Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
How Does Information Opacity Affect Public Firms’
Borrowing Cost? Evidence from Syndicated Loans
Jiayuan Chen*, Di Gong† and Cal Muckley‡
United States listed firms’ information opacity and bargaining power, vis-à-vis their
lenders, influence their cost of borrowing. Unreliable bond market access and stock
market microstructure illiquidity can reduce borrowers' bargaining power and
increase the cost of borrowing. Relationship lending can mitigate information
asymmetry with lenders and reduce the cost of borrowing. This latter benefit to
borrowers, however, is more pronounced for firms with access to bond markets and
high stock market liquidity. Our main findings are robust to default risk, a wide variety
of firm and loan specific features, and firm and year fixed effects.
JEL Codes: G21 and G32
1. Introduction
Borrowers’ opacity, with respect to pertinent information, affects the cost and terms
of their borrowing in the syndicated loan market (Sufi, 2007, Saunders and Steffen,
2011). This “information premium” is documented in the cost of syndicated loans for
private firms compared to public firms (Pagano & Panetta 1998; Schenone 2010;
Saunders & Steffen 2011). In this paper, we assess whether this “information
premium” is also evident in public firms; and what is the role of information opacity in
setting loan contract spreads for these firms.
Our paper contributes to the extant literature in several ways. We study stock market
microstructure illiquidity measures including the closing percent bid-ask (Chung and
Zhang, 2014) and Roll (1984) effective spreads, the Goyenko et al. (2009) and
Holden (2009) effective ticks, Lesmond, Ogden and Trzcinka (1999) trading days
with zero returns measure and the Amihud illiquidity ratio (2002). We test for a
relation between these illiquidity measures and syndicated loan spreads. Our
findings, which are consistent across this wide range of illiquidity measurements,
indicate that firms with more stock market liquidity experience significantly lower
costs of borrowing in the syndicated loan market. This relationship holds when we
control for firm and loan specific features, firms’ credit ratings, and firm and year
fixed effects. We identify two channels which can account for the stock illiquidity-loan
spread relationship. The information asymmetry component of these measurements
can account for the relation with loan spread (George, Kaul & Nimalendran 1991;
Bharath, Pasquariello & Wu 2009). In addition, an increase in a borrowing firm’s
stock market illiquidity renders more expensive equity finance (Amihud and
Mendelson, 1986). Such a deterioration of an alternative finance channel can
diminish the bargaining power, vis-à-vis its lenders, of the borrowing firm.
*
Jiayuan Chen, Department of Banking & Finance, University College Dublin, Ireland. Email:
Jiayuan.chen@ucdconnect.ie
†
Di
Gong,
Department
of
Economics,
Tilburg
University,
Netherlands.
Email:
d.gong@tilburguniversity.edu
‡
Dr. Cal Muckley, Department of Finance, Yale University and University College Dublin. Email:
cal.muckley@yale.edu
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
On the one hand, using exogenous market microstructure information asymmetry
measures (George et al. 1991; Bharath et al. 2009), we find that stock market
information asymmetry does explain the loan spread premium. Much of the prior
literature uses dichotomous indicator variables such as corporate structure (public or
private firms), stock market listings (Saunders & Steffen 2011) and public credit
ratings to infer borrowing firms’ information opacity. Such binary indicators can
provide valuable insight into the importance of information asymmetry to influence
loan spreads. However, they necessarily neglect cross-sectional variation within
each category. In contrast, our approach allows near continuous information opacity
measurements from stock market data, which reveals variation in information
asymmetry across firms and through time. Further, we find that relationship lending
reduces borrowing costs due to the mitigation of private information production cost
and/or adverse selection risk (Boot & Thakor 1994). As a result, we show that even
for publicly listed firms with compulsory regular information disclosure, there is
substantial heterogeneity in information opacity which still matters in terms of the
borrowing cost. These empirical findings are consistent with lenders requesting
higher loan spreads of informational opaque firms as compensation for the cost of
private information production and/or adverse selection risk.
An additional advantage of our measures of information asymmetry is that unlike
dichotomous stock or bond market listing, our information opacity measures are less
likely to be manipulated by firms’ managers. Therefore, our measures are less prone
to endogeneity concerns. For instance, we estimate our information opacity proxies
using stock market data up to one year before the loan origination to circumvent the
possibility of reverse causality. In addition, we adopt firm fixed effects models to
control for the time-invariant unobserved firm heterogeneity.
Lastly, by separating information asymmetry from market illiquidity, and by means of
analysing the impact of lender-borrower past relationship on loan spread, we are
able to identify the two channels via which information opacity influences loan
spread. On the other hand, the benefit of relationship lending is significantly greater
for firms with more liquid stocks, and hence a more feasible financing alternative in
the capital market. But, we do not find that the benefit of relationship lending is
significantly different between firms with different level of information asymmetry.
This finding supports Sharpe’s (1990) theory that lenders can “hold up” their
relationship borrowers and charge them with higher interest rates if they have limited
ability to alternative financing. Our results are robust when we control for bond
market access. Winton and Santos (2008) suggest that firm-level access to the bond
market can be an indicator of information opacity. It is also a clear alternative
source of finance for the firm which can ascribe to the borrower superior bargaining
power in negotiations with lenders in a syndicate, relative to when such an alternate
finance source is not available.
The remainder of the paper is organised as follows. We review the extant literature
in Section 2. In Section 3, we describe our testing methodologies, our base-line
model and we develop our hypotheses tests. Data description and our main
empirical results are reported in Section 4. Section 5 concludes.
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
2. Literature Review
Determinants of firms’ borrowing cost have been discussed extensively among
finance researchers. Fama and French (1993) identify two factors that affect
corporate bonds yields related to maturity and default risks. From a corporate
liquidity demand aspect, Holmström and Tirole (2001) find leverage ratios, capital
adequacy requirements, and the composition of savings also affect interest rates.
Moreover, borrowers’ information opacity increases the adverse selection risk, and
hence lenders may request higher interest rates. Nevertheless, this information
asymmetry can be greatly mitigated in a single-lender circumstance (Diamond
1984), as the lender has more incentives to investigate and monitor the borrowing
firm. However, this does not necessarily result in lower interest rates: on the one
hand, lenders possessioning private information can “hold up” their customers and
request for higher spreads (Sharpe 1990); on the other hand, production of such
private information is costly (Bharath et al. 2011; Saunders & Steffen 2011). There
are several other channels via which information asymmetry can directly or indirectly
impact loan spreads, for example, the existence and liquidity of a secondary loan
market (Gupta, Singh & Zebedee 2008), the ownership concentration (Lin et al.
