The Impact of Strong Bank-Firm Relationship on the Borrowing Firm
Nishant Dass† and Massimo Massa*
Commercial banks acquire inside information about the firms they lend to. We study the impact of this
informationally privileged position on the borrowing firm using a broad panel of U.S. firms over the 1993–
2004 period. We consider three facets of the strength of the borrower-lender relationship: proximity of the
lender, significance of the loan to the borrower, and insider potential of the lender. We show that a stronger
relationship, by inducing better monitoring by the bank, improves the quality of the borrower’s corporate
governance. At the same time, a stronger relationship enhances the bank’s potential to use its privileged
information in the equity market. This increases adverse selection for other market participants, raises
information asymmetry on the stock, and lowers stock liquidity. This trade-off between improved corporate
governance and greater information asymmetry is reflected in the firm’s value. Better governance increases
the firm’s Tobin’s Q, while greater information asymmetry lowers Tobin’s Q. Our results have normative
implications for the role of banks in the development of financial markets.
JEL Classification: G10, G21, G30, G34
Keywords: Banks, corporate governance, information asymmetry, lending relationship
†
Georgia Tech. *INSEAD. Please address all correspondence to Massimo Massa, INSEAD, Boulevard de Constance, Fontainebleau
77305, FRANCE, Telephone: +33160724481, Fax: +33160724045 Email: [email protected]. Nishant Dass is at College of
Management GA Tech, 800 W. Peachtree St. NW, Atlanta, GA 30308, USA, Telephone: +14048945109, Email:
[email protected]. This paper was initially circulated under the title “The Dark Side of Bank-Firm Relationships: The
(Market) Liquidity Impact of Bank Lending”. We have benefited from the comments of V. Acharya, M. Baker, S. Bharath, J.
Dermine, M. Giannetti, D. Gromb, R. Inderst, K. John, R. Masulis, M. Roberts, E. Stafford, J. Stein, A. Sufi. Comments of seminar
participants and our discussants – M. Flannery, L. Klapper, A. Rampini, and C. Schenone – at the NYU/NYFed, JFI/World Bank,
RFS/Wharton/NYFed, and Wash U. (Olin) conferences, respectively, as well as seminar participants at the Federal Reserve Atlanta’s
2007 All-Georgia Conference were also extremely useful. We thank A. M. Ranganathan and W. Fisk for their invaluable help with the
name-recognition algorithm. All errors are our own.
The Impact of Strong Bank-Firm Relationship on the Borrowing Firm
Commercial banks acquire inside information about the firms they lend to. We study the impact of this
informationally privileged position on the borrowing firm using a broad panel of U.S. firms over the 1993–
2004 period. We consider three facets of the strength of the borrower-lender relationship: proximity of the
lender, significance of the loan to the borrower, and insider potential of the lender. We show that a stronger
relationship, by inducing better monitoring by the bank, improves the quality of the borrower’s corporate
governance. At the same time, a stronger relationship enhances the bank’s potential to use its privileged
information in the equity market. This increases adverse selection for other market participants, raises
information asymmetry on the stock, and lowers stock liquidity. This trade-off between improved corporate
governance and greater information asymmetry is reflected in the firm’s value. Better governance increases
the firm’s Tobin’s Q, while greater information asymmetry lowers Tobin’s Q. Our results have normative
implications for the role of banks in the development of financial markets.
JEL Classifications: G10, G21, G30, G34
Keywords: Banks, Corporate Governance, Information Asymmetry, Lending Relationships,
Stock-market Liquidity
1
It is widely accepted that bank loans are special because of inside information that comes with
lending. More inside information enhances the monitoring ability of the bank (e.g., Diamond
(1984), James (1987), and Besanko and Kanatas (1993)) and “thereby [improves] capital
allocation and corporate governance” (Levine (2002)). However, the bank may also exploit its
informational advantage in the equity market and effectively become an insider (Kahn and
Winton (1998)). This dual effect on the borrower makes bank loans special.
Even though commercial banks cannot directly trade in shares, they may still manage the
portfolios held in trust on behalf of customers (Santos and Wilson (2005)).
Moreover,
commercial banks are often part of bigger financial conglomerates, with affiliated investment
arms (such as investment banks, mutual funds, pension funds, and insurance companies) that can
trade on the basis of the information acquired through lending (Massa and Rehman (2005),
Acharya and Johnson (2007)). Thus, the privileged information of the commercial bank and its
potential to impact the borrower’s stock price by trading through its asset-management arm may
increase information asymmetry and adverse selection for the investors in the borrower’s stock.
This creates disincentives for other investors to trade in this stock, thus lowering its liquidity.
There is plenty of anecdotal evidence suggesting that access to privileged information
encourages the bank to exploit this information advantage through its trading arm. For instance,
Barclays was recently accused of trading on confidential information obtained through its
involvement in committees of creditors in distressed firms (International Herald Tribune (2007)).
Market participants have often voiced fears of banks’ informational advantage due to lending.
“Banks’ growing use of credit derivatives to reduce the risks in their loan portfolios have raised
concerns among investors that knowledge about lending plans can be used to gain advantage in
the credit derivatives market, which is sensitive to similar information” (Financial Times (2003)).
As a result, a stronger borrower-lender relationship both benefits a firm’s corporate
governance as well as exacerbates the information asymmetry of the firm’s stock. This induces a
trade-off between better corporate governance and greater information asymmetry (or lower stock
liquidity). While the literature so far has not focused on this trade-off, it is of great significance,
particularly because of its implications for the development of financial markets. The major
focus of this paper is to study this bank-induced governance/liquidity trade-off.
We rely upon the existing literature to construct measures of the strength of lending
relationship. We consider three facets of it: proximity of the bank to the firm, significance of the
loan to the firm, and the bank’s insider potential. Proximity is defined as the geographical
proximity between the borrower and the lender. Significance of the loan to the borrowing firm’s
2
finances is measured by the loan-to-asset ratio (defined later). The bank’s insider potential is
measured by the equity ownership (direct or indirect) of the bank in the borrowing firm. Direct
ownership of equity is measured by the fraction of borrower’s equity held in trust by commercial
banks and indirect ownership is measured by the fraction of borrower’s stock owned by the
bank’s affiliated institutional investors (such as insurance companies, mutual funds, pension
funds, and investment managers). The insider potential proxies for the opportunities the lender
has to exploit its inside information in the equity market.
If availability of inside information and the ability to trade in the equity market make the
lender a potential insider (Kahn and Winton (1998)), then the bank’s insider potential should
affect the information asymmetry in the market, reducing the borrower’s stock liquidity.
Simultaneously, proximity and loan-significance should benefit the borrower’s governance.
Proximity to the borrower directly affects the bank’s information-gathering and monitoring
ability (Coval and Moskowitz (2001) and Berger et al. (2005)). The more significant a loan is for
the borrower, the more influence the lender has over the managers (Rajan (1992)). Therefore,
both proximity and loan-significance should lead to better monitoring and thus alleviate moral
hazard concerns (Diamond (1984), James (1987)), thereby improving its governance.
We test these hypotheses by constructing a dataset containing characteristics of bank loans
for a broad panel of US firms over the 1985–2004 period, and relating specific loancharacteristics (proximity, loan significance, and insider potential) to the borrowing firm’s
liquidity/information-asymmetry as well as to its quality of governance. We use an appropriate
instrumental variables technique to account for the endogeneity of the borrowing decision and the
choice of loan-characteristics. We also recognize the potential reverse causality whereby a more
opaque firm may choose a bank with a bigger stake in its equity or a well-governed firm may
choose a closer bank and/or a larger loan.
We start by studying the change in the trading of the borrower’s stock by the lender-affiliated
institutional investors relative to the stock’s overall trading. We find a strongly positive relation
between the bank’s insider potential and the lender-affiliated institutions’ trading in the
borrower’s stock, after the loan has been granted. Specifically, a standard deviation increase in
insider potential raises lender-affiliated institutions’ relative trading by 0.1 standard deviations.
This suggests that the information asymmetry around the borrower’s stock is exacerbated.
Next, we provide evidence on the impact of the bank’s insider position on the stock’s
secondary market liquidity. We show that a stronger bank-firm relationship increases borrower’s
illiquidity as well as information asymmetry in the equity market. This holds despite the fact that
3
we control for the confounding effects of endogeneity of the borrowing decision and the choice
of loan-characteristics. A stronger bank-firm relationship also increases the probability of
informed trading (Easley et al. (1996)) and lowers the overall trading by institutional investors.
The effect is economically and statistically significant – cross-sectionally, an increase of one
standard deviation in the bank’s insider potential raises the borrowing firm’s illiquidity (or stockprice impact of a trade) by 0.2 standard deviations, increases information asymmetry by 0.8
standard deviations, increases the probability of informed trading by 0.4 standard deviations, and
lowers institutional trading by 0.5 standard deviations. We find consistent results using other
measures of liquidity as well as the stock’s overall trading volume, all of which decrease with a
stronger bank-firm relationship.
On the other side of the trade-off, we find evidence of a beneficial effect of a stronger bankfirm relationship reflected in better firm governance.
Cross-sectionally, a 10% increase in
proximity raises the probability of having independent directors on the board or having a nonexecutive Chairman (as measured by Independent Directors Dummy and Non-Executive
Chairman defined below) by 3% and 3.3%, respectively, and increases the independent directors’
voting power (as measured by Voting Power of Independent Directors defined below) by 2%. A
10% increase in proximity also reduces the probability of having directors with multiple
directorships or interlocking directorships, or having a relative of the CEO on board (as measured
by Multiple Directorships Dummy, Interlocking Directorships Dummy, and Relative-on-Board
defined later) by 7%, 6%, and 2.5% respectively. Using an alternative measure of governance,
we find that a 10% increase in proximity increases the holdings of institutional investors in the
firm (as measured by Unaffiliated Institutional Holdings defined below) by 3%. The same
change in proximity also lowers the probability of the firm having bad governance provisions (as
measured by the Governance Dummy defined later) by 2%.
Analogously, a 10% increase in the loan’s significance raises the probability of having
independent directors on the board or having a non-executive Chairman by 12% and nearly 2%,
respectively, and increases the independent directors’ voting power by 2%. It also reduces the
probability of having directors with multiple directorships or interlocking directorships, or having
a relative on the board by 4%, 3% and 2%, respectively. The same change in the loan’s
significance also increases the holdings of institutional investors in the firm by 4%. It also lowers
the probability of the firm having bad governance provisions by 2%.
Finally, it is interesting to note that a stronger lending relationship also increases the
sensitivity of CEO-compensation to performance, thus indicating an improvement in governance.
4
Specifically, a cross-sectional increase of one standard deviation in proximity raises the
sensitivity of the CEO-compensation to performance by 89% and the same increase in loan’s
significance makes it statistically significant (from being insignificant).
What is the effect on the firm’s value? On the one hand, better governance should lead to
higher stock prices, but on the other hand, more information asymmetry and lower liquidity will
increase the required rate of return on the stock, thus reducing its price. We find that, indeed,
greater proximity and loan significance have a positive influence on Tobin’s Q and profitability,
while a greater insider potential is negatively related to these measures of firm value.
Specifically, a standard deviation increase in proximity (loan’s significance) raises Tobin’s Q by
0.3 (0.6) standard deviations and industry-adjusted ROA by 0.1 (0.1) standard deviations, while a
standard deviation increase in the bank’s insider potential reduces Tobin’s Q (industry-adjusted
ROA) by 0.4 (nearly 2) standard deviations.
Alternatively, higher insider trading potential
reduces firm value by about 40 b.p. per month over 12 months and a corresponding trading
strategy yields 3%–4% over 12 months. Overall, the net effect is negative. This implies that the
beneficial effects in terms of better governance are more than offset by the negative implications
of the lower stock liquidity.
Our paper makes several contributions. First, it quantifies the impact of the different facets of
a lending relationship on the borrowing firm’s stock price. Our results highlight a trade-off
between governance and liquidity, which is similar to the monitoring–liquidity trade-off
established in the corporate governance literature (e.g., Berle and Means (1932), Coffee (1991),
Bhide (1993)).
Second, our paper adds to a broader debate in financial intermediation on the distinction
between banks-based and markets-based financial architecture, and the implications of one
prevailing over the other (e.g., Allen and Gale (2000)). Although the implications of conflicts of
interest due to underwriting or consulting activities of investments banks around M&A deals,
IPOs, and bond-issues have been highlighted in the literature (e.g., Puri (1996), Ritter and Zhang
(2005), and Schenone (2004)), the informational and liquidity implications of the lending activity
of the commercial banks have hardly been considered. Not only does our paper provide that link
but it also shows that this impact on liquidity can be sizable. If the very power that allows the
banks to monitor well has an adverse impact on the stock market, then it may prevent countries in
which lending relationships are strong from developing a well-functioning stock-market. In the
limit, the adverse-selection effects generated by banks may dry up liquidity and diminish stockmarket participation.
5
Third, we add another facet to the literature on liquidity. We are not aware of any study that
relates stock market liquidity to lending relationships or to the flow of inside information
(obtained through lending) within the financial conglomerate. Previous studies have documented
a liquidity impact of internal monitoring by block-holders (e.g., Coffee (1991), Bhide (1993)).
Another strand of literature provides evidence that the price-impact following an IPO
underwritten by a financial conglomerate can be ascribed to the flow of information within the
financial conglomerate (e.g., Ellis, Michaely, and O’Hara (2000), Schenone (2004)). We provide
complementary evidence on the effects of commercial banks’ behavior on stock liquidity.
Also, our study is related to previous findings showing that the announcement of a new loan
or a loan revision has a positive impact on the borrowing firm’s value (James (1987), Lummer
and McConnell (1989), and Slovin et al., (1992))). We qualify these results by focusing not so
much on the granting of the loan in itself, but on the loan-characteristics. We find that portfolios
of firms with a stronger relationship with the lenders earn positive abnormal returns in
comparison with those that have a relatively weaker relationship with the lenders. This suggests
that the signal is related not only to the initiation of the loan, but also to the strength of the bankfirm relationship.
Finally, our findings provide some normative insights and suggest that treating all loans
equivalently ignores important differences. After the complete repeal of the Glass-Steagall Act in
1998, the possibility of banks directly trading on the information acquired through lending has
increased tremendously. However, the role of banks as insiders has gone unnoticed. Our results
suggest that the effects of this on market liquidity may be relevant.
The remainder of the paper is structured as follows. Section 2 lays out the hypotheses,
Section 3 describes the sample and the variables, and Section 4 describes the econometric
methodology. Sections 5, 6, and 7 report the main findings. A brief conclusion follows.
2. Hypotheses and testable propositions
We argue that bank lending induces a trade-off between governance and liquidity for the
borrowing firm, especially when the borrower-lender relationship is stronger.
We start by defining the loan-characteristics which proxy for the strength of the borrowerlender relationship. The first one, called Proximity, is the closeness of the borrower-lender
relationship. It is measured by the geographic distance between the borrower and the lender. The
rationale is that a shorter distance (or greater Proximity) increases the ability to collect “soft”
6
information (Berger et al. (2005)). The interpretation of proximity as a measure of access to
inside information is supported by plenty of evidence (e.g., Coval and Moskowitz (1999),
Garmaise and Moskowitz (1999), Grinblatt and Keloharju (2001)). The precision of the signal the
bank receives on the borrower also decreases with distance (Diamond (1984), Petersen and Rajan
(1994), Berger and Udell (1995), Hauswald and Marquez (2000), and Sufi (2005)).
The second characteristic we focus on is the relative importance of the loan to the borrower’s
finances. We measure it using the Loan-to-Asset Ratio. The rationale is that increasing the loan
size makes the lender a keener monitor (Khalil and Parigi (1998)). More importantly, a bank
lending to a more dependent or needy firm – i.e., when the relative significance of the loan to the
borrower’s finances is greater – will have better access to inside information. An extreme case of
this “dependency” is relationship lending (Mayer (1988), Sharpe (1990), Rajan (1992), and Boot
and Thakor (2000)).
Next, we define insider potential as the ability of the bank to exploit the inside information
that it acquires through lending, in the equity market. We proxy for it with the equity stake that
the bank has, either directly or indirectly, in the firm it lends to. We call this variable the bank’s
Equity Exposure. Although historically US regulations have prohibited banks from investing in
the equity market for their own portfolio, banks can still make these investments through their
trust business. Banks’ trust services include selecting investments and exercising the voting
rights of stocks held in trust. Banks must select trust investments within the confines of the
federal and state law, and their supervisors’ regulations. However, “banks are still left with
significant discretion in the choice of their trust investments. Federal law generally defers to state
trust law ... [that] ... recognizes the duty of loyalty and the duty of care. This rule, however, is
quite general ... Some trust settlers or pension-plan sponsors choose to retain investment
discretion for themselves. Others give the trustee full investment discretion” (Santos and Wilson
(2005)). Banks can also hold positions indirectly through other members of the same financial
conglomerate (Massa and Rehman (2005), Acharya and Johnson (2007)). Seyhun (2007) shows
that when an investment banker joins a firm’s board of directors, profitability from insider trading
decreases. This effect is reversed after the end of the investment banker’s term on the board.
This is interpreted as evidence of ineffectiveness of the “Chinese Walls”. In general, there is
growing evidence of synergies accruing to commercial banks from belonging to a financial
conglomerate (e.g., Puri (1996), Schenone (2004), Ritter and Zhang (2005), and Seyhun (2007)).
Both, Proximity and Loan-to-Asset Ratio improve the bank’s ability to monitor the firm’s
managers. Bank monitoring, even if aimed at recovering the loan, improves overall corporate
7
governance. Banks acquire private information about loans and enhance the value of investment
projects (Diamond (1984), James (1987)). Bank lending also helps improve the quality of the
firm’s projects by reducing the management’s incentive to default strategically (Bolton and
Scharfstein (1996)), or to invest sub-optimally (Jensen (1986), Holmstrom and Tirole (1997)).
More inside information, due to greater proximity and/or higher loan-to-asset ratio, enhances the
monitoring ability of the bank (Besanko and Kanatas (1993)) and “thereby [improves] capital
allocation and corporate governance” (Levine (2002)).
We argue that the bank’s role is not limited to contributing to the general quality of the
governance by preventing inefficient and wasteful practices of the management. In fact, it may
also provide protection for the shareholders. For instance, a bank may be interested in preserving
the market value of the firm to avoid an increase in the firm’s market leverage, or just to preserve
the market valuation of the collaterals posted by the borrower. Therefore, the bank will use its
relationship with the firm to oppose antitakeover provisions or enhance the attractiveness of the
firm to institutional investors. Overall, these considerations suggest a positive correlation between
governance and the strength of the borrower-lender relationship.
However, the strength of the borrower-lender relationship may also have a detrimental effect
on the stock liquidity of the borrowing firm due to the bank’s informationally privileged position
vis-à-vis the other market participants. Indeed, the lender may exploit its equity exposure and
trade on the information obtained through lending. This would widen the information asymmetry
associated with the firm’s stock, reduce overall trading, raise adverse selection, and dry up stock
liquidity. These considerations allow us to define our testable restrictions. We posit that:
H1: A greater insider potential of the bank increases information asymmetry and reduces the
firm’s stock liquidity.
H2: A closer borrower-lender relationship and a more significant loan, both improve the
firm’s corporate governance.
The net effect of the governance/liquidity trade-off on firm-value is uncertain a priori. If the
“better-governance” effect due to a stronger borrower-lender relationship prevails, it would
enhance firm and stock value, while if the “informational asymmetry” aspect prevails, it would
depress the stock price.
