Do Credit Rating Agencies Underestimate Liquidity Risk?

8/11/2019 Do Credit Rating Agencies Underestimate Liquidity Risk?

1/36Electronic copy available at: http://ssrn.com/abstract=1495849

Do Credit Rating Agencies Underestimate Liquidity Risk?

Radhakrishnan Gopalan Fenghua Song Vijay Yerramilli

February 8, 2009

Abstract

We test if credit ratings adequately reflect liquidity risk, i.e., the risk that the firm may face diffi-culty in refinancing its short-term debt. Consistent with credit ratings underestimating liquidity

risk, we find that after controlling for credit ratings and other known determinants, long-term

bonds of firms with a higher proportion of short-term debt trade at higher yields. Using multi-

notch downgrades to identify severe and unexpected rating downgrades, we find that firms with a

higher proportion of short-term debt are more likely to experience multi-notch downgrades. The

association between short-term debt and multi-notch downgrades is stronger in industries that

experience a negative profitability shock, during recessionary periods and when credit conditions

are tight. The relationship is robust to instrumenting the proportion of short-term debt. Overall,

our results highlight that rating agencies underestimate liquidity risk, and offer a potential ex-

planation for the failure of ratings to predict financial difficulties at firms such as Penn Central,

WorldCom, Enron, Bear Stearns and Lehman Brothers.

We thank Long Chen, Paolo Fulghieri, Wei Xiong, and seminar participants at Washington University in St. Louisfor helpful comments.

Olin Business School, Washington University in St. Louis. Campus Box 1133, 1 Brookings Drive, St. Louis, MO63130. Email: [email protected].

Smeal College of Business, Pennsylvania State University. University Park, PA 16802. Email: [email protected] School of Business, Indiana University. 1309 East Tenth Street, Bloomington, IN 47405. Email: vyer-

[email protected].


2/36Electronic copy available at: http://ssrn.com/abstract=1495849

Although we believe that our enhanced analytics will not have a material effect on the majority of our

current ratings, individual ratings may be revised. For example, a company with heavy debt maturities

over the near term (especially considering the current market conditions) would face more credit risk,

notwithstanding benign long-term prospects.

Standard and Poors Ratings Direct (May 13, 2008)

Introduction

Ratings issued by credit rating agencies such as Standard and Poors (S&P), Moodys and Fitch are

an important source of information for investors about the credit quality of corporate and government

bonds (Ederington et al. (1987), and Goh and Ederington (1993)). Over time, regulation has also

enhanced the role of rating agencies in financial markets (White (2009)). 1 Given the increased

dependence of the financial system on credit ratings, the failure of rating agencies to anticipate

distress at financial institutions like Bear Stearns and Lehman Brothers, and defaults in mortgage-

backed securities during the recent financial crisis has focussed the attention of both researchers and

regulators on the credit rating process.

Rating agencies have a long history of slow reaction to financial distress at rated firms. For

example, over the years, they failed to warn investors about high-profile bankruptcies such as those

of WorldCom (2002), Enron (2001), First Executive Corporation (1991), and Penn Central (1970).

During the recent financial crisis, all three major rating agencies were caught by surprise when Bear

Stearns announced on March 14, 2008 that it had obtained emergency funding from JPMorgan

Chase, and all three agencies continued to give a safe rating to Lehman Brothers right until the

day it filed for bankruptcy.2 Interestingly, a common thread running through all these high-profile

failures is the heavy reliance of the firm in question on short-term debt, which it was unable to roll

over. For instance, Penn Centrals bankruptcy was triggered by its reliance on commercial paper,

which it was unable to refinance following heavy operating losses in 1970. A natural question that

1An assets credit rating determines whether regulated institutions such as insurance companies and pension fundscan invest in it. The new Basel Capital Accord also prescribes that small and medium-sized credit institutions usean assets credit rating to determine their risk weights for capital charges (Basel Committe on Banking Supervision(1999)).

2See Bear Stearns Has Credit Ratings Slashed After Bailout (Bloomberg News, March 14, 2008) and FlawedCredit Ratings Reap Profits as Regulators Fail (Bloomberg News, April 29, 2009).

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arises is whether credit rating agencies systematically underestimate the risk of firms not being able

to refinance their short-term liabilities. Our paper addresses this question.

According to rating agencies, a firms credit rating provides their summary assessment of the

firms default likelihood. Such an assessment should take into account not only the risk of the firms

cash flows, but also the risk imposed by different aspects of the firms capital structure including

the maturity structure of its liabilities. Accounting for the maturity structure of liabilities is im-

portant because a firm that has to refinance its liabilities more often is more likely to be exposed

to fluctuations in credit market conditions, and is more likely to be affected by a temporary fall in

collateral values or cash flows.3 Following Diamond (1991), we refer to the risk of a firm not being

able to refinance its short-term liabilities as liquidity risk. Anecdotal evidence presented above

suggests that liquidity risk may be an important determinant of default risk, and hence, should be

incorporated in credit ratings.

We measure a firms exposure to liquidity risk using the variable Short, which we define as the

proportion of the firms total debt that is maturing within the year. As a preamble to our analysis,

we first examine whether investors in the secondary bond market recognize the presence of liquidity

risk and take it into account, over and above the firms credit rating, while pricing corporate bonds.

We follow Campbell and Taksler (2003) and model a bonds yield spread as a function of the issuing

firmss idiosyncratic volatility, market volatility, credit rating, firm financial ratios including Short,

and macroeconomic variables. We find that bonds issued by firms with higher values ofShort havehigher yield spreads, even after controlling for the firms credit rating: a one-standard-deviation

increase in Short leads to a 5 basis point increase in the yield spread. This finding suggests that

bond investors price liquidity risk not captured by credit ratings. 4

Next, we formally address the question of whether rating agencies adequately account for liquid-

ity risk by analyzing a large panel of rated firms with financial information in Compustat, spanning

the time period 19802008. In our empirical analysis, we use multi-notch rating downgrades (hence-

forth severe downgrades) to identify instances in which there is an unexpected decrease in the

rating agencys assessment of the firms overall credit quality. If rating agencies systematically un-

derestimate liquidity risk, then we should expect to observe a positive association between severe

3CFOs surveyed in Graham and Harvey (2001) cite the cost of refinancing in bad times as the second mostimportant factor affecting their choice of maturity structure of corporate debt.

4This result is consistent with previous studies that show that bond markets reflect credit risk information not fullycaptured by ratings (Grier and Katz (1976), Hettenhouse and Sartoris (1976), and Pinches and Singleton (1978)).

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downgrades and firms exposure to liquidity risk, as measured by Short. On the other hand, if rating

agencies correctly estimate liquidity risk when assigning ratings to firms, then we should not expect

any such systematic association. The underlying idea is that if there are two otherwise identical firms

that differ only in the maturity structure of their debt, then a rating agency that correctly estimates

liquidity risk will assign a lower rating ex anteto the firm with the higher proportion of short-term

debt, so that there should be no difference ex postin terms of severity of rating downgrades.

Controlling for a variety of firm characteristics including the firms financial condition, credit

rating, firm and year fixed effects, we find that firms with a higher proportion of short-term debt

are more likely to experience severe rating downgrades. This result is both statistically and econom-

ically significant: a one-standard-deviation increase in Short is, on average, associated with a 2.1%

increase in the annual probability of a severe downgrade. In comparison, the sample average annual

probability of a severe rating downgrade is 4.4%. This positive association betweenShortand severerating downgrade is robust to controlling for recent negative outlooks issued by rating agencies and

the agencies desire to smooth rating changes around the investment-grade cutoff. We also find that

our results are present both for small and large firms, and for firms with investment-grade (S&P

rating of BBB- or above) as well as speculative-grade (S&P rating below BBB-) ratings.

To further establish that the positive association between severe rating downgrade and Short is

due to liquidity risk, we perform a number of cross-sectional tests. Firms with a larger proportion

of short-term debt are likely to face greater liquidity risk if they experience a negative shock totheir cash flows. Consistent with this idea, we find that the positive association between Short and

severe downgrade is stronger for firms in industries that experience a decline in operating profits,

and during periods of economic recession. Consistent with the idea that liquidity risk is likely to be

more severe when credit market conditions are tighter, we find that the positive association between

Shortand severe downgrade is stronger when the spread between the prime interest rate charged by

banks and the federal funds rate is higher.

