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Do banks learn from financial markets? Evidence from loan contract design

Abstract

We find that banks charge higher loan rates and impose stricter non-price loan terms for

borrowers with higher short selling activity. The influence of short selling activity on loan

costs is more pronounced for borrowers with an opaque information environment and for

banks with no prior relationship with the borrower. Firms with higher levels of short selling

are also more likely to choose bank loans over public bonds. Overall, our results indicate that

banks consider the expectation of bad news embedded in equity short selling activity when

designing loan contracts.

Keywords: Short selling ratio, Information spillover, Syndicated loans

JEL Classification: G3; G32

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1 Introduction

Recent studies show that trading in financial markets can influence decision making by

corporate insiders (Bond et al. 2012; Goldstein et al. 2013; Subrahmanyam and Titman 1999).

Financial market trading provides valuable information for managers making investment decisions

(Chen et al. 2007; Dessaint et al. forthcoming; Foucault and Frésard 2012, 2014; Foucault and

Gehrig 2008) or engaging in takeover activities (Bond et al. 2012; Luo 2005). Goldstein et al.

(2013) also argue that the collective action of traders in financial markets influences capital

provision through a feedback effect. In their model, capital providers assess a firm’s profitability

using their own private information and the aggregate information embedded in the trading of the

firm’s securities. Thus, trading activities in financial markets influence a firm’s access to finance

through this feedback effect.

While there is ample empirical evidence of the feedback effect on managerial decisions, there

is a lack of understanding of whether capital providers indeed value financial market information,

as highlighted by Goldstein et al. (2013). Anecdotal evidence seems to suggest that firms are

concerned about how financial market trading could hinder their ability to raise capital. For

example, in Overstock.com Inc. vs. Gradient Analytics Inc., the plaintiff (Overstock.com Inc.)

alleged that predatory short selling by the hedged funds involved caused substantial harm to the

firm’s operations, including its relationship with bankers and lenders.1 In this paper, we empirically

examine the effect of financial market trading on access to capital. In particular, we study the effect

of short selling activities in the stock market on the costs of the largest source of finance: bank

loans.

Relative to arm’s length investors in secondary financial markets, banks have more information

about firms, stemming from monitoring and maintaining ongoing relationships with borrowers

(Diamond 1984, 1991b). From this perspective, banks have better firm-specific information.

1 See http://ak1.ostkcdn.com/05-1012_AmendedComplaint_GRAD.pdf for further information on

the case. We are grateful to Goldstein, Ozdenoren, and Yuan (2013) for citing this case as a

quintessential bear raid.3

However, their lending decisions can be influenced by external factors, such as demand for the

borrower’s products or industry outlook (Edmans et al. 2011). While financial market participants

may possess less firm-specific information, they could know more than banks regarding external

information. Thus, so long as financial markets as a whole possess information unknown to banks,

trading activities in financial markets could have an effect on banks’ lending decisions.

We study the effect of financial market information on bank loan contracting in a sample of

syndicated loans extended to US nonfinancial and non-utility companies from 1982 to 2017. Black

and Scholes (1973) show that holding a debt instrument is equivalent to having a short put position

on the firm’s assets, since debtholders own the firm’s assets, but allowing shareholders the option to

buy the assets back. Given the payoff of a short put position, we argue that banks are more

concerned about the downside risk than the upside gain. Therefore, banks could value information

reflecting a negative outlook for the firm more than information reflecting a positive outlook. We

measure expected negative outlook using the level of short selling in the borrower’s stocks. We

focus on short sellers because evidence suggests that a high level of short selling predicts negative

information, including negative future stock returns (Asquith and Meulbroek 1995; Boehmer et al.

2010; Desai et al. 2002), negative earnings surprises (Christophe et al. 2004), financial misconduct

(Karpoff and Lou 2010), and analyst downgrades (Christophe et al. 2010). In addition, trading by

short sellers accounts for a significant proportion of the share volume (Diether et al. 2009).

Our results show that loan spreads increase with the short selling ratio. This effect is

statistically and economically significant. A one standard deviation increase in the short selling ratio

is associated with an increase of 39 basis points (bps) in the loan spread, or USD 1.84 million in

annual interest costs, for an average loan facility. Our findings remain robust after controlling for

the availability of lendable shares, thus alleviating concerns that the relation between short selling

and loan spreads is driven by the lendable share supply rather than by actual shorting activity.

To mitigate concerns that our results could be driven by correlated omitted variables, we

conduct a number of tests. First, we take advantage of the Taxpayer Relief Act of 1997 (TRA

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1997), which changed the tax incentives for short selling against the box (i.e., shorting against the

box) as an exogenous shock to the information content of short selling activity. A short sale against

the box allows traders with a long position in an asset to short sell the asset without the requirement

of immediately delivering the long position to cover the short sale. This strategy gives the trader the

option to maintain a long position, and any gain from this position will be offset by the shorting

position. This strategy preserves a capital gain while postponing the capital gains tax until a later

tax year (Arnold et al. 2005). This tax savings strategy was eliminated under the TRA 1997, in

which any capital gains or losses from short selling against the box are immediately recognized.

Arnold et al. (2005) find that the TRA 1997 makes it more costly for uninformed short sellers to

short against the box, driving more uninformed short sellers out of the market, resulting in a higher

information content of short interest. Examining the loan contracts granted to firms that borrowed

both before and after the TRA 1997, we find a significant increase in the impact of the short selling

ratio on loan costs. Our finding is consistent with the argument that the short selling ratio becomes

more informative after TRA 1997 and plays a more important role in determining loan costs in the

post-implementation period.

Second, we use propensity score matching to compare the loan costs of firms with a high level

of short selling and the loan costs of similar firms with a low level of short selling. Firms are

defined as having a high (low) level of short selling if the short selling ratio is higher (lower) than

the two-digit Standard Industrial Classification (SIC) code industry median value. We find that the

matched control and treatment firms have similar characteristics. However, on average, the costs of

the loans granted to the treatment firms are higher than those of the loans granted to the control

firms. Moreover, when we re-estimate the baseline regression on the sample of loan facilities

granted to the matched control and treatment firms, we find results consistent with the baseline

results. Finally, we obtain consistent results when we use regression specifications with firm and

year fixed effects to control for unobservable time- and firm-invariant determinants of loan costs.

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Besides the omitted variable bias, our results could be subject to reverse causality. Existing

literature reports that loan syndicate participants trade on private information obtained from the loan

negotiation process (Ivashina and Sun 2011; Massoud et al. 2011). Thus, the positive relation

between the short selling ratio and loan spreads could indeed be driven by insider trading by loan

syndicate participants in response to unfavorable loan terms during the negotiation process. We

argue that, if syndicate participants engage in insider trading, it should take place over the duration

from the starting date of the loan negotiation to the date the loan contract is signed. Consistent with

this argument, Massoud et al. (2011) show that hedge fund lenders short sell the borrower’s equity

over a short time frame (up to 10 days) after loan origination. Thus, one strategy to rule out hedge

funds trading on the borrower’s stock is to ensure that the time window over which we measure

short selling occurs prior to the start of the loan negotiation process. Although the starting date of

the negotiation process is unobservable, Murfin (2012) argues that this process typically takes six

months. Assuming a loan negotiation process takes six months, we identify the starting date of the

negotiation as six months before the loan origination date recorded in Dealscan. We next set the

condition that the short selling ratio is measured prior to the starting date of the loan negotiation

process. Using this refined sample, we still observe the short selling ratio to have a positive effect

on loan costs. We further extend the time threshold to nine months and find similar results.

To corroborate the inference from the main analyses, we conduct a number of cross-sectional

analyses. We argue that the short selling interest influences loan contract terms because it conveys

valuable information that the banks have yet to possess. If learning from secondary financial

markets improves a bank’s knowledge about a borrower’s future performance, such information

should be more valuable when the borrower is difficult to evaluate. First, we hypothesize that the

effect of financial market information on loan pricing is stronger when the borrower’s information

environment is more opaque. Using various measures of the information environment based on

analyst forecast dispersion and financial report readability and comparability, we show that the

effects of the short selling ratio are indeed stronger among firms with a higher degree of

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information asymmetry. Further investigation reveals that the effect of short selling activity on loan

costs is stronger when short selling risk is high. To the extent that short selling risk is positively

correlated with stock mispricing (Engelberg et al. 2018), this finding is consistent with our

argument that banks value the information content of short selling more highly when it is more

difficult to price the borrowing firm.

Second, we explore how relationship and non-relationship banks value financial market

information. Banks that maintain a lending relationship with the borrower benefit from obtaining

propriety information that helps reduce information asymmetry between the bank and the borrower

(Bharath et al. 2011; Boot and Thakor 2000). These banks might therefore not value information

from secondary financial markets as much as non-relationship (i.e., transactional) banks do.

Consistent with this conjecture, we find that the effect of the short selling ratio on loan spreads is

more pronounced for loans from non-relationship banks.

We conduct further analyses to show that financial market information also influences loan

features that reflect bank monitoring. Specifically, an increase in the short selling ratio shortens loan

maturity, increases the likelihood of collateral requirement, and increases the number of financial

covenants. These findings reveal that banks react to negative market expectations by both

increasing loan costs and tightening monitoring mechanisms. Further analysis suggests that the

short selling ratio is relevant to the imposition of performance covenants, rather than capital

covenants. This finding implies that short selling activity conveys valuable information about future

firm performance that is relevant to banks in designing loan contracts.

In the final set of analyses, we examine the effect of financial market information on the choice

of bank loans versus bonds. Diamond (1984) and Gilson et al. (1990) suggest that, for borrowers

with a troublesome outlook, the benefit of bank monitoring is higher and the renegotiation costs of

bank loans are lower, relative to public bond issues. Thus, these borrowers prefer bank loans over

corporate bonds. We find support for this prediction. We first show that firms with a higher short

selling ratio have a larger bond spread. In addition, a higher level of short selling is associated with

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a higher likelihood of choosing bank loans rather than public bonds. Collectively, these findings

support the notion that banks (and bond investors) use information in other financial markets to

infer borrower creditworthiness.

Our paper contributes to the following strands of literature. First, the theoretical model of

Goldstein et al. (2013) highlights the real impact of financial market trading activities on capital

providers’ expectations of a firm’s value. Our paper provides empirical evidence on this effect by

studying how banks’ assessment of their borrowers’ value is influenced by short selling activities.

Our study also complements the recent literature on the effect of short selling on corporate

outcomes. Prior studies document the importance of short selling constraints or the supply of shares

for shorting (i.e., lending supply) on earnings management (Fang et al. 2016; Massa et al. 2015),

corporate investment (Grullon et al. 2015) and mergers decisions (Chang et al. forthcoming),

incentive contracts (De Angelis et al. 2017), and audit pricing (Hope et al. 2017). Ho et al. (2016)

show that the relaxation of short selling constraints under the Regulation SHO Pilot Program

resulted in reductions in loan costs for treated firms compared to controlled firms. Our paper is

distinguished from these studies by our focus on actual short selling activities. We show that the

effect of actual short selling activities on loan costs is not driven by lending supply, highlighting the

independence of the demand and supply sides of the short selling market in affecting decision

makers.