2011), and the “unobserved opacity” (Saunders & Steffen 2011). Since the
secondary loan market liquidity and the ownership concentration are of less a
concern to U.S. public firms1, in this study, we focus on two channels, 1) borrowers’
bargaining power, and 2) lenders’ cost of private information production and/or
adverse selection risk.
Sharpe (1990) develops a model where informed lenders can take advantage of
captive customers (i.e., the borrowing firms) and charge prices that exceed the
marginal cost of funds. This theory is empirically supported by Santos and Winton
(2008), who find that during recessions, banks raise rates more for bank-dependent
borrowers than for those with bond market access. Studying the changes in bankloan pricing around IPO, Schenone (2010) find that the average interest rate is
significantly higher before IPO, and that relationship banks have greater ability to
exploit their information advantage before IPO. Consistent results are found in the
UK syndicated loan market (Saunders & Steffen 2011). UK private firms on average
pay 27-bp premium in syndicated loans compared to public firms. However, it is
much less pronounced if the private firms have issued public bonds prior to loan
origination; and public firms do not get such discount if they are listed on small
secondary market, such as AIM.
Bharath, Pasquariello and Wu (2009) relate exogenous market microstructure
measures of information asymmetry to financial markets’ perception of the
information advantage held by firm insiders. Using principle component analysis,
they construct an adverse selection index as the first component of seven market
microstructure measures of adverse selection or price impact. This approach differs
from all the studies discussed above in that it does not rely on endogenous
information asymmetry indicators such as public credit rating, stock exchange listing
or bond market access2. In contrast to Schenone (2010), Bharath, Dahiya, Saunders
and Srinivasan (2011) find that repeated borrowing from the same lender lowers the
loan spreads, especially for opaque borrowers.
In spite of the above empirical evidence, to the best of our knowledge, no paper has
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
studied whether the heterogeneous cost of equity among public firms has an impact
on lenders’ “hold up” power, and thus affects firms’ cost of debt. Well-known factors
that explain the expected equity returns include market return (Rm-Rf) (Sharpe 1964;
Lintner 1965), return on small stocks minus return on big stocks (SMB), return on
high book-to-market ratio stocks minus return on low book-to-market stocks (HML)
(Fama & French 1993), momentum (Carhart 1997), as well as profitability (RMW)
and investment (CMA) factors (Fama & French. 2015). Moreover, with the
development of research in market microstructure, increasing studies have extended
traditional asset-pricing models to including a new category of market illiquidity
factors, for example, bid-ask spread (Amihud & Mendelson. 1986), probability of
information-based trading (Easley, Hvidkjaer & O’hara 2002) and number of zerovolume days (Liu 2006). Since information asymmetry is a key component of market
illiquidity, in this paper, we focus on this new category of factors and test for whether
higher cost of equity due to market illiquidity diminishes borrowing firms’ bargaining
power, and hence increase their borrowing cost. Following Bharath et al. (2009), we
extract the adverse selection component from bid-ask spread and Roll’s effective
spread and test the alternative channel that lenders raise loan spreads due to the
cost of private information production and/or compensation for adverse selection
risk. To further identify the two channels, we analyse how market illiquidity and
information asymmetry affect loan spread in case of relationship lending, and
additional robust test is conducted controlling for corporate bond market access.
3. Methodology and Hypotheses
3.1 Measuring information opacity
The key variable of interest is the market-based measures of firm opacity. In the
paper, we use overall eight proxies for firm opacity. In particular, we adopt six
proxies for stock illiquidity and two proxies for adverse selection.
3.1.1 Illiquidity proxies
(i) Closing percent bid-ask spread
Liquidity is often measured by the bid-ask spread, which captures the cost
dimension of liquidity. Many researchers rely on high-frequency databases such as
NYSE Trade and Quote (TAQ); however, it is not only financially expensive and
time-consuming to process large sample over a long period, it is also subject to
errors mainly due to withdrawn quotes, and the spreads may vary substantially using
different “quote timing rules” (Holden & Jacobsen 2014).
Recently, scholars in market microstructure studies have evaluated a variety of
spread measures estimated from low-resolution data, and many of them well
simulate high-frequency liquidity measures. For example, Corwin and Schultz (2012)
derive a simple way to estimate bid-ask spread from daily Ask/High and Bid/Low
prices from CRSP, which is highly correlated with TAQ based spread measure;
Chung and Zhang (2014) find that using daily Bid and Ask fields provided by the
CRSP database, one can construct a spread measure, which has cross-section
correlation with TAQ based spread of more than 0.9. We follow Chung and Zhang
(2014) and calculate daily bid-ask spread
as the daily closing percent bid-ask
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
spread on day :
(1)
where
is the average of end-of-day bid and ask quotes. The bid-ask
spreads for longer horizons, e.g., monthly and yearly, are calculated as the average
daily bid-ask spread during the period3.
(ii) Closing percent effective spread
While bid-ask spread provides a way to calibrate the transaction cost, many trades
are executed between the best bid and ask price. Therefore, an alternative measure
of spread accounting for the “inside-spread” transactions is also widely used. Our
percent effective spread is defined as twice as the difference between the closing
trade price and the closing bid-ask midpoint, as a proportion to the bid-ask midpoint.
The daily effective spread is calculated as
|
|
(2)
and the longer-horizon effective spreads are calculated as the average of daily
effective spread during the period.
(iii) Roll’s effective spread
Roll (1984) develops theoretical framework that connects price reversal with spread.
