentrated, when the private firm is high tech, when the
IPO market is “hotter” than the takeover market, and when the cost of debt is high. With
respect to deal characteristics, they show that post-deal insider ownership is higher and
liquidity is lower in firms that choose the IPO route. Though the choice of IPO versus
takeover is quite interesting, BFK’s research design does not allow them to assess
whether a firm’s decision to undertake an IPO might be part of a larger plan to sell the
firm to an acquirer later.
CFB examine 36 thrifts that go public from 1988-1992, some of which are
subsequently acquired and some of which remain independent. They also construct a
sample of 73 thrifts that convert from mutual to stock ownership and subsequently sell
out to an acquirer without doing an IPO first. CFB investigate how thrifts that conduct
IPOs and subsequently sell out differ from those that remain independent, and they ask
why some firms that sell out to an acquirer go through an IPO first when a private sale is
possible. They find that acquired thrifts that do not go public first are smaller, perhaps
because fixed IPO costs are prohibitive for small firms. In addition, prior to the IPO,
thrifts that ultimately sell to acquirers are less risky than those that stay independent. As
with many of the studies cited above, CFB do not begin with a random sample of private
firms. The problem of interpreting the joint “IPO and mutual-to-stock” event and the
regulatory “push” that thrifts received as financial conditions in the industry deteriorated

14

in the late 1980s and early 1990s makes it difficult to know the extent to which these
results provide support for theories such as Mello and Parsons (1998).
Lastly, Brau, Ryan, and DeGraw (2005) attempt to uncover the motivations
driving firms’ decisions to go public by conducting surveys of 380 Chief Financial
Officers of firms that went public during 1996-2002. The study concludes that CFO
opinions support two motivations for going public: (1) the desire to increase the firm’s
visibility and reputation and (2) market timing. The answers to survey questions
designed to explore control-related motivations for the IPO are not consistent with firms
going public to enable subsequent control changes. CFOs also report that a significant
concern about going public is disclosure of confidential information. Presumably
disclosure related concerns play a smaller role in the banking industry given the
significant disclosure requirements imposed on private and public institutions.
In addition to all of the standard concerns that one might raise about drawing
conclusions from survey evidence, Brau, Ryan, and DeGraw do not examine why some
firms chose not to go public. Rather, only CFOs of firms that had already gone public
received surveys to complete. As with many of the other papers reviewed here, it is not
clear how the firms going public compare to their private counterparts. It is the ability to
study an unbiased sample of private firms and compare those that go public to those that
remain private that constitutes the primary advantage of our bank-focused approach.

15

3. Data
Our ability to observe both public and private firms derives from the requirement
that financial institutions disclose balance sheet and income statement figures to
regulators. To build our sample, we begin with the Thomson Financial Securities Data,
SDC Platinum new issues database. From SDC we extract all firm-commitment IPOs
from 1981-2002 by banks and bank holding companies. This yields a sample of 59 bank
IPOs and 181 bank holding company IPOs.10 For reasons mentioned above, we do not
include in our sample the 168 thrift IPOs that took place during this period.
Data on banking organizations come from the Call Reports of Income and
Conditions that banks and bank holding companies (BHCs) are required to submit. We
are able to match the SDC data to the Call Report data for 140 IPOs, of which 25 are
banks and 115 are BHCs. This constitutes our base sample, although in the tables below
the sample may shrink due to missing data required for some of our tests. Missing data is
often an issue for the BHCs because frequently the holding company structure did not
exist prior to the IPO. We address this problem by constructing an artificial holding
company structure to backfill the data for BHCs that own a single bank.11
To construct a control sample of private institutions, we match based on size,
location, and organizational structure. We match on size because large banks may
compete in different ways and in different markets than small banks.12 In addition, many
of the costs of going public are fixed, making it less likely that very small banks will find

10

In most of what follows, we will use the term bank to refer to both banks and bank holding companies.
We use the Call Report data on the bank to backfill.
12
See, for example, Berger, Rosen, and Udell (2005) which discusses how bank size affects competition
for small business lending.
11