Second, prior studies highlight the importance of the role of the feedback effect from secondary

financial markets on management’s decisions and compensation (Brandenburger and Polak 1996;

Chang and Yu 2010; Dessaint et al. forthcoming; Edmans et al. 2011; Foucault and Frésard 2012,

2014, 2019; Foucault and Gehrig 2008; Goldstein et al. 2011, 2013; Luo 2005; Zuo 2016). We

show that secondary financial markets affect another group of informed decision makers: bank

lenders. Toward this end, our paper is related to the literature that examines the effects of credit

default swap trading (Ashcraft and Santos 2009; Norden and Wagner 2008) and trading activities in

the secondary loan markets (Gupta et al. 2008) on the cost of bank loans. We complement these

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studies by documenting empirical evidence of the spillover of information from the stock market to

the primary loan market.

Third, we contribute to an understanding of the determinants of bank loan contracts. The

theoretical literature outlines the roles of borrower credit quality, agency costs, information

asymmetry, monitoring, and negotiability in determining loan contracts (Boyd and Prescott 1986;

Christensen et al. 2016; Diamond 1984, 1991b; Leland and Pyle 1977). A large body of empirical

research explores the role of information asymmetry on the design of bank loan contracts. While

Bharath et al. (2011), Engelberg et al. (2012), and Hollander and Verriest (2016) examine banks’

information production through their interactions with firms and managers, we focus on an

information channel external to the borrowing company (and thus less likely to be influenced by the

borrowing company): short selling activity in financial markets.

The paper proceeds as follows. We survey the literature and present our hypotheses in Section

2. We discuss the data collection and variable construction in Section 3. The main results are

presented in Section 4. In Section 5, we conduct further robustness tests. The concluding remarks

are drawn in Section 6.

2 Literature review and hypothesis development

Recent literature documents evidence of a feedback effect between secondary financial markets

and the firm in which corporate insiders learn from financial markets and adjust their decisions

accordingly. There are various reasons why information from secondary markets is relevant to

corporate insiders. First, although the real decision maker (i.e., managers) can be more informed

than individual investors about their firm, the market as a whole could be more informed than the

decision maker (Grossman 1976; Hellwig 1980). Second, market participants can be more informed

than the manager about the outside environment, such as the industry or economic outlook (Bond et

al. 2012). Bakke and Whited (2010) and Chen et al. (2007) find evidence that managerial

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investment decisions are a function of stock price informativeness.2 Taken together, these studies

highlight that trading activities in secondary financial markets can alter firm value through the

feedback channel. The literature, however, is silent about whether trading activity in financial

markets also contains valuable information for banks.

In this study, we focus on the role of information from financial markets on banks’ decision

making. We are particularly interested in the expectation of bad news because debtholders’ payoff

function represents that of a short put position (Black and Scholes 1973). Consequently, we expect

information regarding a negative outlook to be more important to banks when structuring loan

contract terms. We explore whether the expectation of a negative outlook from short selling activity

influences loan contract terms. Short selling is often attributed to informed traders, since short

selling is more costly and subject to more restrictions that selling the shares outright (Diamond and

Verrecchia 1987). Prior studies also show that short selling activity predicts negative future returns

(Asquith and Meulbroek 1995; Beber and Pagano 2013; Berkman et al. 2017; Desai et al. 2002) and

increases prior to negative news such as unfavorable earnings announcements (Christophe et al.

2004), analyst downgrades (Christophe et al. 2010), and corporate misconduct (Karpoff and Lou

2010).

Overall, as suggested by the feedback effect argument, financial market participants possess

incremental information that is not observed by banks. A major channel through which negative

information is incorporated into prices is through short selling activity. In this case, the information

embedded in trading activities in financial markets has an influence on bank loan pricing. In

particular, given that banks’ payoff resembles that of a short put position on the borrowing firm’s

assets, the expectation of poor future performance, as reflected by higher short interest, should be

positively associated with the costs of bank loans.

We acknowledge that the alternative hypothesis is also possible. Prior literature shows that

banks improve information efficiency due to screening and monitoring activities (Diamond 1991b;

2 Other studies highlight the fact that regulators also pay close attention to market prices (Burton and Seale 2005), especially since the financial crisis of 2007–2009 (Flannery 2016; Hart and Zingales 2011; McDonald 2013).

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Rajan 1992; Ramakrishnan and Thakor 1984). Through the due diligence process, banks obtain

material non-public information, giving them an information advantage over arms-length

bondholders and other financial market participants (Rajan 1992). Consistent with this idea, several

studies document that bank loan contract terms convey crucial information regarding the future

prospects of the borrowers (Besanko and Thakor 1987; Chan and Kanatas 1985; Garleanu and

Zwiebel 2009).

Other studies find that stock market participants extract information from banks’ decision to

lend. Billett et al. (1995), James (1987), and Lummer and McConnell (1989) find positive stock

market reactions to bank loan announcements. This is because, in the presence of information

asymmetry, banks’ willingness to lend is a credible signal of the borrower’s creditworthiness.

Recent studies also document the spillover of information from the loan markets to other financial

markets through the trading activity of loan market participants. Ivashina and Sun (2011) find that

institutional investors involved in loan renegotiations earn abnormal returns subsequent to loan

amendments. Similarly, Massoud et al. (2011) find that hedge funds use private information

obtained during the loan syndication process to trade on the stock market.

Overall, this branch of the literature suggests that, due to access to private information during

the loan negotiation process and through monitoring activity, banks are more informed than other

market participants about the borrower’s true value, and bank lending activity thus provides

valuable information to investors in secondary financial markets. Therefore, banks might not find

financial market information valuable. In this case, there is no relation between financial market

information and bank loan contracting.

3 Variable construction and sampling procedure

We discuss the measures for expected bad news in Section 3.1. We outline the control variables

in Section 3.2. In Section 3.3, we discuss the characteristics of our sample.

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3.1 Measuring expected bad news with short selling activity

We measure financial market participants’ expectation of future bad news using short selling

activities in the borrower’s stocks (Beber and Pagano 2013; Berkman et al. 2017; Desai et al. 2002).

We collect short interest information from Compustat’s Supplemental Short Interest File. The short

selling ratio (SIR) is calculated as follows:

SIR= Number of shorted stocksNumber of sharesoutstanding

, (1)

where the number of shorted stocks is measured on a monthly basis. The total number of shares

outstanding is measured from the last month of the fiscal year. This measure is consistent with prior

studies (Boehmer et al. 2010; Callen and Fang 2015; Desai et al. 2002). To compute the short

selling ratio for each fiscal year, we take the simple average of the monthly values of SIR within a

fiscal year.

3.2 Loan contract terms and firm characteristics

We construct our loan sample using data from the DealScan database from 1982—the first

year DealScan data are available—through 2017. We include all US dollar–denominated loan

facilities syndicated for US nonfinancial and non-utility firms during this period. Our loan variables

of interest include the loan spread, defined as the difference (in bps) between the interest charged on

the loan and the London Interbank Offered Rate (LIBOR) or LIBOR equivalent rate. Our regression

analysis includes as control variables the logarithm of the principal amount and the logarithm of

loan maturity (in months). Additionally, following the literature (Balachandran et al. forthcoming;

Hasan et al. 2014; Ivashina 2009), we control for loan purpose, loan type, and loan syndication

fixed effects.

We compute firm-specific characteristics using financial statement information collected from

the Compustat Annual Industrial Files. Following prior literature (Hasan et al. 2014; Valta 2012),

we include in our analysis the following control variables: firm size (LNASSETS, logarithm of total

assets), book leverage (LEVERAGE, total debt scaled by total assets), cash holdings (CASH, cash

and cash equivalents, scaled by total assets), return on assets (ROA, earnings before interest, taxes, 12

depreciation, and amortization, divided by total assets), earnings volatility (EARNVOL, standard

deviation of earnings in the previous four quarters), Altman’s (1968) Z score (Z),3 asset tangibility

(PPE, plant, property, and equipment, scaled by total assets), the market-to-book ratio (MTB), and a

categorical variable indicating the borrower’s Standard & Poor’s long-term credit ratings

(RATING).4 The detailed descriptions of all the variables used in this paper are provided in

Appendix 1.

3.3 Sample selection

We match loans from the universe of loan facilities collected from DealScan with the

borrowers’ latest financial statements to obtain their accounting information. We map the

borrowers’ identities in DealScan with GVKEY using the linking table of Chava and Roberts

(2008).5 We remove financial and utility companies (SIC codes in the 6000s or 4900–4999). In

addition, we exclude borrowing firms whose i) total assets are less than USD 1 million, ii) share

price is less than USD 1, and iii) total sales are nonpositive, following Kahle and Stulz (2013) and

Kim and Zhang (2014). We also exclude loan observations that are missing information regarding

the loan spread, the principal amount, and maturity. The aforementioned sampling procedure yields

a sample of 23,039 loan–firm observations with non-missing short selling data. To avoid bias

caused by outliers, we winsorize all continuous variables at 1% and 99%.

Table 1 provides the descriptive statistics of the borrowers and loans in our sample. The

average size of a loan facility (SIZE) in our sample is USD 471 million. The average maturity

(MAT) is just over four years (49.7 months), and the average loan spread (AIS) is 198.30 bps. The

number of financial covenants (FIN_COV) is approximately 2.45 per loan. About half of the loans

3 We follow Hasan et al. (2014) and modify the Altman Z score by removing the market value of equity. This is because our regression models include the market-to-book ratio (MTB).4 High credit ratings are indicated with a smaller number: an AAA rating takes a value of one and a

CC rating takes a value of 20. Ratings that are missing are assigned a value of 21.

5 We thank Michael Roberts for making the DealScan–Compustat linking table publicly available at http://finance.wharton.upenn.edu/~mrrobert/styled-9/styled-12/index.html. We include only exact matches in our analysis, where the matching score is 100%.

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are secured, and the majority are syndicated. Overall, these statistics are in line with prior literature

on bank loan contracts (Anantharaman et al. 2013; Hasan et al. 2014; Valta 2012).

[Insert Table 1 here]

4 Effect of expected bad news on the cost of private debt

4.1 Baseline results

We model the relation between the loan price and the short selling ratio using the following

equation:

SPREADi , j , t=β0+β1 SIR i ,t−1 ,+∑q=2

n

βq Controlq+εi , j ,t, (2)

where SPREADi,j,t indicates the natural logarithm of the spread between the interest charged on loan

facility j obtained in year t by firm i over the LIBOR rate (AIS), and SIRi,t-1 denotes the average

short selling ratio in firm i’s stocks over fiscal year t - 1. The control variables include the following

loan-specific control variables: the logarithm of the loan size (LNSIZE); loan maturity (LNMAT);

loan syndication, loan purpose, and loan type fixed effects; and the firm-specific control variables,

namely, firm size (LNASSETS), book leverage (LEVERAGE), cash holdings (CASH), profitability

(ROA), earnings volatility (EARNVOL), the Z score (Z), asset tangibility (PPE), the market to book

(MTB), and credit rating (RATING). We further include the year and two-digit SIC industry fixed

effects to control for time- and industry-invariant factors, respectively.