Let be the unobservable fundamental value of the stock on day , which follows a
simple random walk, and the observed transaction price of stock is the fundamental
value plus half spread :
(3)
(4)
Combining the two equations above, effective spread can be interpreted as twice as
the square root of the negative autocovariance of price change; and we substitute
positive serial autocovariance with zero following Goyenko et al. (2009):
√
{
(5)
(iv) Effective tick
Goyenko et al. (2009) and Holden (2009) point out that it is possible to estimate
effective spread from the end-of-day trade price cluster. Let
be the number of
days where price cluster correspond to the th spread , where
, and let
be the corresponding probability,
∑
The unconstrained probability of the th spread is
(6)
5
Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
{
(7)
To control for unconstrained probability going below zero due to reverse price
clustering, the constrained probability of the th spread is calculated as:
[
[
] ]
{
(8)
[
[
]
∑
]
And finally, the effective tick is calculated as the probability-weighted average
spread divided by average price4:
∑
(9)
̅
The spread set
is dependent on the price grid, which is related to
the minimum ticksize. In this paper, we infer the spread set based on the three
minimum ticksize regimes:
⁄
⁄
⁄
⁄
⁄
⁄
(10)
{ ⁄
(v) Zero
Calculated as the number of days with zero returns over total number of trading
days,
(Lesmond, Ogden & Trzcinka 1999) indicates the value of information
relative to the transaction cost. When the transaction costs are high, traders will
refrain from trading if the value of new information cannot over come the transaction
cost. Therefore, in a limit order market, we will observe more zero-return days.5 Let
and
be the return and trading volume (in shares) on day respectively, and
be the number of days during the period,
can be interpreted as:
|
(11)
(vi) Amihud’s illiquidity ratio
Price impact of trades as a liquidity measure has gained much popularity in market
microstructure research, as it reflects the cost associated with trades. Amihud (2002)
employs a market illiquidity measure as “the daily ratio of absolute stock return to its
dollar volume”. It calibrates the daily price change in response to one million U.S.
dollars volume. Let and
denote the return and dollar volume (in millions)
on day , the Amihud illiquidity measure of a period with days can be calculated
as:
| |
(12)
∑
3.1.2 Adverse selection proxies
Extensive literature has found that transaction cost mainly comes from three
6
Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
sources, order processing cost, inventory cost and adverse selection. George, Kaul
and Nimalendran (1991) develop a simple model to decompose the quoted spread
into an order processing cost component and an adverse selection component. Let
and
be the daily return calculated from closing transaction prices
and the
subsequent bid quotes
respectively, the difference between the two,
, can be used to estimate the order processing cost
during month ,
(13)
√
where
is the observed quoted spread in month (calculated as the daily
average), and is the unobservable proportion of the quoted spread due to order
̂ from
processing cost. An unbiased yearly measure of can be estimated as ̂
the following regression:
̂
(14)
Therefore, our yearly adverse selection component
can be calculated as
̂ multiplied by the averaged daily bid-ask spread over the year
. Also, in
line with Bharath, Pasquariello and Wu (2009), we also estimate
by
substituting bid-ask spread with Roll’s effective spread
:
(15)
̂
(16)
̂
3.2 Controls
We include a number of firm level controls that may affect the lending interest rates.
First, we include firm size, as larger firms are less risky and more information
transparent. Next, we control for leverage and ROA, as highly leveraged firms and
less profitable firms are more likely to default. As for the firm specific controls that
affect loss given default (LGD), we include new working capital (NWC) and tangibles
assets. Firms with more net working capital and a higher fraction of tangible assets
are expected to lose less value in the event of default. We also control for Market-toBook ratio (Firm MKTBOOK), an imperfect proxy of Tobin's Q, which is a ratio of the
market value of a firm to its accounting value. We expect a firm with a higher Marketto-Book ratio to have lower spreads. Finally, we include industry dummies that
classify borrowers into ten sectors based on 4-digit SIC codes6, considering that loss
given default (LGD) is strongly correlated with industry characteristics (Hertzel &
Officer 2012; James & Kizilaslan 2014).
We also include several non-pricing loan features as they may reflect the default
risks (Sufi 2007). In specific, we include Facility Size and Maturity to proximate these
features. The signs of their impact on loan spread are both ambiguous: large loans
are likely to be associated with greater credit risk in the underlying project and lower
liquidity, but could also be borrowed by larger firms which tend to have lower risks; it
is similar in regard to maturity. Next, we use the number of lenders in a facility (No.
of Lenders) and the number of facilities within a deal (No. of Facilities) to proxy the
syndicated structure. To measure the liquidity exposure of each facility, we classify a
loan as a line of credit (Revolver) or a term loan (Term Loan)7. Moreover, we include
dummy variables that indicate whether a loan is senior (Senior) in the borrowers'
liability structure and whether the loan is secured by collateral (Secured). Seniority
and collateral may reduce the lenders' loss in the event of borrower default and
therefore reduce lending rates, however, the contractual arrangement may be
required ex-ante to protect lenders towards specifically risky borrowers. Therefore,
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
the relation between seniority, collateral and loan pricing is an empirical question.
Last, we control for loan purpose dummies into five categories: Corporate Purpose,
Debt Repayment, Takeover, Working Capital and Other.
In particular, we use the accounting information of the borrower from the fiscal year
ending in the calendar year
for loans made in calendar year . To eliminate the
bias from outliers, we winsorise loan spreads, firm specific variables and borrowers'
opacity measures at 1 and 99 percentile levels. We include year dummies to capture
time trends throughout the analysis as Santos and Winton (2008) have shown the
business cycle effect on loan contracts.
3.3 Loan pricing model
Finally, our baseline loan pricing model is defined as follows:
∑
∑
∑
(17)
where , , and denote firm, loan and year, respectively. The dependent variable,
, is the all-in-drawn spread in Dealscan which denotes an interest rate
spread over LIBOR measured in basis points. It is a measure provided by Dealscan
of overall costs of the loan, accounting for both one time and recurring fees.
is a market microstructure measure related to firm’s information opacity; it can be
one our illiquidity or adverse selection measures. Moreover, we include firm specific
variables
and loan specific variables
. We also include year dummies to
control for year fixed effect.
is the error term. We estimate the baseline loan
pricing model by cross-sectional OLS regressions that pool together all valid
observations. Robust standard errors are clustered at the borrower level to correct
for correlation across observations of a given firm.
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
4. Empirical Studies
4.1 Data
The data for this study come from LPC Dealscan8, Compustat and CRSP over the
period between 1988 and 20119. We collect syndicated loan data taken out by U.S.
firms from LPC Dealscan. We exclude loans borrowed by companies in regulated
utilities and financial sectors from the sample (SIC codes 6000 to 6999, Finance and
Insurance). Syndicated loans are usually structured in a number of facilities, also
called tranches. We treat facilities in each deal as different loans because spreads,
identity of lenders and other contractual features often vary within a syndicated loan
deal 10 . Therefore, each observation in the regressions denoted by Eq. (17)
corresponds to a syndicated loan facility.