8
3. Data and Definitions of Main Variables
3.1 Data description
We draw data from several different sources and merge them to construct our final sample.
Primarily, we construct our sample using two groups of companies – firms that initiate a loan
contract and firms whose historical location is known. To construct our base sample, we start
with data on bank loans, which are collected from Loan Pricing Corporation’s (LPC) DealScan
database. We pick all loan contracts over the period 1985–2004 between borrowers and lenders
located in the United States. These data provide information such as the size of the loan, the date
when the contract is effective, the tenor of the loan, and the location of the borrowing firm at the
time of the loan contract, etc. The other component of our base sample consists of all firms
whose historical location is known. These historical location data (at county-level) are from
1991–onwards, so merging the LPC Dealscan data with these historical-locations data from
Compustat constrains our sample to 1991–2004. The resulting sample consists of loan-taking as
well as non-borrowing firms, whose historical location is known over 1991–2004.
For all the banks listed as members of the lending syndicate in our LPC data, we obtain the
location of the parent company (or “bank holding company”) either from the Federal Reserve’s
Report of Condition and Income (a.k.a. “Call Reports”), or from the Federal Deposit Insurance
Corporation’s (FDIC) Institution Directory, or else from the Bureau van Dijk’s BankScope
database. To obtain these locations, banks are matched by name and year in which the loan
becomes active (the time dimension is added in order to account for possible changes in the
banks’ location). The name-matching is first done using an algorithm designed for this purpose
and then further enhanced by manually searching for the remaining (unmatched) LPC-banks in
the above three databases and identifying their parent company’s location.
We then calculate the distance between the borrower and the nearest “large” branch of any
bank within the borrower’s lending-syndicate. We identify the geographical coordinates (i.e.,
latitude and longitude) for the borrower and for the nearest “large” branch of any of its lending
banks. We obtain these county-level coordinates from the Census 2000 US Gazetteer Files and
plug them into the formula for calculating the spherical distance. The distance di,j (in miles)
between the branch of bank i and firm j is:
d i , j = arccos (deg latlon ) ⋅ r
9
(1)
where deglatlon is given by:
cos (lat i ) ⋅ cos (lon i ) ⋅ cos (lat j )⋅ cos (lon j ) + cos (lat i ) ⋅ sin (lon i ) ⋅ cos (lat j )⋅ sin (lon j ) + sin (lat i ) ⋅ sin (lat j )
and lat and lon refer to the latitude and longitude in radians (converted from degrees by
multiplying with π / 180 ); r is the radius of Earth in miles.
All accounting variables for the borrowing firms are measured through the life (or tenor) of
their corresponding loans, and are obtained from the CRSP-Compustat Merged (CCM) database.
Information regarding the local banking market is obtained using branch-level data from FDIC’s
Summary of Deposits. The earliest available Summary of Deposits is dated June 1994 and covers
the preceding year. Hence, using these data adds a further constraint and restricts our sample
period to 1993–2004.
We use Thomson Financials’ 13F Reports to obtain the fraction of
borrowing company’s outstanding shares held by financial institutions. In order to calculate
aggregate volatility of returns and stock market illiquidity, we use CRSP Daily data. Average
trading volume is based on CRSP Monthly data.
We report summary statistics for our loan-taking sample in Table 1, Panel B. We can see that
in our sample the median firm-size (as measured by assets) is about $738 million. More than half
of the loan-taking firms are listed on the NYSE. The average loan size is four times the assets.
These loan-taking firms are on average located about 200 miles away from the largest branch of
their lender(s). Approximately 14% of the firms in our loan-taking sample are located in large
metropolitan areas. We distinguish the following six major metropolitan areas in the U.S. because
of their prominent capital markets – Boston, Chicago, Los Angeles, New York, Philadelphia, and
San Francisco. The median firm has 9 out of the 24 features that compose IRRC’s Governance
Index, a number very similar to the figures reported in Gompers, Ishii and Metrick (2003). Our
mean statistic for Illiquidity is 0.58, which is close to the mean statistic of 0.32 reported in
Amihud (2002).
3.2 Strength of the borrower-lender relationship
In all the definitions below, n represents the tenor (or duration) of the loan and t represents the
year in which the loan is initiated. We use three features of the borrower-lender relationship:
Proximity, which reflects the degree of closeness between the borrower and the lender; Loan-toAsset Ratio, which is a measure of how significant the loan is for the borrower; and Equity
Exposure, which proxies the insider potential of the bank vis-à-vis the borrower. Proximity is
defined as –ln(1 + Borrower-Branch Distance), where Borrower-Branch Distance is the distance
10
between the borrower and the nearest “large” branch of any bank within the borrower’s lendingsyndicate. “Large” branch is taken to be one with deposit size greater than that year’s median
deposit size in branches across the country. Loan-to-Asset Ratio is the “drawn amount” of the
loan, calculated as the percentage of the borrower’s asset size. “Drawn amount” refers to the
actual amount drawn by the borrower (as opposed to what might be available through a line of
credit). Also, the borrower’s asset size in this ratio is the size of its assets (item 6) averaged over
[t-n, t-1] years. Equity Exposure is the fraction of borrower’s equity held by all institutional
investors (i.e., commercial banks, insurance companies, mutual funds, pension funds, and
investment managers) affiliated with the lending banks in the syndicate. We assume that banks
can (indirectly) dispose of these shares and therefore affect the price of the stock. We measure
the value of these holdings on the last filing date in the fiscal year before the loan is initiated.1
3.3 Firm characteristics
We control for several firm-characteristics that may be indirectly related to the degree of
information asymmetry between the borrower and the lender. Following is a brief description of
those. Size is the logarithm of book value of assets (item 6) averaged over [t-n, t-1] years. Sizesquared is the square of Size. Leverage is the long-term debt (item 9) to assets ratio averaged
over [t-n, t-1] years. Cash is total cash (item 1) to lagged assets ratio averaged over [t-n, t-1]
years. Capital Expenditure is capital expenditures (item 128) to lagged assets ratio averaged over
[t-n, t-1] years. Market-to-Book is market equity (item 25 x item 199) to book equity (item 60)
ratio averaged over [t-n, t-1] years. All these data are obtained from CRSP-Compustat Merged
database. Institutional Holdings is the institutional investors’ equity stake averaged over all
quarters in [t-n, t-1] years; these data are obtained from 13F filings. Analysts is the number of
analysts following the stock (obtained from the I/B/E/S Summary database), averaged over [t-n, t1] years. (Our results are robust to measuring all these characteristics only in year t-1.) Firm’s
1
All our results are robust to using the following alternative measures of the loan characteristics. Proximity of the firm
can be measured with respect to the lending banks’ headquarters, and defined as –ln(1 + Borrower-HQ Distance),
where Borrower-HQ Distance is the average distance between the borrower and the headquarters of all banks in the
lending syndicate. We can also define Proximity as the fraction of total loan obtained from banks headquartered in the
same state as the firm’s headquarters, or as the fraction of total loan obtained from banks headquartered within 200
miles of the borrower, or as reciprocal of the average distance of the borrowing firm from all the lending banks in the
syndicate. Loan-to-Assett-1 Ratio is defined in a manner similar to the measure described above, except that it is
standardized by assets from year t-1 (instead of assets averaged over [t-n, t-1] years). Bank Holdings is an alternative
to Equity Exposure; it is constructed like the measure used in the paper, except we use only “Type-1” holdings to
construct this alternative measure. (“Type” refers to the classification used for categorizing institutional investors in
the CDA/Spectrum 13F Holdings database by Thomson Financial, and “Type-1” indicates a Bank.) Potential Equity
Exposure is another alternative to Equity Exposure, and it is measured as the ratio of the overall assets (as opposed to
the holdings in the borrower) of all lender-affiliated institutions to the market equity of the borrower.
11
Relative Age is the firm’s age standardized by the age of other firms in the same industry. Age is
calculated as the number of years since the firm first appeared in CRSP-Daily database (Denis,
Denis, and Sarin (1997)). NYSE is a dummy variable equal to 1 if the firm is listed on the New
York Stock Exchange, and 0 otherwise. Ratings Dummy is a dummy equal to 1 if the firm has a
credit-rating, and 0 otherwise. We also use year- and 48 industry-dummies (Fama and French
(1997)).
Size, leverage, profitability (Frank and Goyal (2003)), level of institutional ownership (Best,
Hodges, and Lin (2004)), and number of analysts (Krishnaswami and Subramaniam (1998),
Lowry (2003)) are all indicative of the firm’s information asymmetry. The use of these and other
firm-specific characteristics as controls allows us to strengthen our claim that the impact on
liquidity/governance is a direct result of the borrowing relationship and not due to the underlying
informational asymmetry between the borrower and the lender. Some of the firm-characteristics
that we control for also reflect the firm’s investment opportunities.
4. Econometric methodology
We focus on the loan deal as the unit of our cross-sectional analysis. For each deal-based
observation, we average the dependent variable over n years after the year in which a loan is
initiated and we also control for the average value of the dependent variable over n years before
the loan is initiated (here n is the tenor of the loan). This pre-loan average (over [t-n, t-1] years)
of the dependent variable is like a “lagged dependent variable”. For example, for a three-year
loan, we analyze the average value of liquidity over years [t+1, t+3] while controlling for average
liquidity over the years [t-3, t-1] (t being the year in which the loan is initiated). The fact that the
sampling is event-based eliminates the issue of autocorrelation. As a robustness check, we also
employ a sampling at the firm level in which the unit of observation is the firm as opposed to the
loan deal. The results obtained using these alternative specifications are consistent. Our results
are also robust to controlling for the independent variables lagged only by one year. Given that
the results do not differ, we leave these additional robustness checks unreported.
All the estimates of the impact of bank-firm relationship on the firm’s liquidity/governance
are conditional on the firm’s decision to borrow from a bank. This may induce a selection bias if
the variables that determine such an impact were the same as those explaining the decision to
borrow. It may be, for instance, that the impact of a stronger relationship on stock liquidity is due
12
to the fact that less liquid firms are the ones more likely to borrow from banks with a greater
equity exposure in the first place. I.e.,:
si*,t = β xi ,t + ε i ,t
(2)
bi*,t = αzi ,t + ηi ,t
(3)
if bi*,t > 0 , si ,t = si*,t ,bi ,t = 1 ; otherwise, if bi*,t ≤ 0 , si,t not observed and bi ,t = 0 , (4)
where equation (2) relates stock-specific characteristics (e.g., si,t be stock liquidity) and equation
(3) represents the firm’s decision to borrow from a bank, where zi,t and xi,t are the explanatory
variables. The conditions in (4) say that we do not observe the relationship between bank loans
and stock liquidity for the firms that have not borrowed. Thus, the decision to borrow is
endogenous: bi,t depends on the latent variable bi*,t , which itself is a function of firm
characteristics and other determinants. We follow the solution proposed by Heckman (1979) and
we first estimate equation (3) using a Probit model. Then, we estimate:
s i ,t = xi ,t β + σLambda i ,t + ε i ,t
(5)
where Lambdai,t is Heckman’s (1979) Lambda, calculated using estimates from the Probit model
in (3). We estimate equation (3) using a panel of loan-taking as well as non-borrowing firms
whose historical location is known from Compustat. bi*,t is a dummy variable (named Loantaking Decision in Table 2, Panel A) that equals 1 in the year when the firm initiates a loan, and 0
otherwise. The vector xi′,t contains the main determinants of the decision to borrow, a set of
firm- and industry-specific control variables, and year and 48 industry dummies (Fama and
French (1997)). The main determinants are: a dummy for whether the firm already has an
outstanding loan in the given year; an interaction of the firm’s age and the Metropolis dummy
(which equals 1 if the borrower is headquartered in one of the six metropolises identified above,
and 0 otherwise); an interaction of firm’s size and the Metropolis dummy; number of segments
the firm operates in; concentration of the firm’s local banking market (measured by the lagged
Herfindahl Index of bank-deposits at all the branches located in the same county as the firm); the
borrower’s median distance (inversely weighted by the corresponding number of branches) from
the headquarters of all the bank-branches located in the same county as the firm; the median size
(weighted by the corresponding number of branches) of all the bank-branches located in the same
county as the firm; the regulatory environment in the firm’s home state, as measured by the
fraction of years in our sample for which interstate-branching was deregulated in the state.
13
The firm-specific control variables are lagged by one year and include: Size, Size-squared,
Leverage, Cash, Capital Expenditure, ROA, Market-to-Book, Kaplan-Zingales Index (constructed
following the methodology of Baker, Stein, and Wurgler (2003)), Institutional Holdings,
Analysts, Firm’s Relative Age, NYSE dummy, and Ratings Dummy.2 All these variables are
defined in the same manner as in Section 3.3 above, except here they are measured in year t-1 as
opposed to being averaged over [t-n, t-1] years. Details on the definitions can be found in Table
1. We also include several industry-specific control variables in some specifications of the model
but do not report their estimated coefficients in Table 2, Panel A, for economy of space. These
industry-specific variables are lagged by one year and include the following characteristics
averaged across other firms in the same industry: Size, Leverage, Cash, Capital Expenditure,
ROA, Market-to-Book, Kaplan-Zingales Index, Institutional Holdings, Analysts, and Age.
The estimated coefficients from the Probit model are reported in columns (1)–(4) of Table 2,
Panel A. We calculate the Heckman’s (1979) Lambda using the estimates reported in column (3).
As a robustness check, we also consider a Logit specification and report those estimates in
columns (5)–(8). It is evident that, even after controlling for firm- and industry-characteristics,
the nature of the local bank-lending market has a critical influence on the firm’s loan-taking
decision. Given that this is not the main focus of the paper, we will not dwell on these results. It
is just worth mentioning that the firms more likely to borrow are the ones that are not located in
financial centers, have more segments, are bigger in size and less levered, have less cash but more
capital expenditures, are more profitable and younger, and have a past loan outstanding. Distance
from banks reduces the likelihood to borrow. These results are consistent with the existing
literature.
In the second stage, we include the Inverse Mills’ Ratio in the main equation and estimate
this main equation using 2SLS, as described in Procedure 17.2 under Section 17.4.2 in
Wooldridge (2001). The results show that, while the selection model for the loan taking decision
appears to have good explanatory power, the Inverse Mills Ratio (λ) is insignificant in the
governance regressions but seems to play a role in the case of information asymmetry
regressions. This suggests that the selection bias due to the endogenous choice of the loan is
significant and affects the degree of information asymmetry around the borrower’s stock. If λ is
2
The Kaplan-Zingales Index is included to control for the likelihood that the firm faces financial constraints. Like
Baker, Stein, and Wurgler (2003), we define it as per the following linearization: KZ = (3.139 x Leverage) + (0.283 x
Tobin’s Q) – (1.002 x Cashflow) – (39.368 x Dividends) – (1.315 x Cash). Cashflow is the sum of Compustat Items 14
and 18, as a fraction of lagged assets (item 6), and Dividends are the sum of Compustat items 19 and 21, as a fraction
of lagged assets as well. The remaining variables are as defined above.
14
not included, then the least squares methodology overestimates the value of the relationship
between information asymmetry and loan characteristics. In other words, the stocks of firms that
borrow in general display higher information asymmetry and are less liquid. This bias is properly
accounted for by including λ. No such effect of λ is found in the case of governance, which
means that borrowing firms are not significantly different from the non-borrowing firms in terms
of corporate governance.
All the second-stage estimations are based on instrumented loan-characteristics because these
are also endogenous. The main instrument for each of the three loan characteristics is the
unconditional value of the corresponding variable.
Specifically, the main instrument for
Proximity is proximity of the borrower to the nearest “large” branch of any bank, and that for
Loan-to-Asset Ratio and Equity Exposure is the industry average of the corresponding loancharacteristic in the same year as the given loan’s start. Definitions of the other instruments and
the estimated coefficients are reported in Table 2, Panel B. The suitability of these instruments is
checked in all the second-stage regressions by the statistics from Hansen’s test of overidentification. The p-values are reported at the bottom of each table and confirm the quality of
our instruments. In the Appendix, we provide a detailed description of the set of instruments
used for the loan-characteristics and discuss the estimation results from this stage.
Recall that only Equity Exposure is expected to affect the information asymmetry of the
borrowing firm. The other two loan-characteristics – Proximity and Loan-to-Asset Ratio – are
expected to affect the governance of the firm. Therefore, in all our tests, we will first consider the
individual impact of these three loan-characteristics on the dependent variable and subsequently
employ a pair of loan-characteristics for robustness. The pair of loan-characteristics consists of
one that affects governance (either Proximity or Loan-to-Asset Ratio) and one that affects the
information asymmetry (i.e., Equity Exposure).
5. The Dark Side of a Stronger Borrower-Lender Relationship
5.1 Lender-Affiliated Institutions’ Trading
Given the role of the bank’s informational advantage and the bank’s ability to exploit this
information in the equity market, we start by focusing directly on the lender-affiliated
institutional investors’ trading in the borrower’s stock after the loan’s initiation. The dependent
variable, Lenders’ Relative Trading, measures trading by the lender-affiliated institutional
investors in the borrowing firm’s stock relative to the overall trading by all institutional investors
15
in that stock. It is defined as ln(1 + Lenders’ Trading/Institutional Trading), where Institutional
Trading is the average quarterly turnover in institutional investors’ holdings over [t+1, t+n] years.
Lenders’ Trading is calculated in the same manner but using only those institutional investors
that are affiliated with banks involved in the lending syndicate. Lenders’ Relative Trading thus
captures the adverse selection faced by other market-participants. We regress this measure on the
instrumented loan-characteristics and the full set of explanatory variables defined above.
The results are reported in Table 3. They show a strongly positive relation between our proxy
for the bank’s insider potential and the lender-affiliated institutions’ trading in the borrower’s
stock.3 The results are also economically significant – a standard deviation increase in Equity
Exposure raises lender-affiliated institutions’ relative trading (calculated using all “Types”) by
0.1 standard deviations.
Next, we split Lenders’ Relative Trading into two sub-components by computing the
dependent variable either using only the holdings of banks or using the holdings of all the
institutional investors except the banks. The results thus obtained are very similar to those for the
overall Lenders’ Relative Trading and show a very strongly positive impact of Equity Exposure,
while Proximity and Loan-to-Asset Ratio have no significant impact.
Later, we will also show that equity exposure is negatively related to overall trading volume
as well as institutional trading. Taken together, these results suggest that the bank’s insider
potential directly translates into more insider-trading by the lender-affiliated institutional
investors at the cost of trading by other institutions.
This provides evidence for how the
borrowing firm’s strong relationship with the lenders can lead to adverse selection for the other
market participants.
5.2 The effect on Stock Liquidity and Information Asymmetry
We now relate the strength of the borrower-lender relationship to the firm’s liquidity and
information asymmetry in the stock market (H1). We estimate:
LAM i ,[ t +1,t + n ] = β 0 + β 1 BLR i ,t + β 2 X i ,[ t − n ,t −1] + β 3 LAM i ,[ t − n ,t −1] + σLambda i ,t + ε i ,[ t +1,t + n ] , (6)
where LAMi,[t+1, t+n] is the liquidity/asymmetry measure. We consider two alternative measures:
Amihud’s (2002) Illiquidity ratio and the measure of Information Asymmetry developed by
3
All the results reported in the paper are robust to the exclusion of 5% extreme observations. These results using the
censored data are left unreported for economy of space.