Table 2 reports the baseline ordinary least squares (OLS) results of estimating the effect of

the expectation of future bad news in financial markets on the cost of bank loans. The dependent

variable in all the models is the natural logarithm of the All in drawn spread variable from

DealScan (AIS). In Model 1, we regress the short selling ratio (SIR) against the logarithm of the

loan spread (SPREAD). In Model 2, we control for the full set of firm- and loan-specific controls. In

Model 3, we include all control variables and year and industry fixed effects. Across all models, we

find a positive and significant coefficient for SIR, indicating that banks charge higher loan spreads

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when the level of short selling in the borrower’s stock is higher. This relation is robust after

controlling for firm- and loan-specific characteristics and industry and year fixed effects. The

coefficient of SIR in the full model (Model 3) is 1.4798 and significant at 1%, suggesting that a one

unit increase in SIR is associated with a 4.39 increase in loan spreads (e1.4798 = 4.39). Given a

standard deviation of 4.5%, a one standard deviation increase in the short selling ratio increases the

loan spread by 19.76%. Since the average loan spread is 198.303 bps, this is equivalent to an

increase of 39 bps in loan costs, or USD 1.84 million (39 bps * 471 milllion = USD 1.84 million).

The control variables in all the models have signs consistent with the findings of prior

studies (Balachandran et al. forthcoming; Hasan et al. 2014; Valta 2012). Larger firms and less

levered firms have lower loan costs. Profitable firms and firms with more growth opportunities

(proxied by ROA and MTB, respectively) also borrow for cheaper. On the other hand, loans to

riskier firms with higher earnings volatility (EARNVOL) or a lower Z score (Z) have higher costs.

Similarly, firms of lower credit quality (i.e., a higher RATING value) face higher loan costs. With

respect to loan characteristics, loan size (LNSIZE) has a negative relation with loan costs.

We also estimate Equation (2) with firm fixed effects and report the results in Model 4. We

include year fixed effects to control for the time-invariant determinants of loan costs. The inclusion

of firm fixed effects allows us to isolate within-firm variations in SIR and loan costs. The coefficient

of SIR is positive and significant, suggesting that an increase in SIR is associated with an increase in

loan costs.

Finally, we consider the effect of increases or decreases in the short selling ratio on loan

costs. If the short selling ratio conveys negative information to lenders, we would expect its effect

on loan costs to be stronger following an increase in short selling. To test this conjecture, we use

annual changes in the short selling ratio (ΔSIR) as the main independent variable, instead of the raw

value of the short selling ratio. The result in Model 5 shows a positive and significant coefficient

estimate for ΔSIR. This finding implies that loan costs increase as the short selling ratio increases.

[Insert Table 2 here]

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4.2 Robustness checks

We perform a battery of additional tests to ensure our baseline results are robust to alternative

model specifications. Table 3 reports the results. For brevity, we only report the coefficient and t-

statistics of SIR. First, one could argue that the relation between SIR and loan costs is driven by

differences in lender characteristics. To address this concern, we isolate within-lender variations by

including lead bank fixed effects.6 We identify 15,937 loans with non-missing information on the

lead lender. The positive and significant coefficient of SIR in Model 1 indicates that our results are

not driven by differences across lead lenders. In Model 2, we include both lead lender and borrower

fixed effects. We find that the coefficient of SIR remains positive and significant. Overall, the

results in Models 1 and 2 indicate that our results are not driven by differences across lead lenders.

In addition, thus far we have treated each loan facility as an independent observation,

according to standard practice in the literature. There are two potential issues regarding this

practice, however. First, since loans initiated by the same lender can be correlated with one another,

the standard errors could be biased. We adjust for this issue by estimating Equation (2) with

standard errors clustered at the firm–lead bank level. The findings are reported in Model 3, and

suggest that the effect of SIR on loan costs remains positive and significant. Second, a loan package

can include multiple loan facilities. Loan facilities belonging to the same package could be

correlated, since they are provided to the same borrower, at the same point in time. Although our

results are robust to the inclusion of firm and year fixed effects (Model 4 of Table 2), the t-statistics

could be biased because the errors terms of loan facilities obtained from the same package are

correlated. To avoid this problem, we follow Anantharaman et al. (2013) and Hasan et al. (2014) to

restrict our sample to only the largest loan facility for each loan package. This condition reduces our

sample to 16,199 observations. Model 4 shows that we find similar results from estimating the

baseline regression in this sample.

6 To identify the lead lender, following Ivashina (2009), we first identify the administrative agent. If

the loan does not have an administrative agent, we then search for the terms agent, arranger, book

runner, lead arranger, lead bank, and lead manager in the lender role field. 16

Next, we adjust for the joint determination of loan costs and non-price loan terms (collateral,

covenants, and maturity) using a two-stage least squares (2SLS) regression framework method

analogous to that of Bharath et al. (2011), Dennis et al. (2000) and Hollander and Verriest (2016).

For brevity, we report the coefficient of SIR from the second-stage regression in Model 5. We

provide a detailed description of the 2SLS estimation in Appendix 2. We find that the effect of SIR

on loan costs remains positive and significant after controlling for the joint determination of loan

costs, collateral requirement, covenant provisions, and loan maturity.

In the final set of analyses, we consider different model specifications and alternative

proxies for the variables of interest. First, our main measure of loan costs (LOAN_SPREAD) reflects

only the interest costs of bank loans. To provide a more comprehensive analysis of the effect of

short selling activity on loan costs, we employ as the dependent variable the estimate of total loan

costs of Berg et al. (2016), which include both interest costs and fees.7 The results of this test are

reported in Model 6.

Second, we consider an alternative measure of expected bad news, using information from

options trading activity. Specifically, we employ the steepness of the option implied volatility (IV)

skewness (i.e., the IV smirk), following Kim and Zhang (2014). This IV skewness reflects the

difference between the value of an out-of-the-money put option and an at-the-money call option

written on the firm’s stock. High out-of-the-money put volatility indicates that investors are keen to

sell. Similarly, low at-the-money volatility indicates less interest to buy the stock. The IV skewness

value therefore reflects investors’ perception of a future negative event that depresses stock prices

(Bollen and Whaley 2004; Kim and Zhang 2014). We describe the construction of the option IV

skewness in detail in Appendix 3. We present the result for this analysis in Model 7.

Third, one could argue that heavily shorted stocks are driving our results. To address this

concern, we perform two tests. In the first test, we conduct a median regression analysis to rule out

7 We thank Tobias Berg for making the measure publicly available at

http://www.tobias-berg.com/index.php/research. Since this measure is available only until 2012, our

sample is reduced to 13,315 loan observations.17

the effect of outliers on our results. We report the results of this test in Model 8. In the second test,

for each fiscal year, we sort firms into deciles based on their short selling ratio, so that each firm is

assigned a value from one to 10. We then replace the raw value of SIR with this rank variable in the

regression analysis. By sorting firms into 10 bins, we effectively remove the effect of any extreme

values of the short selling ratio on our results. We report the findings for this analysis in Model 9.

Finally, to rule out any potential influence of the 2007–2009 global financial crisis on our

results, we remove loan facilities whose origination date falls between 2007 and 2009 from the

sample. We report the results of estimating the baseline model in this refined sample in Model 10.

Overall, the robustness tests documented in Models 6 to 10 show results similar to those of our

baseline model. These analyses thus provide assurance that our findings are not driven by the

selection of sample periods, proxies, or extreme outliers.

[Insert Table 3 here]

4.3 Identification strategies

Thus far, we have documented a strong positive relation between measures of investors’

expectation of bad news, as reflected by a higher short selling ratio, and loan costs in Tables 2 and

3. We acknowledge that this relation could be driven by unobservable factors that influence both the

behavior of financial market participants (hence the short selling ratio) and the bank lender. We

identify potential sources of endogeneity in this section and discuss our empirical strategy to

address each of these concerns.

4.3.1 TRA 1997

We examine changes in the effect of SIR on loan costs after the TRA 1997. A common

strategy for the relatively less informed short seller is to short against the box. Shorting against the

box involves the short position of a trader who already holds a long position in the asset. The trader

is not required to immediately deliver the long position to cover the short sale, and any gains

(losses) from the long position will be offset by the losses (gains) from the short position. The net

position is the postponement of capital gains tax until a later tax year (Arnold et al. 2005). The

18

strategy of shorting against the box became more costly after implementation of the TRA 1997,

since any capital gains or losses are now immediately recognized for tax purposes. Therefore, the

information content of short interest increases in the post-TRA 1997 period due to the departure

from the market of relatively uninformed short sellers against the box (Arnold et al. 2005).

We argue that the TRA 1997 represents an exogenous shock to the information content of

SIR. If SIR is an important consideration for banks when designing loan contracts, we should

observe an increase in the effect of SIR on loan costs in the post-TRA 1997 period, since the

information content of short selling increases subsequent to the implementation of TRA. To conduct

this analysis, we restrict our sample to firms that borrowed in both the pre- and post-TRA 1997

periods during the five years before 1997 and after 1997. We then estimate the following regression

with OLS:

SPREADi , j , t=β0+β1 SIR i ,t−1+β2 POST +β3 POST × SIRi , t−1+∑q=4

n

βqControlq+ε i , j , t , (3)

where POST is a dummy variable that takes the value of one for the post-TRA1997 period, and zero

otherwise. We include all control variables specified under Equation (2). We require each firm to

have at least one loan facility in each of the pre- and post-periods.

We report the results for this analysis in Table 4. Our variable of interest is the interaction

term POST×SIR. The coefficient β3 of the variable POST×SIR reflects the change in the effect of

SIR on loan costs after the implementation of the TRA 1997. We find that, in models with or

without firm and loan control variables, the coefficient estimates for β3 of this variable (POST×SIR)

are positive and statistically significant. Overall, consistent with our conjecture, the findings in this

section suggest that SIR plays a more important role in explaining loan costs after the

implementation of the TRA 1997.

[Insert Table 4 here]

19

4.3.2 Propensity score matching

We further use propensity score matching, whereby we compare the loan costs of firms with

a high level of short selling interest and the loan costs of similar firms with a low level of short

selling activity. The purpose of this test is to examine how banks would have determined loan

interest costs had the borrower not had a high level of short selling. For each fiscal year, we classify

firms as having a high (low) level of short selling activity if the short selling ratio is above (below)

the two-digit SIC code industry median value. Our treatment group includes loans granted to

borrowers with a high short selling ratio, and our control group consists of loans granted to

borrowers with a low short selling ratio.

We obtain the propensity score for each loan observation using a logit regression, with all

control variables as specified in the baseline regression [Equation (2)]. We then perform matching

using a caliper of 5%. The descriptive statistics of the control and treatment firms are reported in

Panel A of Table 5. Our matching procedure yields 990 treatment firms and 990 matched control

firms. The total number of loans obtained by the treatment (control) firms is 1,646 (1,596). Column

3 of Panel A (Treatment – Control) shows no statistical difference between the characteristics of the

treatment and control firms. Nevertheless, the average loan spread is higher for the treatment group

relative to the control group.

In addition to documenting the average treatment effect, we perform a regression analysis on

the reduced sample of matched control and treatment loans. We report the results of estimating the

baseline regression with year and industry fixed effects in Model 1 of Panel B of Table 5.

Consistent with the baseline results (Model 3 of Table 2), we find a positive and significant

coefficient for SIR with a similar magnitude (1.144 vs. 1.4798). We further estimate the baseline

model with firm and year fixed effects and find that the within-firm variations between SIR and loan

spreads are also significant and in the same direction as our expectation (β1 = 0.8686, t-statistic =

2.34). Taken together, the analyses presented in Table 5 highlight that our findings are not driven by

systematic differences between firms with high and low short selling ratios.