Compustat collects annual report data of publicly listed American companies. By
merging Dealscan with Compustat, we have detailed annual accounting information
of the borrowers11. We rely on the CRSP database to calculate our market-based
proxies for stock illiquidity and adverse selection. In particular, we collect daily return
data over the year leading up to the facility activation date for borrowers listed in
NYSE, AMEX and NASDAQ12.
Finally, our sample consists of 10877 loans taken out by 1779 U.S. firms. All firm specific variables are
winsorised at the 1% and 99% levels. We present the definitions and data sources of all the variables in Table
A1 and the summary statistics in
Table A2.
4.2 Empirical results
We apply the loan pricing model denoted by Eq. (17) to examine whether our
informational opacity measures can explain the variations of borrowing costs among
U.S. public firms. In particular, we alternatively regress the all-in-drawn spreads on
the six stock illiquidity measures, and we control for the sets of firm and loan specific
variables described in Section 3.2 as well as year dummies. The results are shown
in Column (1) to (6) in Table 1.
The results in Column 1 suggest that firms with higher bid-ask spread (baspr) in the
stock market pay higher lending interest rates. Using effective spread (effspr) in
replace of bid-ask spread, shown in Column 2, we find similar results. In Column 3
and 4, we show that the coefficients of alternative effective spread measures, Roll’s
effective spread (Roll) and Holden’s effective tick (efftick), are also positive and
highly significant, consistent with the previous findings. Furthermore, Column 5
implies that borrowers with more stock zero-return days (zero) are charged at higher
loan spreads. When measuring market illiquidity as the price impact of one million
dollar volume, in Column 6, the Amihud measure shows a significant and positive
coefficient, indicating that firms with greater stock market price impact and thus less
liquid stocks tend to pay higher interest rates in the syndicated loan market.
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
Table 1 Pooled OLS Regression
In all specifications, we run cross-sectional OLS regressions that pool together all valid observations. The
dependent variable is the all-in-drawn spread. Standard errors are adjusted for clustering at the borrower level.
P-value is reported in parentheses below the coefficients. ***, **, * denote coefficients significantly different from
zero at the 1%, 5% and 10% levels, respectively.
baspr
(1)
6.683***
(0.000)
effspr
(2)
(3)
(4)
(5)
(6)
(7)
11.673***
(0.000)
Roll
1011.342***
(0.000)
efftick
13.579***
(0.000)
zero
88.942***
(0.004)
Amihud
3.724**
(0.029)
GKN
323.407**
(0.016)
RGKN
Firm size
Firm leverage
Firm ROA
Firm NWC
Firm tangibility
Firm MRTBOOK
No. of lenders
No. of facilities
Facility size
Maturity
Revolver
Term loan
Secured
Senior
_cons
Year dummies
Loan purpose
dummies
Industry dummies
No. of observations
No. of firms
R-sq
(8)
-7.702***
-6.369***
-7.513***
-7.696***
-8.830***
-9.286***
(0.000)
(0.001)
(0.000)
(0.000)
(0.000)
(0.000)
69.591***
67.459***
69.891***
68.648***
72.838***
74.539***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-242.554*** -235.221*** -231.101*** -234.379*** -249.307*** -248.384***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-39.998***
-39.540***
-41.687***
-40.808***
-44.022***
-43.369***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
2.159
2.632
2.068
2.892
2.463
2.161
(0.666)
(0.599)
(0.677)
(0.567)
(0.623)
(0.667)
-1.761
-1.820
-3.001*
-2.419
-2.235
-3.071*
(0.336)
(0.313)
(0.090)
(0.185)
(0.219)
(0.087)
-1.057***
-1.055***
-1.040***
-1.028***
-1.014***
-1.058***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
10.060***
9.848***
10.091***
9.917***
9.865***
9.889***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-9.532***
-9.280***
-9.334***
-9.461***
-10.000***
-9.846***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-5.468**
-5.183**
-5.666**
-5.408**
-5.949**
-5.844**
(0.025)
(0.034)
(0.020)
(0.027)
(0.014)
(0.017)
-64.398***
-64.634***
-64.468***
-64.101***
-64.037***
-64.167***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-3.829
-4.024
-3.734
-3.481
-3.098
-3.359
(0.705)
(0.691)
(0.711)
(0.733)
(0.761)
(0.741)
82.254***
82.294***
81.410***
82.551***
82.770***
83.018***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-247.653*** -247.121*** -244.632*** -247.678*** -251.217*** -249.766***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
481.910*** 465.932*** 479.909*** 479.168*** 498.360*** 512.308***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Yes
Yes
Yes
Yes
Yes
Yes
-9.620***
(0.000)
74.472***
(0.000)
-252.116***
(0.000)
-42.301***
(0.000)
4.728
(0.359)
-3.687*
(0.065)
-1.068***
(0.000)
10.223***
(0.000)
-9.804***
(0.000)
-6.078**
(0.014)
-64.172***
(0.000)
-1.495
(0.885)
82.454***
(0.000)
-229.985***
(0.000)
479.906***
(0.000)
Yes
695.047***
(0.002)
-9.670***
(0.000)
74.104***
(0.000)
-250.138***
(0.000)
-42.478***
(0.000)
4.828
(0.347)
-4.065**
(0.039)
-1.065***
(0.000)
10.314***
(0.000)
-9.761***
(0.000)
-6.098**
(0.014)
-64.710***
(0.000)
-1.970
(0.849)
82.408***
(0.000)
-229.408***
(0.000)
481.112***
(0.000)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
10877
1779
0.550
Yes
10877
1779
0.552
Yes
10877
1779
0.552
Yes
10877
1779
0.551
Yes
10877
1779
0.547
Yes
10877
1779
0.547
Yes
10363
1737
0.552
Yes
10363
1737
0.553
Turning to the controls, it shows in Table 1 that most of our control variables have
significant and expected signs. In specific, we find that large firms, more profitable
firms and firms with higher net working capital tend to pay lower interest rate,
whereas highly leveraged firms are charged higher loan spreads. The coefficients of
tangible assets and the market-to-book ratio, however, are not significantly different
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Proceedings of 10th Annual London Business Research Conference
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from zero. In terms of loan-specific features, we find that loans with more lenders,
larger size and longer maturity are associated with lower interest rates, but the
number of facilities has an opposite impact. Credit lines and senior loans are also
cheaper, however, loans secured by collaterals are charged at higher interest rates,
which is likely because risky firms are more likely requested of collaterals.