16
Llorente et al. (2002). The first is based on Kyle’s (1985) λ and measures the percentage price
response for a given level of trading volume. It reflects the compensation that liquidity providers
demand for transacting with better-informed traders, and increases with the degree of information
asymmetry. We define it as ln(1 + AvgILLIQ[t+1, t+n]), where AvgILLIQ[t+1, t+n] is the average of
AvgILLIQ over [t+1, t+n] years. AvgILLIQ is the yearly average of ILLIQ (multiplied by 107).
ILLIQ, for month t is:
ILLIQi ,t =
1
Daysi ,t
Days i ,t
Ri ,t , d
d =1
DVoli ,t , d
∑
.
(7)
Daysi,t is the number of valid observation days for stock i in month t, and Ri,t,d and DVoli,t,d are the
daily return and daily dollar volume, respectively, of stock i on day d of month t.
The measure of Information Asymmetry developed by Llorente et al. (2002) relies on the
positive (negative) autocorrelation in returns when the level of speculative trading is
proportionately high (low). The degree of information asymmetry for stock i in a given fiscal
year is measured by the coefficient C2 estimated from the following regression using daily data:
Ri ,d + 1 = C0 + C1 .Ri ,d + C2 .(Vi ,d × Ri ,d ) + ε i ,d + 1 ,
Vd = log Turnoverd −
(8)
1 −1
∑ log Turnoverd + s , log Turnover = log (Turnoverd + 0.00000255)
200 s = −200
C2 is then averaged over [t+1, t+n] years. Turnoverd is the total number of shares (of stock i)
traded on day d as a fraction of shares outstanding, and Ri,d is stock i’s return on day d. The
intuition is that the interaction of higher trading volume and stock return autocorrelation helps
identify firms with high degree of speculative trading driven by private information. When
volume is high, stocks characterized by more speculative trading exhibit positive autocorrelation
in returns, while stocks characterized by more hedging-motivated trading display negative
autocorrelation.
BLRi,t is the proxy for the strength of borrower-lender relationship (i.e., Proximity, or Loanto-Asset Ratio, or Equity Exposure). The vector of control variables (Xi,[t-n, t-1]) consists of Size,
Size-squared, Leverage, Cash, Capital Expenditure, ROA, Market-to-Book, Institutional
Holdings, and Analysts averaged over [t-n,t-1], besides Firm’s Relative Age, NYSE, and Ratings
Dummy. We also include the pre-loan average of the dependent variable (LAMi,[t-n,
t-1] )
and
Heckman’s (1979) Lambda (Lambda). To address the issue of endogeneity, we instrument our
17
various proxies for the strength of borrower-lender relationship.
A detailed description of
instruments and their characteristics is reported in the Appendix.
Our working hypothesis (H1) requires β1 > 0 for Illiquidity as well as Information
Asymmetry. The results, reported in Table 4, support it. There is a strongly positive and
statistically significant relationship between Illiquidity (or Information Asymmetry) and Equity
Exposure.
Not only are the results statistically significant, but they are also economically
relevant.
A standard deviation increase in Equity Exposure raises illiquidity (information
asymmetry) by 0.2 (0.8) standard deviations. Also, the individual impact of proximity and loan’s
significance on illiquidity and information asymmetry appears to be very strongly positive.
However, when we pair these loan-characteristics with equity exposure, that effect disappears.
The signs on the coefficients of the control variables are as expected. Illiquidity is positively
related to leverage and negatively related to the size of the firm, its profitability, and to the
amount of cash it holds. Rated firms as well as firms held by institutional investors, firms
belonging to the NYSE, and firms with higher market-to-book ratio have lower illiquidity/
asymmetry levels. It is important to stress that these results hold even after controlling for the
pre-loan level of illiquidity/asymmetry.
Overall, these findings suggest that the lending relationship directly affects the level of
liquidity and information asymmetry in the equity market. Banks are perceived as insiders and
this increases asymmetry while simultaneously reducing overall institutional trading.
The
potential of trading on insider information by banks (and their affiliated institutions) crowds out
trading by other institutional investors.
As institutional trading drops, less information is
impounded in prices, which then become less transparent, leading to a rise in information
asymmetry and a drop in liquidity. This is also consistent with the results (described below)
showing that a stronger lending relationship reduces overall trading.
One important additional element is the fact that the impact of the borrower-lender
relationship on liquidity is more evident in the sub-sample of firms that are relatively more
transparent to the market. In unreported tests, we investigate how the impact of the lending
relationship differs by the degree of ex-ante transparency of the borrowing firm. The intuition is
that, the insider role of the bank affects illiquidity more for firms with greater transparency.
Liquidity of firms that were already more opaque will be less affected by the lending relationship.
And indeed, the results show that the lending relationship impacts stock Illiquidity and
Information Asymmetry more strongly for those firms that before the loan either have a creditrating, or have greater analyst coverage, or are relatively older than other firms in the industry, or
18
are listed on the NYSE. The effect is minimal in the case of unrated firms, or firms with less
analyst coverage, or relatively younger firms, or firms that are not listed on the NYSE. Similarly,
a stronger impact for the sub-sample of more transparent firms is found using the alternative
measures of information asymmetry that are described in the next section.
Overall, these findings suggest that for more opaque firms, the benefit of getting a loan
dominates the adverse impact a strong borrower-lender relationship has on the stock’s liquidity.
In other words, the positive information signaled by the bank loan (e.g., James (1987)) offsets the
negative effect due to the bank’s insider potential. Therefore, the bank loans are important for
young companies in building reputation (Diamond (1989)), but once the borrower is well
established, a strong lending relationship can have adverse effects on the firm (Rajan (1992)).
5.4 Robustness checks
We perform a series of robustness checks based on alternative measures of stock illiquidity or
information asymmetry. In particular, we consider the Probability of Informed Trading, the
Liquidity Ratio, Trading Volume, and Institutional Trading. The Probability of Informed Trading
is constructed as ln(1+PIN[t+1,t+n]), where PIN[t+1,t+n] is the average of PIN over [t+1, t+n] years,
and PIN is the measure proposed by Easley et al. (1996).4 This measure is built on the structural
sequential trade model of Easley and O’Hara (1987, 1992). It posits that differences in the
probability of informed trading across stocks and the changes in this probability for a given stock
are related to the level of information asymmetry. It proxies for informed trading, as calculated
from the relative imbalance in a stock’s buy and sell orders.
The Liquidity Ratio (Cooper, Groth, and Avera (1985), Amihud, Mendelson, and Lauterbach
(1997), and Berkman and Eleswarapu (1998)) can conceptually be considered as the reciprocal of
Illiquidity used above. Operationally, however, it is constructed by using high-frequency TAQ
data. It is the logarithm of “the average ratio of volume to absolute return, where the average is
taken over all days in the sample for which the ratio is defined (Hasbrouck (2005)).5
Another measure related to both information asymmetry and liquidity is the stock’s trading
volume. This is based on the fact that information asymmetry reduces trading volume (Milgrom
and Stokey (1982), Foster and Viswanathan (1990), and Easley et al. (1996)). Also, trading
volume is positively related to stock liquidity (Amihud and Mendelson (1986), and Brennan et al.
4
5
We obtained this measure from Soeren Hvidkjaer’s website: http://www.smith.umd.edu/faculty/hvidkjaer/.
We obtained this measure from Joel Hasbrouck’s website:
19
(1998)). We define Trading Volume as the logarithm of annual volume, averaged over [t+1, t+n]
years. Annual volume is the average monthly volume over the year, where monthly volume is the
number of shares traded (reported in CRSP Monthly) as a fraction of total shares outstanding.
Finally, we also consider a measure of trading by institutional investors. Since institutions
tend to be better informed, a reduction in their trading would be a signal of adverse selection
problems. The variable Institutional Trading is the average over [t+1, t+n] years of turnover in
institutional investors’ holdings (obtained from 13F-Spectrum). Yearly turnover is the average
quarterly turnover in a given fiscal year.
Our working hypothesis (H1) requires β1 > 0 for Probability of Informed Trading, but β1 < 0
for Liquidity Ratio, Trading Volume, and Institutional Trading. Unreported results confirm those
described in the previous sub-section and support our main hypothesis. Specifically, there is a
strongly positive and statistically significant relationship between our alternative measures of
information asymmetry and equity exposure. Equity Exposure is positively related to Probability
of Information Trading and negatively related to Liquidity Ratio, Trading Volume, and
Institutional Trading.
Not only are the results statistically significant, but they are also
economically relevant. A standard deviation increase in Equity Exposure raises (reduces) the
Probability of Informed Trading (Institutional Trading) by 0.4 (0.5) standard deviations. The
results for Liquidity Ratio and Trading Volume are also very similar.
6. The Benefits of a Stronger Borrower-Lender Relationship
We now study the impact of a strong lending relationship on the firm’s corporate governance.
6.1 Measures of corporate governance
In this section, we focus on the benefits of a strong lending relationship for the governance of the
firm. We use three sets of measures for the quality of corporate governance that are prevalent in
the literature and relate them to the strength of the borrower-lender relationship.
The first set of governance measures proxies for the internal governance provided by the
board composition.
In line with the existing literature (e.g., Davis (1996), Hermalin and
Weisbach (1998), Core, Holthausen, and Larcker (1999), Klock, Mansi, and Maxwell (2005),
Fich and Shivdasani (2006)), we identify the following measures of governance: the fraction of
http://pages.stern.nyu.edu/~jhasbrou/Research/GibbsEstimates/Liquidity%20estimates.htm
20
board of directors that has multiple board memberships, the fraction of board of directors that is
independent, the voting power of the independent directors, whether the Chairman is also an
executive of the firm, the presence of a CEO’s relative on the board, and the fraction of board of
directors that has interlocking directorships.6 Specifically, Multiple Directorships Dummy equals
1 if the fraction (averaged over [t+1, t+n] years) of total directors with multiple directorships is
above median and 0 otherwise. We define Independent Directors Dummy and Interlocking
Directorships Dummy in a similar manner.7 Voting Power of Independent Directors is the voting
power on equity shares (averaged over [t+1, t+n] years) controlled by the independent directors.
Non-Executive Chairman is a dummy that equals 1 if the firm’s Chairman during the [t+1, t+n]
years is not an executive manager of that firm, and 0 otherwise. Relative-on-Board is a dummy
that equals 1 if there is a relative of the CEO on the board at any time during [t+1, t+n] years, and
0 otherwise.
The second measure of corporate governance is related to the equity stake of institutional
investors in the borrowing firm. The literature has argued that institutional investors provide a
measure of “external governance” (Maug (1998), Kahn and Winton (1998), Bolton and von
Thadden (1998), Noe (2002), and Faure-Grimaud and Gromb (2004)). In order to filter out the
effect of the loan on overall institutional holdings, we only measure the holdings of those
institutional investors that are unaffiliated with banks in the lending syndicate. Specifically, we
take the equity holdings of all these unaffiliated institutional investors in the borrower and
average them across all quarters through [t+1, t+n] years. The resulting dependent variable is:
Unaffiliated Institutional Holdings.
The final set contains measures of governance based on by-laws. These are the Gompers,
Ishii, and Metrick (2003) governance index (henceforth GIM), the anti-takeover provisions of
GIM as well as the complementary part of GIM that is not made of anti-takeover provisions.
GIM is calculated by adding one point for each of 24 provisions, compiled by the Investor
Responsibility Research Center (IRRC), that restrict shareholder rights. Higher levels of the
index represent weaker shareholder rights. Our first dependent variable is Governance Dummy,
which is equal to 1 if the GIM index averaged over [t+1, t+n] years is greater than 9, and 0
otherwise (i.e., 1 for “dictatorship” firms and 0 for “democratic” firms).
6
All the measures based on the fraction of board of directors are calculated as the ratio between the number of
directors with those characteristics (such as independent or interlocking) and the total number of directors on the board.
7
The sample-medians for the fraction of total directors with multiple directorships, independent status, and
interlocking directorships are 0, 2/3, and 0, respectively.
21
Further, in line with Cremers and Nair (2005) and Cremer, Nair, and Wei (2007), the
provisions used to construct the GIM index can be divided in two broad categories. One category
relates to takeover defenses, such as bylaws to delay hostile bids (e.g., a staggered board, limits to
call special meetings, limits to act by written consent), and general defense tactics (e.g., poison
pills and blank check). Using these five measures, we create the Anti-takeover Dummy, which
equals 1 if a firm has more than 3 of these provisions in place (when averaged over [t+1, t+n]
years), and 0 otherwise. The second category relates to power-sharing arrangements between the
management and the shareholders. It includes the amount of protection given to officers and
directors (e.g., golden parachutes), and the effective voting rights of shareholders (e.g., absence
of confidential voting). Using these remaining provisions unrelated to anti-takeover measures,
we create the Complementary Governance Dummy, which equals 1 if a firm has more than 6 of
these provisions (when averaged over [t+1, t+n] years), and 0 otherwise. The power-sharing
category of provisions partially reflects the bargaining power of existing management vis-à-vis
inside monitors (Hellwig (2000)).
To test for the exact impact of lending relationship on governance, we regress the above
measures of governance on our proxies for the strength of lending relationship as well as on the
set of control variables defined earlier. We also control for Product-Market Competition, which
is the Herfindahl Index of industry sales (item 12), averaged over years [t-n, t-1]. This is done to
account for the disciplining effect competition has on the firm’s governance. We estimate:
Gi ,[ t +1,t + n ] = β 0 + β 1 BLR i ,t + β 2 X i ,[ t − n ,t −1] + β 3 Gi ,[ t − n ,t −1] + σLambda i ,t + ε i ,[ t +1,t + n ] , (9)
where Gi,[t+1, t+n] is one of the measures of corporate governance, averaged over the years [t+1,
t+n]. The other variables and the econometric specification are the same as before, i.e., we use
Heckman’s Lambda to correct for the selection bias, we instrument the loan-characteristics, and
we average the right-hand side control variables over [t-n, t-1] years. In order to rule out reversecausality we control for the pre-loan level of the corresponding dependent variable, averaged over
[t-n, t-1] years.8 This is indicated by Gi,[t-n, t-1] on the right-hand side of (9).
When the dependent variable is a dummy variable, equation (9) is estimated as a Probit
model. When the dependent variable is a fraction between 0 and 1, equation (9) is estimated as a
Tobit model. We expect Proximity and Loan-to-Asset Ratio to have a favorable impact on
governance. The results are reported in Table 5. In Panel A, we report results for the six
22
measures of governance based on the board structure (specifically, Multiple Directorships
Dummy, Independent Directors Dummy, Voting Power of Independent Directors, Non-Executive
Chairman, Relative-on-Board, and Interlocking Directorships Dummy). In Panel B, we report the
results for Unaffiliated Institutional Holdings, and in Panel C, we report the results for
Governance Dummy, Anti-takeover Dummy, and the Complementary Governance Dummy,
derived from the GIM Index.
All the results display a strongly positive relation between the strength of the lending
relationship and the quality of governance. In particular, there is a positive relation between
Proximity (or Loan-to-Asset Ratio) and the number of independent directors, voting power of
independent directors, probability of having a non-executive Chairman (Panel A) as well as the
holdings of unaffiliated institutional investors (Panel B). Conversely, there is a negative relation
between Proximity (or Loan-to-Asset Ratio) and multiple directorships, probability of relatives
sitting on the board, and interlocking directorships (Panel A) as well as the three GIM-based
indices (Panel C).
These results are not only statistically significant, but are also economically relevant. A 10%
increase in Proximity reduces the probability of having multiple and interlocking directorships by
7% and 6%, respectively. It also lowers the probability of a relative sitting on the board and the
firm having bad overall governance (as per Governance Dummy) by 2.5% and 2%, respectively.
The same increase in Proximity also raises the probability of having more independent directors
by 3% and the probability of having a non-executive Chairman by 3.3%. A 10% increase in
Proximity also increases the voting power of independent directors by 2% and unaffiliated
institutional holdings by 3%.
Analogously, a 10% increase in Loan-to-Asset Ratio reduces the probability of having
multiple and interlocking directorships by 4% and 3%, respectively.
It also reduces the
probability of a relative sitting on the board and the firm having bad overall governance (as per
Governance Dummy) by 2% and 2%, respectively. The same increase in Loan-to-Asset Ratio
also raises the probability of having more independent directors by 12% and the probability of
having a non-executive Chairman by nearly 2%. A 10% increase in Loan-to-Asset Ratio also
raises the voting power of independent directors by 2% and the unaffiliated institutional holdings
by 4%.
8
In unreported tests, we also use change-in-governance, measured by Gi,[t+1,t+n] – Gi,[t-n,t-1] , as the dependent
variable in order to rule out reverse causality, and we find qualitatively similar results.
23
Although we do not hypothesize any relationship between governance and equity exposure,
we still find that equity exposure has a positive impact on some of our proxies for governance. A
potential explanation is that a bigger equity exposure may provide the affiliated institutions with
more “power of persuasion” on the borrowing firm.
This can happen either by the direct
representation of lender-affiliated institutions on the borrowing firm’s board or by the mere threat
of voting with their feet (e.g., Kahn and Winton (1998)). Alternatively, it is possible that the
lender-affiliated institutions might use their voting power to vote against the board of directors on
corporate governance matters.9
Overall these findings provide substantial evidence that a strong borrower-lender relationship
improves the governance of the firm.10 They support our working hypothesis (H2), showing a
strong bank-firm relationship improves the firm’s governance and suggest that we have identified
a separate dimension of governance: one based on the role of lending institutions. It is important
to note that these results hold even after adjusting for selection bias and controlling for reverse
causality.
6.2 Robustness check
One alterative measure of corporate governance that is often used is the degree of alignment of
managers’ incentives with those of the shareholders. Therefore, we conduct a robustness check
based on how the lending relationship affects the sensitivity-to-performance of the CEO’s
compensation. We define CEO’s Compensation as ln(1 + Total Compensation), where Total
Compensation is the CEO’s total compensation for the given year, as given in Compustat’s
ExecuComp. We estimate:
Ci , t = β 0 + β1 BLR i , t + β 2 (BLR i , t × Ri , t −1 ) + β 3 Ri , t −1 + β 4 X i , t −1 + β 5Ci , t −1 + σLambda i , t + ε i , t ,
where Ci,t is CEO’s Compensation, as defined above.
(10)
Here, again we adopt a Heckman
specification with instrumented loan characteristics. However, given that we measure the change
in sensitivity-to-performance over time with the coefficient β2 in equation (10), we use a panel
specification (as opposed to the cross-section of loan-deals that we have been using so far). This
9
We thank the referee for suggesting this alternative.
Also, in unreported tests, we also find that a standard deviation increase in proximity (loan’s significance) leads to a
0.3 (0.8) standard deviations increase in sales-growth, 0.1 (0.2) standard deviations increase in return-on-equity, and a
0.4 (0.6) standard deviations decrease in expenditures on mergers and acquisitions that have a negative return over the
subsequent 12 months. These further confirm the benefits that accrue to the borrower from the bank’s monitoring role.
10
24
implies that all the right-hand side control variables are measured in year t-1 as opposed to being
averaged over [t-n, t-1] years.
We measure stock-market performance (Ri,t-1) as the firm’s stock return in excess of the
industry’s return, calculated in year t-1.
Ci,t-1 refers to the one-year lagged level of CEO
compensation. The other variables as well as the econometric specification are the same as in
equation (9). We also control for the firms’ excess returns in years t-1 and t-2 as well as
industry’s returns in years t-1 and t-2. We also control for Stock Return Volatility, which is the
standard deviation of daily stock returns (from CRSP-Daily database) calculated over the fiscal
year t-1. Year- and 48 industry-dummies (Fama and French (1997)) are included as well.