20

[Insert Table 5 here]

4.3.3 Reverse causality concerns

Our results could also be subject to reverse causality concerns, whereby insiders to the loan

negotiation process take advantage of this information by shorting the shares of the borrowing firm.

For example, the loan negotiation process could reveal unfavorable information regarding the

borrower which could lead to lower share value. An insider could profit from such knowledge by

shorting the borrowing firm’s stocks. Massoud et al. (2011) suggest that hedge funds that participate

in loan syndications short the borrower’s stock prior to loan amendments events. This finding

implies the possibility of reverse causality whereby the trading activity of insiders involved in the

loan syndication process causes an increase in the short selling ratio, rather than lenders learning

from the short selling activity of other financial market participants.

Our strategy for addressing this possibility relies on the distance between the loan contract

date and the window for computing the measures of expected bad news. A typical loan negotiation

process takes approximately six months (Murfin 2012). If insiders trade on private information

obtained from loan negotiations, we expect these trading activities to take place from the start of the

negotiation process to the day the loan contract is signed (i.e., within six months prior to the loan

facility’s starting date). In other words, insider trading related to the loan contract in negotiation

must occur within this period. Thus, to circumvent the potential that insider trading prior to the loan

contract drives our results, we set the condition that the loan’s starting date is at least six months

after the closing date of the previous fiscal year. Since our measures of expected bad news are at the

fiscal year level, this condition ensures that we do not use short selling data during the loan

negotiation period to calculate our short selling ratio. We then re-estimate the baseline regression on

this subset of loan observations. We report the results of this test in Table 6. Models 1 to 4 show the

results when we impose a time window of six, seven, eight, and nine months, respectively. We find

that, across all these models, the coefficient of SIR is positive and significant, consistent with the

21

baseline results. This result alleviates the reverse causality concerns regarding our baseline

regression result.

[Insert Table 6 here]

5 Lending supply and short selling risk

Our implicit assumption is that the relation between SIR and loan costs, documented in the

previous sections, is driven by stock market investors’ intention to sell short. However, this

interpretation could be problematic if we are unable to control for the availability of lendable

shares. In particular, since short sellers are required to borrow shares prior to shorting, our results

could be driven by an increase in the quantity of lendable shares (which could mechanically

increase short selling), rather than informed trading.8 To mitigate this concern, we conduct the

following analyses. First, we control for the effect of lending supply in our baseline regression and

report the results in Model 1 of Table 7. We define lending supply as the ratio of the number of

shares available to lend to the number of shares outstanding. We find that the effect of SIR on loan

costs remains robust after controlling for lending supply. In Model 2, we control for lending supply

by replacing SIR with the utilization ratio (Utilization), defined as the ratio of the number of shares

shorted to the number of shares available to lend. The higher value of Utilization reflects the larger

percentage of shares actually on loan (i.e., shares that have been shorted) to the number of shares

available to lend. We find that, similar to our baseline regression, a higher value of Utilization is

associated with higher loan costs.

Besides the quantity of lendable shares, we consider short selling risk (D’Avolio 2002;

Engelberg et al. 2018). Engelberg et al. (2018) find that stocks with higher short selling risk exhibit

greater mispricing. We therefore argue that the value of the information from the short selling ratio

will be more relevant to banks when short selling risk is higher. Following D’Avolio (2002) and

8 Recent literature also highlights the importance of lending supply or the threat of short selling as

an important external governance mechanism (e.g., Chang et al. 2018; Fang et al. 2016; Ho et al.

2016; Hope et al. 2017; Massa et al. 2015).22

Engelberg et al. (2018), we measure short selling risk as the logarithm of the variance of loan fees

over the previous fiscal year. Subsequently, for each fiscal year–SIC two-digit industry code

combination, we rank firms into terciles based on the level of short selling risk. We then create a

dummy variable that takes the value of one if a firm belongs to the top tercile of short selling risk

(HIGH) and a dummy variable that takes the value of one if a firm belongs to the bottom decile

(LOW). For our empirical analysis, we replace the main independent variable of interest (SIR) with

two interaction terms: SIR×HIGH and SIR×LOW. We report the results of estimating this

regression in Model 3. The coefficient of SIR×HIGH is positive and significant, while the

coefficient of SIR×LOW is insignificant. The difference between these coefficients is statistically

significant. Consistent with our conjecture, this finding highlights the fact that the short selling ratio

is more relevant to loan pricing in the subsample of firms with higher short selling risk. Given that

higher short selling risk indicates higher uncertainty regarding short selling costs (fees), our result

lends support to Diamond and Verrecchia (1987), who argue that higher costs of short selling

increase the information content of short interest, since those willing to bear the higher costs are

those with the greatest anticipated benefits.

[Insert Table 7 here]

6 Information environment

We argue that the positive relation between the short selling ratio and loan costs arises because

banks consider information in the short selling ratio when pricing loans. If this is the case, we

expect the effect of SIR on loan costs to be more pronounced when the information environment of

the borrower is more opaque, since banks are likely to value information from the short selling ratio

more in these situations. In addition, systematic cross-sectional variations in line with the

predictions from previous literature would strengthen our inference, since it is arguably harder for

an omitted correlated variable to explain both our main results and cross-sectional findings (Cheng

et al. 2016).

23

In the discussion that follows, we provide a comprehensive examination of the mediating effect

of the information environment on the effect of SIR on loan pricing. First, we investigate the

information environment at the firm level in Sections 6.1 and 6.2. We then proceed to explore the

role of analysts as third-party information providers in Section 6.3. Finally, we study the premise of

information asymmetry between the borrower and the bank lender in Section 6.4.

6.1 Financial report opaqueness

We measure the degree of information opaqueness at the firm level, using the readability of

financial reports provided by the SEC Analytics Suite from Wharton Research Data Services.

Loughran and McDonald (2014) show that the readability of financial reports, measured by the size

of the financial statement (in megabytes), determines the effective communication of valuable

information about the firm. This implies that more readable (i.e., smaller) financial reports are

associated with a lower degree of information asymmetry. In addition to the financial report size,

we further analyze the separate effects of the number of negative words and the number of positive

words. We argue that banks are more concerned about expected negative information regarding the

borrower, as opposed to positive information. If this is the case, we expect the relation between SIR

and loan pricing to be increasing with the proportion of negative words in the financial report. In

contrast, we expect the number of positive words to have no influence on the relation between SIR

and loan pricing.

We test the mediating effect of the firm-specific information environment on the relation

between SIR and loan pricing as follows. For each two-digit SIC code industry in each fiscal year,

we divide firms into terciles based on one dimension of financial report readability discussed above.

We then create 1) a dummy variable that takes the value of one if the firm belongs to the top tercile

(HIGH), and zero otherwise, and 2) a dummy variable that takes the value of one if the firm belongs

to the bottom tercile (LOW), and zero otherwise. We interact these dummy variables with SIR and

include these interactions in the regression, as follows:

24

SPREADi , j , t=β0+β1 HIGH i ,t−1× SIR i ,t−1+β2 LOW i ,t−1× SIR i ,t−1+∑q=3

n

βq Controlq+εi , j , t ,

(4)

where HIGH and LOW are as described above. The other variables are analogous to those described

in Equation (2).

We report the results of examining the mediating effect of financial report readability on the

relation between SIR and loan pricing in Table 8. For this analysis, we exclude firms in the middle

tercile. In Model 1, we report the results for financial report size. We find that the coefficient of

HIGH×SIR is higher than the coefficient of LOW×SIR. The difference between these coefficients

(HIGH×SIR – LOW×SIR) is positive and significant. This result indicates that the effect of SIR on

loan spreads is stronger in firms with a longer financial report. Since a longer financial report is

associated with a higher degree of information asymmetry (Loughran and McDonald 2014), this

finding suggests that banks value the information content of the short selling ratio more highly

when the borrower is more opaque, which supports our conjecture that information asymmetry

drives the relation between SIR and loan spreads.9

We further explore how the effect of SIR on loan pricing varies with different tones of the

financial report. In Models 2 and 3, we analyze the relation between SIR and loan spreads across

different groups of firms based on the number of negative words and the number of positive words

separately. We find that the effect of SIR on loan pricing is stronger when there are larger numbers

of negative words (Model 2), whereas we find no significant variation in this effect across firms

with different numbers of positive words (Model 3). This result indicates that banks value negative

information more than positive information, consistent with our conjecture and the notion that

debtholders have a short put position on firm assets (Black and Scholes 1973).

9 As an alternative measure of financial report opaqueness, we employ the Fog Index. This index

measures the number of years of education required to understand the financial report in the first

reading. Hence, a lower Fog Index suggests that the financial report is easier to read. Consistent

with Model 1, we find that firms with a more complex financial report (i.e., a higher Fog Index)

have higher loan costs. 25

Overall, the above analysis shows that the informational value of the short selling ratio is

higher when financial readability is low. Further investigation reveals that the amount of negative

information and litigation risk in the borrowing firm’s financial report also increases the effect of

SIR on loan pricing, whereas this is not the case for positive information in the financial report. This

finding is consistent with banks valuing the information content of short selling activity more highly

when financial report readability is low. Moreover, banks value negative information more highly

than positive information.

[Insert Table 8 here]

6.2 Financial report comparability

Our next measure of the information environment is financial report comparability (De

Franco et al. 2011; Kim et al. 2016).10 Financial report comparability is defined as “The quality of

information that enables users to identify similarities in and differences between two sets of

economic phenomena.” (Financial Accounting Standards Board, p.9, 1980). De Franco et al. (2011)

measure how close a firm’s earning figures are to another firm’s, holding economic events constant.

If two firms have comparable financial statements, they should report similar accounting figures

when exposed to the same economic conditions. Greater financial report comparability implies that

the information of comparable firms is more available, meaning that the cost of information

acquisition is lower. Thus, a higher level of financial report comparability should be associated with

a lower degree of information asymmetry (De Franco et al. 2011).

Similar to Equation (4) outlined above, we examine the mediating effect of financial report

comparability on the relation between SIR and loan pricing as follows. First, we create i) a dummy

variable that takes the value of one if the firm belongs to the top tercile of financial report

comparability, and zero otherwise (HIGH), and ii) a dummy variable that takes the value of one if

the firm belongs to the bottom tercile of financial report comparability, and zero otherwise (LOW).

10 We obtain the measures for financial report comparability from Rodrigo Verdi’s website (http://mitmgmtfaculty.mit.edu/rverdi/) for the period 1981–2013. Our sample for this analysis stops in 2013, due to the availability of the financial report comparability measures.

26

We then interact these dummy variables with SIR and include the interaction terms in our

regression. Our findings are documented in Model 4 of Table 8.

We find that the coefficient of HIGH×SIR is lower than the coefficient of LOW×SIR, and

the difference is negative and significant. This result suggests that the effect of SIR on loan pricing

is stronger when the borrowing firm’s financial report is less comparable, relative to those of

industry peers.11 De Franco et al. (2011) find that higher comparability reduces the costs of

information acquisition, making the firm’s information environment more transparent. Thus, our

finding implies that banks value the information content of SIR more highly when the firm is less

transparent, as reflected by lower financial report comparability.

6.3 Analyst forecast dispersion

We next investigate the information channel of SIR on loan pricing from the angle of third-

party information provided by analysts. A divergence of opinions among analysts following the

same stock implies that it is more difficult to assess the firm’s future performance (Sadka and

Scherbina 2007; Yu 2008). This result is consistent with a greater degree of information asymmetry.