These results can be explained in line with Sharpe (1990) that firms with less liquid
stocks face higher cost and limited ability to finance from the capital market, and
therefore more dependent on debt financing. Hence, lenders are more likely to “hold
up” these firms and charging them higher interest rates. Nevertheless, since adverse
selection is an important component of stock market illiquidity (Bharath, Pasquariello
& Wu 2009), the stock illiquidity premium in the syndicated loan market may reflect
the adverse selection risk or the cost of private information production of firms with
illiquid stocks. We formally test the latter hypothesis using two adverse selection
measures, GKN and RGKN following George et al. (1991) and Bharath et al. (2009).
As reported in the last two columns in Table 1, both of the measures enter the
regression with positive and significant signs. In sum, we find strong evidence that
firms illiquidity and information asymmetry in the stock market are positively and
significantly associated with firms’ borrowing cost.
Next, two robustness tests are applied to our baseline loan pricing model. To show
that our results are not driven by imperfect control of firms’ default risk, we include
credit rating from Standard & Poor in Table 2 despite that our sample shrinks
substantially since many listed firms are not rated by S&P. In particular, we adopt
the S&P domestic long term issuer credit rating. The variable S&P rating takes value
of 1 if the firm has AAA rating, the value increases as the rating deteriorates. The
highest value is 17 for ratings below “B-“. Good rating score takes a lower value. We
find the coefficient of rating is positive and significant, indicating that firms of high
default risk pay higher interest rates; and the coefficients of the six market illiquidity
measures (Column 1 to 6) and the two adverse selection measures (Column 7 and
8) are quantitatively and qualitatively alike the baseline regression reported in Table
1, indicating that our findings are robust to omitted default risks.
The second robustness test is in regard to the potential endogeneity due to omitted
variables of the baseline specification if unobserved time-invariant firm
characteristics drive both firms’ stock features and loan spreads. We restructure the
data set into panel data in which we have
as the cross section unit and
as the time series unit. We estimate a firm fixed effects model, allowing for
arbitrary correlation between the unobserved borrower effect and the observed
explanatory variables. The identification comes from variations in opacity and loan
spreads within the same firm. In particular, we compare loan spreads of the same
firm across different loans when equity volatilities differ before the loan origination.
The results of fixed effect regression are reported in Table 3. It further confirms the
findings that firms with illiquid stocks and higher informational asymmetry are faced
with higher interest rates.
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
Table 2: Robustness check: Control for S&P ratings
In all specifications, we run cross-sectional OLS regressions that pool together all valid observations. The
dependent variable is the all-in-drawn spread, and all control variables except S&P ratings are suppressed from
reporting. Standard errors are adjusted for clustering at the borrower level. P-value is reported in parentheses
below coefficients. ***, **, * denote coefficients significantly different from zero at the 1%, 5% and 10% levels,
respectively.
baspr
(1)
10.945***
(0.000)
effspr
(2)
(3)
(4)
(5)
(6)
(7)
22.221***
(0.000)
Roll
1419.608***
(0.000)
efftick
16.238***
(0.002)
zero
122.501***
(0.009)
Amihud
17.302***
(0.001)
GKN
570.576***
(0.008)
RGKN
S&P rating
_cons
Year dummies
Loan purpose
Industry dummies
No. of observations
No. of firms
R-sq
(8)
18.992***
(0.000)
132.206
(0.128)
Yes
Yes
Yes
5769
943
0.679
18.609***
(0.000)
115.787
(0.162)
Yes
Yes
Yes
5769
943
0.682
18.542***
(0.000)
156.030*
(0.052)
Yes
Yes
Yes
5769
943
0.680
19.153***
(0.000)
151.219*
(0.079)
Yes
Yes
Yes
5769
943
0.677
19.497***
(0.000)
145.248*
(0.095)
Yes
Yes
Yes
5769
943
0.676
19.436***
(0.000)
134.705
(0.142)
Yes
Yes
Yes
5769
943
0.678
19.450***
(0.000)
156.795*
(0.077)
Yes
Yes
Yes
5645
934
0.678
696.852**
(0.018)
19.490***
(0.000)
170.005*
(0.050)
Yes
Yes
Yes
5645
934
0.677
Table 3: Robustness check: Firm fixed effect
In all specifications, we estimate firm fixed effects models. The dependent variable is the all-in-drawn spread,
and all control variables are suppressed from reporting. Standard errors are adjusted for clustering at the
borrower level. P-value is reported in parentheses below coefficients. ***, **, * denote coefficients significantly
different from zero at the 1%, 5% and 10% levels, respectively.
baspr
(1)
7.799***
(0.000)
effspr
(2)
(3)
(5)
(4)
(6)
(7)
13.486***
(0.000)
Roll
1030.082***
(0.000)
efftick
16.967***
(0.000)
zero
71.385***
(0.003)
Amihud
5.959***
(0.000)
GKN
432.287***
(0.000)
RGKN
Firm FE
Year dummies
Loan purpose
Industry dummies
No. of observations
No. of firms
R-sq
(8)
Yes
Yes
Yes
Yes
10877
1779
0.716
Yes
Yes
Yes
Yes
10877
1779
0.717
Yes
Yes
Yes
Yes
10877
1779
0.716
Yes
Yes
Yes
Yes
10877
1779
0.717
Yes
Yes
Yes
Yes
10877
1779
0.714
Yes
Yes
Yes
Yes
10877
1779
0.714
Yes
Yes
Yes
Yes
10363
1737
0.720
683.529***
(0.000)
Yes
Yes
Yes
Yes
10363
1737
0.720
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Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
To further identify the two potential channels, we construct a dummy for lending
relationship, which takes the value of one if the borrowing firm has taken a
syndicated loan within five years from the same lead lender, and zero otherwise.
Under Sharpe’s (1990) theory, relation lending results in higher loan spreads, as
relationship lenders are more likely to “hold-up” their captive customers. However,
under the alternative channel (Boot & Thakor 1994), repeated lending reduces the
information asymmetry between lenders and borrowers, as well as the cost of
private information production, and therefore lowers the interest rates. We include
the relationship dummy
and the interaction term
into our baseline
loan pricing regression denoted by Eq. (17). We report the regression output in
Table 4.