We recognize that the maturity structure of corporate debt is endogenous. Theory suggests

that high-risk and low-risk firms may pool together to issue more short-term debt as compared to

medium-risk firms (Diamond (1991)). Thus, an important alternative explanation for the positive

association between Shortand severe downgrade is that firms which rely more on short-term debt

tend to be riskier, and hence, have more volatile credit ratings. Here, we must note that, based on

observable risk characteristics such as size, leverage and idiosyncratic volatility, firms in our sample

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with higher values ofShort are actually less risky. So we expect endogeneity to have a downward

bias on our coefficient estimate on Short. Nonetheless, we perform three sets of tests to distinguish

our liquidity risk hypothesis from this alternative explanation.

First, if firms with a higher proportion of short-term debt are riskier firms that tend to have

more volatile credit ratings, then we should expect a positive association between Short and multi-

notch rating upgrades as well. By contrast, the liquidity risk hypothesis does not predict a positive

association betweenShortand multi-notch rating upgrades, because liquidity risk should only matter

on the downside. Consistent with the liquidity risk hypothesis, we do not find a positive association

betweenShortand multi-notch rating upgrades.

In our second set of tests, we follow Almeida et al. (2009) and use the proportion of a firms long-

term debt maturing within the year as a measure of the firms exposure to liquidity risk, and repeat

our tests with this new measure. Note that this new measure excludes short-term debt securities

issued by the firm that mature within the year. The underlying idea is to identify firms exposed to

liquidity risk as a result of long-term debt maturity profile decisions made more than a year ago. Such

maturity decisions are less likely to depend on the firms current risk profile. Consistent with our

hypothesis that rating agencies underestimate liquidity risk, we find a positive association between

the proportion of long-term debt maturing within a year and severe rating downgrades.

Finally, we employ instrumental variable (IV) regressions to control for any possible endogeneity

bias. Specifically, we use the yield on the 10-year treasury bond, and the delta (i.e., sensitivity of

compensation to the firms share price) and vega (the sensitivity of compensation to the firms stock

return volatility) of the firms CFOs compensation to instrument for Short. The use of the 10-year

treasury yield as an instrument is motivated by the market-timing argument which suggests that firms

tend to borrow short term when long-term interest rates are high (Baker et al. (2003), Barclay and

Smith (1995), and Guedes and Opler (1996)). The use of delta and vega of the CFOs compensation

as instruments is motivated by Chava and Purnanandum (2009), who find that the structure of

the CFOs compensation affects the firms debt maturity choice. Specifically, they find that CFOs

with higher delta choose significantly less short-term debt, whereas CFOs with higher vega choose

significantly more short-term debt. The identifying assumption is that the 10-year treasury rate

and the structure of the CFOs compensation package do not directly affect the severity of rating

downgrades, and only have an indirect effect through the firms debt maturity choice. This is a

reasonable assumption because the CFO of a firm mainly influences the firms financing policies, and

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is likely to have less direct influence on the firms investment policy and hence operational risk (see

Chava and Purnanandum (2009) for empirical evidence). We find that our results continue to hold

even in our IV estimation. In fact, the coefficient estimates in the IV estimation are significantly

larger than our OLS estimates, which underscores our earlier observation that endogeneity has a

downward bias on our estimates.

While we use a firms reliance on short-term debt as a measure of its exposure to liquidity risk,

a rating downgrade may itself increase a firms exposure to liquidity risk by triggering covenants

that necessitate refinancing, and also by making refinancing more difficult (as happened in the case

of AIG). So it can be argued that the reason rating agencies resort to a multi-notch downgrade is

because they correctly factor in the increase in liquidity risk resulting from a rating downgrade. Note

that this explanation is not counter to ours, because if a rating downgrade increases liquidity risk

for firms with high Short, then rating agencies should anticipate such an eventuality and lower theex ante rating of such firms. At a minimum, our results indicate that ratings are more likely to be

subject to jumps downward for firms with a larger proportion of short-term debt. Moreover, we find

that firms with a higher proportion of short-term debt also have a higher default likelihood even

after we control for lagged credit ratings. Since the default rating is assigned automatically when a

firm defaults on its debt obligations, and not at the discretion of the rating agency, this finding lends

further support to our hypothesis that rating agencies underestimate liquidity risk.

Our paper makes two important contributions. First, it contributes to the literature on creditratings by providing evidence that rating agencies systematically underestimate liquidity risk. It is

important to emphasize that our results are not about ratings of complex structured products, nor

are they confined to a specific time period when there may have been a credit bubble. We obtain

our results by examining corporate bond ratings over a long period of time spanning 28 years. In

comparison, recent papers have focused on ratings of structured products, the problems with the

issuer-pay model of credit ratings, and the structure of the rating agency (Benmelech and Dlugosz

(2009), Bolton et al. (2009), Skreta and Veldkamp (2009), White (2001), and White (2009)). 5 Our

paper contributes to this literature by highlighting an important dimension of risk not adequately

taken into account by rating agencies, and hence, an important reason behind the repeated failures

of rating agencies to anticipate financial distress at rated firms.

5It has also been suggested that credit rating agencies collude with banks to suppress adverse information about thefirms credit quality. See Enrons credit rating: Enrons bankers contacts with Moodys and government officials,2003 Report prepared by the Staff of the Committee on Governmental Affairs, United States Senate

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Second, our paper contributes to the literature on debt structure and financial contracting by

highlighting the liquidity risk associated with short-term debt. While theoretical literature identifies

liquidity risk as an important determinant of debt maturity choice (Diamond (1991,1993), Flannery

(1986)), the empirical literature on debt maturity choice (Barclay and Smith (1995), Berger et al.

(2005), Guedes and Opler (1996), Stohs and Mauer (1994)) largely sidesteps this issue because of the

difficulty of measuring liquidity risk. By looking at credit rating transitions, we are able to identify

whether an important intermediary adequately estimates liquidity risk. In this regard, our paper

is related to studies that exploit the current crisis to highlight liquidity risk inherent in short-term

debt. For example, Almeida et al. (2009) show that firms with a large proportion of their long-term

debt maturing right after August 2007 (when the subprime crisis unfolded) experienced large drops

in their real investment rates, and Duchin et al. (2009) find that the decline in corporate investment

following the subprime crisis was more pronounced for firms that had more net short-term debt.

The paper proceeds as follows. In Section 1, we discuss the theoretical literature and outline

our hypotheses. Section 2 describes our data and empirical specification. Section 3 presents the

empirical results. Section 4 concludes.

1 Theory and Hypotheses

Diamond (1991) argues that short-term debt creates liquidity risk for the borrower because thelender may refuse to roll over the debt if bad news arrives, forcing the firm into inefficient liquidation

even when it is solvent in the long run. Such inefficient liquidation may arise due to constraints

on pledging future rents to lenders because of agency costs or due to strategic uncertainty about

other lenders actions (Diamond and Dybvig (1983), and He and Xiong (2009)). Even if liquidation

is avoided, short-term debt can still cause loss of value if it has to be refinanced at an overly high

interest rate because of credit market imperfections (Froot et al. (1993), Sharpe (1991), and Titman

(1992)). The upshot is that short-term debt can exacerbate the impact of temporary fall in the firms

cash flows, either by drying up the external sources of cash or increasing its cost. All these are going

to dilute the long-term creditors. Morris and Shin (2009) examine how the risk of a run on a firms

short-term debt undermines its long-term creditors and argue that the measure of an institutions

credit risk should incorporate the probability of a default due to a run [on its short-term debt] when

the institution would otherwise have been solvent. He and Xiong (2010) examine how short-term

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debt exacerbates the conflict of interests between a firms equity and debt holders and consequently

precipitates bankruptcy at higher fundamental thresholds. One basic takeaway from both Morris and

Shin (2009) and He and Xiong (2010) is that liquidity risk of short-term debt is an important source

of a firms overall credit risk. If rating agencies underestimate such liquidity risk while assigning

ratings to firms, we should observe more severe rating downgrades for firms that are more exposed

to liquidity risk, i.e., firms with a larger proportion of their debt maturing in the short term. On the

other hand, if rating agencies correctly estimate liquidity risk, then the severity of rating downgrade

should not be systematically related to the proportion of the firms debt maturing in the short term.