16

an IPO feasible. We match on location because competition in banking, especially for
the smaller banks that do an IPO, is largely local in nature. Both the level of competition
and the economic conditions of bank customers are likely to vary significantly across the
country. Finally, we match on organizational structure to capture the differences in
investments available to stand-alone institutions and banks with a holding company
structure.
For each banking organization that goes public, we select up to five matching
firms. We require that matching firms be in the same state as the IPO firm and have the
same corporate organization (bank or BHC). For stand-alone institutions and BHCs with
only a single bank, we also require matching firms to be in a rural area if the IPO firm is
in a rural area or in an urban area if the IPO firm is in an urban area.13 If a firm is less
than five years old, we also require all matching banks to be within five years of the IPO
firm’s age (since there is evidence that de novo banks are different than established
banks). Once this is done, we choose up to five matching firms based on size (total
assets). We choose the firm that is closest in size plus up to four other matching firms
conditional on those firms being within 50% of the size of the IPO firm. (Henceforth,
unless otherwise noted, ‘banks’ refers to both stand-alone institutions and BHCs.)
In our empirical analysis below, we compare the IPO banks to both the entire
control group and to a control group that includes only the single closest match for each
IPO bank. This bank, which we refer to as the best control, is the one with total assets
closest to that of its matching IPO bank in the year prior to the IPO.

13

We do not do this for BHCs that own multiple banks since the BHC may be have banks and branches in
both rural and urban areas.

17

4. Results
Table 1 lists descriptive statistics for both our IPO banks and the private control
institutions. By design, the IPO banks and controls are similar in size (assets). In the
year prior to the IPO, banks that go public grew faster than those that remained private,
reminiscent of the result for Italian firms obtained by Pagano, Panetta, and Zingales
(1998; henceforth PPZ). In terms of profitability and leverage, IPO banks and their
control banks appear quite similar, while the IPO banks’ asset portfolios tilt more heavily
toward loans than their peers’ portfolios. IPO banks also are younger than private banks
and have more branches.
4.1. Which Banks Go Public and Which Remain Private?
In this section, we examine how the probability of going public is affected by
bank characteristics. In Table 2, we report results from a logistic regression on the
characteristics that are associated with a firm going public. We include one observation
for each IPO firm for the year prior to when it goes public and one for each control firm
for the year prior to the one in which its matched IPO firm goes public. The basic model
is
IPO bank = f(log total assets, asset growth rate, return on assets (ROA), equityto-assets ratio, loans-to-assets ratio, bank age, young bank dummy, bank
dummy, branches, branches per market, deposits per branch)
where IPO bank is a dummy variable that equals one for IPO banks and zero for control
banks. The independent variables in the regressions include bank size and growth rate;

18

measures of profitability and risk; age of the bank; and bank branch and deposit
characteristics.
The first thing to note is that with the full sample, we find larger banks are more
likely to go public. This may seem surprising, since we match based on size, but it
reflects the fact that some of the controls differ significantly in size from the IPO bank.
This is one reason why we run all of our tests on the sample that includes only the best
controls. For the best control sample, the coefficient on bank size is insignificant. In
addition to the role of size, we find that faster growing firms are more likely to go public,
consistent with prior work.
Banks with higher profits and more leverage are more likely to go public. The
coefficients on ROA and equity-to-assets ratio in Table 2 are positive and significant. A
bank may be more likely to go public after a period of strong profitability since it may
allow the bank to get a better price. More levered banks have less equity available to
expand, so it also not surprising that they are more likely to go public. IPO banks also
have asset portfolios more heavily tilted towards loans.
Chemmanur and Fulghiere (1999) predict that firms go public when more
information about them has accumulated in the public domain or when the costs that
investors bear to gather information declines. Of course information gathering costs are
difficult to observe. In the logistic regression we include several variables intended to
proxy for costs of information gathering. The first is bank age (as well as young bank
dummy). Presumably investors have more knowledge about a firm the longer is its
operating history. However, we find that newer banks, whether measured by the age of
the bank or the young bank dummy, are more likely to go public. We also conjecture that