Our hypothesis (H2) posits that firms characterized by a stronger lending relationship should
have managerial compensation tied more closely to firm performance. So, we expect β2 > 0 for
Proximity and Loan-to-Asset Ratio. We do not expect Equity Exposure to affect governance.
The results, reported in Table 6, show a significantly positive relation between the bank’s
influence (Proximity or Loan-to-Asset Ratio) and sensitivity-to-performance of CEO’s
Compensation.
A standard deviation increase in Proximity raises the sensitivity of CEO
Compensation to performance by 89% and a standard deviation increase in Loan-to-Asset Ratio
makes the sensitivity of CEO’s Compensation to performance significant (from being statistically
insignificant). This suggests that the benefits of bank monitoring also permeate out to the equityholders in other ways.
These results confirm those reported earlier and underline the link
suggested between the lending relationship and the quality of governance. We now study the net
impact of stronger lending relationships on the firm’s value.
7. Strength of the Borrower-Lender Relationship and Firm’s Value
Estimating the effect of the borrower-lender relationship on the firm’s value is equivalent to
studying the value implication of the governance/liquidity tradeoff illustrated above.
We
therefore relate two measures of firm value – Tobin’s Q and profitability – to our proxies of the
borrower-lender relationship, and estimate:
Vi ,[ t + 1, t + n ] = β 0 + β 1 BLR i , t + β 2 X i ,[ t − n , t −1] + β 3Vi ,[ t − n , t −1] + σ Lambda
i ,t
+ ε i ,[ t + 1, t + n ] ,
(11)
where Vi,[t+1,t+n] is alternatively Tobin’s Q and Industry-Adjusted ROA. Tobin’s Q is calculated
as (item6 + item25 x item199 – item60 – item74)/(item6)), averaged over [t+1, t+n] years, while
all the other variables are same as defined in the previous Section. We also control for the pre-
25
loan Tobin’s Q of the borrowing firm, averaged over [t-n, t-1] years.11 The alternative dependent
variable, Industry-Adjusted ROA, is defined as ROAi,[t+1,t+n] – IndustryROA[t+1,t+n]. ROAi,[t+1,t+n]
is income before extraordinary items (item 18) as a percentage of lagged assets (item 6), averaged
over years [t+1, t+n], and IndustryROA[t+1,t+n] is the median ROA across all the other firms in the
same industry as the borrower. We also control for the pre-loan industry-adjusted ROA, which is
constructed the same way, except being averaged over [t-n, t-1] years. The control variables and
instrumental variables as well as the econometric specification are the same as in equation (9)
above, i.e., we revert to the loan-deal based cross-sectional specification.
As in the previous specification, we first look at the separate impact of the three loancharacteristics on the firm’s value and subsequently employ a pair of loan-characteristics. The
pair of loan-characteristics consists of one that affects governance (either Proximity or Loan-toAsset Ratio) and the one that affects the information asymmetry (i.e., Equity Exposure). The
specification with the pair of loan-characteristics is especially relevant in this case as it shows the
net result of the conflicting effects that Proximity (or Loan-to-Asset Ratio) and Equity Exposure
have on the firm’s value.
The results are reported in Table 7. Columns (1)-(5) report the results for Tobin’s Q, while
Columns (6)-(10) report the results for Industry-Adjusted ROA. We find a positive relation
between Proximity (or Loan-to-Asset Ratio) and firm value, as measured by Tobin’s Q and
Industry-Adjusted ROA. Cross-sectionally, a standard deviation increase in Proximity (Loan-toAsset Ratio) raises Tobin’s Q by 0.3 (0.6) standard deviations. Similarly, a standard deviation
increase in the Proximity (Loan-to-Asset Ratio) raises Industry-Adjusted ROA by nearly 2 (more
than 1) standard deviations.
Interestingly, we also find a negative impact of the bank’s insider potential on Tobin’s Q and
Industry-Adjusted ROA. Again, the impact is not only statistically significant but economically
relevant, too.
Cross-sectionally, a standard deviation increase in Equity Exposure reduces
Tobin’s Q (Industry-Adjusted ROA) by 0.4 (nearly 2) standard deviations. The coefficients of the
control variables are in line with previous studies (Gompers, Ishii, and Metrick (2003)).
Although few control variables are statistically significant, we find that firms with greater
analyst-coverage and, not surprisingly, firms with high pre-loan Tobin’s Q (market-to-book ratio)
display a higher Tobin’s Q (ROA) after the loan.
11
Unreported results show that including the average pre-loan Tobin’s Q of other firms in the corresponding industry
as an additional control doesn’t affect our results.
26
Overall, these results reflect the governance/liquidity trade-off that we have described above.
That is, the positive impact of governance due to the closeness of the lender or the loan’s
significance to the borrower appears to enhance the firm-value. The adverse effect of the lender’s
insider potential is reflected in the negative impact on firm-value.
As an additional test, we calculate the abnormal returns from trading strategies based on the
various characteristics of the borrower-lender relationship. We use two methodologies: returns
across time and securities (RATS) and the calendar-time portfolio regressions (CTPR). The
RATS methodology (Ibbotson (1975)) is based on the monthly average abnormal returns in event
time. One cross-sectional regression is run for each event month j (j=0 is the month in which the
firm enters the loan), with j varying from 1 to 12:
(R
i ,t
− R f ,t ) = a j + b j (Rm ,t − R f ,t ) + c j SMBt + d j HMLt + g jUMDt + ε i ,t ,
(12)
where Ri,t is the monthly return on security i in calendar month t. Rf,t and Rm,t are the risk-free
rate and the return on the equally-weighted CRSP index, respectively. SMBt, HMLt, and UMDt,
are the monthly returns on the size, book-to-market and momentum portfolios in month t,
respectively. We report the sums of the intercepts of cross-sectional regressions aj over the
relevant event-time periods.
The alternative methodology is based on portfolios formed according to the intensity of
lending relationship. We construct equally-weighted portfolios consisting of firms whose loancharacteristic (Proximity to Bank-Branch, or Loan-to-Asset Ratio, or Equity Exposure) is above
median in a given month (Hi) and portfolios of those firms whose loan-characteristic is either
equal to or below median in that month (Lo). Hi – Lo represents a trading strategy going long in
the Hi portfolio and going short in the Lo portfolio. That is, each month we look backward and
add stocks (that have borrowed) to one of the two portfolios for each loan-characteristic; we keep
these stocks in the portfolios for a certain number of months. We consider horizons of 1, 3, 6 and
12 months. Then, we calculate the abnormal returns for these portfolios and their differences
using a four factor- model.
We report the results in Table 7, Panels B and C. Panel B presents the returns using the
Ibbotson’s (1975) RATS estimation. The returns in this panel are returns over the indicated
holding period. The numbers in brackets at the head of each column represent months after the
loan, over which these stocks are held.
E.g., [1, 6] would represent the 6-month period
immediately after the month in which the loan started. Panel C presents returns using the
portfolio strategy in calendar-time. The returns in this panel are returns per month over the
27
indicated period (i.e., the returns under [1, 6] are monthly returns for a period of 6 months
immediately after the month in which the loan started.)
The results are broadly consistent with the ones based on Tobin’s Q and show a significantly
negative relationship between stock returns and Equity Exposure. After the inception of the loan,
the stock prices of firms borrowing from potential insiders drop and the returns are negative. The
reduction in value is not only statistically significant but also economically relevant – it is more
than 40 b.p. per month over 12 months using the calendar-time portfolio strategy and more than
4% over 12 months using RATS. There seems to be some evidence of an overall negative effect
of the bank-firm relationship on firm value.12
One contradictory finding is the fact that the portfolio with smaller (Lo) Proximity yields a
positive return and that with greater (Hi) Proximity yields a negative return. While this result is
inconsistent with our working hypothesis, and with the findings on Tobin Q, it can be explained
by the fact that the portfolio analysis is only univariate and cannot control for different competing
effects. Indeed, here proximity may just be proxying for some liquidity effect. This is controlled
for in the multivariate analyses and hence, does not conflict with our earlier findings.
8. Conclusion
We document the trade-off facing a firm that borrows from a bank. We show that a stronger
bank-firm relationship has a favorable effect on the firm’s corporate governance and an adverse
effect on its stock liquidity and information asymmetry in the equity market. We argue that with
the privileged information obtained from lending, monitoring by the bank improves the firm’s
corporate governance.
Simultaneously, however, the ability of the bank to use this inside
information in the equity market increases adverse selection for other market participants. This
translates into lower stock market liquidity and greater information asymmetry for the borrowing
firm.
We consider three facets of the borrower-lender relationship: proximity of the lender to the
borrower, significance of this loan to the borrower, and the insider potential of the lending bank.
To explain why there is greater adverse selection, we document a strongly positive relation
12
We know that a one standard deviation increase in proximity raises Q by 0.3 standard deviations, while a one
standard deviation increase in equity exposure lowers Q by 0.4 standard deviations. The net negative effect seems be
provided by the robust negative relation between stock returns and equity exposure, in the lack of significance of
proximity and loan size.
28
between the bank’s insider potential and the lender-affiliated institutions’ relative trading in the
borrower’s stock after the loan has been granted. Then, we provide evidence of the impact this
insider position has in the equity market. We show that a stronger borrower-lender relationship,
as measured along any of the three dimensions, increases stock illiquidity and information
asymmetry in the stock market, and lowers the trading volume. On the other side of this tradeoff, a stronger relationship with the lender improves the borrower’s governance. The net effect is
reflected in the firm’s value – greater proximity and loan-significance increase it while a greater
inside potential of the bank lowers it.
Our findings have important normative implications. Indeed, since the final repeal of the
Glass-Steagall Act in 1998, the ability of banks to directly trade on the basis of information
acquired during the course of their lending activity has increased tremendously. This can further
compound the liquidity effects of bank lending.
29
Appendix
The main explanatory variables – Proximity, Loan-to-Asset Ratio, and Equity Exposure – are, at
least to some extent, endogenously determined by the firm. They are affected by the
characteristics of the firm and its external constraints as well as by some of our dependent
variables – information asymmetry. More opaque firms are more likely to borrow from closer
banks (Sufi (2005)).
While we can control for many firm-specific characteristics, a residual unwarranted
correlation between the omitted variables and the errors may bias our estimates. To address this
issue, we follow a two-pronged approach. First, we concentrate on a variable – equity exposure –
that is less subject to endogeneity. Second, we adopt an instrumental-variables approach, similar
to the one of Berger et al. (2005), to deal with endogeneity of the remaining loan-characteristics.
The significance of equity exposure provides evidence for our results even in the presence of
endogeneity for the other variables.
Equity exposure represents the overall holdings of the financial conglomerate in the
borrowing firm before the loan is granted. It is less endogenous since it is unlikely that firms will
borrow from a bank because of that bank’s overall holdings in the firm’s equity. Moreover, if
investment in the firm’s equity is influenced by lower asset transparency, we would expect a
positive spurious correlation between stock liquidity and the bank’s equity holdings (Falkenstein
(1996)) as opposed to the negative one that we hypothesize.
To address the endogeneity of all the loan-characteristics, we need instrumental variables that
are correlated with proximity, loan-to-asset ratio, and equity exposure, but orthogonal to other
omitted characteristics. That is, the instruments should be uncorrelated with the dependent
variable through any channel other than their effect via the endogenous explanatory variables.
We consider several sets of instruments. The first set is firm-specific and includes: the
number of different segments that the borrower has, the product between the borrower’s size and
13
a financial-centre dummy (Metropolis) , and the product between the borrower’s age and the
Metropolis dummy. The number of segments captures the different borrowing opportunities
available to a firm that operates in many segments, presumably with many subsidiaries. If, as per
standard practice, the assets of the subsidiary are pledged against the loan, the firm’s borrowing
30
ability will be related to the number of its subsidiaries/segments. Next, the age of the firm is
related to the stage of the “financing cycle” of the firm. Young firms are more likely to rely on
bank-lending, while older firms are more likely to access the bond market (Diamond (1989)).
Finally, if dependence on the local banking market varies with the size and age of the firm, then
being located in a financial centre (which would presumably have more deposit-taking and
lending activity) should affect a big (old) firm’s dependence in a way different from the way it
affects a small (young) firm’s dependence. We therefore use as instruments the product between
borrower’s size (age) and the Metropolis dummy. This is similar in spirit to Chen et al. (2005).
While location in a big city, size and age of the firm may directly affect the liquidity/governance
of the firm, their interactions should not be directly related to liquidity/governance through a
channel different from their influence on the loan-characteristics.
The second set of instruments is the “unconditional expected value” of the instrumented
variables. Proximity from the nearest large branch of any bank is defined as –ln(1 + BorrowerBranch Distance), where the Borrower-Branch Distance is the distance between the borrower
and the nearest “large” branch of any bank (whether lending to the firm or not). A “large” branch
is one with deposit size greater than that year’s median deposit size across all branches in the
country. The other two instruments are the Industry-average of Loan-to-Asset Ratio and Industryaverage of Equity Exposure, which are respectively defined as the average of Loan-to-Asset Ratio
and Equity Exposure of peer firms in the same industry that also initiate a loan in the same year as
this loan-deal.
A third set of instruments is related to the firm’s product market. We define some industryspecific variables proxying for the industrial structure of the product market. We rely on the
literature linking leverage and borrowing choice to product market competition (e.g., Kovenock
and Phillips (1995)). If there is a direct relationship between the financial structure of different
firms operating in the same industry, we can use industry-level averages of firm-characteristics to
explain the firm’s borrowing needs. Therefore, we use as instruments: Size of Other Firms in
Borrower’s Industry, Leverage of Other Firms in Borrower’s Industry, Cash of Other Firms in
Borrower’s Industry, Capital Expenditure of Other Firms in Borrower’s Industry, ROA of Other
Firms in Borrower’s Industry, Market-to-Book of Other Firms in Borrower’s Industry,
Institutional Holdings of Other Firms in Borrower’s Industry, Analysts of Other Firms in
13
This is a dummy variable that equals 1 if the firm is headquartered in one of the six largest metropolises in the U.S.
(Boston, Chicago, Los Angeles, New York, Philadelphia, and San Francisco), and 0 otherwise. It tells whether the
borrower has easy access to other sources of capital.
31
Borrower’s Industry, Age of Other Firms in Borrower’s Industry. These variables are the yearly
averages of the corresponding firm characteristic (defined in the paper) for all the firms belonging
to the borrower’s industry, and are measured before the loan-deal (that is under consideration) is
initiated.
A fourth set of instruments consists of the characteristics of the local commercial-bank
market. These instruments proxy for the availability and/or relative cost of other sources of
capital available to the firms. For example, firms located in areas with few banks are more likely
to have a closer relationship with few banks simply because bank competition may not be severe.
At the same time, bigger banks are more likely to be part of a conglomerate, and therefore, likely
to hold a bigger stake in the borrowing firm. However, this larger equity exposure will be related
to where the firm is located as opposed to its characteristics, such as informational transparency
vs. opaqueness. This suggests that the banking-market characteristics provide instruments for the
loan-characteristics that are orthogonal to the residuals. The impact of the local banking market is
stronger for smaller/younger firms, and decreases as the firm grows bigger/older, and is able to
access alternative capital markets more easily. Therefore, this set of instruments complements the
previously-defined instruments based on the interactions between the size/age of the borrower
and the Metropolis dummy. We borrow from Berger et al. (2005) and use the following proxies
to characterize the local banking market: Concentration of the Banking Market, Median Distance
from HQs of all Banks, and Median Size of all Banks. All these variables are measured in the year
before the inception of the loan. We measure concentration of the commercial-bank market by
calculating a Herfindahl Index (ranging between 0–1) based on the bank-deposits of all the
branches located in the same county as the firm.
14
As an alternative robustness check, we use a
Herfindahl Index based simply on the number of branches in the county; the results, left
unreported, are consistent with those reported in the paper. The concentration of the banking
market should reduce total bank-borrowing as well as make access to other sources of capital
easier (Boot and Thakor (2000)). Next, the geographical composition of lending market in the
firm’s county of location is proxied by the median distance (inversely weighted by the number of
branches) between the borrower and the headquarters of all bank-braches present in the same
county as the borrower. Finally, we define the median size of the lending-market as the median
13
We use deposits data from FDIC’s Summary of Deposits database for calculating the Herfindahl Index because these
are the most comprehensive data available for branch-level details. Location of banks is not refined beyond countylevel because historical Compustat data on location of borrowing firms is available to us only at the county-level.
32
size of all bank-branches (weighted by the number of branches) in the borrower’s county of
location.
We also include among the instruments a regulatory dummy that measures how permissive
the state in which the firm is located has been with respect to inter-state bank-branching. Similar
to Berger et al. (2005), we define it as the fraction of years in our sample-period for which the
borrowing firm’s state was neither a unit-banking state nor limited-branching state. The idea is
that if a firm is located in a state where regulation has not constrained bank-branching, then the
firm is more likely to have a “large” branch located closer to it.
In Table 2, Panel B, we report the results for the regressions of the loan-characteristics on the
above instrumental variables. Proximity is positively related to the unconditional proximity of the
firm to any bank’s branch, to the concentration of the banking market, and to its median size.
Also, the incentive to borrow from a close bank increases with size for a firm located in big
financial centers – presumably due to the higher bargaining power that comes with size – and
decreases with the number of its segments – as the ability to borrow through subsidiaries reduces
the incentive to go to branches closer to the firm’ s headquarters. Proximity is also affected by the
characteristics of the firm’s industry (average profitability and number of analysts), and strongly
positively related to the local banking market having been deregulated.
The loan-to-asset ratio is positively related to its own unconditional average as well as to
characteristics of the firm’s industry (market-to-book and size). It is negatively related to the
number of segments the firm operates in. This suggests that multiple-segment conglomerates are
better able to resort to other capital markets. This intuition is confirmed by the fact that the size of
the loan taken by a firm located in big financial centers decreases with the firm’s size and age –
presumably due to its ability to resort to the bond market, for instance.
Finally, equity exposure is positively related to its own unconditional average as well as to
the number of segments the firm operates in. The incentive for a firm located in a large financial
centre to borrow from a bank with a stake in its equity increases with the firm’s size and age –
presumably due to the higher bargaining power of the firm. It is also affected by characteristics of
the firm’s industry (such as, capital expenditure, market-to-book, institutional holdings, and
leverage) and of the local banking market (concentration and distance).
With regards to the validity of our instruments, the least-squares regression of Proximity
(Loan-to-Asset Ratio and Equity Exposure) on the instruments and the exogenous variables
reports an F-test statistic of 36.47 (9.76 and 27.10) with p-value <0.0001 (p-value <0.0001 and p-
33
value <0.0001). The Hansen’s J-test of over-identification in the “second-stage regressions”
(reported in the paper) provides evidence of the lack of residual correlation of the instruments
with the “second-stage” residuals. In sum, for all the specifications, the instruments are
statistically correlated with the potentially endogenous variables of interest and do not seem to
affect the dependent variables through a channel other than their effect via the endogenous loancharacteristics.