We refer to this as the liquidity-riskhypothesis.

It is important to recognize that the choice of debt maturity structure is endogenous and is

likely to be determined by firm characteristics such as firm size, growth opportunities (Myers (1977))

and information asymmetry (Diamond (1991,1993), Flannery (1986), and Kale and Noe (1990)).The existing empirical literature documents that small firms, firms with more growth opportunities,

riskier firms, and firms with larger information asymmetry rely more on short-term debt (Barclay

and Smith (1995), Stohs and Mauer (1994), Titman and Wessels (1988)).6 In our empirical tests,

we control for known determinants of debt maturity structure that may also affect the severity of

rating downgrade.

A positive association between the proportion of short-term debt and severe rating downgrade

may also result if riskier firms have both higher proportion of short-term debt (see Stohs and Mauer(1994) for empirical evidence) and are also more likely to subject to severe rating downgrade. Rating

agencies are known to avoid frequent rating changes due to market participants desire for rating

stability (Altman and Rijken (2004), Fons et al. (2002), and Ellis (1998)). Such stickiness may

result in rating transitions only after significant changes in credit quality. Since riskier firms are

more likely to experience significant change in their credit quality, they may be especially prone to

severe rating transitions as compared to less risky firm. Apart from explicitly controlling for firm

risk based on observable characteristics, we also perform Difference-in-Difference estimations and

instrumental variable (IV) regressions to better distinguish the liquidity-risk hypothesis from this

alternate explanation.

6Examining new bond issues, Guedes and Opler (1996) come to a somewhat different conclusion from Barclay andSmith (1995) and Stohs and Mauer (1994). They find that large firms with investment-grade credit ratings typicallyborrow both at the short end and at the long end of the maturity spectrum, whereas firms with speculative-grade creditratings typically borrow in the middle of the maturity spectrum.

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2 Data, Empirical Specifications, and Preliminary Results

2.1 Data

Our data comes from three sources. We obtain data on monthly firm credit ratings from Standard and

Poors (S&P), which we complement with firm financial information from Compustat. Our sample

consists of all firms with S&P long-term credit rating and financial information in Compustat during

the period 1980-2008. We transform the firms credit rating into an ordinal scale ranging from 1 to

22, with 1 representing a rating of AAA and 22 representing a rating of D, i.e., a smaller numerical

value represents a higher rating (see Appendix for details). To align the monthly credit rating data

with annual firm financial information, we drop years in which firms change the month they end

their fiscal year in.

We obtain data on long-term corporate bond yields from two modules of the Mergent Fixed

Income Securities Database (FISD). The first module provides issue characteristics and the second

provides transaction prices for all bond trades among insurance companies from the National Asso-

ciation of Insurance Commissioners (NAIC) since 1995. Our sample selection criteria mirrors that of

Campbell and Taksler (2003). Specifically, we focus on trades for investment-grade bonds, because

by regulation insurance companies often limit their investment to non-investment-grade bonds.7 We

restrict our sample to fixed-rate U.S. dollar-denominated bonds in the industrial, financial and util-

ity sectors that are not defeased, defaulted or in default process. We exclude any bonds that are

callable, puttable, convertible, exchangable, with sinking fund or with refund protection. We also

exclude issues that are asset-backed or include credit-enhancement features to ensure that the bonds

are backed solely by the creditworthiness of the issuer. We estimate the yield to maturity for each

bond trade using the transaction price, time to maturity and coupon rate. We then calculate the

yield spread for a bond during a month as the difference between the average yield to maturity on

all transactions for the bond during the month and the treasury yield on the government bond with

closest maturity. We obtain benchmark treasury yields from the Federal Reserve Board website.

Finally, we winsorize the data on yield spreads at the 1% level to reduce apparent data recording

error in FISD.7Also, as pointed out by Campbell and Taksler (2003), non-investment-grade bond trades in the FISD database are

unlikely to be representative of the general market.

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2.2 Empirical Specifications and Key Variables

We begin our empirical analysis by examining whether bond prices reflect liquidity risk as measured

by the proportion of short-term debt. To achieve this, we estimate the regression model in Campbell

and Taksler (2003) after including Short as an additional regressor. Specifically, we estimate thefollowing panel regression where each observation represents a bond-month pair:

Spreadb, = 0+1Shorti,t1+2Xi,t1+3Xb+4Xm,

+ Rating FE + Firm FE + Year FE, (1)

where the subscripts b, i, m, and t indicate the bond, the firm, the market, the month and the

year, respectively, and the term FE denotes fixed effect. The dependent variable Spreadb, is the

average yield spread for a bond as measured from all the transactions for the bond during the

month. The main independent variable in our analysis is Short, the proportion of the firms debt

due within one year. We define this as the ratio of total debt in current liabilities (Compustat item

dlc) to total debt (the sum of dlc and long-term debt dltt). We use the lagged values of Short

as the main independent variable to measure the extent of liquidity risk arising from short-term

liabilities. The firm characteristics that we employ as control variables include (Xi,t) include: (i) the

mean and standard deviation of the firms daily excess return the difference between the firms

stock return and the return on the CRSP value-weighted index over the 180 days proceeding

(not including) the bond trade, Average Excess Return and Equity Volatility respectively, (ii) the

ratio of the firms market capitalization to the market capitalization of the CRSP value-weighted

index, Market Cap/Index, (iii) the accounting ratios Long-Term Debt/TA (the ratio of total long-

term debt to the book value of total assets), Total Debt/Market Value (the ratio of total debt to

the sum of market value of equity and book value of total liabilities) and Operating Income/Sales

(the ratio of operating income before depreciation to net sales), and (iv) four dummy variables that

identify firms withInterest Coverage(the ratio of the sum of operating income after depreciation and

interest expense to interest expense) below 5, between 5 and 10, between 10 and 20 and above 20,

respectively. The bond characteristics (Xb) that we control for are the bonds remaining maturity

in years, Maturity, the yield offered at the time of the bonds issue, Offering Yield, and the natural

logarithm of the dollar size of the issue,Log(Amount). We also control for market conditions (Xm,),

including the mean and standard deviation of daily return on the CRSP value-weighted index over the

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180 days prior to (not including) the bond transaction date,Average IndexandSystematic Volatility

respectively, and the slope of the term structure which is the difference between the 10-year and

2-year treasury rates,Treasury Slope.

We then examine the relationship between the proportion of short-term debt on a firms balance

sheet and the severity of rating downgrade of the firm. We do so by estimating panel regressions

that are variants of the following form where each firm-year combination represents one observation

in the panel:

yi,t= 0+1Shorti,t1+2Xi,t1+ Industry or Firm FE + Year FE. (2)

The dependent variable yi,t measures the severity of rating downgrade for firm i in year t. We use

two alternate measures for severe rating downgrade. The first measure,Notches Downgrade, is the

maximum number of notches by which a firms credit rating is downgraded during any month of the

year. The variable takes the value zero if the firms rating is not downgraded during the year. The

second measure,Multi-notch Downgrade, is a dummy variable that takes the value one for firms that

experience multi-notch downgrade at least once during a year. Thus,Multi-notch Downgradeequals

one ifNotches Downgradeis greater than one.8

We also control for a number of firm characteristics (Xi,t) that may affect the likelihood of

rating downgrade. Following prior literature that identifies a nonlinear relationship between firm

size and the amount of short-term debt and hence credit quality, we include a piecewise linear term

to control for firm size. Specifically, we divide our sample into three terciles based on the book

value of total assets (TA) and include three interaction terms between the natural logarithm of book

value of total assets (Size) and dummy variables identifying firms belonging to these terciles. We

control for the firms credit quality with Investment Grade, a dummy variable that identifies firms

with investment-grade ratings (BBB- or better) at the end of the previous year. We also control

for Long-Term Debt/TA,Total Debt/Market Value, Operating Income/Sales and Interest Coverage.