19

investors have more information about banks with more branches or more branches per
market. The coefficients on these variables do not tell a consistent story, however.
Banks with more total branches are more likely to go public, but banks with more
branches per market are less likely to go public.
4.2. Do Banks Go Public to Sell Out to an Acquirer?
Several theories of the going public decision postulate that firms conduct IPOs to
facilitate a subsequent sale to an acquirer. These predictions run contrary to the more
conventional wisdom that firms go public to raise equity to finance growth. The banking
industry may be uniquely well suited to test these theories due to the rapid pace of bank
mergers and acquisitions during our sample period. With the number of banks shrinking
rapidly, it seems plausible to assume that an owner of a private bank would recognize the
significant probability that the bank might be purchased by another institution and would
consider how going public might affect the chances of being acquired and the price
offered in any acquisition attempt.
In Table 3, we report estimates from logistic regressions in which the dependent
variable equals one if a bank (either the IPO bank or its controls) is acquired within four
years of the IPO date. The main variable of interest is IPO bank, a dummy that is one if
the bank did an IPO and is zero if the bank is part of the matched sample. The logistic
model includes controls for bank size, profitability, leverage, and bank age. We include a
salary measure as a proxy for private control benefits and an indicator variable equal to
one if a bank itself had made an acquisition in recent years. Finally, for those banks that
conducted IPOs we include measures of the fraction of shares sold and the amount of
proceeds raised in the IPO. It turns out that, as shown in Table 3, none of these variables

20

help to distinguish banks that become takeover targets from those that remain
independent. The only variable with a significant link to the probability of being
acquired is the IPO dummy. Banks that went public are much more likely to become
takeover targets themselves than are the privately-held control institutions. This is true
when we include all the controls or when we restrict attention to only the best controls,
and whether or not we include year dummies.
Banks may also decide to go public in order to may future acquisitions easier.
There are several ways that an IPO can make it easier for a bank to make an acquisition.
The bank can use the capital it raises in the IPO to finance all or part of an acquisition. In
addition, going public may give the bank a more acceptable currency for a stock-financed
acquisition. The owners of the target bank may prefer the relatively liquid stock of a
publicly-traded bank to the less liquid stock of a private bank. To test whether the IPO is
used to facilitate acquisitions, Table 4 reports estimates from logistic regressions
estimating the probability that a bank becomes an acquirer. The logistic models reported
in Table 4 use the same set of covariates as the model for becoming a target. We find
that IPO banks are more likely to be acquirers than are banks in the control samples.
This is true when we include all the controls or when we restrict attention to the best
control banks, and whether or not we include year dummies.
There are several other factors that affect the probability of making an
acquisition. Consistent with conventional wisdom, larger banks are more likely to be
acquirers. Note that the coefficient on the log of total assets is at best marginally
significant when we drop all but the best control banks. This is not surprising, since in
these regressions we match banks most closely on size. There is also evidence that banks

21

paying lower salaries are more likely to be acquirers. One explanation for this is that
these banks are more efficient than their rivals. Finally, among the IPO banks, those that
raise more capital in the offering are more likely to make acquisitions in subsequent
years. This is consistent with banks using capital raised in the IPO process to finance
their acquisition strategy.
The results in Tables 3 and 4 suggest that IPOs can be a part of a larger merger
strategy for a firm, regardless of the role they will play in the merger. The result in Table
3 that IPO banks are more likely to be acquired is consistent with the hypothesis that
firms go public with a subsequent sale in mind (e.g., Zingales, 1995, and Mello and
Parsons, 1998). The results in Table 4 support the intuition that an IPO bank raises
money to finance growth, albeit growth through acquisition.
4.3. Do Banks Go Public in Hot Markets?
Loughran and Ritter (1995), Ritter and Welch (2002), and others argue that firms
may choose to go public when the market places a particularly high value on their shares.
PPZ find that Italian firms go public when the market-to-book ratios of existing public
firms in the same industry are high. Though our data do not allow us to make crossindustry comparisons in market valuations, we can look at the time series to see if banks
go public when the market is hot. In Table 5 we test the market timing hypothesis by
comparing returns on various stock indexes for the periods preceding bank IPOs to