34
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37
Table 1, Panel A: Summary Statistics for the Overall Sample
Panel A of Table 1 presents the summary statistics for the main variables used in our analyses of the loan-taking decision; this is the “overall
sample” (as opposed to only the loan-taking sample used further on). The data in Panel A are based on a panel of all those firms for which
the headquarters’ historical location is known. The dependent variable in this “overall sample” is a dummy variable, Loan-taking Decision,
that equals 1 for those firm-years in this panel data when a loan is initiated; the variable is zero for the complementary firm-years. The firmyears where Loan-taking Decision is equal to 1 are derived from the Loan Pricing Corporation’s DealScan database. The instrumental
variables in this “overall sample” are: a dummy variable for whether the firm already has an active loan in the given year; an interaction of
the firm’s age and a Metropolis dummy; Metropolis dummy takes a value of 1 if the firm is located in one of six largest metropolises in the
US (Boston, Chicago, Los Angeles, New York, Philadelphia, and San Francisco); an interaction of firm’s size and the Metropolis dummy;
the number of segments that the firm has; concentration of the firm’s local banking market, as measured using the lagged Herfindahl Index
of the bank-deposits at all branches present in the same county as the firm; median distance (inversely weighted by the number of branches)
from headquarters of all banks present in the same county as the firm; median size (weighted by the number of branches) of all banks
present in the same county as the firm; the regulatory environment in the firm’s home state, as measured by the fraction of years in our
sample for which interstate-branching was deregulated in the state. The firm-specific control variables in this “overall sample” are lagged
by one year and include the following: Size is the logarithm of book value of assets (item 6); Size-squared is the square of Size; Leverage is
long-term debt (item 9) standardized by assets; Cash is total cash (item 1) standardized by lagged assets; Capital Expenditure is capital
expenditures (item 128) standardized by lagged assets; ROA (return on assets) is income before extraordinary items (item 18) as a
percentage of lagged assets; Market-to-Book is the ratio of market equity (item 25 x item 199) to book equity (item 60); Kaplan-Zingales
Index is constructed following the methodology of Baker, Stein, and Wurgler (2003); Institutional Holdings is the fraction of firm’s shares
held by institutional investors; Analysts is the number of analysts following the firm’s stock; Firm’s Relative Age is the firm’s age
standardized by the age of all other firms in the same industry, and firm’s age is the number of years since the firm first appeared in CRSPDaily database (Denis, Denis, and Sarin (1997)); NYSE is a dummy variable that takes a value 1 if the firm is listed on the New York Stock
Exchange, and zero otherwise; Ratings Dummy is a dummy variable that equals 1 if the firm has a credit-rating, and equals zero otherwise.
DEPENDENT VARIABLE
Units
0/1
Loan-taking Decision
N
56243
Mean
0.211
Median
0.000
Std. Dev.
0.408
INDEPENDENT VARIABLES
Instrumental Variables
Firm has another loan outstanding
(Firm's Age) x (Metropolis)
Metropolis
(Firm's Size) x (Metropolis)
Number of segements
Concentration of the Banking Market
Median Distance from HQs of all Banks in the Region
Median Size of all Banks in the Region
Interstate-branching Deregulation
Firm Characteristics
Size
Size-squared
Leverage
Cash
Capital Expenditure
ROA
Market-to-Book
Kaplan-Zingales Index
Other Control Variables
Institutional Holdings
Analysts
Firm's Relative Age
NYSE
Ratings Dummy
Units
N
Mean
Median
Std. Dev.
0/1
56243
56243
56243
56243
56243
56243
56243
56243
56243
0.363
1.911
0.124
0.691
3.757
1398
25.463
1385
0.854
0.000
0.000
0.000
0.000
3.000
1213
4.889
945
1.000
0.481
8.088
0.330
2.030
2.129
717
67.467
1475
0.184
52978
52978
52863
48590
47801
48538
52501
48430
5.061
30.184
0.157
0.235
0.079
-3.415
4.392
0.318
4.871
23.726
0.094
0.086
0.045
3.161
2.028
0.425
2.138
24.460
0.176
0.554
0.183
42.955
53.422
3.533
56243
56243
56243
55558
56243
0.231
4.008
1.016
0.293
0.237
0.171
1.700
0.677
0.000
0.000
0.231
6.051
1.086
0.455
0.425
0/1
miles
MM$
fraction
logarithm
fraction
fraction
fraction
%
fraction
fraction
fraction
0/1
0/1
38
Table 1, Panel B: Definitions and Summary Statistics
of variables used in the Loan-taking Sample
Panel B of Table 1 presents the summary statistics for the main variables used in our analyses of the impact of lending relationship on the
firm’s information asymmetry and corporate governance. The data for these analyses are based on the cross-section of loan-taking firms
(except when we look at CEO-specific variables, where we use a panel data of CEOs in order to calculate the sensitivity-to-performance
over time.)
The dependent variables in this “loan-taking sample” are the following. Lenders’ Relative Trading is defined as ln(1 + Lenders’
Trading/ Institutional Trading), where Lenders’ Trading and Institutional Trading are averaged over [t+1,t+n] years. Illiquidity is defined as
ln(1 + AvgILLIQ[t+1,t+n]), where AvgILLIQ[t+1,t+n] is the average of AvgILLIQ over [t+1,t+n] years (n being the tenor of the loan), AvgILLIQ is
the yearly average (multiplied by 107) of ILLIQ (Amihud, 2002), and ILLIQ is (1/Daysj,t)∑d[|Rj,t,d|/DVolj,t,d]. Daysj,t is the number of valid
observation days for stock j in the month t; the summation is over d=1 through Daysj,t; Rj,t,d is the return and DVolj,t,d the dollar volume of
stock j on day d of month t. Information Asymmetry is based on the model of Llorente, Michaely, Saar, and Wang (2002) and is represented
by the coefficient C2 (averaged over [t+1,t+n] years) from the regression: Ri,t+1 = C0 + C1Ri,t + C2(Vi,t x Ri,t) + εi,t+1, where Vt = log(Turnovert
+ 0.00000255) – [∑s log(Turnovert+s + 0.00000255)]/200, with s ranging from –200 through –1; and Turnover is the total number of shares
traded each day as a fraction of total shares outstanding.
Multiple Directorships Dummy equals 1 if the fraction of total directors on the board with multiple directorships is above the
sample-median for this fraction, and 0 otherwise. Independent Directors Dummy and Interlocking Directorships Dummy are defined in a
similar manner. Voting Power of Independent Directors is the independent directors’ voting power on the equity shares. Non-Executive
Chairman is a dummy that equals 1 if the Chairman is not an executive of the firm, and zero otherwise. And finally, Relative-on-Board is a
dummy that equals 1 if there’s a relative of the CEO on the board, and zero otherwise. (All the fractions of total directors are averaged over
[t+1,t+n] years before constructing the corresponding Dummy variables.) Unaffiliated Institutional Holdings are the equity holdings in the
borrower, averaged across all quarters during the years [t+1, t+n], of all those institutional investors that are not affiliated with any of the
lending banks. Governance Dummy equals 1 if the Gompers, Ishii, and Metrick index is greater than 9 (when averaged over [t+1,t+n]
years), and 0 otherwise. Anti-takeover Dummy equals 1 if a firm has more than 3 of the anti-takeover provisions (identified by Cremers and
Nair (2005)) in place (when averaged over [t+1,t+n] years), and 0 otherwise. Complementary Governance Dummy equals 1 if a firm has
more than 6 of the provisions unrelated to anti-takeover (when averaged over [t+1,t+n] years), and 0 otherwise. CEO’s Compensation is
defined as ln(1 + Total Compensation), where Total Compensation is the CEO’s total compensation for the given year. Tobin’s Q is the
firm’s Q, calculated as (item6 + item25 x item199 – item60 – item74)/(item6), averaged over [t+1,t+n] years. Industry-adjusted ROA is
(ROA – IndustryROA), where ROA is income before extraordinary items (DATA 18) as a percentage of lagged assets (DATA 6), averaged
over years [t+1, t+n]. IndustryROA is the median ROA across all firms in the borrower’s industry group (excluding the borrower itself), and
the 48 industries are defined as per Fama and French (1997).
The “lagged” values of all dependent variables, used as control variables on the right-hand side, are also defined similarly, except
being averaged over years [t-n, t-1]. In all cases, n is the tenor of the loan.
The right-hand side variables of interest are as follows. Proximity is –ln(1 + Borrower-Branch Distance), where BorrowerBranch Distance is the distance between the borrower and the nearest “large” branch of any bank within the borrower’s lending syndicate.
“Large” branch is taken to be one with deposit size greater than that year’s median deposit size across all bank branches in the country.
Loan-to-Asset Ratio is defined as the “drawn amount” (of loan) as a percentage of borrower’s asset size. “Drawn amount” refers to the
amount drawn by the borrower as opposed to what might be available as a line of credit, for instance. Also, the borrower’s asset size in this
ratio is the average over [t-n,t-1] years of assets (item 6), where n is the tenor of the loan deal. Equity Exposure is the fraction of borrower’s
equity held by all institutional investors affiliated with the lending banks, and measured on the last filing date in the fiscal year before the
loan is initiated.
Size is the logarithm of book value of assets (item 6) averaged over [t-n,t-1] years. Size-squared is simply the square of Size.
Leverage is the long-term debt (item 9) to assets ratio averaged over [t-n,t-1] years. Cash is total cash (item 1) to lagged assets ratio
averaged over [t-n,t-1] years. Capital Expenditure is capital expenditures (item 128) to lagged assets ratio averaged over [t-n,t-1] years.
ROA is income before extraordinary items (item 18) as a percentage of lagged assets, averaged over [t-n,t-1] years. Market-to-Book is
market equity (item 25 x item 199) to book equity (item 60) ratio averaged over [t-n,t-1] years. Firm’s Excess Return in year (t-1) is the
difference between the firm’s stock return over the year (t-1) and the average stock return over the same period of other firms in the same
industry. Institutional Holdings is the institutional investors’ equity stake averaged over all quarters in [t-n,t-1] years. Analysts is the
number of analysts following the stock, averaged over [t-n,t-1] years. Product-market concentration is the pre-loan Herfindahl Index of
industry sales, where the 48 industries are defined as per Fama and French (1997). Firm’s Relative Age is the firm’s age standardized by the
age of other firms in the same industry, and Age is calculated as the number of years since the firm first appeared in CRSP-Daily database
(Denis, Denis, and Sarin (1997)). NYSE is a dummy variable that takes a value 1 if the firm is listed on the New York Stock Exchange, and
0 otherwise. Ratings Dummy is a dummy variable that equals 1 if the firm has a credit-rating, and equals 0 otherwise. In all cases, n is the
tenor of the loan.
39
DEPENDENT VARIABLES
Units
N
Mean
Median
Std. Dev.
Post-loan Levels of Information Asymmetry
Lenders' Relative Trading
Illiquidity
Information Asymmetry
logarithm
logarithm
logarithm
6521
8206
8553
0.732
0.582
0.007
0.629
0.058
0.012
0.580
1.153
0.124
Post-loan Levels of Corporate Governance
Multiple Directorships Dummy
Independent Directors Dummy
Voting Power of Independent Directors
Non-Executive Chairman
Relative-on-Board
Interlocking Directorships Dummy
Unaffiliated Institutional Holdings
Governance Dummy
Anti-takeover Dummy
Complementary Governance Dummy
CEO's Compensation
Tobin's Q
Industry-Adjusted ROA
0/1
0/1
%
0/1
0/1
0/1
fraction
0/1
0/1
0/1
logarithm
fraction
%
3950
3950
2229
3950
3950
3950
7750
4763
4763
4763
6814
8022
4078
0.611
0.519
6.742
0.088
0.083
0.070
0.318
0.496
0.373
0.502
1.376
1.597
0.274
0.857
1.000
6.539
0.000
0.000
0.000
0.300
0.000
0.000
1.000
1.239
1.286
0.346
0.442
0.500
3.366
0.246
0.251
0.232
0.185
0.500
0.484
0.500
0.777
1.148
7.715
Units
N
Mean
Median
Std. Dev.
logarithm
%
%
miles
7721
9846
9874
7721
-2.600
4.146
0.722
216.325
-1.720
0.000
0.033
2.865
2.852
15.465
1.525
489.617
logarithm
9874
9874
9874
9874
9874
9874
9874
8102
6.796
50.243
0.226
0.121
0.100
4.508
3.139
3.151
6.605
43.620
0.217
0.051
0.066
4.611
2.156
-4.009
2.014
29.124
0.158
0.209
0.137
10.583
4.062
63.983
9859
9677
9874
9854
9874
9874
0.416
9.398
0.088
1.229
0.650
0.669
0.410
6.515
0.063
0.891
1.000
1.000
0.195
8.050
0.082
1.426
0.477
0.471
INDEPENDENT VARIABLES
Loan-characteristics
Proximity
Loan-to-Asset Ratio
Equity Exposure
Distance from the nearest “large” branch
Firm-characteristics
Size
Size-squared
Leverage
Cash
Capital Expenditure
ROA
Market-to-Book
Excess Return in year (t-1)
fraction
fraction
fraction
%
fraction
%
Other Control Variables
Institutional Holdings
Analysts
Product-market concentration
Firm's Relative Age
NYSE
Ratings Dummy
fraction
fraction
0/1
0/1
40
Table 2, Panel A: The Loan-taking Decision of the Firm
In this panel dataset of all Compustat firms, whose headquarters’ historical location is known from 1991 onwards, the dependent variable
(Loan-taking Decision) is a dummy variable that equals 1 for those firm-years when a loan is initiated, and 0 in the complementary firmyears. The firm-years where Loan-taking Decision is equal to 1 are derived from the Loan Pricing Corporation’s DealScan database.
The independent variables can be divided into three groups: a) instruments, b) firm-specific control variables, and c) industryspecific control variables. The instruments are: a dummy variable for whether the firm already has an active loan in the given year; a
Metropolis dummy that takes a value of 1 if the firm is located in one of six largest metropolises in the US (Boston, Chicago, Los Angeles,
New York, Philadelphia, and San Francisco) and 0 otherwise; an interaction of the firm’s age and the Metropolis dummy; an interaction of
firm’s size and the Metropolis dummy; the number of segments that the firm has; concentration of the firm’s local banking market, as
measured using the lagged Herfindahl Index of the bank-deposits at all branches located in the same county as the firm; median distance
(inversely weighted by the number of branches) from headquarters of all banks located in the same county as the firm; median size
(weighted by the number of branches) of all banks located in the same county as the firm; the regulatory environment in the firm’s home
state, as measured by the fraction of years in our sample for which interstate-branching was deregulated in the state.
The firm-specific control variables are lagged by one year and include the following: Size, Size-squared, Leverage, Cash, Capital
Expenditure, ROA, Market-to-Book, Institutional Holdings, Analysts, Firm’s Relative Age, NYSE, Ratings Dummy, and Kaplan-Zingales
Index, which is constructed following the methodology of Baker, Stein, and Wurgler (2003). Remaining definitions can be found in Table
1B above.
Industry-specific control variables are also lagged by one year and include the following characteristics averaged across other
firms in the same industry: Size, Leverage, Cash, Capital Expenditure, ROA, Market-to-Book, Kaplan-Zingales Index, Institutional
Holdings, Analysts, and Age. These industry-specific control variables are only included in columns (4) and (8). Firms are grouped into 48
industries, as defined in Fama and French (1997). Coefficient estimates from a Probit regression for the loan-taking decision are reported in
columns (1)–(4) of Table 2. For robustness, the same loan-taking decision is presented using a Logit model in columns (5)–(8) of Table 2.
Heckman’s (1979) Lambda (Inverse Mill’s Ratio), included in subsequent “second-stage” regressions, is calculated using the estimates from
column (3). Time and industry dummies are included to capture any year- and industry-specific effects that might affect the loan-taking
decision of the firms.
41
LOAN-TAKING DECISION
Probit Model
Logit Model
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.737***
0.500***
0.528***
0.526***
1.279***
0.880***
0.934***
0.930***
[46.36]
[26.51]
[26.90]
[26.78]
[46.19]
[26.01]
[26.57]
[26.41]
-0.004***
-0.001
0.000
0.000
-0.006***
-0.003
0.001
0.001
[-3.10]
[-0.97]
[0.12]
[0.19]
[-2.93]
[-1.00]
[0.21]
[0.26]
-0.788***
-0.222***
-0.284***
-0.290***
-1.396***
-0.395***
-0.485***
-0.500***
[-12.34]
[-2.76]
[-3.49]
[-3.56]
[-11.98]
[-2.65]
[-3.24]
[-3.34]
(Firm's Size) x (Metropolis)
0.150***
0.043***
0.041***
0.042***
0.257***
0.073***
0.066***
0.069***
[13.95]
[3.17]
[2.95]
[3.00]
[13.66]
[3.10]
[2.73]
[2.81]
Number of segments
0.039***
0.000
0.011**
0.010**
0.065***
-0.001
0.018**
0.017**
[11.50]
[0.02]
[2.45]
[2.29]
[11.36]
[-0.17]
[2.44]
[2.25]
0.000
-0.000*
0.000
0.000
0.000
-0.000*
0.000
0.000
[1.19]
[-1.87]
[0.76]
[0.82]
[1.07]
[-1.88]
[0.60]
[0.66]
-0.001***
-0.001***
-0.000***
-0.000***
-0.001***
-0.001***
-0.001***
-0.001***
[-7.62]
[-5.38]
[-3.31]
[-3.33]
[-7.25]
[-5.16]
[-3.14]
[-3.16]
-0.000***
-0.000**
0.000
0.000
-0.000***
-0.000**
0.000
0.000
[-2.78]
[-2.57]
[0.14]
[0.16]
[-2.60]
[-2.35]
[0.33]
[0.38]
Instruments and Independent Variables:
Firm has another loan outstanding
(Firm's Age) x (Metropolis)
Metropolis
Concentration of the Banking Market
Median Distance from HQs of all Banks
Median Size of all Banks
Interstate-branching Deregulation
-0.208***
-0.010
-0.069
-0.067
-0.357***
-0.003
-0.122
-0.121
[-4.82]
[-0.22]
[-1.42]
[-1.38]
[-4.79]
[-0.04]
[-1.42]
[-1.40]
0.099***
0.125***
0.132***
0.162***
0.210***
0.224***
[4.78]
[5.74]
[6.03]
[4.34]
[5.37]
[5.69]
Size-squared
-0.007***
-0.005**
-0.005***
-0.011***
-0.008**
-0.009***
[-3.76]
[-2.45]
[-2.76]
[-3.45]
[-2.24]
[-2.59]
Leverage
-0.196***
-0.354***
-0.357***
-0.381***
-0.678***
-0.684***
[-3.53]
[-6.02]
[-6.09]
[-3.75]
[-6.26]
[-6.28]
Cash
-0.349***
-0.307***
-0.313***
-0.728***
-0.630***
-0.641***
[-8.60]
[-7.53]
[-7.73]
[-7.81]
[-6.98]
[-6.94]
Capital Expenditure
0.581***
0.456***
0.452***
1.108***
0.875***
0.861***
[6.62]
[5.36]
[5.38]
[7.89]
[6.02]
[5.90]
ROA
0.001***
0.001***
0.001**
0.004***
0.004***
0.003***
[2.96]
[2.60]
[2.48]
[3.57]
[3.57]
[3.41]
0.000
0.000
0.000
0.000
0.000
0.000
[0.38]
[1.15]
[1.12]
[0.47]
[1.31]
[1.28]
-0.010***
-0.010***
-0.010***
-0.022***
-0.021**
-0.022**
[-3.19]
[-3.09]
[-3.19]
[-2.69]
[-2.50]
[-2.46]
0.152***
-0.024
-0.033
0.273***
-0.032
-0.049
[3.52]
[-0.50]
[-0.69]
[3.67]
[-0.39]
[-0.60]
Size
Market-to-Book
Kaplan-Zingales Index
Institutional Holdings
Analysts
0.007***
0.002
0.002
0.011***
0.002
0.003
[3.34]
[0.75]
[1.10]
[3.19]
[0.46]
[0.84]
-0.021**
-0.047***
-0.048***
-0.033**
-0.081***
-0.083***
[-2.32]
[-4.90]
[-4.90]
[-2.14]
[-4.86]
[-4.88]
0.046**
0.045*
0.045*
0.065
0.064
0.064
[1.97]
[1.83]
[1.83]
[1.62]
[1.51]
[1.51]
0.711***
0.724***
0.721***
1.193***
1.230***
1.225***
[25.37]
[24.96]
[24.87]
[24.82]
[24.64]
[24.54]
-1.075***
-1.512***
-2.735***
-3.169***
-1.806***
-2.557***
-4.817***
-5.562***
[-25.03]
[-21.43]
[-12.73]
[-5.76]
[-24.31]
[-19.89]
[-12.74]
[-5.80]
56243
46922
46610
46610
56243
46922
46610
46610
Time Dummies
No
No
Yes
Yes
No
No
Yes
Yes
Industry Dummies
No
No
Yes
Yes
No
No
Yes
Yes
Industry-specific Control Variables
No
No
No
Yes
No
No
No
Yes
Firm's Relative Age
NYSE
Ratings Dummy
Constant
Observations
Robust and firm-clustered z-statistics in brackets; *** significant at 1%, ** significant at 5%, * significant at 10%
42
Table 2, Panel B: Choice of Loan Characteristics
In Table 2, Panel B, we address the endogeneity of the firm’s choice of loan characteristics. The three instrumented loan characteristics,
Proximity, Loan-to-Asset Ratio, and Equity Exposure, are reported in columns (1)–(3), respectively. The instrumental variables include: a
Metropolis dummy that takes a value of 1 if the firm is located in one of six largest metropolises in the US (Boston, Chicago, Los Angeles,
New York, Philadelphia, and San Francisco) and 0 otherwise; an interaction between the firm’s size and Metropolis dummy; an interaction
between the borrower’s age and Metropolis dummy; number of segments that the borrowing firm has; Proximity from the nearest “large”
branch of any bank (and not just banks lending to that borrower); average Loan-to-Asset Ratio of other firms belonging in the same industry
group and initiating a loan in the same year as the given loan-taking firm; average Equity Exposure of other firms belonging in the same
industry group and initiating a loan in the same year as the given loan-taking firm; yearly average Size, Leverage, Cash, Capital
Expenditure, ROA, Market-to-Book, Institutional Holdings, Analysts, and Age of all other firms in the same industry; concentration of the
borrower’s local banking market, as measured using the year (t-1) Herfindahl Index of the bank-deposits at all branches present in the same
county as the firm; median distance (inversely weighted by the number of branches) from headquarters of all banks present in the same
county as the borrower, measured in year (t-1); median size (weighted by the number of branches) of all banks present in the same county as
the borrower, measured in year (t-1); the regulatory environment in the borrower’s home state, as measured by the fraction of years in our
sample for which interstate-branching was deregulated in the state. Remaining definitions can be found in Table 1 of the paper. Time and
industry dummies are included to capture any year- and remaining industry-specific effects that might affect the loan-characteristics of the
firms. Absolute values of t-statistics are reported in brackets.