We measure analyst forecast dispersion as the standard deviation of all analysts’ forecasts. We scale

analyst forecast dispersion with the mean forecast value.12

Similar to the analyses in Sections 6.1 and 6.2, to examine the mediating effect of analyst

information on the effect of SIR on loan pricing, we first group firms into terciles of analyst forecast

dispersion. For each of these measures, we then interact SIR with the dummy variables that indicate

whether the firm belongs to the top (or bottom) tercile and include the interaction terms in our

regression analysis. The findings of these analyses are reported in Model 5. We find that the

11 This finding is robust to the use of alternative benchmarks to compute financial report

comparability. In particular, following De Franco et al. (2011), we consider the comparability of a

firm’s financial report with i) the top 10 industry peers by market share, ii) the top four industry

peers by market share, and iii) the top 10 industry peers by size.

12 As an alternative measure, we scale analyst forecast dispersion by the firm’s stock price. We find

similar results using this alternative measure of analyst dispersion. 27

coefficient of SIR×HIGH is larger than the coefficient of SIR×LOW. The difference between

SIR×HIGH and SIR×LOW is positive and significant at 1%. We conclude that the short selling ratio

is more important for the pricing of loan contracts when the borrowers’ degree of information

asymmetry is higher.

6.4 Relationship banking

Maintaining a relationship with a borrower allows a bank to extract proprietary information

from the borrower, as well as soft information (Bharath et al. 2011). Banks with an existing

relationship with the borrowing firm are therefore more informed than first-time lenders. Therefore,

we expect that financial market information is less important for relationship lenders, relative to

non-relationship lenders. We measure relationship lending following Bushman et al. (2017). A loan

is considered a relationship loan when the lead arranger accounts for at least 50% of the total loan

amount lent to the borrower in the previous five years. We only consider the relationship between

the borrower and the lead lender rather than with the other loan participants because the lead

arranger is in charge of screening and monitoring the borrower (Bharath et al. 2011; Ivashina 2009).

The value of information is thus greater for lead lenders. Finally, for this analysis, our sample stops

in 2012, since we rely on Michael Schwert’s file to identify banks’ mergers and acquisitions to

determine bank relationships.13

We test for the effect of bank relationships on the value of financial market information by

including the interaction terms SIR×NO_RELATION and SIR×RELATION in our regression. The

variable NO_RELATION is a dummy variable that takes the value of one for a non-relationship

loan, and zero otherwise. The variable RELATION is a dummy variable that takes the value of one

for a relationship loan, and zero otherwise. Our hypothesis implies that the coefficient of

SIR×NO_RELATION is higher, since financial market information is more valuable to non-

relationship banks.

13 We thank Michael Schwert for providing the bank merger data (see

https://sites.google.com/site/mwschwert ) . 28

We report the empirical results of this test in Model 6 of Table 8. The coefficient of

SIR×RELATION is 1.7005, whereas the coefficient of SIR×NO_RELATION is 2.1642. The

difference between these coefficients (SIR×RELATION – SIR×NO_RELATION) is statistically

significant. These results indicate that financial market information has a stronger effect on the

costs of loan provided by non-relationship lenders, relative to relationship lenders. Overall, the

results reported in Table 8 suggest that the effect of SIR on loan spreads is stronger when the firm

faces a greater degree of information asymmetry. This effect is robust across different ways of

measuring information asymmetry. These findings further support our conjecture that short selling

activity influences loan pricing through its information role.

7 Effect of short selling on non-price loan terms

In this section, we explore whether information obtained from financial markets influences

other loan terms besides the risk premium. The type of information that we focus on is the market’s

expectation of poor future performance, as reflected by the short selling interest. From a lender’s

perspective, this information is valuable because it signals the borrower’s ability to repay the loan.

Banks can impose a number of monitoring mechanisms to limit the downside risk of lending to

these borrowers, including debt maturity, collateral requirements, and covenant restrictions. We

document the results of examining the effect of short selling activity on these loan terms in Table 9.

7.1 Loan maturity and security

Diamond (1991a) shows that banks use short-term debt to monitor borrowers, since the

former can refuse to reprice the maturing debt if the borrower takes excessive risks. Early repricing

also forces the borrower to disclose information more frequently, thus improving transparency

(Graham et al. 2008). These studies imply that banks shorten debt maturity when lending to firms

with a negative outlook. Consistent with this hypothesis, in Model 1 of Table 9, we document a

negative and significant effect of SIR on the logarithm of loan maturity.

29

We next investigate the effect of short selling activity on the likelihood of loan security.

Rajan and Winton (1995) argue that banks are more likely to require collateral when lending to

riskier borrowers. Collateralization benefits risky borrowers because it incentivizes banks to

monitor. Following this argument, we conjecture that banks are more likely to require collateral

when the expectation of bad news (reflected in short selling activity) increases, since the risk of the

borrower defaulting is higher. To examine the effect of SIR on loan security, we estimate a probit

model where the dependent variable is DSECURED, a dummy variable that takes the value of one if

the loan is secured, and zero otherwise. We find a positive coefficient for SIR for this analysis

(Model 2 of Table 9), indicating that a higher level of short selling is associated with a higher

chance of the bank requiring collateral on the loan.

7.2 Covenant restrictions

Alternatively, banks can impose restrictive covenants to prevent borrowers from taking

actions that could harm their stake. Covenants allow ownership to be transferred to the bank when

the covenant is breached, hence motivating the managers to perform (Armstrong et al. 2010). If the

firm’s future performance is in doubt, banks should impose more covenants to better monitor the

borrower. Therefore, we expect a positive relation between the short selling ratio and the number of

covenant restrictions.

We analyze the effect of short selling activity on various angles of loan covenants and report

the findings in Models 3 to 7 of Table 9. 14 First, we construct a covenant intensity index, following

Bradley and Roberts (2015). This index consists of six categories: 1) security, 2) dividend

restrictions, 3) whether the loan has more than two financial covenants, 4) an asset sweep, 5) an

equity sweep, and 6) a debt sweep. We assign a value of one for each category whose condition is

14 For these analyses, we follow Anantharaman et al. (2013) and restrict our sample to the largest

loan facilities per loan package. We only retain observations with non-missing financial covenant

information. Loan covenants are determined at the package level. Thus, there is no variation in loan

covenants among the loan facilities of a same package. The inclusion of all loan facilities could

therefore introduce bias into our estimates. 30

met, and zero otherwise. A higher index value indicates stricter loan covenant restrictions. To study

the effect of short selling activity, we regress SIR against the covenant index in a Poisson regression

framework. We find a positive and significant coefficient for SIR (Model 3), suggesting that banks

increase covenant restrictions when SIR is higher.

In the second analysis, we focus on the number of financial covenants. Similar to Model 1,

we perform a Poisson regression analysis to examine the effect of SIR on the number of financial

covenants in the loan contract. We find that, consistent with Model 1, the effect of SIR on the

number of financial covenants is positive and significant, showing that banks increase the number

of financial covenants when the level of short selling on the borrower’s stock is higher.

We further decompose financial covenants into capital covenants and performance

covenants. Capital covenants impose restrictions on capital structure, whereas performance

covenants are tied to the borrower’s periodic performance (tripwire covenants; see Christensen and

Nikolaev 2012). Christensen and Nikolaev (2012) argue that capital covenants are used to align the

interests of shareholders and debtholders, whereas performance covenants facilitate bank

monitoring, since they allow banks to take control when firm performance deteriorates. If SIR

reflects possible problems with firm performance in the future, we expect banks to take this

information into account by increasing the use of performance covenants rather than capital

covenants.

Consistent with these arguments, we find no significant effect of SIR on the number of

capital covenants (in Model 5), whereas the effect of SIR on the number of performance covenants,

as shown in Model 6, is positive and significant. Similarly, SIR has a positive effect on the ratio of

performance covenants over capital covenants, suggesting that banks are likely to rely more on

performance covenants when there is more short selling activity in the borrower’s stocks.

Overall, the relation between the non-price loan terms and SIR suggests that financial

markets’ expectations of a negative outlook have a material impact on not only the loan price, but

31

also the monitoring mechanisms in the loan contract, as reflected by loan maturity, security and

covenant restrictions.

[Insert Table 9 here]

8 Debt choice and the cost of public debt

Thus far, we have shown that loan contract terms are influenced by the short selling ratio in the

borrower’s stocks. One explanation for this effect is that, since the short selling ratio conveys

information of future bad news, it could signal a borrower’s inability to pay debt obligations in the

future. Although private debt and public debt are starkly different (Denis and Mihov 2003;

Diamond 1991b), their payoff functions are similar, with both having fixed claims on the

borrower’s assets, but unlimited downside losses. Consequently, we expect bond investors to also

charge higher rates to borrowers with a more negative outlook.

To examine the effect of the short selling ratio on the cost of public debt, we collect all bonds

issued by US nonfinancial and non-utility corporations over the period 1986 to 2017 from the

Mergent Fixed Income Securities Database database. We match these bond issues with the issuer’s

financial statements reported by Compustat, using the six-digit issuer CUSIP number. We exclude

bonds with missing information on bond spreads, offering amounts, and maturity. We also exclude

firm–year observations missing short selling interest and other control variables. The final sample

includes 6,738 bond issue observations. We then estimate the following equation:

BOND¿=β0+β1 SIR i ,t−1 ,+∑q=2

n

βq Controlq+εi , j ,t, (5)

where BOND_SPREADj,i,t is the spread between the yield at issuance and a government bond of

equivalent maturity (Treasury spread) for bond j obtained in year t for firm i. The vector of bond

characteristics includes the natural logarithm of the bond offering amount (Log bond size), the

natural logarithm of bond maturity (Log bond maturity), a dummy variable indicating whether the

bond is senior, and a dummy variable indicating whether the bond is flagged as a private issue. All

32

other firm characteristics are analogous to those in the baseline model [Equation (2)]. Our control

variables are consistent with those of previous studies, including those of Bharath et al. (2008) and

Hasan et al. (2014). We document the findings in Table 10.

In Model 1 of Table 10, we report the results of estimating the effect of SIR on bond yield

spreads with no control variables. In Model 2, we include firm-level control variables. Model 3

further includes bond-level control variables. Model 4 includes all of control variables mentioned

above, as well as industry and year fixed effects. In Model 5, we include firm fixed effects to

estimate the within-firm effect of SIR on bond spreads. We observe a positive and significant

coefficient for SIR across all model specifications in Table 10, indicating that a higher level of

shorting is associated with a higher cost of public debt. This finding is also consistent with our main

results for the cost of private bank loans.

We further examine whether bearish signals in the short selling ratio influence the choice

between bank loans and public bonds. Prior literature shows that short selling interest is associated

with performance problems (Desai et al. 2002). Therefore, these firms should have a higher chance

of renegotiating their debt in the future. Prior literature finds that the renegotiation of bank loans is

more efficient than public debt because the number of lenders involved in a bank loan is much

lower than the number of bond investors (Bolton and Freixas 2000; Gilson et al. 1990). This result

implies that firms with a high SIR value would prefer bank loans, because the renegotiation costs

are lower than for public bonds. Based on these arguments, we expect a positive relation between

expected future bad news and the likelihood of borrowing from banks (relative to issuing public

bonds).