22

average returns across the entire sample period. If banks go public to time the market,
then we expect above-normal market returns leading up to the IPO.14
Table 5 presents results for four market indices over several horizons. The four
indices we include are a value-weighted portfolio of all banks listed on CRSP, a portfolio
of small banks, and the value-weighted and equally weighted CRSP indexes. Table 5
reports gross returns calculated by taking the average daily return over the given pre-IPO
horizon. We calculate pre-IPO returns for each IPO bank and then take averages across
all IPO firms. Given the lead time required to execute an IPO, we examine index returns
over several pre-IPO event windows and compare them to the average (annualized) daily
return over our entire sample period. The t-tests in Table 5 assess whether the average
daily return in the event period differs from the average daily return for the entire sample.
There is some evidence of a hot market effect, but the results are sensitive to the
stock market index and event horizon chosen. For example, the top panel shows that the
annualized daily return in the year leading up to IPOs is 21.6 percent for the CRSP valueweighted index, whereas the annualized daily return on the same index for the entire
sample period is just 14.3 percent. That difference is highly significant, but differences
for the other three market indexes are insignificant in the year leading up to an IPO.
Other event windows in Table 5 show mixed results. Our strongest evidence for a
hot market effect emerges when we measure average daily returns from nine months to
three months prior to the IPO. Over that pre-event horizon, the average daily returns are
14

In a sense, this test is biased against finding that IPOs occur in hot markets because the average returns
for the benchmark indexes include some hot and cold periods. An alternative test could compare
benchmark returns prior to periods of high IPO volume to returns during low IPO volume periods.

23

above normal for three of our four market indexes. However as was the case in the 1year pre-event horizon, only the CRSP value-weighted index return shows any sign of a
market timing effect in the horizon covering the six months leading up to the IPO.
4.4. How Does the Financial Performance of IPO Banks Differ from Private Banks
If firms go public to raise capital which enables them to pursue profitable growth
opportunities, then we might expect IPO banks to grow faster and to improve profitability
after going public. A somewhat surprising result from PPZ is that firms in Italy appear to
go public after a period of rapid growth, not before one. In this section, we examine
various financial performance measures for public and private banks, before and after the
IPO, to see how financial performance varies across institutions before and after the IPO.
Table 6 shows regression results for five different performance measures: return
on assets (ROA), return on equity (ROE), leverage (the equity-to-assets ratio), ratio of
chargeoffs to loans, and asset growth. To estimate these regression models, we pool the
data for IPO banks and their controls across years (the best control only results are in the
lower panel). We gather data from up to 3 years prior to the IPO for up to 5 years
afterwards. By requiring banks to have at least one year of pre-IPO data, we eliminate
banks that are created at the time of the IPO. We also require two years of post-IPO
data.15
As above, our key variable is IPO bank, a dummy variable equal to 1 in all years
for banks that conduct IPOs and zero in all years for control banks. Since we are
examining performance over time we have to control for changes in economic conditions.

24

We do this in two ways. The first is to create dummy variables for before and after the
bank IPO. Pre-IPO is a dummy that takes the value 1 for IPO banks in the years prior to
their IPO and for the control banks in the years before their matched IPO banks go
public. In the IPO year and subsequently, the dummy is zero. Post-IPO is defined
similarly for the years following the IPO (so the IPO year is the omitted date). We also
include year dummies to control for macro effects.
To determine whether the performance of IPO banks changes relative to the
matched sample in period following the IPO, we use the interaction term IPO bank *
post-IPO, the product of the IPO dummy and the post-IPO dummy. Since performance
might be affected by the amount of cash raised in the IPO (since we know this affects
acquisition activity), we include percent sold * post-IPO and new cash-to-equity * postIPO, which allow us to determine whether banks that give up more control or raise a
larger share of new equity perform differently than other IPO banks.
The results reported in Table 6 suggest that the profitability of IPO banks may
decline relative to their peers after the IPO. When we include all control banks, the
coefficients on IPO bank * post-IPO are significantly negative for both ROA and ROE.
When we tighten the focus to the best controls only, the magnitudes of the coefficients
change very little, but the statistical significance is weaker. The drop in significance is
likely due to the loss of two-thirds of our observations.
We find a little evidence that IPO banks differ in profitability from control banks
prior to the IPO, consistent with the univariate results in Table 1. The coefficients on the
15

Control banks are only in the sample for the years where their matched IPO bank has data. Also, for the
best control results, we use the control bank with at least one year of pre-IPO data and two years of post-