These accounting ratios have been shown in prior literature to affect firm credit ratings (Blumeet al. (1998), Pinches and Mingo (1973), and Pogue and Poldofsky (1969)). We control for firm

growth opportunities using Market-to-Book(the ratio of market value of total assets to book value

8The following example illustrates how we construct the two measures. Suppose a firm starts with a rating of AAin January. In March during the same year, its rating drops to AA- (1-notch downgrade), and in August the ratingcontinues to drop to A- (3-notche downgrade from March), and stays at A- until the end of the year. In this example,Notches Downgrade= 3, and Multi-notch Downgrade= 1.

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of total assets) andR&D/TA(the ratio of R&D to book value of total assets). We control for firms

operating risk using the annual cross-sectional operating income volatility of all firms in the industry,

Industry Volatility, and idiosyncratic volatility of the firms stock return, Idiosyncratic Volatility. We

control for the firms asset structure using the ratio of property, plant and equipment to total assets,

Tangibility, and the proportion of cash on the firms balance sheet, Cash/TA.

In our next set of tests, we use (2) to test whether the proportion of short-term debt affects

the likelihood of default. In these tests our dependent variable is Default, a dummy variable that

identifies the year in which a firms long-term credit rating is downgraded to D.

2.3 Short-Term Debt and Yield Spreads on the Firms Long-Term Bonds

As a preamble to our analysis, we first examine whether bond yield spreads are higher for firms witha higher proportion of short-term debt on their balance sheets. We focus on bond returns because

liquidity risk is more likely to impose costs on bondholders.

In Panel A of Table 1, we divide the firms into two subsamples based on whether Short is above

or below the sample median and compare the yield spreads of bonds issued by the firms. We present

this comparison separately for the different sectors (financial firms, utilities and industrial firms),

rating categories and maturity categories. We classify firms into three rating categories: High-

Rated firms (those with S&P rating {AAA, AA+, AA, AA-}), Medium-Rated firms (S&P rating

{A+, A, A-}), and Low-Rated firms (S&P rating {BBB+, BBB, BBB-}). Recall that we limit

bond transaction data only for investment-grade bonds. In terms of maturity categories, we classify

bonds as Short-Maturity bonds (maturity less than 7 years), Medium-Maturity bonds (maturity

between 7 and 15 years) and Long-Maturity bonds (maturity between 15 and 30 years). As can be

seen from Panel A, regardless of the sector, rating category or maturity category, bonds issued by

firms with above median proportion of short-term debt on average trade at higher yield spread as

compared to bonds issued by firms with below median proportion of short-term debt.

In Table 2, we perform multivariate tests by estimating (1). As mentioned, (1) is similar to the

model in Campbell and Taksler (2003) with Shortas an additional regressor. The dependent variable

is the bond yield spread, which is the difference between the average yield to maturity of all bond

transactions during a month and the yield on the treasury with the closest maturity. Recall that we

restrict attention to fixed-rate U.S. dollar-denominated bonds without any special features (call and

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put provisions, sinking fund, credit enhancements, etc.). Moreover, we limit our data to investment-

grade bonds. In Column (1) of Table 2, we estimate the regression on the bonds issued by all the

firms in our sample and include year and industry fixed effects. We identify industry at the level of

the four-digit SIC code. The positive coefficient onShort indicates that bonds issued by firms that

have a high proportion of their debt maturing in the short term trade at a higher yield, even after

controlling for the firms credit rating and all the other factors that are known to affect bond yields.

This finding is consistent with credit rating agencies systematically underestimating liquidity risk.

In Column (2), we repeat our estimation with firm fixed effect instead of industry fixed effect and

obtain similar results. Our results are economically significant. The coefficient estimates in Column

(2) indicate that a one-standard-deviation change in Shortresults in a 5 basis points change in the

bond yield spread. In comparison, the average bond yield spread in our sample is 113 basis points.

The coefficients on the control variables are consistent with those in Campbell and Taksler (2003). In

particular, bond yield spreads are higher for firms with higher equity volatility, higher excess return

and during times of higher market volatility, and higher market return. Bond yield spreads are also

lower for large bond offerings, with lower maturity and for bonds of large firms.

In Columns (3) and (4), we repeat the regression separately on the subsamples of bonds issued

by small and large firms, respectively. The coefficient on Short is significant in Column (3) but not

in Column (4), indicating that the return premium we identified in Column (2) is confined to bonds

issued by small firms. In Columns (5) and (6), we repeat the regression separately on the subsamples

of high-rated bonds (i.e., bonds with credit rating {AAA, AA+, AA, AA-}) and low-rated bonds

(i.e., bonds with credit rating {BBB+, BBB, BBB-}). In both subsamples, we find that bonds

issued by firms that rely more on short-term debt trade at higher yields.

Overall, the evidence in Table 2 indicates that, regardless of the bonds credit rating, bond market

investors seek a premium for taking on liquidity risk arising from short-term debt. This illudes that

the liquidity risk with short-term debt may not be adequately reflected in credit ratings.

3 Empirical Results

We now look at rating downgrades to examine if rating agencies adequately account of liquidity risk

when they assign ratings to firms. Before we discuss the results of our multi-variate analysis, we first

provide some summary statistics.

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3.1 Short-Term Debt and Severity of Rating Downgrade

3.1.1 Summary Statistics and Univariate Tests

Descriptive statistics for the full sample are presented in Panel B of Table 1. The mean value of

Size of 8.015 in our sample corresponds to an average book value of total assets of approximately

$3 billion. The corresponding value for the full Compustat sample during the same time period is

$82 million. Thus, our sample of rated firms includes the larger firms in Compustat. Firms in our

sample have an average market-to-book ratio of 1.456 and spend about 1% of their total assets in

R&D. The median value of firm credit rating in our sample is 9 which corresponds to a rating of

BBB. Consistent with this, we find that about 64% of the firms in our sample have investment-

grade ratings (BBB- or above). The likelihood for an average firm in our sample to experience a

rating downgrade within a year is 13%, and that likelihood is 4.4% for a multi-notch downgrade.

Multi-notch Downgrade(Conditional) identifies the instances when a firm experiences a multi-notch

rating downgrade conditional on a downgrade during the same year. The mean value ofMulti-notch

Downgrade(Conditional) indicates that about 32% of the downgrades in our sample are multi-notch

downgrades. From the mean value of Notches Downgrade (Conditional), we find that conditional

on a downgrade during a year, the notches downgraded is on average about 1.5. The mean value

ofShort is 0.19, meaning that the average firm in our sample has 19% of its total debt maturing

within one year. Finally, the summary statistics of the other variables are consistent with our sample

comprising of the larger firms in Compustat.

In Panel C, we divide the firms into two subsamples with below and above sample median value

ofShortand compare their characteristics. We find that larger firms, firms with marginally lower

market-to-book ratios and better credit ratings are more likely to have above-median proportion of

short-term debt. We also find that while firms with more short-term debt are no more likely to

experience a rating downgrade (the mean value ofDowngrade is not significantly different across the

two subsamples), such firms are more likely to experience multi-notch downgrade (the mean value of

Multi-notch Downgradeis significantly different across the two subsamples). This is consistent with

the idea that firms with more short-term debt are more likely to be exposed to liquidity risk, which

is not adequately taken into account by rating agencies. We also find that conditional on a rating

downgrade, such firms experience a more severe downgrade (the mean value ofNotches Downgrade

(Conditional) is significantly different across the two subsamples).

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Comparing the other financial characteristics, we find that firms with above-median value of

Shorttend to be more profitable, have a lower proportion of long-term debt to total assets, and have

a higher interest coverage. These firms are also from industries with lower volatility of operating

profits and tend to have lower idiosyncratic risk. All these indicate that these firms are less risky

and are in better financial position as compared to firms with below median values ofShort. This

is consistent with the central prediction of Diamond (1991) that firms with good credit quality are

more likely to issue short-term debt.

Overall, the results in Panels B and C of Table 1 indicate that firms with more short-term debt are

better firms based on observable characteristics but experience more severe rating downgrade within

a year. This is consistent with both the liquidity risk hypothesis and the alternative explanations.

We now turn to formal multivariate analysis that will help distinguish between the two.