43
CHOICE OF LOAN CHARACTERISTICS
Instrumented variables:
(1)
(2)
(3)
Proximity
Loan-to-Asset Ratio
Equity Exposure
0.100**
-0.661***
0.136***
[2.50]
[-3.29]
[7.13]
-0.005
-0.048**
0.007***
Instruments:
(Borrower's Size) x (Metropolis)
(Borrower's Age) x (Metropolis)
[-1.30]
[-2.41]
[3.54]
Number of segments
-0.026**
-0.374***
0.078***
[2.12]
[-5.91]
[13.08]
Proximity to the nearest large branch of any bank
0.600***
[10.15]
Industry-average of Loan-to-Asset Ratio in that year
0.112***
[2.60]
Industry-average of Equity Exposure in that year
0.325***
[7.96]
Size of Other Firms in Borrower's Industry
Leverage of Other Firms in Borrower's Industry
Cash of Other Firms in Borrower's Industry
Capital Expenditure of Other Firms in Borrower's Industry
ROA of Other Firms in Borrower's Industry
-0.067*
-0.387**
0.021
[-1.78]
[-2.02]
[1.16]
0.495*
0.055
0.866
[0.10]
[0.32]
[1.96]
0.117
0.221
-0.451*
[0.22]
[0.08]
[-1.78]
0.021
2.891
-0.918***
[-3.31]
[0.04]
[1.00]
0.029***
-0.073
0.008
[2.99]
[-1.50]
[1.63]
0.024**
Market-to-Book of Other Firms in Borrower's Industry
Institutional Holdings of Other Firms in Borrower's Industry
Analysts of Other Firms in Borrower's Industry
Age of Other Firms in Borrower's Industry
0.011
0.328***
[0.49]
[2.75]
[2.17]
1.089*
2.073
-0.529**
[-2.00]
[1.95]
[0.74]
-0.037**
-0.008
0.000
[-2.30]
[-0.10]
[0.03]
-0.001
0.009
-0.003
[-0.14]
[0.37]
[-1.11]
0.000***
0.000**
0.000***
[3.08]
[1.97]
[3.49]
Median Distance from HQs of all Banks
-0.003***
-0.003
-0.001***
[-5.30]
[-1.45]
[-3.85]
Median Size of all Banks
0.000***
-0.000*
0.000
[3.08]
[-1.89]
[0.95]
Concentration of the Banking Market
Metropolis
-0.340
6.592***
-1.199***
[-1.10]
[4.36]
[-8.35]
1.297***
1.146
-0.030
[7.42]
[1.30]
[0.36]
-4.100***
3.790*
-0.208
[-10.46]
[1.95]
[-1.10]
Observations
7990
10305
10333
F-statistic
36.47
9.76
27.10
p-value of F-statistic
0.00
0.00
0.00
Adjusted R-squared
0.11
0.02
0.07
Time and Industry Dummies
Yes
Yes
Yes
Interstate-branching Deregulation
Constant
t statistics are in brackets; *** significant at 1%; ** significant at 5%; * significant at 10%.
44
Table 3: Lending Relationships and Adverse Selection
We look at the relationship between the bank-firm’s relationship and firm’s degree of adverse selection in the market. Columns (1)-(5) use
all “Types” of holdings reported in 13F while columns (6) and (7) only use Type-1 holdings to calculate the Lender’s Relative Trading
(“Banks” are identified as Type-1 institutions in 13F). Columns (8) and (9) use holdings of all Types except Type-1. Pre-loan level of
dependent variable on the right-hand side is constructed similarly, except being averaged over [t-n,t-1] years.
LENDERS' RELATIVE TRADING (LRT)
HOLDINGS OF ALL TYPES
(1)
Independent variables:
Proximity
(2)
0.001
[0.18]
Loan-to-Asset Ratio
(4)
0.001*
[1.82]
0.379***
[16.1]
(5)
-0.004
[-1.28]
Equity Exposure
Pre-Loan L R T (all Types)
(3)
0.375***
[16.3]
0.052***
[11.8]
0.323***
[14.8]
0.053***
[9.76]
0.326***
[14.3]
ONLY TYPE-1
HOLDINGS
ALL EXCEPT
TYPE-1
(6)
(8)
(7)
-0.002
[-0.73]
0.000
[0.93]
0.052***
[11.7]
0.323***
[14.7]
Pre-Loan L R T (only Type-1)
-0.011*
[-1.90]
0.035***
[5.86]
-0.000
[-0.33]
0.037***
[7.29]
0.061***
[5.12]
0.000
[0.35]
0.047***
[4.76]
0.314***
[14.0]
0.315***
[14.7]
-0.022
[-0.48]
0.005*
[1.65]
0.253***
[3.04]
-0.036
[-0.46]
0.169**
[2.07]
0.002
[0.93]
0.004
[1.33]
-0.093
[-1.46]
0.010***
[4.66]
-0.003
[-0.38]
-0.032
[-1.16]
0.066**
[2.21]
0.348
[0.98]
0.227***
[6.44]
-0.231**
[-2.46]
0.022***
[3.66]
-0.231
[-1.49]
-0.243*
[-1.77]
-0.037
[-0.37]
0.003
[1.36]
0.011**
[2.06]
-0.065
[-0.58]
0.008**
[2.03]
-0.004
[-0.23]
0.036
[0.80]
-0.005
[-0.11]
0.571
[1.46]
0.223***
[7.16]
-0.239***
[-3.13]
0.023***
[4.51]
-0.239*
[-1.82]
-0.174
[-1.55]
0.024
[0.25]
0.003
[1.46]
0.011***
[2.71]
-0.096
[-0.99]
0.005
[1.58]
-0.003
[-0.24]
0.060
[1.59]
0.021
[0.56]
-0.316
[-0.61]
6269
Yes
3831
Yes
5251
Yes
Pre-Loan L R T (except Type-1)
Size
Size-squared
Leverage
Cash
Capital Expenditure
ROA
Market-to-Book
Institutional Holdings
Analysts
Firm's Relative Age
NYSE
Ratings Dummy
Constant
-0.087**
[-2.05]
0.011***
[4.30]
0.058
[0.80]
0.011
[0.18]
0.101*
[1.72]
0.003**
[2.41]
0.006**
[2.43]
-0.037
[-0.62]
0.007***
[3.36]
0.003
[0.29]
-0.015
[-0.59]
0.040
[1.47]
0.323
[0.96]
-0.058
[-1.62]
0.009***
[4.09]
0.025
[0.37]
-0.004
[-0.087]
0.065
[1.17]
0.003***
[2.66]
0.005**
[2.47]
-0.058
[-1.09]
0.007***
[3.98]
0.002
[0.46]
-0.021
[-0.93]
0.033
[1.33]
0.384**
[2.32]
-0.073**
[-2.08]
0.010***
[4.41]
0.028
[0.43]
0.011
[0.22]
0.072
[1.35]
0.003**
[2.54]
0.005**
[2.50]
-0.100*
[-1.93]
0.007***
[4.35]
0.001
[0.11]
-0.018
[-0.83]
0.040*
[1.70]
0.443***
[2.63]
-0.091**
[-2.16]
0.011***
[4.31]
0.060
[0.85]
0.029
[0.51]
0.100*
[1.77]
0.003**
[2.30]
0.005**
[2.14]
-0.081
[-1.40]
0.008***
[3.75]
-0.002
[-0.23]
-0.010
[-0.41]
0.036
[1.34]
0.372
[1.15]
-0.070**
[-1.97]
0.009***
[4.33]
0.029
[0.45]
0.013
[0.25]
0.072
[1.34]
0.003**
[2.55]
0.005**
[2.43]
-0.101*
[-1.93]
0.007***
[4.34]
0.001
[0.13]
-0.018
[-0.84]
0.037
[1.53]
0.428**
[2.56]
-0.028
[-0.52]
0.005*
[1.66]
0.258***
[2.98]
-0.012
[-0.13]
0.264***
[2.81]
0.002
[0.73]
0.003
[0.73]
-0.113*
[-1.69]
0.010***
[4.07]
0.006
[0.60]
-0.028
[-0.95]
0.053*
[1.67]
0.641**
[2.37]
Observations
5042
7099
7124
5042
7099
4647
Time and Industry Dummies
Yes
Yes
Yes
Yes
Yes
Yes
Robust and firm-clustered t-statistics in brackets; *** significant at 1%, ** significant at 5%, * significant at 10%
45
(9)
Table 4: Lending Relationships and Information Asymmetry
We look at the relationship between the bank-firm’s relationship and the degree of illiquidity and information asymmetry in the stock of the
firm.
ILLIQUIDITY
(1)
Independent variables:
Proximity
(2)
0.109**
[2.16]
Loan-to-Asset Ratio
(4)
(5)
-0.025
[-0.79]
Size-squared
Leverage
Cash
Capital Expenditure
ROA
Market-to-Book
Institutional Holdings
Analysts
Firm's Relative Age
NYSE
Ratings Dummy
(7)
(8)
0.018**
[2.15]
(9)
(10)
0.002
[0.24]
-0.011
0.005**
-0.001
[1.97]
[-0.71]
0.202**
[2.03]
[2.08]
[-0.45]
0.045**
[2.15]
0.181*
[1.95]
0.182**
[2.22]
0.067*
[1.90]
0.038**
[2.10]
0.011
[0.67]
-0.007
[-0.94]
-0.001
[-0.95]
0.017
[0.93]
-0.019
[-1.24]
0.010
0.012
[0.65]
[0.86]
-0.003
-0.015
[-0.46]
[-0.71]
-0.000
0.000
[-0.92] [0.023]
0.015
0.020
[0.96]
[0.82]
-0.024* -0.029***
[-1.89]
[-2.65]
0.431*** 0.390*** 0.408*** 0.395*** 0.418***
[12.1]
[11.1]
[12.8]
[11.3]
[12.2]
Pre-Loan Information Asymmetry
Size
(6)
0.026**
Equity Exposure
Pre-Loan Illiquidity
(3)
INFORMATION ASYMMETRY
0.005
0.013
[0.31]
[0.91]
-0.002
0.034*
[-0.24]
[1.73]
-0.000 -0.002*
[-0.24] [-1.77]
0.016
-0.034
[1.03]
[-1.51]
-0.037*** -0.034***
[-3.57] [-2.96]
-0.469***
[-6.16]
0.025***
[5.82]
0.427***
[3.61]
-0.433***
[-4.81]
-0.379***
[-3.41]
0.022***
[3.84]
0.369***
[2.67]
-0.433***
[-4.17]
-0.551***
[-7.81]
0.028***
[6.79]
0.559***
[4.53]
-0.409***
[-4.02]
-0.523***
[-7.12]
0.026***
[6.21]
0.429***
[3.70]
-0.387***
[-4.00]
-0.616***
[-5.29]
0.030***
[5.38]
0.625***
[3.91]
-0.418***
[-4.08]
-0.082
[-0.80]
-0.011***
[-4.08]
-0.007*
[-1.79]
-0.488***
[-5.63]
0.013***
[4.53]
-0.017
[-1.49]
-0.101**
[-2.44]
-0.099*
-0.114
[-1.18]
-0.010***
[-4.93]
-0.019***
[-3.81]
-0.529***
[-5.78]
0.011***
[4.93]
-0.019
[-1.57]
-0.097**
[-2.14]
-0.207***
-0.196**
[-2.20]
-0.009***
[-4.88]
-0.016***
[-4.08]
-0.627***
[-5.98]
0.010***
[4.39]
-0.040***
[-2.65]
-0.082*
[-1.95]
-0.146**
-0.211**
[-2.20]
-0.007***
[-3.32]
-0.014***
[-3.60]
-0.609***
[-5.91]
0.008***
[3.11]
-0.038**
[-2.45]
-0.064
[-1.57]
-0.139**
-0.223**
0.009
0.016
-0.006
-0.009
-0.006
[-2.21]
[0.54]
[0.94]
[-0.42] [-0.54]
[-0.42]
-0.008*** -0.000
-0.000
0.000
0.000
0.000
[-4.03]
[-0.79] [-0.26] [0.037]
[0.44]
[0.81]
-0.013***
0.000
-0.001* -0.001* -0.001
-0.001
[-2.69]
[0.59]
[-1.77] [-1.65] [-1.44]
[-0.75]
-0.630*** -0.029** -0.024** -0.067*** -0.050*** -0.054***
[-5.86]
[-2.28] [-2.02] [-2.72] [-2.85]
[-3.17]
0.011***
0.001*
0.001
0.000
0.000
0.000
[4.31]
[1.95]
[1.53]
[0.66]
[0.61]
[0.94]
-0.044***
0.001
0.003
-0.004
-0.003
-0.002
[-2.67]
[0.67]
[1.57]
[-0.85] [-1.04]
[-0.51]
-0.076* -0.025*** -0.021*** -0.018*** -0.019*** -0.018***
[-1.73]
[-4.51] [-4.12] [-3.31] [-3.42]
[-3.70]
-0.115
0.013
-0.005
0.002
0.004
0.009
[-1.66]
[-2.60]
[-2.31]
[-2.31]
[-1.45]
[1.54]
[-0.45]
[0.19]
0.162** 0.202** 0.137*
0.101
0.116
0.019** 0.024** 0.006
[1.99]
[2.46]
[1.84]
[1.29]
[1.50]
[1.96]
[2.40]
[0.60]
Constant
2.597*** 1.835*** 3.014*** 2.939*** 3.284*** -0.149* -0.131 0.138***
[7.27]
[3.63]
[7.30]
[6.25]
[4.66]
[-1.69] [-1.26]
[2.71]
Observations
6533
7972
7999
6535
7977
6512
8218
8247
Time and Industry Dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Hansen's J (p-value)
0.54
0.50
0.89
0.87
0.90
0.38
0.43
0.58
Robust and firm-clustered z-statistics in brackets; *** significant at 1%, ** significant at 5%, * significant at 10%
Lambda
46
[0.48]
0.009
[0.94]
-0.031
[-0.38]
6516
Yes
0.78
[0.81]
0.007
[0.71]
0.162
[1.25]
8219
Yes
0.56
Table 5: Lending Relationships and Corporate Governance
We look at the relationship between the bank-firm’s relationship and alternative measures of quality of governance. Three different sets of
measures are used for corporate governance. In Panel A, the quality of corporate governance is measured by the board-structure of the
borrowing firm (obtained from IRRC). In Panel B, the quality of corporate governance is measured by the equity holdings of unaffiliated
institutional investors. In Panel C, provisions used by Gompers, Ishii, and Metrick (2003) (also obtained from IRRC) are used as proxies for
the quality of governance. Definitions of all variables can be found in Table 1B.