25

IPO bank dummy are weakly significant when focusing on ROE and including all control
banks. In addition, the coefficients are smaller in magnitude than those on IPO bank *
post-IPO.
Not surprisingly, going public reduces leverage for IPO banks. Raising new
equity capital increases the equity-to-assets ratio unless the bank immediately uses the
capital for something such as an acquisition. One question is whether a bank does an
IPO because regulators pressure it to add equity. To address the concerns that IPO banks
might be going public to meet minimum regulatory capital standards, in unreported
regressions we exclude banks that are close to or below minimum capital thresholds.
Leaving these firms out of our sample does little to change our results.
We find at most weak evidence that loan losses rise after an IPO. The coefficient
on IPO bank * post-IPO is weakly significant when we include all controls, but it is not
significantly different from zero for the best control sample. Consistent with the results
in PPZ, we find no evidence that IPO banks experience faster asset growth after going
public.
The coefficient on new cash-to-equity indicates how performance measures are
correlated with the amount of money raised in the IPO for those banks that went public.
The interaction term on this variable indicates whether the performance measures
changed after the IPO. A robust result is that more profitable institutions raise more
money in their IPOs, but there was no correlation between money raised and subsequent
changes in performance. The amount of money raised in the offering does not appear to

IPO data that is closest in size to the IPO bank.

26

be strongly correlated with leverage, loan charge offs, or asset growth rates before or
after the IPO.
Our results in this section provide some evidence against the hypothesis that
banks go public to undertake profitable investment opportunities. Banks do not grow
faster after going public, and there is some evidence that their profitability deteriorates.
5. Summary
In this paper we propose that a sample of private banks and bank holding
companies can shed light on the going public decision. Obtaining empirical results in
this area is a challenge because most private firms do not disclose much information.
Regulators require that all banks, both public and private, disclose their financial results,
and this requirement enables us to compare banks that go public to those that remain
private, an essential comparison in any test of a theoretical model of the IPO decision.
One interesting finding is that banks that choose to go public face a higher
probability of being acquired in subsequent years than do firms that remain private. This
result, which is predicted by several theoretical models, is in contrast to findings of other
empirical papers such as Fischer (2000), Helwege and Packer (2004), Brau, Ryan, and
DeGraw (2005), and Brau, Francis, and Kohers (2003).
But going public also raises the probability that the IPO bank will subsequently
acquire other banks. This supports the more conventional view of IPOs as capital raising
events designed to finance future growth. However, our analysis of financial
performance metrics before and after the IPO indicates that IPO firms on average do not
grow faster after going public, and their profitability deteriorates. This may be because

27

banks choose when to go public based on how well they have performed recently. We
find evidence of rapid growth and high profitability leading up to the IPO, however since
we use accounting measures of profitability, it possible that we are capturing the effects
of banks manipulating their accounting data to inflate pre-IPO profit at the expense of
future profitability.

28

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31
Table 1
Descriptive statistics for IPO bank vs. control banks
This table presents descriptive statistics for IPO banks and the matched sample of control banks in the year of the IPO.
Asset growth rate is the current year’s total assets divided by the prior year’s total assets. ROA (return on assets) is net
income divided by total assets. ROE (return on equity) is net income divided by shareholders’ equity. The equity-toassets ratio is shareholders’ equity divided by total assets. The loans-to-assets ratio is total loans divided by total assets.
The chargeoffs-to-loans ratio is loan chargeoffs divided by total loans. Bank age is the age of the bank in years at the
time of the bank IPO date. The young bank dummy is equal to 1 for banks that are less than 3 years old at the bank IPO
date. The bank dummy is equal to 1 for stand-alone banks and 0 for bank holding companies. Branches is the number
of branches for a bank. Branches per market is the average number of branches per local market for a bank. Deposits
per branch is the average amount of deposits per branch for a bank. Target is equal to 1 for banks that are acquired
within four years and 0 otherwise. Salary per employee is the total salaries divided by the total number of employees.
Prior acquirer is equal to 1 for banks that have completed an acquisition within the prior four years and 0 otherwise.
Percent sold is equal to the IPO primary shares sold divided by the post IPO total shares outstanding for IPO banks and
0 otherwise. New cash raised is IPO proceeds divided by shareholder’s equity prior to the IPO. New cash-to-equity is
total IPO proceeds divided by the post IPO shareholder’s equity for IPO banks and 0 otherwise. With respect to t-tests
of equal means between control banks and IPO banks, ***, **, and * indicate significance at the 1, 5, and 10 percent
level respectively.