To test for firms’ preference for bank loans relative to public bonds, following Becker and

Ivashina (2014), for any given year in our sample, we identify whether a firm has taken out any new

loans or issued any new debt in the public market. We exclude years with no new debt, as well as

years in which the firm has both a new loan and a new bond issue. We create a bank loan issue

indicator that equals one for any year the firm has a new loan, and zero for any year the firm issues

33

a new bond. We then estimate the likelihood of having a bank loan relative to a bond issue, using a

probit model. We include all firm control variables and industry and year fixed effects in our model.

We report the results of the aforementioned tests in Model 6 of Table 10. We find a positive

relation between the short selling ratio and the likelihood of borrowing from banks rather than

issuing bonds. Overall, our results in this section highlight the fact that both private bank lenders

and public bond investors value information obtained imbedded in short selling activity, and that

firms with a higher SIR value prefer private debt (bank loans) over public debt (corporate bonds).

[Insert Table 10 here]

9 Conclusions

Information asymmetry creates an information advantage for private lenders over arm’s length

investors in the secondary financial markets. Although there is ample evidence that outsiders

attempt to learn about the quality of a firm from the activities of private lenders, whether the

information possessed by financial market participants is valuable to private lenders is not well

understood. We explore this issue by examining the reaction of the dominant group of private

lenders, that is, banks, to expected bad news embedded in short selling activity. We show that,

despite the banks’ information advantage, they value the information conveyed in short selling

activity in the borrower’s stocks. In particular, banks charge higher loan rates, impose more

covenants and collateral requirements, and shorten loan maturity for borrowers with more short

selling activity. Furthermore, we find that the information content of these ratios becomes more

valuable in higher information asymmetry scenarios, such as when the bank has no previous

banking relationship with the borrower, when the borrower’s financial reports are opaque, and when

the information provided by analysts is mixed. Besides banks, we also show that bond market

participants value the information embedded in the short selling ratio. Finally, consistent with the

conjecture that riskier firms value bank monitoring and the renegotiability of bank loans more, we

find that firms with a higher short selling ratio are more likely to borrow from banks than to issue

34

bonds. Overall, our paper highlights that policies that enhance information efficiency in financial

markets increases social welfare for not only the market participants, but also corporations and

capital suppliers.

35

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39

Appendix 1: Variable description

Variable DescriptionBank loan characteristics obtained from DealScanAIS The spread between the loan’s interest and the LIBOR or LIBOR-equivalent rate (item All in drawn). DSYND A dummy variable that equals one if the loan involves more than one lender, and zero otherwise. DSECURED A dummy variable that equals one if the loan is secured, and zero otherwise.FIN_COV The total number of financial covenants in the loan package.LNMAT The natural log of the number of months until maturity (item Maturity). LNSIZE The natural log of the size of the loan facility (item Facility amt). LOAN_PURPOSE A categorical variable representing different loan purposes, including corporate purposes, debt repayment, working capital, acquisitions, backup

loans, and others (item Primary purpose). We create the loan purpose fixed effects for each of the above categories.LOAN_TYPE A categorical variable representing different loan types, including term loans, revolver less than one year, revolver greater than one year, 364-day

facility, bridge loans, and others (item Type). We create the loan type fixed effects for each of the above categories.MAT The number of months until maturity (item Maturity)SIZE The face value of the loan facility (item Facility amt).SPREAD The log of the difference (in bps) between the interest charged on the loan facility and LIBOR or LIBOR-equivalent rate (item All in drawn). Bond characteristics obtained from Mergent FISDBOND_MAT The natural log of the difference between the bond’s issuing date and maturity date (in months)BOND_SIZE The natural log of the bond’s offering amount (item Offering amount)BOND_SPREAD The natural log of the spread between the bond’s yield at issue and a government bond of equivalent maturity (item Treasury spread)DPRIVATE A dummy variable indicating whether the bond is a private placementDSENIOR A dummy variable indicating whether the bond is classified as “senior”, “senior subordinate”, or “senior secured”Firm characteristics obtained from CompustatCASH Cash = che/ atEARNVOL Standard deviation of quarterly earnings (epspiy) in the previous four yearsLEVERAGE Leverage = (dltt + dlc)/ atLOGASSETS Log assets = log(at)MTB MTB=(prcc_f*csho)/ceqRATING Categorical variable ranging from one (AAA rating) to 21 (missing rating). We use the borrower’s S&P long-term issuer rating. A smaller number

indicates a higher rating.ROA ROA = oibdp/ atSIR Yearly average short selling interest ratio

SIR=Number of shorted shares/ Total shares outstandingZ Modified Z score in Hasan et al. (2014)

Z= (1.2*wcap + 1.4*re + 3.3*pi + 0.999*sale)/atShort-selling data from MarkitUtilization The number of shares shorted to the number of shares available to lendLending supply The log of the variance of loan fees over the previous fiscal year

40

Appendix 2: Joint determination of loan terms

In this appendix, we describe our empirical test to address the joint determination of loan terms. In a

loan negotiation process, parties to the loan contract negotiate non-price loan terms prior to agreeing on

the dollar costs (Bharath et al. 2011). This process means that loan spread has a unidirectional relation

with other non-price loan terms whereas the relations between loan maturity, covenant intensity and

collateral are bidirectional. We model the relation between loan spread, loan maturity, covenant

intensity and security using 2SLS regressions. In the first stage, we use instrumental variables to

estimate the predicted values of loan maturity, security and covenant intensity as follows:

LNMATi,j,t = f(IVs, CONTROLS), (A2.1)

DSECUREDi,j,t = f(IVs, CONTROLS), (A2.2)

COVi,j,t = f(IVs, CONTROLS), (A2.3)

where LNMAT denotes the log of loan maturity; DSECURED denotes a dummy variable that

equals one when the loan is secured and zero otherwise; COV denotes Bradley and Roberts (2015)

covenant intensity index. IVs denotes the set of instruments for loan maturity, security and covenant

intensity.

We estimate Equations (A2.1), (A2.2) and (A2.3) using the OLS, logit, and Poisson regressions,

respectively. Following Hollander and Verriest (2016) and Bharath et al. (2011), we use the average

loan maturity in the previous three months as the instrument for loan maturity. In addition, the

instruments for collateral requirement are loan concentration (measured as the current loan amount

divided by the sum of the loan amount plus existing debt) and the four-digit SIC industry median

tangibility ratio. For covenant intensity, we use the 360-day historical default of the lead bank as an

instrument (Balachandran et al. forthcoming). The 360-day historical default is the total size of the lead

bank’s defaulted loans in the previous 360 days prior to the facility start date scaled by the total amount

41

of defaulted loans experienced by the lead bank in the three years from year t-4 to year t-2.15 The

rationale for using the lead bank’s loan default history as an instrument for covenant intensity can be

found in Murfin (2012), who finds that the lead bank’s recent default experience influences the

strictness of covenants in subsequent loans.

In the second stage, we estimate the following equation:

SPREADi , j , t=β0+β1 LNMAT i , j ,t+ β2 DSECUREDi , j ,t+β3 COV i , j ,t+εi , j ,t , (A2.4)

where the fitted values for loan maturity, security and covenant intensity are obtained from estimating

Equations (A2.1) to (A2.3) above. We include all control variables as in previously reported tests.

Finally, we include SIR to estimate the effect of short selling activity on loan spread while adjusting for

the joint determination of loan spread, maturity, security and covenant intensity.

Appendix 3: Constructing option IV skewness

We collect stock option information from the OptionsMetric database, which is available from 1996.

Following Kim and Zhang (2014) and Kim et al. (2016), we measure the IV skewness as the difference

between the implied volatility of an out-of-the-money (OTM) put option and the implied volatility of

an at-the-money (ATM) call option written on the same stock, as depicted in Equation (A2.1) below:

IV_SKEWit=IVOTMPit – IVATMC

it, (A3.1)

where IV_SKEWit denotes the measure of expected crash risk of stock i on day t – the implied volatility

smirk; IVOTMPit is the implied volatility of stock i’s OTM put option on day t; and IVATMC

it is the implied

volatility of the ATM call option written on stock i on day t. Option moneyness is defined using the

delta value. A put option is OTM if its delta value is between -0.375 and -0.125, whereas a call option

is defined as being ATM if its delta value is between 0.375 and 0.625.

If the stock has multiple options traded on the same day, the implied crash risk is calculated as

the average of the implied volatilities of puts and calls as follows:

15 See Balachandran et al. (forthcoming) for further details.42

IV_SKEWit=

∑j

OPEN∫¿ j× IV itj

OTMP

∑j

OPEN∫¿ j−∑

kOPEN∫¿k

× IV itkATMC

∑k

OPEN∫¿k¿

¿¿

¿, (A3.2)

where OPEN_INTj denotes the open interest of put option j, and OPEN_INTk denotes the open interest

of call option k. The daily skewness measure is then averaged over the fiscal year prior to the loan

initiation date. A higher value of the option skewness measure implies a greater expected crash risk.

43

Table 1: Descriptive statisticsThis table shows the characteristics of the borrowers and the features of loan contracts in the sample. The sample period spans from 1982 to 2017 and includes all loans to U.S. non-financial and non-utility borrowers. Loan data come from the Dealscan database and firm-specific accounting information comes from Compustat Annual Industrial Files. All continuous variables are winsorized at the 1st and 99th percentiles. All variables are described in Appendix 1.

Variable N Mean SD P25 Median P75Loan characteristics

Loan spreads (bps) 23,039 198.303 150.246 92.000 175.000 275.000Loan amount (US Mil) 23,039 471.000 1,040.000 69.000 200.000 500.000Loan maturity (months) 23,039 49.697 23.082 36.000 60.000 60.000Syndication 23,039 0.934 0.248 1.000 1.000 1.000Security 23,039 0.519 0.500 0.000 1.000 1.000Financial covenants 13,309 2.448 1.072 2.000 2.000 3.000

Firm characteristicsSIR 23,039 0.037 0.045 0.008 0.022 0.048Total assets 23,039 5,531.269 16,798.720 408.269 1,292.700 4,088.122Leverage 23,039 0.301 0.206 0.156 0.280 0.414Asset tangibility 23,039 0.309 0.231 0.125 0.248 0.441Cash holdings 23,039 0.089 0.107 0.016 0.048 0.120ROA 23,039 0.137 0.080 0.093 0.132 0.178MTB 23,039 2.832 4.487 1.304 2.123 3.489Z 23,039 1.717 1.265 0.965 1.706 2.459Earnings volatility 23,039 0.728 1.184 0.185 0.366 0.757Rating 23,039 15.224 5.775 10.000 15.000 21.000

44

Table 2: Baseline results This table presents the baseline results of the relation between the short selling ratio (SIR) and loan costs. The dependent variable in all models is the log of the all-in-drawn spread (SPREAD). Model 1 shows the results of regressing SPREAD on the short selling ratio (SIR) by itself. Model 2 includes both firm and loan control variables. Model 3 includes the full set of control variables and industry and year fixed effects. Model 4 includes the full set of variables and firm and year fixed effects. Model 5 uses the changes in SIR as the main independent variable. We compute the change in SIR from two years prior to the loan origination to the previous year (ΔSIR). All continuous variables are winsorized at the 1st and 99th percentiles. We cluster standard errors at the borrowing firm level. T-statistics are reported in parentheses. The symbols *, **, and *** denote statistical significance at 10%, 5% and 1% level, respectively. See Appendix 1 for detailed description of all variables.