3.1.2 Regression Results

We now perform multivariate tests to investigate whether firms with a higher proportion of debt

maturing in the short term are likely to experience more severe rating downgrade. To this end,

we estimate the panel OLS regression (2) with Notches Downgradeas the dependent variable and

lagged values of Short as the main independent variable. We include firm and year fixed effects

in all specifications. The standard errors are robust for heteroscedasticity and autocorrelation and

clustered at the individual firm level. The results are presented in Panel A of Table 3.

In Column (1), we estimate the regression on all the firms in our sample. The positive and

significant coefficient on Short indicates that firms with a higher proportion of short-term debt

experience more severe rating downgrades. Since we have firm fixed effects in the specification,

the coefficient measures the correlation between within-firm changes in the proportion of short-

term debt and rating downgrade severity. The coefficient is also economically significant: a one-

standard-deviation increase inShortis associated with an increase of 0.0714 in the number of notches

downgrade. In comparison, the sample mean value ofNotches Downgrade is 0.205.

In terms of the coefficients on the control variables, the insignificant coefficients onSize (1),Size

(2) andSize(3) indicate that firm size does not affect the severity of rating downgrades. Furthermore,

we find that firms with smaller market-to-book ratios, those with less idiosyncratic risk and those with

investment-grade credit ratings are more likely to experience multi-notch downgrades. These results

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indicate that the firms that experience multi-notch downgrades are not the riskier firms in terms

of observable characteristics. These results are consistent with our univariate results. We further

find that firms that experience multi-notch downgrades have lower cash balance (negative coefficient

on Cash/TA), lower profitability (negative coefficient on Operating Income/Sales), higher leverage

(positive coefficient on Total Debt/Market Value) and lower interest coverage (negative coefficient

onInterest Coverage). All these indicate that such firms experience a deterioration in credit quality.

In Column (2), we repeat the estimation after including dummy variables to represent the 22 rating

categories and obtain similar results.

As noted earlier, the choice of debt maturity structure is likely to be determined by firm charac-

teristics such as firm size and credit quality, which may also affect the severity of rating downgrade.

For instance, small firms rely more on short-term debt (Barclay and Smith (1995)) and are also more

likely to be financially constrained (Rauh (2006)), which may make them more likely to experiencesevere rating downgrades. Although we control for firm size in our estimation and find the coefficient

to be insignificant, to further test if our results are driven by a subset of firms, we repeat our esti-

mation separately in Columns (3) and (4) on subsamples of small and large firms. We identify small

(large) firms as those with below (above) sample median book value of total assets. As can be seen,

the positive association between the proportion of short-term debt and severe rating downgrades is

present for both small and large firms.

In a similar vein, in Columns (5) and (6) we repeat the estimation separately in subsamples ofinvestment-grade (S&P credit rating of BBB- or better) and below investment-grade firms. As can

be seen, the positive association between the proportion of short-term debt and the severity of rating

downgrades is present for both above and below investment-grade firms.

Even if rating agencies do not change a firms credit rating, they frequently issue a negative

(positive) outlook to signal deterioration (improvement) in the firms credit quality. To test if rating

agencies anticipate the liquidity risk facing firms with a higher proportion of short-term debt and

issue a negative outlook in anticipation of severe rating downgrade, in unreported tests we control

for the issuance of a negative outlook. Our results are similar to the ones reported here. We find

an insignificant relationship between the issuance of a negative outlook and the severity of rating

downgrade. Also, ratings are likely to be particularly sticky around the investment-grade cutoff,

because a downgrade to below investment grade imposes large costs on regulated institutions such

as insurance companies (Ellul et al. (2009)). Such institutions may be forced to sell the bonds at

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significant discounts due to their poor liquidity. Hence, rating agencies may be particularly sensitive

to avoid rating volatility around the investment-grade threshold. In unreported tests, we control for

instances when the firms lagged credit rating is at the investment-grade threshold (i.e., BBB-) and

obtain similar results.

Overall, our results in Panel A show that firms with a higher proportion of short-term debt are

more likely to experience severe rating downgrade. This result is consistent with such firms having

greater exposure to the liquidity risk arising from short-term debt.

In Panel B, we repeat our estimation with Multi-notch Downgradeas the dependent variable.

As mentioned, Multi-notch Downgradeis a dummy variable that identifies instances of multi-notch

downgrades. The results in Panel B are qualitatively similar to those in Panel A, and indicate that

firms with a higher proportion of short-term debt are more likely to experience multi-notch rating

downgrades. The results are again economically significant. The result in Column (2) indicates that

a one-standard-deviation increase in Shortis associated with a 2.1% increase in the likelihood of a

multi-notch downgrade. In comparison, the average likelihood of a multi-notch downgrade in our

sample is 4.4%. In unreported tests, we find similar results when we repeat the regression with

Triple-notch Downgrade, a dummy variable that identifies downgrades of at least three notches, as

the dependent variable.

Next, we examine how the positive association between severity of rating downgrade and firms

reliance on short-term debt varies with industry, firm and macroeconomic characteristics. The results

of our estimation are presented in Panel C. Our set of control variables in this panel are similar to

those in Panels A and B, but to conserve space we do not report the coefficient estimates on all

the control variables. In Column (1), we repeat our estimates from Column (1) of Panel A for

comparison. In Column (2), we examine if the positive association between the severity of rating

downgrade and a firms reliance on short-term debt is stronger when the firm experiences a negative

shock to its profitability. We identify a negative shock to profitability from declines in industry

profitability. Specifically, we identify industry at the level of two-digit SIC code and measure the

industry profitability as the median Operating Income/Salesof all firms in the industry. We code the

dummy variable Profit Declineequal to one for firms in an industry in a given year if that industry

experiences a decline in profitability from the previous year. As can be seen, a negative shock to

profitability not only increases the likelihood of a multi-notch downgrade (positive coefficient on

Profit Decline), but this increase is higher for firms with a higher proportion of short-term debt

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(positive coefficient on Profit Decline Short). This is consistent with the idea that liquidity risk

arising form short-term debt exacerbates the impact of negative operating shocks.

On a similar note, in Column (3) we test to see if the effect of short-term debt on the severity

of rating downgrade is stronger during recessions. We use the data on the NBERs website and

classify the years 1981, 1982, 1990, 1991 and 2001 as recessionary during our sample period. We

then repeat our estimation after including a dummy variable Recessionthat identifies the recession

years and an interaction term Recession Short. Our results in Column (3) indicate that while

rating downgrades are no more severe during recessions, the effect of short-term debt on the severity

of rating downgrade is greater during recessions (positive coefficient on Recession Short).

In Column (4), we examine the impact of credit market conditions on the association between the

proportion of short-term debt and severe rating downgrade. Following Hartford (2005), we measure

credit market conditions using the spread between the prime rate on bank loans and the federal

funds rate. We obtain data for both variables from the Federal Reserve Board website. We code the

variable High Bank Spreadequal to one for the years in which the bank spread is above the sample

median. We repeat our estimation after including both High Bank Spreadand an interaction term

High Bank Spread Short. The positive coefficient estimates on both these terms indicate that not

only is the severity of downgrade greater in years of high bank spread but the association between

the proportion of short-term debt and severe rating downgrade is also stronger. This offers strong

evidence that credit market conditions may affect a firms liquidity risk by affecting the likelihoodand terms at which it can refinance its short-term debt.

3.2 Liquidity Risk or Operating Risk?

In this section, we perform tests to see if the positive association between Shortand severe rating

downgrade is due to liquidity risk or operating risk. As explained, we exploit the asymmetry in

the effect of liquidity risk in comparison to operating risk to distinguish the two. That is, liquidity

risk should only matter on the downside and hence should predict a positive correlation between

the proportion of short-term debt and the severity of rating downgrade. In contrast, operating risk

should matter both on the upside and downside and should predict a positive correlation between

short-term debt and severity of both rating upgrade and downgrade. To exploit this differential

prediction, in Table 4 we use (2) and relate the proportion of short-term debt to the extent of rating

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upgrade. The dependent variable in this table is Notches Upgrade, which is the maximal number of

notches by which a firms credit rating is upgraded during any month of the year. Otherwise, the

specification is identical to the one in Table 3. The results indicate that in all but one specification,

the coefficient on Short is either insignificant or negative. This finding indicates that the positive

association between the proportion of short-term debt and severe rating downgrade is more consistent

with liquidity risk as opposed to operating risk.