Log total assets
Asset growth rate
ROA (percent)
ROE (percent)
Equity-to-assets ratio (percent)
Loans-to-assets ratio (percent)
Chargeoffs-to-loans ratio (percent)
Bank age
Young bank dummy
Bank dummy
Branches
Branches per market
Deposits per branch in millions
Salary per employee in thousands
Prior acquirer
Percent sold
New cash raised in thousands
New cash-to-equity

IPO Banks
8.358
0.555
1.057
11.203
9.038
62.021
0.375
5.941
0.361
0.126
7.269
4.348
0.056
32.217
0.09
0.288
10606.640
0.727

Means
All Control Banks
8.200 ***
0.127 ***
1.106
9.489
8.868
56.058 ***
0.543 **
8.268 ***
0.256 **
0.114
5.522 **
3.827
0.048
32.065
0.09
-

Best Control Banks
8.260 *
0.160 ***
1.265
10.179
9.027
55.690 ***
0.440
7.866 ***
0.294
0.126
6.849
4.539
0.059
33.781
0.08
-

32
Table 2
What factors make a bank go public?
This table presents logistic regressions of the incidence of going public for the IPO banks and the matched sample of
control banks. The dependent variable is equal to 1 for IPO banks and 0 for control banks. Asset growth rate is the
current year’s total assets divided by the prior year’s total assets. ROA is net income divided by total assets. The
equity-to-assets ratio is shareholders’ equity divided by total assets. The loans-to-assets ratio is total loans divided by
total assets. Bank age is the age of the bank in years at the time of the bank IPO date. The young bank dummy is equal
to 1 for banks that are less than 3 years old at the bank IPO date. The bank dummy is equal to 1 for stand-alone banks
and 0 for bank holding companies. Branches is the number of branches for a bank. Branches per market is the average
number of branches per local market for a bank. Deposits per branch is the average amount of deposits per branch for a
bank. The regressions are estimated using robust standard errors. With respect to t-tests of parameter estimates equal
to zero, ***, **, and * indicate significance at the 1, 5, and 10 percent level respectively.

Log total assets
Asset growth rate
ROA
Equity-to-assets ratio
Loans-to-assets ratio
Bank age
Young bank dummy
Bank dummy
Branches
Branches per market
Deposits per branch

All controls
1.314**
0.733***
0.572**
-0.424***
0.043***
-0.040*
0.854**
0.823
0.060*
-0.153*
-0.595

Best controls
0.519
0.546***
0.676**
-0.494***
0.039***
-0.028
1.036*
0.436
0.036
-0.107
-1.283

All controls
1.210*
0.733***
0.648**
-0.445***
0.042***
-0.039*
0.935**
0.769
0.070**
-0.176**
-0.339

Best controls
0.444
0.611***
0.783**
-0.563***
0.038***
-0.025
1.141**
0.433
0.057
-0.152
-0.972

Number of observations
Pseudo R-square
Year dummies included

374
20.7%
NO

159
18.3%
NO

369
21.4%
YES

158
19.4%
YES

Table 3
Is a bank that goes public more likely to be acquired than a bank that stays private?
This table presents logistic regressions of the likelihood that a bank is acquired in the next four years. The sample includes IPO
banks plus the matched controls as of the year of the IPO. The dependent variable is equal to 1 for banks that are acquired in the
following four years and 0 otherwise. ROA is net income divided by total assets. The equity-to-assets ratio is shareholders’
equity divided by total assets. Bank age is the age of the bank in years at the time of the bank IPO date. The young bank dummy
is equal to 1 for banks that are less than 3 years old at the bank IPO date. The bank dummy is equal to 1 for stand-alone banks
and 0 for bank holding companies. Salary per employee is the total salaries divided by the total number of employees. Prior
acquirer is equal to 1 for banks that have completed an acquisition within the prior four years and 0 otherwise. Percent sold is
equal to the IPO primary shares sold divided by the post IPO total shares outstanding for IPO banks and 0 otherwise. New cash
raised is IPO proceeds divided by shareholder’s equity prior to the IPO. The regressions are estimated using robust standard
errors. With respect to t-tests of parameter estimates equal to zero, ***, **, and * indicate significance at the 1, 5, and 10 percent
level respectively.