Table 4: OLS regression with relationship dummy
In all specifications, we run cross-sectional OLS regressions that pool together all valid observations. The
dependent variable is the all-in-drawn spread, and column (1) to (8) reports the estimated coefficients
corresponding to each opacity measure labelled in the first row respectively. Control variables are identical to
Table 1 and are suppressed from reporting. Standard errors are adjusted for clustering at the borrower level. Pvalue is reported in parentheses below coefficients. ***, **, * denote coefficients significantly different from zero at
the 1%, 5% and 10% levels, respectively.
(1)
rel
opacity
opacity*rel
Year dummies
Loan purpose
Industry dummies
No. of observations
No. of firms
R-sq
(2)
(3)
(4)
(5)
(6)
(7)
(8)
baspr
effspr
Roll
efftick
zero
Amihud
GKN
RGKN
-10.728***
-11.912***
-12.143***
-9.889***
-10.714***
-7.686***
-9.136***
-7.046***
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.007)
6.176***
10.698***
937.563***
12.763***
77.950**
3.657**
286.039**
725.100***
(0.000)
(0.000)
(0.000)
(0.000)
(0.013)
(0.035)
(0.042)
(0.004)
2.409*
5.455***
308.047*
4.242**
43.514**
-0.221
149.620
-143.279
(0.055)
(0.005)
(0.087)
(0.049)
(0.046)
(0.924)
(0.330)
(0.617)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
10877
10877
10877
10877
10877
10877
10363
10363
1779
1779
1779
1779
1779
1779
1737
1737
0.551
0.553
0.553
0.551
0.548
0.548
0.553
0.554
Consistent with Bharath et al. (2011), the coefficient of relationship dummy is
negative and significant throughout all specifications, indicating that past relationship
lowers the interest rates for repeated borrowers. Turning to the interaction terms,
except Amihud’s price impact measure, we find that the interaction terms of
relationship dummy and stock illiquidity proxies unanimously show positive and
statistically significant coefficients, whereas the interaction terms with Amihud and
the two adverse selection measures (GKN and RGKN) are statistically insignificant.
To interpret these results, we need to consider the two competing roles of
relationship lending: 1) it increases lenders’ monopoly, but 2) it reduces the
information asymmetry. Firms with illiquid stocks have limited and/or more costly
alternative financing ability, and are hence more likely to be held up by the lenders;
therefore the benefit result from information asymmetry reduction brought about by
relationship lending is limited by the increase in lenders’ monopoly. This finding is
consistent with Schenone (2010). In contrast, since relationship lending reduces
information asymmetry (
if true), its interaction with adverse selection
measures (GKN and RGKN) and Amihud, which is highly related to adverse
13
Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
selection (Bharath et al. 2011), become less meaningful, and therefore the
coefficients are not significantly different from zero.
To account for alternative sources of financing, i.e., corporate bond, firstly, we create
a dummy variable
, which equals to one if the borrowing firm has no access
to corporate bond market, and equals to zero otherwise. If a firm has access to the
bond market, the lender should have less monopoly over the borrowing firm, and
less likely to charge higher spread even if it is a relationship lender. Also, firms with
access to bond market are more informational transparent (Santos & Winton 2008).
Therefore we add a dummy
and its interaction with each opacity measure
to our baseline regression model Eq. (17). The results, as shown
in Table 5, are in line with our hypothetical analysis. First, firms without record of
bond issuance in the past five years before loan initiation on average pay about 1015 basis points premium. Second, the coefficients on all opacity measures are
positive and significant, whereas positive but insignificant on the two information
asymmetry measures; these indicate that stock market illiquidity increases lenders’
monopoly even if we control for borrowers’ bond market financing ability, and that
bond issuance history which reflects borrowing firms’ information transparency, is
highly correlated with our information asymmetry measures GKN and RGKN. Third,
the coefficients on the interaction
are generally insignificant,
except for effective spread (effspr) and Roll’s effective spread (Roll). On the one
hand it suggests that bond market access does not affect the loan’s sensitivity to
stock market illiquidity; on the other hand, it implies multicollinearity between
and the two illiquidity measures,
and
Table 5: OLS regression with no bond access dummy
In all specifications, we run cross-sectional OLS regressions that pool together all valid observations. The
dependent variable is the all-in-drawn spread, and column (1) to (8) reports the estimated coefficients
corresponding to each opacity measure labelled in the first row respectively. Control variables are identical to
Table 1 and are suppressed from reporting. Standard errors are adjusted for clustering at the borrower level. Pvalue is reported in parentheses below coefficients. ***, **, * denote coefficients significantly different from zero at
the 1%, 5% and 10% levels, respectively.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
baspr
effspr
Roll
efftick
zero
Amihud
GKN
RGKN
nobond
12.631***
15.773***
21.705***
10.237**
11.627***
10.761***
9.994***
9.348**
(0.001)
(0.000)
(0.000)
(0.013)
(0.002)
(0.002)
(0.010)
(0.024)
opacity
8.858***
19.545***
1911.372***
99.919**
17.382***
12.403*
326.764
603.99
(0.000)
(0.000)
(0.000)
(0.021)
(0.000)
(0.084)
(0.125)
(0.136)
opacity*nobond
Year dummies
Loan purpose
Industry dummies
No. of observations
No. of firms
R-sq
-2.192
-8.104**
-1000.801***
-9.961
-3.738
-8.783
2.951
113.138
(0.292)
(0.040)
(0.002)
(0.795)
(0.407)
(0.235)
(0.989)
(0.797)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
10877
10877
10877
10877
10877
10877
10363
10363
1779
1779
1779
1779
1779
1779
1737
1737
0.551
0.553
0.554
0.548
0.552
0.548
0.553
0.554
Furthermore, we study interactively the joint effect of relationship lending, bond
market access and stock market illiquidity. We add dummies
and
, which
equals to one if the firm has issued corporate bond within five years before loan
14
Proceedings of 10th Annual London Business Research Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1-922069-81-8
initiation, their interaction
, their respective interactions with each of our
opacity measure
and
, as well as the triple interaction
, to Eq. (17). We report the estimation in Table 6.