3.3 Is Endogeneity Driving Our Results?

As we noted earlier, the maturity structure of corporate debt is endogenous. While we have controlled

for observable firm characteristics known to affect firms debt maturity choice such as firm size, growth

opportunities, volatility and asymmetric information, it is possible that our results are driven by

some unobserved firm characteristic that determines both the proportion of short-term debt and

the severity of rating downgrade. Such an unobserved variable, if existing, should be time varying

because we have firm fixed effects in our specifications. In this section, we report the results of

additional tests that help address this potential endogeneity problem. We do two sets of tests to

help control for potential endogeneity. In the first set of tests, instead of the proportion of short-term

debt, we use the proportion of long-term debt due within one year (Compustat item dd1) as our

main independent variable. In the second set of tests, we employ an IV estimation.

In Panel A of Table 5, we repeat the estimation of (2) with Long-Term Debt Due/Total Debt

as the main independent variable. Long-Term Debt Due/Total Debt is the ratio of long-term debt

due within one year to total debt. Note that similar to short-term debt, a higher proportion of

long-term debt due within a year is also likely to lead to greater liquidity risk. Since the proportion

of long-term debt due within a year is likely to depend on the firms long-term debt structure and its

repayment schedule, both of which are likely to have been determined in the past, this measure is less

likely to systematically identify riskier firms (see Almeida et al. (2009) for a similar argument). The

results in Panel A indicate that in all our specifications, the coefficient on Long-Term Debt Due/Total

Debtis positive and significant, indicating that firms with a higher proportion of long-term debt due

within a year are more likely to experience severe rating downgrades. This evidence highlights that

endogenous choice of debt maturity structure is unlikely to drive our results.

In Panel B of Table 5, we directly deal with the potential endogeneity problem by using instru-

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mental variable (IV) regressions. We instrumentShortwith three instruments, namely the 10-year

treasury rate (10-Year T-Rate), and Log(Vega) and Log(Delta) both of which are calculated from

the CFO compensation data. Log(Delta) is the natural logarithm of the delta of the CFOs annual

compensation andLog(Vega) is the natural logarithm of the vega of the CFOs annual compensation.

The identifying assumption behind 10-Year T-Rateis that firms are more likely to issue short-term

debt when long-term interest rates are high (i.e., the market timing argument of Baker et al. (2003),

Barclay and Smith (1995), and Guedes and Opler (1996)), but that 10-Year T-Rateshould not have

a direct impact on the severity of rating downgrade. The identifying assumption behind Log(Delta)

andLog(Vega) is that a compensation contract that provides incentives for the CFO to take on more

risk as characterized by a higher vega and/or a lower delta should also provide incentives to the CFO

to take on a higher proportion of short-term debt. Consistent evidence is provided by Chava and

Purnanandum (2009) who show that a firms proportion of riskier debt increases with CFOs vega

and decreases with CFOs delta. But the structure of CFO compensation should not directly affect

the severity of rating downgrade. We obtain CFO compensation data from Standard and Poors

Execucomp for the time period 1992-2008. We identify the CFO from the annual title of the top 5

officers (Execucomp item titleann). Specifically, we search the annual title and include all executives

with treasurer, finance, controller, vp-finance, or CFO in their title. We then construct delta and

vega for CFO compensation following Core and Guay (1999).

The results of our IV estimation in Column (2) show that the coefficient on Short is positive

and significant. Interestingly, when we employ the IV approach, the magnitude of the coefficient

estimation is significantly higher than that in Column (1) that is estimated as an OLS. This indicates

that endogeneity in firms debt maturity structure is likely to attenuate our coefficient estimates. This

is reasonable because as seen from our univariate results, the larger, less risky and more profitable

firms are more likely to have a higher proportion of short-term debt. These firms are less likely to

experience severe rating downgrade. Thus, when we control for this potential endogeneity in our

IV estimation, we obtain much larger effect of the proportion of short-term debt on the severity of

rating downgrade.

3.4 Short-Term Debt and the Firms Propensity to Default

Our results so far show that firms with a higher proportion of short-term debt are more likely to

experience severe rating downgrade. While this evidence is consistent with credit rating agencies

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systematically underestimating liquidity risk, given that downgrades themselves happen at the dis-

cretion of the rating agencies, another possible interpretation of this finding is that rating agencies

are tougher on firms that have a larger proportion of short-term debt and downgrade them more

severely. This is likely to happen if rating agencies correctly recognize that such firms are riskier.

This is similar to the argument proposed in Blume et al. (1998). Note that this argument does

not invalidate our point that even among similarly rated firms, those with a higher proportion of

short-term debt are riskier.

One way to differentiate between these competing explanations is to examine if the proportion

of short-term debt affects the likelihood of defaults. Since defaults are not at the discretion of rating

agencies and happen automatically when the firm is either liquidity constrained or insolvent, this

will help distinguish between the two explanations. Specifically, we examine if firms with a higher

proportion of short-term debt are more likely to default on their long-term debt obligations aftercontrolling for lagged rating status. To do this, we estimate (2) with Default as the dependent

variable, where Default is a dummy variable that identifies firms that are downgraded to rating of

D in a year. The results of our estimation are presented in Table 6.

In Columns (1) and (2), we estimate panel OLS regressions on our entire sample of firms. In

addition to year fixed effect, in Column (1) we employ industry fixed effects at the level of the

four-digit SIC code, and in Column (2) we employ firm fixed effects. The positive and significant

coefficient estimates on Short indicate that firms with a higher proportion of short-term debt aremore likely to default. The coefficient is also economically significant: a one-standard-deviation

increase inShort is associated with a 0.52% increase in the propensity to default. In comparison, the

sample average probability of default is just 0.5%. In Columns (3) and (4), we repeat the estimation

in Column (2) separately on the subsamples of small and large firms, respectively. In Column (5),

we estimate a Cox-Hazard Model on the entire sample of firms, and in Column (6) we estimate a

logistic specification. In all specifications, other than that in Column (4), the coefficient estimates

onShortare positive and significant. Overall, the results in Table 6 indicate that firms with a higher

proportion of short-term debt are more likely to experience default, even after controlling for their

current credit rating. This offers further support to our thesis that rating agencies underestimate

liquidity risk.

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4 Conclusion

In this paper we test to see if rating agencies adequately take into account the liquidity risk of short-

term debt and find that they do not. We provide two pieces of evidence to support this contention.

First, we find that long-term bonds of firms with a higher proportion of short-term debt have higher

yields after controlling for all known determinants including their credit ratings. Second, we show

that firms with a higher proportion of short-term debt are more likely to experience severe rating

downgrade within a year, suggesting that rating agencies are more likely to be surprised by the

deterioration in the firms credit quality. We find that our results are not driven by operating risk

or by the endogenity of a firms debt maturity structure.

Recent studies on rating agencies have focused on how the distorted incentives of the agencies

in rating structured investment products results in inflated ratings that may not truly reflect theinstruments default risk. Our analysis, on the other hand, indicates that the problems with the

rating process may not be confined to rating of structured investment products. We show that the

rating agencies need to pay greater attention to the finer aspects of a firms capture structure, such

as the maturity structure of its liabilities. While our current analysis is agnostic about the reason

why rating agencies underestimate liquidity risk arising from short-term debt, in future we hope to

explore that issue. The higher yield on long-term bonds for firms with greater short-term debt is an

important cost of short-term debt which firm managers need to take into account while determining

debt maturity structure.

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Profit Decline: dummy variable that takes the value 1 if the firms residing industry experiences a

decline in profitability from the previous year, where industry is defined at the two-digit SIC code level

and profitability is measured as the median value of op erating income after depreciationsales

of all firms in that

industry, and 0 otherwise.

Recession: dummy variable that takes the value 1 for years 1981, 1982, 1990, 1991 and 2001, and 0

otherwise.

High Bank Spread: dummy variable that takes the value 1 for the years when the spread between the

prime rate on bank loans and the federal funds rate is above its sample median, and 0 otherwise.