IPO bank
Log total assets
ROA
Equity-to-assets ratio
Bank age
Young bank dummy
Bank dummy
Salary per employee
Prior acquirer
Percent sold
New cash raised

All controls
1.519***
-0.582
-0.059
-0.094
-0.011
-0.289
0.119
-0.003
0.181
-0.289
-0.691

Best controls
1.814***
-0.478
-0.380
-0.007
-0.005
-0.504
0.034
-0.014
0.421
-0.716
-0.617

All controls
1.540***
-0.355
-0.095
-0.067
-0.008
0.045
-0.018
0.000
-0.122
-0.294
-0.741

Best controls
2.082***
-0.638
-0.297
-0.049
-0.033
-0.365
0.273
-0.032
0.101
-0.548
-1.193

Number of observations
Pseudo R-square
Year dummies included

547
5.3%
No

233
9.8%
No

499
9.9%
Yes

207
16.6%
Yes

34
Table 4
Is a bank that goes public more likely to acquire another bank than a bank that stays private?
This table presents logistic regressions of the likelihood that a bank makes an acquisition in the next four years. The sample
includes IPO banks plus the matched controls as of the year of the IPO. The dependent variable is equal to 1 for banks that
acquire another bank in the following four years and 0 otherwise. ROA is net income divided by total assets. The equity-to-assets
ratio is shareholders’ equity divided by total assets. Bank age is the age of the bank in years at the time of the bank IPO date. The
young bank dummy is equal to 1 for banks that are less than 3 years old at the bank IPO date. The bank dummy is equal to 1 for
stand-alone banks and 0 for bank holding companies. Salary per employee is the total salaries divided by the total number of
employees. Prior acquirer is equal to 1 for banks that have completed an acquisition within the prior four years and 0 otherwise.
Percent sold is equal to the IPO primary shares sold divided by the post IPO total shares outstanding for IPO banks and 0
otherwise. New cash raised is IPO proceeds divided by shareholder’s equity prior to the IPO. New cash to equity is total IPO
proceeds divided by the post IPO shareholder’s equity for IPO banks and 0 otherwise. The regressions are estimated using robust
standard errors. With respect to t-tests of parameter estimates equal to zero, ***, **, and * indicate significance at the 1, 5, and
10 percent level respectively.

IPO bank
Log total assets
ROA
Equity-to-assets ratio
Bank age
Young bank dummy
Bank dummy
Salary per employee
Prior acquirer
Percent sold
New cash raised

All controls
0.910**
1.620***
0.043
-0.040
-0.030
-0.206
-0.292
-0.031**
0.029
-1.366
0.720***

Best controls
1.298***
0.929*
0.134
-0.008
0.022
-0.455
-0.203
-0.031*
0.013
-1.490*
0.648***

All controls
0.895**
1.599***
-0.027
-0.055
-0.051
-0.218
-0.250
-0.035*
-0.022
-1.451*
0.742***

Best controls
1.316***
0.717
0.162
-0.027
-0.012
-0.492
0.110
-0.049*
0.431
-1.829**
0.737***

Number of observations
Pseudo R-square
Year dummies included

547
12.4%
No

233
13.7%
No

525
13.7%
Yes

225
17.3%
Yes

34

35
Table 5
Returns prior to IPOs compared to average daily returns
This table presents gross average daily returns prior to IPOs compared to average daily returns in the entire sample period. The
table shows gross annualized average daily returns. The t-tests refer to the difference in the mean return during pre-IPO event
windows compared to the mean return throughout the sample period.