Table 6: OLS regression with relationship and bond access dummies
In all specifications, we run cross-sectional OLS regressions that pool together all valid observations. The
dependent variable is the all-in-drawn spread, and column (1) to (8) reports the estimated coefficients
corresponding to each opacity measure labelled in the first row respectively. Control variables are identical to
Table 1 and are suppressed from reporting. Standard errors are adjusted for clustering at the borrower level. Pvalue is reported in parentheses below coefficients. ***, **, * denote coefficients significantly different from zero at
the 1%, 5% and 10% levels, respectively.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Panel A
rel
bond
baspr
effspr
Roll
efftick
zero
Amihud
GKN
RGKN
-12.429***
-12.579***
-13.319***
-12.565***
-11.103***
-8.617***
-11.296***
-9.747***
(0.002)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
-9.485*
-9.401*
-20.193***
-6.773
-5.322
-3.377
-5.818
-5.919
(0.097)
(0.094)
(0.003)
(0.199)
(0.392)
(0.488)
(0.303)
(0.321)
11.284*
7.470
0.226
7.888
4.445
6.392
2.976
8.864*
(0.175)
(0.967)
(0.244)
(0.352)
(0.252)
(0.490)
(0.091)
(0.058)
opacity
5.983***
10.624***
854.116***
12.697***
75.097**
3.753**
266.998*
689.837***
(0.000)
(0.000)
(0.000)
(0.000)
(0.019)
(0.035)
(0.071)
(0.009)
opacity*rel
2.611**
4.459**
276.16
4.124*
49.348**
-0.443
243.001
97.558
(0.047)
(0.025)
(0.160)
(0.081)
(0.046)
(0.851)
(0.151)
(0.761)
255.443
rel*bond
opacity*bond
opacity*bond*rel
Year dummies
Loan purpose
Industry dummies
No. of observations
No. of firms
R-sq
Panel B
3.807
5.945
1280.062**
4.046
24.642
-3.079
163.906
(0.147)
(0.215)
(0.011)
(0.441)
(0.655)
(0.328)
(0.580)
(0.685)
-1.658
12.669*
-91.715
3.788
-12.772
33.389
-504.303
-1174.594
(0.659)
(0.069)
(0.870)
(0.513)
(0.801)
(0.290)
(0.209)
(0.109)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
10877
10877
10877
10877
10877
10877
10363
10363
1779
1779
1779
1779
1779
1779
1737
1737
0.551
0.554
0.554
0.548
0.552
0.548
0.553
0.554
baspr
effspr
Roll
efftick
zero
Amihud
GKN
RGKN
-12.429
-12.579
-13.319
-11.103
-12.565
-8.617
-11.296
-9.747
-14.444
-21.754
-25.624
-13.431
-11.495
-9.018
-8.250
-4.382
-2.015
-9.175
-12.305
-2.328
1.070
-0.401
3.046
5.365
baspr
effspr
Roll
efftick
zero
Amihud
GKN
RGKN
8.594
15.083
1130.276
16.821
124.445
3.310
509.999
787.395
10.743
33.697
2318.623
24.655
136.315
33.620
169.602
-131.756
2.149
18.614
1188.347
7.834
11.870
30.310
-340.397
-919.151
Sum Intercept (excluding constant)
Relationship + No Bond
Relationship + Bond
Bond - No Bond
Sum Opacity Coefficient
Relationship + No Bond
Relationship + Bond
Bond – No Bond
In Table 6, we present in Panel A that after controlling for bond market access, the
relationship dummy
shows a significant and negative coefficient across all
opacity measures, and the interaction
shows a significant and positive
coefficient through all illiquidity measures (excluding Roll and Amihud). These
results are consistent with our previous findings reported in Table 4. Particularly, we
are interested to know weather relationship lenders charge higher rates for
borrowing firms without bond market access than those having such alternative.
Hence we respectively aggregate the intercepts (excluding the constant) and the
15
Proceedings of 10th Annual London Business Research Conference
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coefficients related to each opacity measure, which are reported in Panel B. As
illustrated in the last row “Bond – No Bond” of the two subpanels, for all illiquidity
measures from column (1) to (6), with sole exception of zero, relationship borrowers
with bond market access receive a discount in the intercept coefficient than firms
without; this benefit is reduced if the firm has illiquid stocks. For the two information
asymmetry measures GKN and RGKN, the intercepts are slightly larger for firms
which have previously issued bond, and the coefficient on information asymmetry
measure is smaller. This may be explained as both bond and GKN (RGKN) reflect
the level of information asymmetry, and are hence highly correlated.
5. Conclusions
In this study, we discuss the role of information opacity in determining firms’
borrowing cost. Taking a large sample of U.S. listed firms from 1987 to 2011, we
construct a set of market-based information opacity measures, which are near
continuous across firms and through time, and less prone to endogeneity. Using
stock market microstructure illiquidity measures, we find that firms with more liquid
equities pay significantly lower interest rates in the syndicated loan market. This
relationship is robust when we control for firm and loan specific features, firms’ credit
rating, and firm and year fixed effect.
Second, we further identify two channels of the stock illiquidity-loan spread
relationship. Following George et al (1991) and Bharath et al. (2009), we extract
exogenous market microstructure information asymmetry measures from stock
market illiquidity, and we find that the information asymmetry component explains
the loan spread premium. This empirical finding is supportive of the theory that
lenders request higher loan spreads to informational opaque firms in compensation
of the cost of private information production and/or adverse selection risk.
Furthermore, by analysing the role of relationship lending, we find evidence in line
with Boot and Thakor (1994) that relationship reduces borrowing cost due to the
mitigation of private information production cost and/or adverse selection risk; this
effect is significantly more valuable for firms with more liquid stocks, but statistically
indifferent for firms with heterogeneous level of information asymmetry. We attribute
these results to a decrease in lenders’ monopoly (Sharpe 1990; Schenone 2010)
along with borrowing firms’ increasing alternative financing ability in the capital
market as well as corporate bond market. We additionally find that access to bond
market helps reduce the borrowing cost, and that the effect of lender-borrower
relationship is robust controlling for this alternative source of financing.
16
Proceedings of 10th Annual London Business Research Conference
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End Notes
1
U.S. has world’s most liquid secondary loan market, and public firms in U.S.
generally have widely dispersed ownership structure.