  Models1 2 3 4 5

SIR 4.1349 2.8352 1.4798 0.988(18.86)*** (19.84)*** (10.54)*** (9.76)***

ΔSIR 1.0882(5.34)***

LNASSETS -0.1075 -0.0732 -0.1251 -0.1170(-7.50)*** (-13.81)*** (-13.77)*** (-12.11)***

LEVERAGE 1.0048 0.8257 0.8241 0.8478(20.76)*** (22.48)*** (19.82)*** (21.18)***

PPE -0.077 -0.0674 -0.1485 -0.0352(-2.34)** (-1.52) (-2.97)*** (-0.74)

CASH 0.3764 0.0648 0.031 0.071(5.63)*** (1.03) (0.57) (1.05)

ROA -1.5819 -1.6444 -1.3213 -1.7607(-16.35)*** (-17.14)*** (-17.40)*** (-16.14)***

MTB -0.0066 -0.0049 -0.0013 -0.0052(-4.89)*** (-4.01)*** (-1.63) (-4.12)***

EARNVOL 0.0441 0.0364 0.0407 0.0438(6.83)*** (8.56)*** (4.27)*** (7.95)***

Z -0.0626 -0.0438 -0.0522 -0.0457(-9.34)*** (-6.39)*** (-6.87)*** (-6.04)***

LNSIZE -0.0522 -0.0625 -0.0591 -0.0643(-6.88)*** (-9.25)*** (-15.66)*** (-9.03)***

LNMAT -0.0222 -0.0136 -0.0088 -0.0082(-1.57) (-1.02) (-1.04) (-0.58)

RATING 0.0317 0.0195 0.0159 0.0213(14.55)*** (10.21)*** (12.84)*** (10.55)***

Constant 4.8278 6.2114 6.2315 6.4450 6.1500(221.06)*** (50.83)*** (37.25)*** (19.67)*** (34.95)***

Loan type, purpose, syndication FEs No Yes Yes Yes YesYear FEs No No Yes Yes YesIndustry FEs No No Yes No YesFirm FEs No No No Yes NoR2 0.0478 0.5359 0.6462 0.8046 0.6504N 23,039 23,039 23,039 23,039 21,085

45

Table 3: Robustness checksThis table shows the results of estimating the baseline model [Equation (2)] using alternative model specifications. For brevity, we only report the coefficients and t-statistics of SIR. In Model 1, we include the lead bank fixed effects. Model 2 includes both lead bank fixed effects and borrowing firm fixed effects. In Model 3, we cluster standard errors at the borrowing firm’s and lead bank’s level. In Model 4, we include only the largest facilities per loan package in our sample. In Model 5, we investigate the effect of SIR on loan spread after controlling for the simultaneity in loan contract terms. In Model 6, we use the total loan costs measure, which includes both interest costs and fees, as the dependent variable. In Model 7, we use the option implied volatility skewness as alternative measures of expected bad news. In Model 8, we use the median regression to mitigate the effect of outliers. In Model 9, we use the deciles of SIR instead of the raw SIR values as the explanatory variable. In Model 10, we exclude the GFC period of 2007 to 2009. Except for Model 3, standard errors are clustered at the borrowing firm’s level. In all models, we include all control variables as specified under Equation (2) and industry and year fixed effects. All continuous variables are winsorized at the 1 st and 99th percentiles. T-statistics are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5% and 1% level, respectively. See Appendix 1 for detailed description of all variables. 

    SIR R2/ Pseudo R2 N    Coefficient t-stat

1 Lead lender FE 1.338 (9.90)*** 0.7038 15,9372 Lead lender and firm FE 0.7839 (6.19)*** 0.8359 15,9373 Clustering by firm and lead bank 0.5830 (4.65)*** 0.6009 15,9374 Largest facility 1.3650 (10.24)*** 0.6443 16,1995 Joint determination of loan terms 1.4315 (9.90)*** 0.6627 14,6006 Total loan costs 1.5126 (9.49)*** 0.7875 13,3157 IV Skewness 0.9843 (4.94)*** 0.6517 15,6878 Median regression 1.3662 (17.02)*** 0.4063 23,0399 SIR decile 0.0206 (9.28)*** 0.6457 23,03910 Remove GFC period 1.465 (9.41)*** 0.6545 20,466

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Table 4: Tax Payer Relief Act of 1997 This table presents the results of the effect of the Tax Payer Relief Act of 1997 (TRA) on the relation between the short selling ratio (SIR) and loan costs. The dependent variable in all models is the log of the all-in-drawn spread (SPREAD). Model 1 shows the results of regressing SPREAD on the short selling ratio (SIR), a dummy variable for post-Act period (POST) and the interaction of SIR and POST. Model 2 is similar to Model 1 with the additional inclusion of firm and loan control variables. We include industry and year fixed effects in Models 1 and 2. Model 3 includes the full set of variables and firm and year fixed effects. We restrict our sample to five years before and five years after the TRA (1992 to 2002) and exclude the year 1997. All continuous variables are winsorized at the 1st and 99th percentiles. We cluster standard errors at the borrowing firm level. T-statistics are reported in parentheses. The symbols *, **, and *** denote statistical significance at 10%, 5% and 1% level, respectively. See Appendix 1 for detailed description of all variables.

  Models1 2 3

SIR 2.6528 1.2239 -0.2137(2.33)** (1.78)* (-0.38)

POST 0.3034 0.5086 0.4808(5.69)*** (15.10)*** (19.76)***

SIR*POST 3.1195 2.453 2.6698(2.00)** (2.63)*** (4.06)***

LNASSETS -0.1506 -0.1401(-7.54)*** (-5.91)***

LEVERAGE 0.9871 0.6494(10.67)*** (7.32)***

PPE -0.1121 -0.2603(-1.06) (-1.90)*

CASH 0.575 0.3447(2.86)*** (2.01)**

ROA -2.1151 -1.3426(-8.44)*** (-6.21)***

MTB -0.0097 -0.0102(-3.47)*** (-4.43)***

EARNVOL 0.0267 0.0222(2.35)** (1.78)*

Z -0.0992 -0.1276(-5.15)*** (-5.96)***

LNSIZE -0.0856 -0.0827(-5.24)*** (-7.60)***

LNMAT -0.0328 -0.0287(-1.05) (-1.29)

RATING 0.0200 0.0085(4.85)*** (2.75)***

Constant 4.2181 7.7712 7.5158(88.32)*** (26.74)*** (28.79)***

Loan purpose, type and syndication FEs Yes Yes YesIndustry FEs Yes Yes NoFirm FEs No No YesR2 0.0635 0.6958 0.8209N 3,852 3,852 3,852

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Table 5: Propensity score matchingThis table reports the average treatment effects on the loan spreads obtained from the propensity score matching (Panel A), and the results of estimating the baseline regression [Equation (2)] on the matched sample (Panel B). The treatment group includes loans to firms with above-median SIR. The control group includes loans to firms with below-median SIR. We compute the median value of SIR for each fiscal year and each two-digit SIC code in our sample. In Panel B, Model 1 (2) includes industry (firm) and year fixed effects. Standard errors are clustered at the firm level. All continuous variables are winsorized at the 1st and 99th percentiles. T-statistics are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5% and 1% level, respectively. See Appendix 1 for detailed description of all variables.

Panel A: Characteristics of treatment and control firms  Treatment Control Treatment - ControlLNASSETS 7.075*** 7.008*** 0.066LEVERAGE 0.295*** 0.301*** -0.007PPE 0.343*** 0.348*** -0.005CASH 0.076*** 0.080*** -0.004ROA 0.135*** 0.139*** -0.004MTB 2.727*** 2.737*** -0.01Z 1.917*** 1.918*** -0.001EARNVOL 0.670*** 0.605*** 0.064RATING 15.394*** 15.536*** -0.142SPREAD 5.048*** 4.967*** 0.081**N (firms) 990 990N (loans) 1,646 1,596  

Panel B: Regression results on the matched sample  Models

1 2SIR 1.1440 0.8686

(4.59)*** (2.34)**LNASSETS -0.1268 -0.1198

(-6.99)*** (-4.25)***LEVERAGE 0.8576 0.8542

(11.14)*** (7.30)***PPE -0.0756 0.0574

(-0.96) (0.34)CASH 0.3197 0.5258

(2.35)** (2.37)**ROA -1.8724 -1.7525

(-9.20)*** (-5.86)***MTB 0.0032 0.0074

(1.26) (2.48)**EARNVOL 0.0422 -0.0038

(3.05)*** (-0.27)Z -0.0514 -0.0655

(-3.39)*** (-2.10)**LNSIZE -0.0471 -0.0425

(-3.78)*** (-4.14)***LNMAT -0.0407 -0.016

(-1.34) (-0.66)RATING 0.0135 0.0313

(3.67)*** (8.08)***Constant 6.6194 6.3819

(17.76)*** (17.58)***Loan type, purpose, syndication FEs Yes YesYear FEs Yes YesIndustry FEs Yes NoFirm FEs No Yes R2 0.6531 0.8906

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N 3,242 3,242Table 6: Test of reverse causalityThis table reports the results of the tests of reverse causality. From Models 1 to 4, we condition that the loan’s starting date is at least six to nine months after the closing date of the previous fiscal year when measuring short selling ratio (SIR). We then re-estimate the baseline model [Equation (2)] on these samples. All continuous variables are winsorized at the 1st and 99th percentiles. We cluster standard errors at the borrowing firm level. T-statistics are reported in parentheses. The symbols *, **, and *** denote statistical significance at 10%, 5% and 1% level, respectively. See Appendix 1 for detailed description of all variables.

  ModelsSix months

1Seven months

2Eight months

3Nine months

4SIR 1.4856 1.427 1.4485 1.5157

(8.86)*** (8.27)*** (7.87)*** (7.96)***LNASSETS -0.1188 -0.121 -0.1219 -0.1238

(-11.19)*** (-10.63)*** (-9.79)*** (-9.35)***LEVERAGE 0.835 0.7941 0.8131 0.7692

(18.47)*** (16.53)*** (15.70)*** (13.90)***PPE -0.0445 -0.0278 0.0108 0.0076

(-0.89) (-0.52) (0.19) (0.13)CASH 0.0526 0.0301 0.0743 0.0754

(0.70) (0.37) (0.86) (0.79)ROA -1.7893 -1.7164 -1.8038 -1.7026

(-15.93)*** (-14.63)*** (-14.66)*** (-12.70)***MTB -0.0051 -0.0058 -0.0052 -0.0051

(-3.29)*** (-3.52)*** (-2.88)*** (-2.53)** EARNVOL 0.0326 0.032 0.0333 0.0369

(6.22)*** (5.83)*** (5.50)*** (5.56)***Z -0.0359 -0.0375 -0.0365 -0.0397

(-4.57)*** (-4.54)*** (-4.22)*** (-4.18)***LNSIZE -0.0686 -0.0703 -0.0692 -0.0681

(-8.13)*** (-7.59)*** (-6.72)*** (-6.36)***LNMAT -0.0144 -0.0108 -0.0024 0.0065

(-0.87) (-0.61) (-0.13) (0.31)RATING 0.0191 0.019 0.0199 0.0208

(8.50)*** (7.95)*** (7.96)*** (7.99)***Constant 6.0994 6.228 6.1667 5.7134

(18.66)*** (17.69)*** (17.07)*** (22.59)***Loan type, loan purpose, loan syndication FEs

Yes Yes Yes Yes

Year FEs Yes Yes Yes YesIndustry FEs Yes Yes Yes YesR2 0.6435 0.645 0.6513 0.6539N 13,894 11,546 9,716 7,972

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Table 7: Lending supply and short selling risk This table presents the results of the effect of the short selling ratio (SIR) on loan costs, controlling for the effect of lending supply and short selling risk. The dependent variable in all models is the log of the all-in-drawn spread (SPREAD). Model 1 shows the results of regressing SPREAD on the short selling ratio (SIR), controlling for lending supply (LS, the number of lendable shares to total shares outstanding). Model 2 shows the results when we replace the short selling ratio with Utilization, defined as the ratio of the number of shares shorted to the number of shares available to lend. Model 3 reports the results of the moderating role of short selling risk on the relation between short selling ratio and loan costs. Short selling risk is defined as the variance of loan fees in the year preceding the fiscal year before the loan start date. We rank firms into terciles based on the short selling risk. Variables HIGH and LOW are dummy variables indicating firms belonging to the top and bottom tercile, respectively. All continuous variables are winsorized at the 1st and 99th percentiles. We cluster standard errors at the borrowing firm level. T-statistics are reported in parentheses. The symbols *, **, and *** denote statistical significance at 10%, 5% and 1% level, respectively. See Appendix 1 for detailed description of all variables.