Improve: dummy variable that takes the value 1 if the firms rating improves from below investment

grade to investment grade, and 0 otherwise.

Negative Outlook: dummy variable that takes the value 1 if S&Ps rating outlook for the firm is negative,

and 0 otherwise.

CP Spread: spread of commercial paper over (3-month) treasury bill rate.

CP Rating: dummy variable that takes the value 1 if the S&P short-term issuer credit rating is higher

thanC, and 0 otherwise.

Average Excess Return: mean of daily excess returns relative to the CRSP value-weighted index for

each firms equity over the 180 days prior to (not including) the bond transaction date.

Equity Volatility: standard deviation of daily excess returns relative to the CRSP value-weighted index

for each firms equity over the 180 days prior to (not including) the bond transaction date.

Average Index: mean of the CRSP value-weighted index returns over the 180 days prior to (not includ-

ing) the bond transaction date.

Systematic Volatility: standard deviation of the CRSP value-weighted index returns over the 180 days

prior to (not including) the bond transaction date.

Market Cap/Index: market value of equityCRSP valued-weighted index

.

Treasury Slope: 10-year treasury rate 2-year treasury rate.

Maturity: years to maturity.

Offering Yield: yield to maturity at the time of bond issuance.

Log(Amount): natural logarithm of bond issue size.

Debt Due in One Year: long-term debt due in one yeartotal debt

.

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Table 1: Summary Statistics

Panel A provides descriptive statistics of yield spreads (in basis points) for three categories of firms: financial, utilities and

industrial. The data are collected from the Mergent Fixed Income Securities Database (FISD) for the period 1995-2008. For

each category, we split the sample into three subcategories depending on the rating of the firm: High-Rated (AAA, AA+, AA,

AA-), Medium-Rated (A+, A, A-) and Low-Rated (BBB+, BBB, BBB-). For each subcategory, we report the mean yield spread

of debts with short-term (maturity 7 years), Medium-Maturity (maturity (7 years, 15 years]) and Long-Maturity (maturity (15 years, 30 years]), for subsamples of firms with proportion of short-term debt, as measured by Short, above or below its

sample median (High-Short and Low-Short, respectively). Panels B and C provide descriptive statistics of the firms. The data

are collected from Compustat and CRSP for the period 1980-2008. Panel B summarizes the full sample. Panel C divides the

full sample into two subsamples depending on whether the variable Short is below or above its sample median (Low-Short and

High-Short, respectively) and compares the two subsamples, unconditional and conditional on there being a rating downgrade.

Details on the definition of the variables are provided in the Appendix. Asterisks denote statistical significance at the 1% (***),

5% (**) and 10% (*) levels.

Panel A: Yield Spread

Financial Firms

High-Short Low-Short High Low

High-Rated short-Maturity 74.583 72.810 1.773

High-Rated Medium-Maturity 97.138 92.111 5.027High-Rated Long-Maturity 138.551 118.417 20.134

Medium-Rated Short-Maturity 89.397 77.638 11.759

Medium-Rated Medium-Maturity 108.407 108.412 -0.005

Medium-Rated Long-Maturity 147.204 135.428 11.776

Low-Rated Short-Maturity 154.589 133.548 21.041

Low-Rated Medium-Maturity 158.324 151.037 7.287

Low-Rated Long-Maturity 167.362 172.610 -5.248

Utilities


High-Rated Short-Maturity 82.800 68.596 14.204

High-Rated Medium-Maturity 70.275 64.816 5.458

High-Rated Long-Maturity 147.484 125.078 22.406


Medium-Rated Medium-Maturity 120.591 112.993 7.598


Low-Rated Short-Maturity 120.017 121.353 -1.336

Low-Rated Medium-Maturity 144.010 131.332 12.678

Low-Rated Long-Maturity 176.548 156.339 20.209

Industrial Firms


High-Rated Short-Maturity 60.701 51.444 9.257

High-Rated Medium-Maturity 66.218 57.784 8.433

High-Rated Long-Maturity 98.177 82.928 15.249


Medium-Rated Medium-Maturity 92.849 91.658 1.191


Low-Rated Short-Maturity 141.781 135.131 6.651

Low-Rated Medium-Maturity 148.373 149.551 -1.178

Low-Rated Long-Maturity 205.143 194.037 11.106

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Panel B: Descriptive Statistics for the Full Sample

N Mean Median S.D.

Size 25142 8.015 7.864 1.661

Market-to-Book 25140 1.456 1.218 0.759

R&D/TA 25142 0.012 0 0.029

Ratingt1 25142 9.245 9 3.764

Investment Grade 25142 0.626 1 0.484

Downgrade 25142 0.133 0 0.339Multi-notch Downgrade 25142 0.044 0 0.206

Notches Downgrade 25084 0.205 0 0.669

Multi-notch Downgrade (Conditional) 3332 0.317 0 0.465

Notches Downgrade (Conditional) 3332 1.547 1 1.137

Short 24801 0.190 0.093 0.236

Operating Income/Sales 25103 0.135 0.113 0.170

Total Debt/Market Value 24956 2.122 0.448 7.512

Long-Term Debt/TA 25133 0.282 0.260 0.195

Interest Coverage 23142 7.194 4.119 11.723

Industry Volatility 23908 0.114 0.091 0.076

Idiosyncratic Volatility 23459 0.023 0.019 0.014

Tangibility 25142 0.311 0.255 0.272

Cash/TA 25082 0.079 0.041 0.101

Panel C: Low-Short versus High-Short

Low-Short High-Short Low High

Size 7.440 8.606 -1.166

Market-to-Book 1.539 1.504 0.035

Ratingt1 10.470 7.985 2.485

Downgrade 0.131 0.136 -0.005

Multi-notch Downgrade 0.040 0.049 -0.009

Notches Downgrade (Conditional) 1.498 1.595 -0.097

Operating Income/Sales 0.119 0.152 -0.033

Total Debt/Market Value 0.916 0.922 -0.006

Long-Term Debt/TA 0.360 0.203 0.157

Interest Coverage 6.287 8.254 -1.967

Industry Volatility .117 .112 .005

Idiosyncratic Volatility .025 .021 .004

Tangibility .343 .277 .066

Cash/TA .081 .077 .004

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Table 2: Bond Yield Spread and Short-Term Debt

This table reports the results of the regressions relating yield spread to the proportion of short-term debt: Spreadb,t = 0+ 1

Shorti,t +2Controls+ Firm or Industry FE+ Year FE. Details on the definition of the variables are provided in the Appendix.

Columns (1) and (2) report the results for the full sample, with Column (1) including year and industry fixed effects and Column

(2) including year and firm fixed effects. Columns (3) and (4) report the results for the subsamples of small firms and large firms,

respectively, where small (large) firms are those with size (as measured by book value of total assets) below (above) the samplemedian. Columns (5) and (6) report the results for the subsamples of firms with high ratings (ratings better than sample median)

and low ratings (ratings worse than sample median), respectively. Robust standard errors, reported in parentheses, are clustered

at individual firm level. Asterisks denote statistical significance at the 1% (***), 5% (**) and 10% (*) levels.