Index
Bank
Small bank
CRSP EW
CRSP VW
Bank
Small bank
CRSP EW
CRSP VW
Bank
Small bank
CRSP EW
CRSP VW
Overall averages
Bank
Small bank
CRSP EW
CRSP VW

Time period
IPO - 1 year --> IPO-1 day

Mean
1.281
1.267
1.266
1.216
1.327
1.324
1.311
1.239
1.262
1.252
1.266
1.211

IPO - 9 mns --> IPO - 3 mns

IPO - 6 mns --> IPO - 1 day

Time period
Average 1 year return

Mean
1.26
1.272
1.279
1.143

35

t-test
1.561
-0.276
-0.914
7.831
3.083
2.019
1.291
5.857
0.077
-0.808
-0.583
4.428

Std Dev
0.195
0.241
0.201
0.133
0.317
0.37
0.359
0.238
0.292
0.352
0.328
0.224
Std Dev
0.217
0.286
0.245
0.168

36
Table 6
OLS panel regressions of pre- and post-IPO performance for IPO banks vs. control banks
This table presents ordinary least squares panel regressions of pre- and post-IPO performance (3 years prior through 5 years post,
with at least one year prior and two years post of data) for IPO banks and the matched sample of control banks. The dependent
variables are ROA, ROE, equity-to-assets, chargeoffs-to-loans, and asset growth rate. ROA is net income divided by total assets.
ROE is net income divided by shareholders’ equity. Equity-to-assets is shareholders’ equity divided by total assets. Chargeoffsto-loans is loan chargeoffs divided by total loans. IPO bank is equal to 1 for sample IPO banks and 0 for control banks. IPO
bank*post is an interaction term that is equal to 1 for IPO banks for observations subsequent the bank IPO date and 0 otherwise.
Pre-IPO is equal to 1 for observations prior to the IPO date of the bank or its matched partner and 0 otherwise. Post-IPO is equal
to 1 for observations subsequent to the bank IPO date of the bank or its matched partner and 0 otherwise. Percent sold is equal to
the IPO primary shares sold divided by the post IPO total shares outstanding for IPO banks and 0 otherwise. New cash-to-equity
is total IPO proceeds divided by the post IPO shareholder’s equity for IPO banks and 0 otherwise. The bank dummy is equal to 1
for stand-alone banks and 0 for bank holding companies. The regressions are estimated using robust standard errors. With respect
to t-tests of equal means between control banks and IPO banks, ***, **, and * indicate significance at the 1, 5, and 10 percent
level respectively.

All Controls

IPO bank
IPO bank*post-IPO
Pre-IPO
Post-IPO
Percent sold
Percent sold*post-IPO
New cash-to-equity
New cash-to-equity*post-IPO
Log total assets
Bank dummy

ROA
0.088
-0.135**
-0.030
0.065**
-0.461***
-0.008
0.045***
0.019
0.261***
-0.130

ROE
1.029*
-2.350***
0.085
0.323
-2.013
0.371
0.337***
-0.599
4.294***
-2.139***

Equity-toassets ratio
-0.770
2.140***
0.395
0.073
2.219
-7.227**
-0.104
1.437
-3.141***
0.718

Chargeoffs-toloans ratio
-0.032
0.183*
-0.055
-0.053
-0.307
0.039
0.001
0.004
-0.115
0.133

Asset growth
rate
0.008
0.063
0.020
-0.037
0.804
-0.683
-0.031
0.010
0.059**
0.119***

Number of observations
Adjusted R-square

3,465
6.2%

3,465
10.7%

3,465
13.5%

3,461
4.3%

3,432
6.1%

IPO bank
IPO bank*post-IPO
Pre-IPO
Post-IPO
Percent sold
Percent sold*post-IPO
New cash-to-equity
New cash-to-equity*post-IPO
Log total assets
Bank dummy

ROA
0.079
-0.115
-0.036
0.084
-0.553***
0.022
0.043***
0.008
0.230*
-0.156

ROE
1.068
-1.861*
0.499
0.405
-2.084
0.380
0.314**
-0.595
4.245***
-2.857*

Equity-toassets ratio
-1.135**
2.179***
-0.206
-0.161
2.288
-7.557**
-0.125
1.423
-3.441***
1.128

Chargeoffs-toloans ratio
-0.035
0.088
-0.034
0.006
-0.175
-0.017
0.011
0.026
0.045
0.268*

Asset growth
rate
-0.021
0.122
-0.047
-0.119*
0.815
-0.711
-0.034*
0.007
-0.011
0.174*

Number of observations
Adjusted R-square

1,443
5.5%

1,443
13.0%

1,443
15.3%

1,441
10.4%

1,430
7.1%

Best Controls

36

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