  Models1 2 3

SIR 1.3098(8.40)***

LS -0.0548(-0.58)

Utilization 0.2394(7.40)***

SIR*HIGH 1.0532(6.50)***

SIR*LOW 0.3881(1.61)

LNASSETS -0.1078 -0.0863 -0.0842(-11.60)*** (-9.35)*** (-8.32)***

LEVERAGE 0.6459 0.5142 0.5537(14.78)*** (11.59)*** (10.99)***

PPE 0.0232 0.0484 0.0442(0.46) (0.96) (0.78)

CASH 0.0201 -0.0415 -0.0055(0.30) (-0.58) (-0.07)

ROA -1.4582 -1.2387 -1.2285(-12.55)*** (-10.72)*** (-9.26)***

MTB -0.0052 -0.0040 -0.0032(-3.70)*** (-2.82)*** (-2.04)**

EARNVOL 0.0445 0.0394 0.0379(7.92)*** (8.05)*** (5.99)***

Z -0.0558 -0.0542 -0.0603(-7.21)*** (-6.86)*** (-6.82)***

LNSIZE -0.0618 -0.0490 -0.0461(-7.92)*** (-6.04)*** (-5.44)***

LNMAT 0.0676 0.1189 0.1247(3.80)*** (6.00)*** (5.21)***

RATING 0.0172 0.0141 0.0164(8.15)*** (6.80)*** (6.74)***

Constant 6.3418 5.7785 5.6506(17.40)*** (34.94)*** (29.38)***

Loan purpose, type and syndication FEs Yes Yes YesYear FEs Yes Yes YesIndustry FEs Yes Yes YesR2 0.6302 0.5856 0.5900N 13,305 9,460 6,295

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Table 8: The effect of information environment on bank learningThis table report the results of estimating the mediating role of information asymmetry on the relation between financial market information and loan pricing. The dependent variable in all models is the log of the All in drawn variable in Dealscan (SPREAD). FSIZE denotes the size of the financial report (in megabytes); FINTERMS_NEGATIVE (FINTERMS_POSITIVE) denotes the proportion of negative (positive) words in the financial report; FRCOMP denotes the comparability of the firm’s financial report relative to all other firms in the same industry. DISP denotes the variation of analyst forecast scaled by the mean forecast. We rank firms into terciles based on the FSIZE, FINTERMS_NEGATIVE,FINTERMS_POSITIVE, FRCOMP, DISP. Variables HIGH and LOW are dummy variables indicating whether the firm belongs to the top and bottom tercile, respectively. RELATION is a dummy variable that takes the value of one if the lead bank accounts for at least 50% of the total amount lent to the borrower within the past five years, and zero otherwise. NO_RELATION equals RELATION – 1. In all models, we include all control variables specified under Equation (2), and industry and year fixed effects. All continuous variables are winsorized at the 1st and 99th percentiles. We cluster standard errors at the borrowing firm level. T-statistics (or F-statistics) are reported in parentheses. *, **, and *** denote statistical significance at 10%, 5% and 1% level, respectively. See Appendix 1 for detailed description of all variables.

  FSIZE FINTERMS_NEGATIVE FINTERMS_POSITIVE FRCOMP DISP Relationship1 2 3 4 5 6

SIR*HIGH 1.7816 2.067 1.1213 0.906 1.8846(8.94)*** (11.49)*** (5.70)*** (2.86)*** (10.73)***

SIR*LOW 0.7644 0.3271 1.457 1.9077 0.8005(3.85)*** (1.44) (7.63)*** (6.04)*** (3.32)***

SIR*RELATION 1.7005(7.25)***

SIR*NO_RELATION 2.1642(8.82)***

Constant 6.0587 6.1952 6.3286 5.9155 5.96 7.0538(29.32)*** (29.39)*** (30.48)*** (19.28)*** (24.98)*** (23.96)***

HIGH – LOW 1.0172 1.7399 -0.3357 -1.0017 1.0841NO_RELATION – RELATION 0.4637F stat (19.91)*** (52.23)*** (2.17) (7.68)** (19.68)*** (3.97)**Other control variables Yes Yes Yes Yes Yes YesLoan type, purpose, syndication FEs

Yes Yes Yes Yes Yes Yes

Year FEs Yes Yes Yes Yes Yes YesIndustry FEs Yes Yes Yes Yes Yes YesR2 0.6483 0.6529 0.663 0.6747 0.6652 0.7031N 14,036 13,874 13,787 7,453 11,744 10,040

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Table 9: Non-price loan termsThis table shows the effect of the short selling ratio on non-price loan features, including maturity (Model 1), security (Model 2) and covenant provisions (Models 3 to 7). We estimate the effect of short selling ratio (SIR) on the log of loan maturity (in months) using the OLS regression method. We use the probit regression in Model 2 to estimate the likelihood of the loan having a collateral requirement. For Models 3 to 6, we use the Poisson regression model. In Model 7, we estimate the effect of SIR on the ratio of performance to capital performance using OLS regression method. We restrict the sample to only the largest loan facilities per package in models 3 to 7. We further remove observations with missing financial covenant information. All continuous variables are winsorized at the 1st and 99th percentiles. We cluster standard errors at the borrowing firm level. T-statistics are reported in parentheses. The symbols *, **, and *** denote statistical significance at 10%, 5% and 1% level, respectively. See Appendix 1 for detailed description of all variables.

 Maturity Security Covenant intensity Financial

covenantsCapital covenants Performance

covenantsPC ratio

1 2 3 4 5 6 7SIR -0.2428 3.557 1.2594 0.4392 -0.1733 0.52 0.1868

(-2.89)*** (4.92)*** (7.07)*** (3.98)*** (-0.94) (4.61)*** (2.69)***Constant 3.1664 -8.6247 -13.8697 1.0903 1.3428 -0.231 -1.4127

(28.21)*** (-8.88)*** (-5.61)*** (7.88)*** (5.36)*** (-1.51) (-13.59)***Other control variables Yes Yes Yes Yes Yes Yes YesLoan type, loan purpose,loan syndication FEs

Yes Yes Yes Yes Yes Yes Yes

Year FEs Yes Yes Yes Yes Yes Yes YesIndustry FEs Yes Yes Yes Yes Yes Yes YesR2/ Pseudo R2 0.6613 0.2985 0.154 0.0454 0.0218 0.024 0.013N 23,039 23,024 9,129 9,129 3,939 7,884 7,884

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Table 10: Bond cost and choice of debt This table shows the effects of SIR on the costs of public debt and on the choice of private versus public debt. We estimate the effect of SIR on the costs of public bonds with OLS regressions in Models 1 to 5. The dependent variable is the log of the spread between bond yield at issue and a government bond of equivalent maturity. We estimate the effect of SIR on the choice of debt using the probit regression in Model 6. The dependent variable is a dummy variable that takes the value of 1 if a firm has a new loan in a certain year, and 0 if the firm issues a new bond in that year. We exclude all firm-year observations where the firm does not have any new debt, or when the firm has both a new loan and a new bond issue. We also exclude firms that only borrows exclusively with either bank loans or bonds throughout the sample period. All continuous variables are winsorized at the 1st and 99th percentiles. We cluster standard errors at the borrowing firm level. T-statistics are reported in parentheses. The symbols *, **, and *** denote statistical significance at 10%, 5% and 1% level, respectively. See Appendix 1 for detailed description of all variables.

  Models  1 2 3 4 5 6SIR 9.8657 4.9527 4.0172 2.0568 1.4706 0.9475

(17.04)*** (12.90)*** (10.81)*** (6.94)*** (6.59)*** (1.86)* LNASSETS -0.0304 -0.1792 -0.1987 -0.1365 -0.2818

(-2.72)*** (-13.78)*** (-19.06)*** (-10.34)*** (-13.13)***LEVERAGE 0.6932 0.693 0.7751 0.7422 -0.2321

(7.49)*** (8.59)*** (10.85)*** (12.03)*** (-1.71)* PPE 0.0283 0.1026 0.1601 0.0141 -0.5546

(0.61) (2.43)** (2.09)** (0.18) (-3.59)***CASH 0.3122 -0.0012 0.0962 0.2704 -0.1513

(2.64)*** (-0.01) (0.96) (2.60)*** (-0.65) ROA -0.9254 -1.4379 -1.8183 -1.3727 -0.992

(-4.77)*** (-7.74)*** (-10.19)*** (-9.96)*** (-3.07)***MTB -0.0107 -0.0129 -0.0078 -0.0037 0.0043

(-3.47)*** (-4.26)*** (-3.72)*** (-3.02)*** (0.92)EARNVOL 0.0011 0.0131 0.036 0.0354 0.0742

(0.12) (1.63) (4.34)*** (5.21)*** (3.30)***Z -0.0405 -0.0288 -0.0378 -0.052 0.0686

(-3.13)*** (-2.26)** (-2.59)*** (-3.66)*** (2.30)** RATING 0.0749 0.0624 0.0477 0.023 0.0622

(17.62)*** (15.59)*** (14.16)*** (11.45)*** (11.96)***BOND_SIZE 0.3588 0.1558 0.1195

(20.50)*** (10.84)*** (11.47)***BOND_MAT 0.1071 0.1895 0.2021

(6.47)*** (15.71)*** (28.05)***DSENIOR -0.0338 0.0004 -0.0839

(-0.24) (0.00) (-1.22) DPRIVATE 0.8006 0.7676 0.6382

(4.30)*** (4.18)*** (3.07)***Constant 4.7863 4.525 0.9881 3.1439 3.4325 2.0334

(153.30)*** (31.40)*** (4.13)*** (12.26)*** (9.24)*** (4.53)***Industry FEs Yes Yes Yes Yes Yes YesYear FEs Yes Yes Yes Yes Yes YesR2 0.1721 0.4758 0.542 0.7368 0.8535 0.3079N 6,738 6,738 6,738 6,738 6,738 13,139

55