All Firms - OLS Small Large High-Rated Low-Rated

(1) (2) (3) (4) (5) (6)

Short .002 .002 .004 .0008 .003 .003(.0009) (.0008) (.001) (.001) (.0007) (.001)

Idiosyncratic Volatility .154 .065 .043 .024 -.002 .032(.034) (.038) (.057) (.072) (.048) (.069)

Systematic Volatility .319 .326 .333 .278 .174 .535(.039) (.038) (.050) (.047) (.035) (.058)

Long-Term Debt/TA -.002 .003 .010 -.003 .004 -.0003(.002) (.002) (.004) (.002) (.002) (.003)

Average Index -1.273 -1.338 -1.115 -1.637 -1.206 -1.636(.112) (.106) (.130) (.163) (.116) (.208)

Average Excess Return -.720 -.123 -.195 .036 -.190 -.034(.208) (.105) (.154) (.147) (.126) (.146)

Market Cap/Index -.146 -.404 -.343 -1.270 -.234 -1.540(.035) (.075) (.076) (.197) (.046) (.293)

Operating Income/Sales -.002 -.002 -.004 -.0009 -.003 -.001(.001) (.002) (.003) (.002) (.002) (.003)

Total Debt/Market Value .00004 .0006 .0004 .002 .0003 .0007(.0002) (.0002) (.0002) (.0007) (.0001) (.0003)

Treasury Slope -.0005 -.0006 -.0004 -.0007 -.0005 -.0009(.0003) (.0002) (.0003) (.0003) (.0002) (.0003)

Maturity .0002 .0002 .0002 .0002 .0002 .0002(1.00e-05) (7.81e-06) (1.00e-05) (1.00e-05) (1.00e-05) (1.00e-05)

Offering Yield .0009 .0007 .0006 .0008 .0006 .0007(.00009) (.00006) (.00008) (.00008) (.00007) (.00008)

Log(Amount) -.0006 -.0002 -.0001 -.0002 -.0002 -.0002(.0002) (.0001) (.0001) (.0002) (.0001) (.0002)

Const. .001 -.003 -.004 -.0001 .0002 .002(.003) (.002) (.003) (.003) (.002) (.003)

Obs. 49098 49098 24271 24827 28875 20223

R2 .519 .631 .648 .625 .581 .642

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Table 3: Severity of Rating Downgrade and Short-Term Debt

This table reports the results of the regressions relating rating downgrade to the proportion of short-term debt: yi,t = 0 +

1 Shorti,t1+ 2 Xi,t+ Firm FE + Year FE, where yi,t is Notches Downgrade in Panel A, and Multi-notch Downgrade in

Panels B and C. Details on the definition of the variables are provided in the Appendix. In Panels A and B, Columns (1) and (2)

report the results for the full sample, Columns (3) and (4) report the results for the subsamples of small firms and large firms,

respectively, where small (large) firms are those with firm size (as measured by book value of total assets) below (above) thesample median, and Columns (5) and (6) report the results for the subsamples of firm with investment-grade (rating BBB- or

above) and below investment-grade (rating below BBB-) ratings, respectively. Robust standard errors, reported in parentheses,

are clustered at individual firm level. Asterisks denote statistical significance at the 1% (***), 5% (**) and 10% (*) levels.

Panel A: Notches Downgrade

All Firms All Firms Small Large Investment Below-Investment

(1) (2) (3) (4) (5) (6)

Short .301 .298 .302 .257 .224 .369(.046) (.045) (.071) (.065) (.052) (.101)

Size (1) -.0004 -.081 .004 -.004 .040(.019) (.021) (.027) (.025) (.030)

Size (2) .003 -.081 .008 .027 .0005 .044

(.018) (.020)

(.026) (.028) (.024) (.028)Size (3) .005 -.074 .030 .004 .039

(.017) (.019) (.027) (.022) (.026)

Market-to-Book -.085 -.107 -.085 -.096 -.080 -.081(.012) (.013) (.020) (.016) (.016) (.019)

Industry Volatility -.053 -.029 -.131 -.008 .014 -.075(.107) (.106) (.152) (.163) (.128) (.171)

Idiosyncratic Volatility -3.682 .122 -3.260 -4.757 -.644 -3.943(1.018) (.684) (1.013) (1.477) (1.670) (1.233)

Tangibility -.017 -.077 .0008 -.027 -.014 .0004(.044) (.047) (.060) (.063) (.055) (.072)

R&D/TA .178 -.424 -.794 .402 .344 -.729(.595) (.589) (.939) (.850) (.896) (.922)

Long-Term Debt/TA .089 .268 .096 .106 .111 .149(.074) (.078) (.092) (.149) (.121) (.095)

Investment Grade .288 .231 .422(.030) (.038) (.051)

Cash/TA -.288 -.207 -.357 -.119 -.034 -.359(.093) (.094) (.114) (.174) (.131) (.126)



Interest Coverage -.002 -.003 -.001 -.001 -.001 -.002(.0005) (.0006) (.0008) (.0008) (.0007) (.0009)

Const. .257 1.936 .366 -.189 .303 .124(.155) (.233) (.209) (.251) (.198) (.231)

Obs. 20258 20258 10481 9777 12592 7666R2 .223 .268 .314 .201 .212 .361

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Panel B: Multi-notch Downgrade


(1) (2) (3) (4) (5) (6)

Short .091 .087 .095 .075 .065 .114(.015) (.015) (.023) (.021) (.018) (.031)

Market-to-Book -.019 -.024 -.019 -.021 -.019 -.018(.004) (.004) (.007) (.005) (.005) (.008)

Industry Volatility -.049 -.042 -.074 -.049 -.046 -.059(.034) (.034) (.048) (.054) (.042) (.056)

Idiosyncratic Volatility -.690 -.039 -.662 -1.086 .512 -.821(.236) (.269) (.244) (.433) (.520) (.272)

Tangibility .005 -.010 .014 -.003 .012 .015(.014) (.014) (.018) (.019) (.016) (.024)

R&D/TA .016 -.141 -.224 .374 -.076 -.197(.195) (.202) (.264) (.368) (.287) (.244)

Long-Term Debt/TA .019 .061 .031 .029 .021 .036(.023) (.025) (.032) (.044) (.042) (.032)

Investment Grade .075 .059 .116(.010) (.014) (.017)

Cash/TA -.069 -.052 -.095 -.009 .034 -.106(.032) (.032) (.042) (.054) (.046) (.043)



Interest Coverage -.0003 -.0006 -.00009 -.0003 -.0002 -.0003(.0002) (.0002) (.0003) (.0003) (.0002) (.0003)

Const. .063 .418 .125 -.096 .103 .068(.044) (.057) (.057) (.084) (.072) (.061)

Obs. 20286 20286 10502 9784 12606 7680

R2 .203 .24 .278 .194 .201 .332

Panel C: Additional Tests on Multi-notch Downgrade

(1) (2) (3) (4)

Short .301 .264 .270 .186(.046) (.047) (.044) (.068)

Profit Decline .019(.010)

Profit Decline Short .087(.052)

Recession -.035(.040)

Recession Short .156(.078)

High Bank Spread .090

(.044)

High Bank Spread Short .148(.064)

Const. .257 .259 .253 .201(.155) (.154) (.155) (.136)

Obs. 20258 20258 20258 20258

R2 .223 .223 .223 .223

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Table 4: Rating Upgrade and Short-Term Debt

This table reports the results of the regressions relating rating upgrade to short-term debt: yi,t = 0 + 1 Shorti,t1 + 2

Controlsit+ Firm F.E. + Year F.E., where yi,t is Notches Upgrade. Details on the definition of the variables are provided in the

Appendix. Columns (1) and (2) report the results for the full sample. Columns (3) and (4) report the results for the subsamples

of small firms and large firms, respectively, where small (large) firms are those with size (as measured by book value of total

assets) below (above) the sample median. Columns (5) and (6) report the results for the subsamples of firm with investment-grade(rating BBB- or above) and below investment-grade (rating below BBB-) ratings, respectively. Robust standard errors, reported

in parentheses, are clustered at individual firm level. Asterisks denote statistical significance at the 1% (***), 5% (**) and 10%

(*) levels.


(1) (2) (3) (4) (5) (6)

Short -.005 -.042 .049 -.042 -.095 .167(.033) (.026) (.057) (.037) (.023) (.096)

Market-to-Book .055 .079 .078 .035 .037 .068(.010) (.009) (.016) (.011) (.009) (.021)

Industry Volatility .051 .023 .127 -.009 .051 .170

(.078) (.071) (.111) (.118) (.082) (.155)Idiosyncratic Volatility 1.845 -1.771 1.336 2.631 2.025 1.466

(.889) (.693) (.892) (1.280) (.825) (1.016)

Tangibility -.050 -.004 .007 -.074 -.065 -.027(.030) (.027) (.041) (.042) (.026) (.057)

R&D/TA -.159 .186 -.152 -.007 -.393 -.103(.432) (.374) (.721) (.502) (.376) (.890)

Long-Term Debt/TA -.150 -.324 -.155 -.111 -.150 -.094(.054) (.048) (.069) (.099) (.052) (.082)

Investment Grade -.338 -.316 -.316(.028) (.035) (.050)

Cash/TA .128 .004 -.023

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