Panel A
MULTIPLE DIRECTORSHIPS DUMMY
(1)
Independent variables:
Proximity
(2)
-0.732***
[-3.40]
Loan-to-Asset Ratio
(4)
(5)
-0.601***
[-3.56]
-0.266**
[-2.57]
Equity Exposure
Pre-Loan Multi. Dir. Dummy
(3)
1.721***
[7.43]
INDEPENDENT DIRECTORS DUMMY
1.097***
[7.68]
-0.923*
[-1.89]
1.043***
[11.4]
-0.248
[-1.05]
1.595***
[8.83]
Size-squared
Leverage
Cash
Capital Expenditure
ROA
Market-to-Book
Institutional Holdings
Analysts
Product-market concentration
Firm's Relative Age
NYSE
Ratings Dummy
Lambda
Constant
0.495
[1.64]
-0.017
[-1.05]
-0.060
[-0.16]
-0.437
[-1.22]
-0.842
[-1.60]
0.019*
[1.73]
0.012
[0.83]
-0.128
[-0.32]
-0.015
[-1.03]
0.398
[0.32]
-0.043
[-0.76]
-0.098
[-0.69]
0.303*
[1.77]
0.054
[0.27]
2.372
[1.34]
-2.139**
[-2.30]
0.112**
[2.37]
0.592
[1.03]
-0.010
[-0.023]
0.448
[0.73]
0.014
[1.06]
0.079***
[2.95]
1.015***
[2.89]
0.010
[0.87]
-1.088
[-0.78]
0.160***
[3.21]
-0.036
[-0.20]
1.082**
[2.42]
-0.061
[-0.23]
8.258**
[2.12]
0.040
[0.19]
0.015
[1.13]
-0.445
[-1.38]
-0.328
[-0.80]
0.165
[0.44]
-0.000
[-0.012]
0.041***
[2.90]
1.948***
[3.39]
0.027***
[3.80]
-0.548
[-0.50]
0.170***
[3.17]
0.054
[0.47]
0.500**
[2.24]
0.052
[0.23]
-2.545**
[-2.15]
0.408
[1.49]
-0.010
[-0.69]
-0.175
[-0.50]
-0.572
[-1.37]
-0.608
[-1.36]
0.018
[1.63]
0.022
[1.50]
0.368
[1.15]
-0.007
[-0.62]
0.141
[0.12]
0.016
[0.37]
-0.098
[-0.73]
0.393**
[2.08]
0.108
[0.49]
2.116
[1.10]
Observations
3255
3833
3824
3237
Industry Dummies
Yes
Yes
Yes
Yes
Wald Exogeneity Test (p-value)
0.00
0.00
0.00
0.00
z-statistics in brackets; *** significant at 1%, ** significant at 5%, * significant at 10%
47
(7)
(8)
0.269***
[4.09]
-0.217**
[-2.04]
0.216
[0.81]
1.096***
[8.73]
Pre-Loan Ind. Dir. Dummy
Size
(6)
(9)
(10)
0.537***
[2.87]
0.104**
[2.10]
1.147*
[1.91]
1.161**
[2.51]
0.110**
[1.98]
-0.154
[-1.04]
-1.826**
[-1.97]
0.093*
[1.93]
0.324
[0.62]
0.163
[0.38]
0.507
[0.88]
0.006
[0.41]
0.064**
[2.33]
0.733*
[1.74]
0.019*
[1.92]
-0.633
[-0.46]
0.137**
[2.45]
-0.040
[-0.25]
0.882*
[1.90]
-0.165
[-0.65]
8.093**
[1.97]
1.514***
[25.6]
-0.105
[-0.59]
0.005
[0.53]
-0.309
[-1.21]
0.022
[0.099]
-0.672*
[-1.68]
-0.013**
[-2.35]
0.015*
[1.74]
0.974***
[4.68]
0.021***
[2.87]
0.102
[0.12]
0.081***
[2.64]
0.120
[1.26]
0.150
[1.24]
0.085
[0.65]
-7.244***
[-5.37]
1.553***
[21.5]
1.061**
[2.38]
-0.050**
[-2.17]
-0.718**
[-2.33]
0.008
[0.034]
-1.253***
[-2.99]
-0.009
[-1.34]
-0.012
[-0.86]
0.372*
[1.89]
0.003
[0.41]
0.633
[0.71]
0.020
[0.67]
0.034
[0.32]
-0.190
[-0.86]
0.087
[0.58]
-5.692***
[-2.95]
1.649***
[14.3]
0.169
[0.74]
-0.020
[-1.36]
-0.233
[-0.62]
0.620
[1.25]
-1.350***
[-2.67]
-0.011
[-1.21]
-0.012
[-0.76]
-1.054
[-1.31]
-0.000
[-0.044]
0.752
[0.63]
-0.139*
[-1.73]
0.083
[0.61]
-0.241
[-0.86]
-0.244
[-0.86]
-6.408***
[-4.79]
1.722***
[13.2]
-0.452
[-1.26]
0.005
[0.27]
-0.166
[-0.37]
1.120*
[1.82]
-0.765
[-1.28]
-0.035**
[-2.48]
-0.021
[-0.99]
-0.142
[-0.27]
0.034**
[2.28]
-0.201
[-0.14]
-0.011
[-0.19]
0.240
[1.37]
-0.289
[-1.05]
-0.419
[-1.32]
-4.353*
[-1.80]
1.536***
[20.0]
1.122**
[2.28]
-0.051**
[-2.02]
-0.758**
[-2.36]
-0.093
[-0.35]
-1.212***
[-2.83]
-0.008
[-1.14]
-0.010
[-0.66]
0.563**
[2.06]
0.002
[0.39]
0.552
[0.61]
0.041
[1.14]
0.024
[0.23]
-0.162
[-0.66]
0.131
[0.81]
-6.122***
[-2.86]
3820
Yes
0.00
3279
Yes
0.00
3856
Yes
0.01
3876
Yes
0.00
3261
Yes
0.00
3855
Yes
0.01
Panel A (contd.)
VOTING POWER OF INDEPENDENT DIRECTORS
(11)
Independent variables:
Proximity
(12)
0.193***
[2.63]
Loan-to-Asset Ratio
(14)
(15)
0.362***
[2.61]
0.166***
[3.09]
Equity Exposure
Pre-Loan Vot. Pwr. Ind. Dirs.
(13)
0.736***
[39.6]
0.734***
[31.1]
0.779**
[2.18]
0.689***
[28.6]
1.136***
[2.64]
0.681***
[20.1]
Size-squared
Leverage
Cash
Capital Expenditure
ROA
Market-to-Book
Institutional Holdings
Analysts
Product-market concentration
Firm's Relative Age
NYSE
Ratings Dummy
Lambda
Constant
1.092***
[3.48]
-0.059***
[-3.27]
-1.207**
[-2.45]
-0.929**
[-2.27]
-0.372
[-0.74]
-0.009
[-0.87]
0.044**
[2.45]
-0.385
[-1.11]
0.005
[0.50]
1.081
[0.72]
0.055
[1.14]
-0.179
[-1.05]
0.031
[0.12]
-0.269
[-0.82]
-1.808
[-1.38]
2.257***
[4.04]
-0.112***
[-3.64]
-1.713***
[-2.72]
-1.277**
[-2.57]
-1.126*
[-1.79]
-0.024*
[-1.90]
0.048**
[2.39]
0.283
[0.55]
0.005
[0.39]
3.124
[1.47]
0.002
[0.027]
-0.289
[-1.34]
-0.110
[-0.32]
0.124
[0.30]
-8.323***
[-3.30]
0.552
[1.49]
-0.031
[-1.55]
-1.360***
[-2.65]
-1.044**
[-2.26]
-0.569
[-1.13]
-0.022**
[-2.02]
0.062***
[3.67]
-0.570
[-1.46]
0.003
[0.24]
-0.248
[-0.084]
-0.065
[-1.28]
-0.325*
[-1.84]
0.074
[0.26]
-0.142
[-0.39]
2.651
[1.03]
0.665
[1.12]
-0.048
[-1.48]
-0.690
[-0.80]
0.407
[0.47]
-0.349
[-0.43]
-0.020
[-1.02]
-0.038
[-0.92]
-0.962
[-1.51]
0.028
[1.34]
2.602
[1.04]
0.010
[0.12]
0.073
[0.22]
-0.666
[-1.32]
-1.130*
[-1.72]
1.023
[0.39]
Observations
1732
2182
2187
1531
Industry Dummies
Yes
Yes
Yes
Yes
Wald Exogeneity Test (p-value)
0.01
0.00
0.01
0.07
z-statistics in brackets; *** significant at 1%, ** significant at 5%, * significant at 10%
48
(16)
(17)
(18)
0.335**
[1.96]
0.173***
[3.02]
-0.636
[-1.55]
0.762***
[25.1]
Pre-Loan Non-Exec. Chair.
Size
NON-EXECUTIVE CHAIRMAN
(19)
(20)
0.357*
[1.91]
0.148**
[2.40]
0.421***
[3.90]
0.634**
[2.21]
0.155***
[3.65]
0.259*
[1.76]
2.624***
[4.25]
-0.128***
[-3.86]
-1.667**
[-2.42]
-1.520**
[-2.49]
-1.142*
[-1.64]
-0.016
[-1.03]
0.062**
[2.46]
0.682
[1.18]
0.008
[0.50]
4.146*
[1.79]
0.044
[0.71]
-0.391
[-1.64]
0.162
[0.39]
0.304
[0.65]
-10.283***
[-3.68]
0.985***
[6.41]
-0.204
[-0.68]
-0.008
[-0.53]
0.542*
[1.71]
1.272***
[4.12]
-0.010
[-0.022]
-0.018*
[-1.81]
0.009
[0.89]
1.272***
[3.27]
0.020
[1.39]
0.301
[0.32]
0.142***
[2.84]
-0.054
[-0.46]
-0.397***
[-2.65]
-0.944***
[-4.60]
1.535
[1.02]
1.120***
[7.09]
1.548***
[2.70]
-0.096***
[-3.18]
0.249
[0.63]
1.004***
[3.38]
-0.683
[-1.55]
-0.015*
[-1.83]
-0.026
[-1.59]
0.580**
[2.33]
0.001
[0.098]
0.714
[0.69]
0.103***
[2.87]
-0.145
[-1.13]
-0.845***
[-3.03]
-0.794***
[-3.88]
-6.497***
[-2.73]
1.204***
[11.6]
0.272
[1.42]
-0.037***
[-3.07]
0.651**
[2.48]
1.102***
[5.08]
-0.455
[-1.50]
-0.008
[-1.64]
-0.004
[-0.52]
0.039
[0.17]
-0.001
[-0.20]
0.165
[0.22]
0.008
[0.45]
-0.120
[-1.32]
-0.480***
[-3.59]
-0.866***
[-5.35]
0.001
[0.0011]
0.996***
[5.57]
-0.182
[-0.55]
-0.019
[-1.07]
0.624*
[1.68]
1.733***
[3.93]
-0.289
[-0.59]
-0.025**
[-2.02]
-0.015
[-0.93]
0.359
[0.71]
0.021
[1.33]
0.443
[0.40]
0.060
[1.03]
-0.002
[-0.013]
-0.687***
[-3.15]
-1.197***
[-4.39]
2.503
[1.36]
1.154***
[7.25]
1.582***
[3.64]
-0.101***
[-4.39]
0.275
[0.71]
1.201***
[3.90]
-0.773*
[-1.80]
-0.018**
[-2.42]
-0.033**
[-2.42]
0.329
[1.06]
0.001
[0.18]
0.879
[0.83]
0.081**
[2.03]
-0.128
[-0.98]
-0.984***
[-4.30]
-0.955***
[-4.46]
-6.298***
[-3.40]
2127
Yes
0.01
3212
Yes
0.02
3868
Yes
0.00
3887
Yes
0.00
3212
Yes
0.01
3867
Yes
0.00
Panel A (contd.)
RELATIVE-ON-BOARD
(21)
Independent variables:
Proximity
(22)
-0.249***
[-2.84]
Loan-to-Asset Ratio
(24)
(25)
-0.226**
[-2.55]
-0.173**
[-2.27]
Equity Exposure
Pre-Loan Relative on Board
(23)
INTERLOCKING DIRECTORSHIPS DUMMY
2.208***
[19.2]
2.379***
[13.3]
-0.727**
[-2.17]
2.177***
[17.3]
0.038
[0.29]
2.215***
[19.4]
Size-squared
Leverage
Cash
Capital Expenditure
ROA
Market-to-Book
Institutional Holdings
Analysts
Product-market concentration
Firm's Relative Age
NYSE
Ratings Dummy
Lambda
Constant
0.053
[0.21]
-0.014
[-1.00]
0.584*
[1.76]
0.044
[0.14]
-0.383
[-0.87]
0.007
[0.89]
-0.022*
[-1.65]
-1.291***
[-4.51]
0.022**
[2.25]
1.038
[0.69]
-0.021
[-0.53]
0.047
[0.39]
0.117
[0.70]
-0.281
[-1.41]
0.277
[0.20]
-1.861***
[-2.67]
0.084**
[2.35]
0.918*
[1.88]
0.191
[0.51]
0.255
[0.49]
0.011
[0.97]
0.020
[1.01]
-0.675**
[-2.23]
0.028***
[2.88]
-4.028
[-1.52]
-0.034
[-0.74]
0.086
[0.55]
0.695**
[2.06]
-0.364
[-1.48]
7.284**
[2.38]
-0.271
[-1.23]
0.011
[0.83]
0.480
[1.35]
-0.224
[-0.60]
0.154
[0.38]
0.003
[0.37]
-0.001
[-0.041]
0.023
[0.048]
0.034***
[4.42]
-3.367*
[-1.68]
0.081
[1.61]
0.067
[0.52]
0.371*
[1.75]
-0.182
[-0.78]
-0.030
[-0.029]
0.030
[0.12]
-0.013
[-0.99]
0.615*
[1.85]
0.110
[0.32]
-0.394
[-0.91]
0.006
[0.74]
-0.024*
[-1.70]
-1.297***
[-4.53]
0.024**
[2.52]
1.016
[0.69]
-0.023
[-0.59]
0.057
[0.46]
0.102
[0.58]
-0.305
[-1.47]
0.440
[0.30]
Observations
3225
3729
3743
3224
Industry Dummies
Yes
Yes
Yes
Yes
Wald Exogeneity Test (p-value)
0.01
0.00
0.01
0.01
z-statistics in brackets; *** significant at 1%, ** significant at 5%, * significant at 10%
49
(27)
(28)
-0.649***
[-4.08]
-0.164**
[-2.16]
-0.185
[-0.88]
2.371***
[13.5]
Pre-Loan Inter. Dir. Dummy
Size
(26)
(29)
(30)
-0.586***
[-4.84]
-0.312***
[-2.69]
-0.584***
[-2.90]
-0.463**
[-2.02]
-0.243***
[-2.88]
0.264
[1.08]
-1.756**
[-2.51]
0.081**
[2.27]
0.907*
[1.89]
0.063
[0.16]
0.297
[0.58]
0.012
[1.09]
0.022
[1.10]
-0.473
[-1.26]
0.029***
[3.03]
-4.273
[-1.63]
-0.012
[-0.24]
0.076
[0.49]
0.731**
[2.19]
-0.301
[-1.19]
6.667**
[2.15]
2.002***
[14.4]
0.531
[1.53]
-0.027
[-1.49]
-0.304
[-0.67]
-0.373
[-0.87]
0.004
[0.0074]
0.027**
[2.41]
-0.035*
[-1.91]
-0.565
[-1.36]
-0.019
[-1.26]
1.173
[0.91]
-0.072
[-1.22]
-0.223
[-1.33]
0.176
[0.82]
0.122
[0.48]
-9.320***
[-7.80]
1.745***
[7.55]
-3.284***
[-3.09]
0.160***
[2.95]
0.963
[1.39]
0.227
[0.43]
1.398*
[1.93]
0.020
[1.31]
0.022
[0.70]
0.493
[1.14]
0.022*
[1.67]
-0.060
[-0.029]
0.071
[1.17]
-0.027
[-0.12]
1.160**
[2.33]
-0.097
[-0.28]
12.504***
[2.72]
1.976***
[18.0]
-0.601***
[-2.82]
0.033***
[2.65]
-0.010
[-0.029]
0.227
[0.68]
0.796**
[2.13]
-0.000
[-0.012]
-0.030*
[-1.91]
1.102***
[3.04]
0.030***
[4.09]
0.073
[0.071]
0.178***
[4.62]
-0.017
[-0.13]
0.299
[1.56]
-0.084
[-0.39]
-0.009
[-0.010]
2.099***
[13.8]
0.382
[1.18]
-0.013
[-0.76]
-0.355
[-0.76]
-0.671
[-1.41]
0.293
[0.56]
0.028**
[2.53]
-0.015
[-0.78]
0.302
[0.69]
-0.014
[-1.11]
1.121
[0.84]
0.009
[0.16]
-0.301*
[-1.69]
0.373
[1.55]
0.251
[0.91]
-10.771***
[-7.41]
1.759***
[9.16]
-2.678***
[-3.40]
0.126***
[3.23]
0.828
[1.43]
0.495
[1.07]
1.178**
[2.00]
0.011
[1.00]
0.003
[0.12]
0.134
[0.29]
0.024**
[2.16]
0.094
[0.056]
0.051
[0.85]
0.000
[0.0010]
0.812**
[2.27]
-0.236
[-0.79]
10.238***
[2.86]
3729
Yes
0.00
3218
Yes
0.00
3765
Yes
0.00
3778
Yes
0.00
3218
Yes
0.00
3765
Yes
0.00
Panel B
UNAFFILIATED INSTITUTIONAL HOLDINGS
(1)
Independent variables:
Proximity
(2)
0.010**
[2.23]
0.029**
[1.99]
Equity Exposure
Size
Size-squared
Leverage
Cash
Capital Expenditure
ROA
Market-to-Book
Analysts
Product-market concentration
Firm's Relative Age
NYSE
Ratings Dummy
Lambda
Constant
(4)
(5)
0.011**
[2.28]
Loan-to-Asset Ratio
Pre-Loan Unaffiliated Inst. Holdings
(3)
0.379***
[23.7]
0.046***
[5.08]
-0.002***
[-3.50]
-0.055***
[-2.96]
-0.006
[-0.36]
0.034
[1.64]
0.000
[1.20]
0.002**
[2.52]
-0.001***
[-2.73]
-0.040
[-0.74]
-0.002
[-0.66]
-0.003
[-0.50]
0.005
[0.56]
0.006
[0.52]
-0.282***
[-2.98]
0.501***
[8.38]
0.285**
[2.25]
-0.013**
[-2.17]
-0.234**
[-2.34]
0.039
[0.84]
0.104
[1.61]
0.001
[1.17]
-0.008
[-1.52]
-0.001
[-1.40]
0.069
[0.42]
-0.011
[-1.44]
-0.011
[-0.47]
-0.091*
[-1.67]
0.079
[1.61]
-1.489**
[-2.42]
-0.066
[-1.35]
0.441***
[14.2]
0.044***
[4.12]
-0.001
[-1.03]
-0.082***
[-3.21]
-0.023
[-1.00]
0.031
[1.63]
0.001***
[3.35]
0.002**
[1.97]
-0.001**
[-2.01]
0.050
[0.77]
0.001
[0.38]
-0.001
[-0.13]
0.016
[1.27]
0.018
[1.28]
-0.023
[-0.27]
0.027
[1.04]
0.365***
[17.0]
0.045***
[4.90]
-0.002***
[-3.32]
-0.049**
[-2.48]
0.005
[0.28]
0.029
[1.28]
0.000
[0.57]
0.001
[1.58]
-0.001***
[-2.74]
-0.057
[-0.98]
-0.005
[-1.12]
-0.002
[-0.31]
0.001
[0.085]
0.005
[0.38]
-0.247**
[-2.34]
0.016*
[1.81]
0.005
[0.079]
0.457***
[6.56]
0.189**
[2.26]
-0.009**
[-2.35]
-0.150**
[-2.03]
0.023
[0.64]
0.059
[1.35]
0.001
[0.88]
-0.005
[-1.38]
-0.002**
[-2.34]
-0.036
[-0.32]
-0.008
[-1.59]
-0.004
[-0.27]
-0.062*
[-1.77]
0.047
[1.39]
-0.942**
[-2.38]
Observations
6231
7279
7568
6231
6451
Time and Industry Dummies
Yes
Yes
Yes
Yes
Yes
Hansen's J (p-value)
0.35
0.45
0.53
0.27
0.45
Robust and firm-clustered z-statistics in brackets; *** significant at 1%, ** significant at 5%, * significant at 10%
50
Panel C
ANTI-TAKEOVER
DUMMY
GOVERNANCE DUMMY
(1)
Independent variables:
Proximity
(2)
-0.148**
[-2.54]
Loan-to-Asset Ratio
(4)
-0.186**
[-2.50]
2.737***
[36.9]
(5)
-0.216***
[-3.06]
Equity Exposure
Pre-Loan Governance Dummy
(3)
2.497***
[24.2]
-0.485***
[-3.49]
2.709***
[40.0]
-0.499***
[-3.21]
2.840***
[31.8]
(6)
(7)
-0.391***
[-2.63]
-0.100**
[-2.09]
-0.262**
[-2.03]
2.572***
[32.5]
Pre-Loan Anti-Takeover Dummy
Size-squared
Leverage
Cash
Capital Expenditure
ROA
Market-to-Book
Institutional Holdings
Analysts
Product-market concentration
Firm's Relative Age
NYSE
Ratings Dummy
Lambda
Constant
0.742***
[3.88]
-0.045***
[-4.08]
-0.206
[-0.77]
0.118
[0.50]
-0.237
[-0.55]
0.006
[1.00]
0.009
[0.98]
0.033
[0.16]
-0.023***
[-3.39]
-0.619
[-0.86]
0.057*
[1.81]
0.074
[0.73]
0.002
[0.019]
-0.064
[-0.45]
-4.187***
[-5.05]
-0.747
[-1.17]
0.029
[0.86]
0.206
[0.54]
0.229
[0.75]
-0.083
[-0.15]
0.018*
[1.84]
0.032**
[2.05]
0.303
[1.28]
-0.018**
[-2.25]
-0.981
[-1.52]
0.027
[0.71]
0.213
[1.61]
0.679**
[2.18]
0.031
[0.16]
2.650
[0.95]
0.758***
[4.31]
-0.043***
[-4.16]
-0.446*
[-1.67]
-0.219
[-0.85]
-0.187
[-0.42]
0.006
[1.21]
0.013
[1.49]
1.071***
[4.18]
-0.010*
[-1.79]
0.414
[0.46]
0.144***
[4.99]
0.067
[0.68]
0.271**
[1.97]
0.145
[0.95]
-5.303***
[-2.98]
0.817***
[3.69]
-0.041***
[-3.22]
-0.395
[-1.25]
-0.370
[-1.16]
-0.076
[-0.15]
0.016**
[2.18]
0.019*
[1.76]
0.663**
[2.19]
-0.028***
[-3.41]
-0.710
[-0.85]
0.116***
[2.86]
0.018
[0.15]
0.218
[1.35]
0.197
[1.06]
-5.399***
[-5.25]
Observations
3878
4669
4675
3878
Industry Dummies
Yes
Yes
Yes
Yes
Wald Exogeneity Test (p-value)
0.00
0.00
0.00
0.00
z-statistics in brackets; *** significant at 1%, ** significant at 5%, * significant at 10%
51
(8)
(9)
-0.228***
[-3.20]
-0.898***
[-2.85]
-0.113**
[-2.02]
-0.407**
[-2.20]
3.323***
[27.1]
3.079***
[30.6]
Pre-Loan Comply. Gov. Dummy
Size
COMPLEMENTARY
GOVERNANCE
DUMMY
-0.505***
[-3.00]
-0.136**
[-2.10]
-0.358**
[-2.22]
2.600***
[32.7]
-0.892
[-1.59]
0.041
[1.41]
0.029
[0.077]
-0.571*
[-1.66]
-0.888*
[-1.65]
0.020**
[2.05]
0.013
[0.83]
0.692**
[2.30]
0.001
[0.100]
-1.239**
[-2.01]
0.148***
[4.53]
0.331***
[2.74]
0.713***
[2.68]
0.200
[1.05]
2.765
[1.12]
4658
Yes
0.00
-0.001
[-0.0033]
-0.006
[-0.26]
-0.031
[-0.11]
-0.057
[-0.21]
-0.081
[-0.18]
0.013*
[1.81]
0.026**
[2.20]
0.598**
[2.50]
-0.015**
[-2.39]
-1.187**
[-2.25]
0.074**
[2.36]
0.159
[1.51]
0.465**
[2.13]
0.086
[0.53]
-0.904
[-0.48]
0.640**
[2.04]
-0.023
[-1.44]
-0.835**
[-2.03]
-0.610
[-1.14]
0.718
[1.17]
0.016
[1.17]
0.053***
[3.37]
1.078**
[2.49]
-0.025*
[-1.89]
0.583
[0.52]
-0.058
[-1.12]
0.039
[0.25]
0.623**
[2.52]
0.405
[1.25]
-5.840***
[-3.30]
-0.641
[-1.33]
0.031
[1.19]
-0.626*
[-1.76]
0.091
[0.26]
0.792
[1.55]
0.005
[0.56]
0.050***
[3.51]
0.859***
[2.60]
0.001
[0.17]
0.187
[0.16]
-0.079**
[-1.97]
0.235*
[1.85]
0.752***
[2.93]
-0.013
[-0.063]
1.008
[0.49]
2.799***
[34.0]
0.433*
[1.93]
-0.022*
[-1.71]
-0.109
[-0.33]
-0.722**
[-2.00]
-1.473**
[-2.55]
0.017**
[2.11]
-0.006
[-0.49]
0.733**
[2.24]
-0.009
[-1.12]
-0.145
[-0.14]
0.099**
[2.28]
0.167
[1.34]
0.258
[1.51]
0.147
[0.75]
-4.736**
[-2.49]
4669
Yes
0.00
3866
Yes
0.00
4469
Yes
0.00
3872
Yes
0.00
Table 6: Lending Relationships and CEO Compensation
The measure of corporate governance used here is the change sensitivity of CEO’s Compensation to firm performance, which implies that
we use a panel dataset of CEOs in this table. The dependent variable, CEO’s Compensation, is defined as ln(1 + Total Compensation),
where Total Compensation is the CEO’s total compensation (from ExecuComp database) for year t.