2
Endogeneity is controlled to some extent by using instrumental variables (Gupta,
Singh & Zebedee 2008; Santos & Winton 2008; Bosch & Steffen 2011; Saunders &
Steffen 2011) or propensity score matching (Saunders & Steffen 2011), these
methods are subject to bias due to weak instrument (in IV method) or
“unobservables” (in PSM).
3
A slightly different version
is also calculated, which is the average daily bidask spread for positive volume days only. The results are suppressed from reporting
as they are similar to
.
4
A slightly different version
is also calculated, which is the probabilityweighted average spread divided by average price for positive volume days only.
The results are suppressed from reporting as they are similar to
.
5
Lesmond et al. (1999) show that the effective number of zero-return days, which
includes the non-zero-return days due to bid-ask bounce, is closely related to the
zero-return days reported by CRSP.
6
Our results hold if we alternatively use dummy variables for two-digit SIC industry
groups.
7
In particular, a loan is classified as a revolver if the loan type is expressed in
Dealscan as “364-Day Facility”, “Revolver/Line < 1 Yr”, “Revolver/Line >= 1 Yr”,
“Revolver/Term Loan”, “Demand Loan” or “Limited Line”. Alternatively, a loan is
defined as a term loan if the loan type is recorded as “Term Loan”, “Term Loan A”,
“Term Loan B”, “Term Loan C”, “Term Loan F” or “Delay Draw Term Loan”.
8
LPC Dealscan is a database of loans provided by Loan Pricing Corporation. It
covers most loans made to large publicly traded companies (Strahan 1999).
9
Before 1987, the coverage of Dealscan is uneven. For an overview of the Dealscan
database, see Strahan (1999).
10
This is a common practice in the loan pricing literature. See similar analyses in
Carey and Nini (2007), Focarelli, Pozzolo and Casolaro (1998), Santos (2011), Gaul
and Uysal (2013).
11
We are indebted to Sudheer Chava and Michael Roberts for providing the link
between Dealscan with Compustat, see Chava and Roberts (2008).
12
We link Dealscan with Compustat via GVKEY. Next, we use PERMNO to link
Compustat with CRSP.
17
Proceedings of 10th Annual London Business Research
Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1922069-81-8
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Proceedings of 10th Annual London Business Research
Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1922069-81-8
Appendix
Table A1: Variable definition and data sources
Variable
AllinDrawn
baspr
effspr
Roll
efftick
zero
Amihud
GKN
RGKN
rel
bond
nobond
Firm size
Firm leverage
Firm ROA
Firm NWC
Firm tangibility
Firm MRTBOOK
No. of lenders
No. of facilities
Facility size
Maturity
Revolver
Term loan
Secured
Senior
S&P rating
Definition
The All-in-Drawn spread is an interest rate spread over LIBOR
measured in basis points for each dollar drawn from the loan.
The difference between ask-price and bid-price, as a percentage of
the quoted bid-ask midpoint.
Twice as the difference between trade price and quoted bid-ask
midpoint, as a percentage of the bid-ask midpoint.
Square root of twice as the negative first order autocovariance of
returns.
Probability weighted price clusters.
Number of zero-return days as a percentage of the total number of
trading days.
Price impact of a million dollar volume.
Adverse selection component of percent bid-ask spread.
Adverse selection component of Roll’s effective spread.
Indicator which takes the value of one if the borrowing firm has taken
a syndicated loan within five years from the same lead lender, and
zero otherwise.
Indicator which takes the value of one if the borrowing firm has
issued corporate bond before loan initiation date
Indicator which takes the value of one if the borrowing firm has not
issued corporate bond before loan initiation date
Log of firm total assets
Sum of long term and short term debt
over total assets
Return on assets
Net working capital over total assets
Tangible assets over total assets
Market to book ratio
Number of lenders in a facility
Number of facilities in a syndicated deal
Log of facility amount
Maturity of the loan
Dummy for lines of credit
Dummy for term loans
Dummy for collateral
Dummy for senior loans
Credit rating from S&P
Source
LPC Dealscan
CRSP
CRSP
CRSP
CRSP
CRSP
CRSP
CRSP
CRSP
LPC Dealscan
Compustat
Compustat
Compustat
Compustat
Compustat
Compustat
LPC Dealscan
LPC Dealscan
LPC Dealscan
LPC Dealscan
LPC Dealscan
LPC Dealscan
LPC Dealscan
LPC Dealscan
Compustat
21
Proceedings of 10th Annual London Business Research
Conference
10 - 11 August 2015, Imperial College, London, UK, ISBN: 978-1922069-81-8
Table A2: Summary statistics
AllinDrawn
baspr
effspr
Roll
zero
efftick
Amihud
GKN
RGKN
Firm size
Firm leverage
Firm ROA
Firm NWC
Firm tangibility
Firm MRTBOOK
S&P rating
No. of lenders
No. of facilities
Facility size
Maturity
Revolver
Term loan
Secured
Senior
No. of obs
10877
10877
10877
10877
10877
10877
10877
10363
10363
10877
10877
10877
10877
10877
10877
5769
10877
10877
10877
10877
10877
10877
10877
10877
Mean
197.141
1.564
1.025
0.016
0.074
0.601
0.444
0.009
0.006
6.828
0.304
0.131
0.151
0.737
1.735
10.852
9.233
1.788
4.899
1.178
0.729
0.245
0.649
0.998
Std Dev
145.071
1.983
1.363
0.014
0.088
1.276
2.047
0.017
0.009
1.742
0.216
0.108
0.200
0.406
1.825
3.091
9.168
1.109
1.571
0.682
0.445
0.430
0.477
0.041
Median
175.000
0.923
0.505
0.012
0.033
0.175
0.008
0.001
0.003
6.795
0.286
0.127
0.130
0.718
1.419
11.000
7.000
1.000
5.011
1.427
1.000
0.000
1.000
1.000
p1
18.750
0.033
0.086
0.002
0.000
0.015
0.000
0.000
0.000
2.988
0.000
-0.146
-0.359
0.064
0.697
4.000
1.000
1.000
0.693
-0.875
0.000
0.000
0.000
1.000
p99
700.000
9.680
6.719
0.070
0.367
5.601
8.412
0.083
0.042
10.663
1.006
0.407
0.651
1.832
5.781
17.000
42.000
5.000
8.011
2.079
1.000
1.000
1.000
1.000
22