The right-hand side variables of interest are the interactions of Firm’s Excess Return in year (t-1) with the three loancharacteristics (Proximity, Loan-to-Asset Ratio, and Equity Exposure), which measure the effect of lending relationships on the sensitivity to
performance. Firm performance, measured by Firm’s Excess Return in year (t-1), is the difference between the firm’s stock return over the
year t-1 and Industry’s Return in year (t-1), which is calculated as the average stock return of other firms in the same industry over t-1.
Control variables Firm’s Excess Return in year (t-2) and Industry’s Return in year (t-2) are defined in a similar manner. Annual returns
used here are calculated by compounding monthly returns obtained from CRSP-Monthly database. Stock Return Volatility is the standard
deviation of daily stock returns (obtained from CRSP-Daily database), calculated over the fiscal year t-1. The control variable CEO’s
Compensation in year (t-1) is constructed just like the dependent variable, except being recorded in year t-1. Other variables are defined in
Tables 1B above.
52
CEO's COMPENSATION
Independent variables:
Proximity
(Proximity) x (Excess Return)
(1)
0.052***
[3.14]
0.231**
[2.13]
Loan-to-Asset Ratio
(2)
0.003
[1.28]
0.022**
[2.25]
(3)
(4)
0.052***
[3.27]
0.198**
[2.02]
(5)
0.003
[1.12]
(Loan-to-Asset) x (Excess Return)
0.021**
[2.02]
Equity Exposure
0.003
-0.006
0.013
[0.21]
[-0.33]
[0.61]
(Exposure) x (Excess Return)
0.104***
0.084
0.017
[2.93]
[0.86]
[0.21]
CEO's Compensation in year (t-1)
0.365*** 0.402*** 0.390*** 0.355*** 0.400***
[14.0]
[16.1]
[16.3]
[11.6]
[15.3]
Firm's Excess Return in year (t-1)
0.685** -0.171*
-0.062
0.481
-0.177
[2.43]
[-1.86]
[-1.35]
[1.57]
[-1.58]
Firm's Excess Return in year (t-2)
0.034*
0.004
0.020
0.029
0.006
[1.86]
[0.15]
[1.41]
[1.49]
[0.26]
Industry's Return in year (t-1)
0.163**
0.056
0.150*** 0.213**
0.071
[2.31]
[0.69]
[2.75]
[2.25]
[1.02]
Industry's Return in year (t-2)
-0.041
-0.082*
-0.013
-0.029
-0.075
[-1.00]
[-1.67]
[-0.42]
[-0.68]
[-1.61]
Stock-return Volatility
0.932
-0.228
0.543
1.488
-0.132
[0.67]
[-0.22]
[0.64]
[1.00]
[-0.11]
Size
0.055
0.059
0.049
0.046
0.045
[1.20]
[1.55]
[1.59]
[0.99]
[1.04]
Size-squared
0.007*** 0.007*** 0.007*** 0.008*** 0.007***
[2.93]
[3.37]
[4.36]
[3.01]
[3.03]
Leverage
-0.251*** -0.391*** -0.218*** -0.232*** -0.390***
[-3.26]
[-2.74]
[-3.66]
[-2.84]
[-2.81]
Cash
0.261**
0.225* 0.274*** 0.256**
0.252*
[2.17]
[1.89]
[3.93]
[2.09]
[1.95]
Capital Expenditure
-0.001
0.208
-0.075
-0.004
0.197
[-0.0029]
[1.13]
[-0.59]
[-0.021]
[1.06]
Market-to-Book
0.008** 0.012*** 0.010*** 0.008** 0.012***
[2.03]
[3.94]
[3.44]
[2.02]
[3.07]
Institutional Holdings
0.151* 0.235*** 0.276*** 0.206** 0.226***
[1.82]
[2.94]
[4.97]
[2.12]
[2.71]
Analysts
0.008*** 0.005*** 0.005*** 0.008*** 0.005***
[3.68]
[2.62]
[3.13]
[3.73]
[2.61]
Firm's Relative Age
0.011
-0.005
-0.002
0.016
-0.005
[1.23]
[-0.41]
[-0.25]
[1.42]
[-0.44]
Product-market concentration
-0.278
-0.189
-0.192
-0.261
-0.206
[-1.21]
[-0.92]
[-1.31]
[-1.12]
[-0.99]
NYSE
0.094***
0.043
0.075*** 0.096***
0.046
[3.13]
[1.32]
[3.90]
[3.27]
[1.47]
Ratings Dummy
0.085
0.015
0.007
0.085
0.013
[1.46]
[0.40]
[0.22]
[1.54]
[0.35]
Governance Index
0.001
-0.001
-0.007
0.003
-0.003
[0.077]
[-0.041]
[-0.49]
[0.16]
[-0.14]
Lambda
0.111
-0.019
-0.074
0.105
-0.033
[1.00]
[-0.26]
[-1.35]
[0.97]
[-0.44]
Constant
-0.257
-0.224
0.046
-0.264
-0.185
[-0.96]
[-0.70]
[0.19]
[-0.88]
[-0.50]
Observations
5467
5772
6750
5467
5772
Time and Industry Dummies
Yes
Yes
Yes
Yes
Yes
Hansen's J (p-value)
0.22
0.44
0.84
0.45
0.43
Robust z-statistics in brackets; *** significant at 1%, ** significant at 5%, * significant at 10%
53
Table 7: Impact of Lending Relationships on Firm Value and Firm Profitability
The dependent variables used in Panel A, Tobin’s Q and Industry-adjusted ROA, are defined in Table 1B. Panels C and D exhibits abnormal
returns from trading strategies devised on the basis of the firms’ loan characteristics. These abnormal returns are calculated with respect to
four factors: the Fama-French 3-factors as well as momentum factor. Panel B presents returns using the Ibbotson Returns Across Time and
Securities (RATS) estimation, so [1, 6] at the head of the column represents the 6-month period immediately after the month in which the
loan started and returns in this column are returns over that 6-month period. Hi (Lo) indicates the return on a portfolio consisting of firms
whose loan-characteristic is above (equal to or below) median in a given month. Panel C presents returns using a calendar-time equallyweighted portfolio strategy, so the column [1, 6] shows returns of a portfolio consisting of all stocks that initiated a loan within the past 6
months and the returns shown are per month over the 6-month period immediately after the month in which the loan started. Hi (Lo)
indicates the return on a portfolio consisting of firms whose loan-characteristic is above (equal to or below) median in a given month. Hi –
Lo represents a trading strategy where we go long in the Hi and short the Lo portfolio.
Panel A
TOBIN's Q
Independent variables:
Proximity to Bank-Branch
Loan-to-Asset Ratio
Actual Equity Exposure
Pre-Loan Tobin's Q
Pre-Loan Ind. Adj. ROA
Size
Size-squared
Leverage
Cash
Capital Expenditure
ROA
Market-to-Book
Institutional Holdings
Analysts
Firm's Relative Age
NYSE
Ratings Dummy
Governance Index
Lambda
Constant
(1)
0.138*
[1.85]
(2)
(3)
INDUSTRY-ADJUSTED ROA
(4)
0.116*
[1.85]
0.046*
[1.87]
-0.336*
[-1.90]
0.177*** 0.182*** 0.198***
[4.09]
[4.24]
[4.58]
-0.248**
[-2.07]
0.177***
[4.00]
(5)
0.077**
[2.23]
-0.364**
[-2.39]
0.177***
[3.35]
(6)
2.734**
[2.16]
(7)
2.014**
[2.27]
(8)
(9)
0.754**
[1.97]
(10)
0.792**
[1.97]
-2.179** -2.039* -2.286*
[-2.07] [-1.94] [-1.93]
0.380*** 0.556*** 0.445*** 0.480*** 0.537***
[5.50]
[4.94]
[8.14] [7.23]
[7.58]
0.119** 0.431** 0.113**
0.135**
0.682**
0.684 16.368** 0.757
0.333 7.208**
[1.97]
[2.23]
[2.01]
[2.16]
[2.35]
[0.57]
[2.23]
[0.78] [0.32]
[2.19]
-0.014*** -0.028*** -0.009** -0.011*** -0.037*** -0.081 -0.852** -0.040 -0.019 -0.384**
[-3.84]
[-3.01]
[-2.36]
[-2.88]
[-2.75]
[-1.14] [-2.32] [-0.68] [-0.30] [-2.28]
-0.358*** -0.847*** -0.534*** -0.396*** -1.213*** -1.592 -5.271 -3.574** -3.011* -3.281
[-2.94]
[-3.59]
[-4.51]
[-3.10]
[-3.44]
[-0.82] [-1.17] [-2.35] [-1.79] [-1.47]
0.196
0.206*
0.067
0.143
0.201
-3.799 -4.090 -3.599 -5.548** -6.920**
[1.51]
[1.69]
[0.57]
[1.00]
[1.19]
[-1.49] [-0.79] [-1.58] [-2.03] [-2.16]
-0.132
-0.093
-0.217*
-0.070
0.047
-2.986 10.584 -6.867** -4.142 -0.058
[-1.03]
[-0.57]
[-1.94]
[-0.52]
[0.21]
[-0.78] [0.98] [-2.54] [-1.45] [-0.011]
0.001
-0.002
0.002
0.003
-0.001
[0.25]
[-0.51]
[0.96]
[0.90]
[-0.29]
0.192*** 0.112 0.223*** 0.212*** 0.161**
[3.07]
[1.02]
[3.25] [2.93]
[2.30]
-0.073
-0.065
0.160
0.139
0.267
2.335
-0.377 2.364* 3.333** 2.803
[-0.75]
[-0.60]
[1.11]
[1.03]
[1.49]
[1.38] [-0.091] [1.71] [2.18]
[1.29]
0.036*** 0.032*** 0.032*** 0.036*** 0.036*** 0.184*** 0.058 0.081** 0.115*** 0.101**
[6.69]
[7.78]
[7.87]
[7.23]
[6.68]
[3.10]
[0.74]
[2.48] [3.27]
[2.12]
0.020
-0.013
0.015
0.044*
0.076**
0.394
0.649 0.360** 0.393* 0.688***
[1.17]
[-0.82]
[0.62]
[1.90]
[2.34]
[1.60]
[1.61]
[2.28] [1.95]
[2.86]
-0.085*
-0.013
-0.035
-0.088*
-0.045
0.574
-4.328
0.516
0.878
-1.597
[-1.77]
[-0.27]
[-0.82]
[-1.69]
[-0.64]
[0.70] [-1.63] [0.76] [1.25] [-1.24]
0.037
-0.118
0.052
0.073
-0.218
-0.888 -5.074* 0.883
0.411
-1.222
[0.55]
[-1.11]
[0.80]
[1.03]
[-1.45]
[-0.78] [-1.70] [1.12] [0.45] [-0.77]
-0.054
0.026
0.003
-0.018
0.069
-0.820
0.956
-0.074 -0.121
0.382
[-1.43]
[0.58]
[0.066]
[-0.43]
[0.99]
[-1.54] [0.93] [-0.20] [-0.30] [0.68]
0.098
0.155
0.073
0.124
0.249
-1.302
3.719
1.566
0.443
2.376
[0.94]
[1.19]
[0.74]
[1.13]
[1.45]
[-0.85] [1.13]
[1.50] [0.41]
[1.46]
2.012***
-0.657
0.448
1.084***
-1.814
5.035 -74.506** -5.065 -1.937 -36.246**
[2.81]
[-0.73]
[1.21]
[2.82]
[-1.36]
[0.79] [-2.39] [-1.04] [-0.40] [-2.54]
Observations
6189
7786
7813
6063
6590
3173
3884
3985
Time and Industry Dummies
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Hansen's J (p-value)
0.89
0.73
0.62
0.34
0.26
0.65
0.21
0.71
Robust and firm-clustered z-statistics in brackets; *** significant at 1%, ** significant at 5%, * significant at 10%
54
3073
Yes
0.74
3884
Yes
0.41
Panel B: RETURNS ACROSS TIME & SECURITIES (RATS)
[1, 1]
Proximity
Loan-to-Asset Ratio
Equity Exposure
[1, 3]
[1, 6]
[1, 12]
Hi
Lo
Hi
Lo
Hi
Lo
Hi
Lo
-0.03%
[0.13]
-0.07%
[0.22]
-0.17%
[1.14]
0.45%*
[1.84]
0.08%
[0.61]
0.44%**
[2.45]
-0.57%*
[1.66]
0.64%
[1.12]
-0.47%*
[1.82]
0.19%
[0.46]
-0.01%
[0.04]
0.53%*
[1.69]
-1.21%**
[2.37]
1.49%*
[1.76]
-1.23%***
[3.26]
0.69%
[1.13]
-0.09%
[0.26]
1.39%***
[2.99]
-1.02%
[1.39]
3.18%***
[2.66]
-1.45%***
[2.65]
1.72%*
[1.89]
0.36%
[0.73]
2.62%***
[3.75]
Panel C: EQUALLY-WEIGHTED CALENDAR TIME PORTFOLIO RETURNS
[1, 1]
Proximity
Loan-to-Asset Ratio
Equity Exposure
[1, 3]
[1, 6]
[1, 12]
Hi
Lo
Hi - Lo
Hi
Lo
Hi – Lo
Hi
Lo
Hi - Lo
Hi
Lo
Hi – Lo
-0.20%
[0.83]
-0.18%
[0.39]
-0.20%
[0.84]
0.45%
[1.32]
0.17%
[0.78]
0.64%**
[2.09]
-0.66%*
[1.73]
-0.35%
[0.74]
-0.84%**
[2.48]
0.01%
[0.06]
0.27%
[0.99]
-0.08%
[0.53]
0.02%
[0.12]
0.12%
[0.77]
0.32%
[1.42]
-0.01%
[0.05]
0.16%
[0.57]
-0.40%*
[1.69]
0.02%
[0.10]
0.47%*
[1.93]
-0.16%
[1.07]
0.07%
[0.38]
0.05%
[0.33]
0.45%**
[2.01]
-0.05%
[0.26]
0.43%**
[2.03]
-0.61%***
[2.91]
0.08%
[0.44]
0.38%*
[1.77]
-0.08%
[0.60]
0.12%
[0.69]
0.10%
[0.72]
0.40%*
[1.90]
-0.04%
[0.23]
0.28%*
[1.75]
-0.48%**
[2.60]
55