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Media Coverage and Debt Financing*

Agnes C.S. Cheng† Liangliang Jiang‡ Wei-Ling Song§

May 2019

Abstract

This study examines how media coverage influences firms’ debt structure and finds that media

coverage reduces firms’ reliance on bank loans but increases amounts of public bonds & notes

on their balance sheets. We document two channels that such effect is concentrated among

firms with less information asymmetry problems suggesting that the media complements other

information sources (information channel), and that such effect is mitigated among firms that

demand less external monitoring suggesting that the media substitutes for banks on firm

monitoring (governance channel). By studying news sentiment, we find that negative earnings

news significantly reduces the likelihood of new debt financing, particularly bank loans and

public bonds, but increases that of private placement. News sentiments predict subsequent

credit rating changes, which affect the terms of debt issuance for both private bank loans and

public bonds. We also show that when the markets are negative about firms’ future prospects,

firms report more negative discretionary accruals, which suggests that media coverage also has

accounting implication in affecting firm debt structure.

JEL classification: D82, L82, G21, G23, G32

Keywords: Media Coverage, Debt Structure and Choice, Corporate Governance, Information

free-riding

* We thank Vikas Agarwal, Tarun Chordia, Inder Khurana, Walid Saffar, and seminar and conference

participants at University of Texas – San Antonio, Shenzhen University, Tongji University, Zhejiang University,

and The Hong Kong Polytechnic University for their helpful comments. This research was substantially

supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China

(Project No. PolyU 13500117). † School of Accounting and Finance, Hong Kong Polytechnic University, Hung Hom, Hong Kong; Phone: 852-

27667771; Email: cs-agnes.cheng@polyu.edu.hk. ‡ School of Accounting and Finance, Hong Kong Polytechnic University, Hung Hom, Hong Kong; Phone: 852-

27667033; Email: liangliang.jiang@polyu.edu.hk. § E. J. Ourso College of Business, Louisiana State University, Baton Rouge, LA 70803, USA; Phone: 1-225-

578-6258; Email: wsong@lsu.edu.

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1. Introduction

Debt financing is an important funding source of U.S. corporations. Data from the

Federal Reserve Board show that total bond issues by U.S. corporations have amounted to over

1.8 trillion dollars in 2017. At the end of 2017, banks also hold more than 2.1 trillion dollars

of commercial and industrial loans on their balance sheets. Public bonds and bank loans

represent two distinct but equally important sources of debt financing. While bank loans come

with close monitoring by their lenders, public bond market is known for its information free-

riding problem, thus less monitoring, due to diffusive bond holdings (Diamond, 1984).

However, the size of bond market suggests that the information free-riding problem has been

mitigated by some mechanisms, such as firms’ own reputation (Diamond, 1991), credit ratings,

and analyst following. In this paper, we investigate whether media coverage can be an

additional mechanism that drives firms’ debt financing choices. The findings can shed light on

the fundamental theories of debt structure and the extent to which the media serves as an

information provider and a governance component in the financial system.1

Media reporting nowadays has a profound effect on business and financial markets. Our

research question is motivated by the recent literature that documents the information role and

corporate governance role of the media in the financial market (e.g. Tetlock, 2007; Tetlock et

al., 2008; Dyck et al., 2008; Kothari et al., 2009; Bushee et al., 2010; Tetlock, 2015). Although

the literature on media coverage in corporate finance is still developing, a key finding is that

media coverage helps firms to raise capital. Nonetheless, current studies on media coverage

have focused extensively on the equity market (e.g. Tetlock, 2007; Tetlock, Saar-Tsechansky,

and Macskassy, 2008; Kothari, Li, and Short, 2009), and evidence on the debt market remains

1 A striking example of the governance effect of media is the cancellation of public bond issuance by Huawei

Technology Co. in April, 2018 after The Wall Street Journal reported that the firm was under investigation by

the Department of Justice despite the fact that investors had placed €2 billion of orders for a €500 million

offering. Although this type of outright cancellation of bond issuance is rare, it suggests the potential for media

outlets to serve as a gatekeeper of the capital markets. See “Huawei Scraps Bond Sale After U.S. Criminal Probe

Comes to Light”, The Wall Street Journal, April 26, 2018.

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limited.2 Our research is built upon existing theoretical research that studies costs and benefits

between the public and private debt markets when the institutional differences are often

highlighted (e.g. Diamond, 1984, 1991; Fama, 1985; Rajan, 1992; Park, 2000). A widely held

view on the benefits of bank borrowing is that banks have superior access to private

information, more resources to process vast information, which enables them to provide

efficient and costly monitoring services that can mitigate asset substitution and

underinvestment problems. Compared to other debt holders, banks are more efficient and

effective monitors, are more flexible in renegotiation debt contracts, and better able to deal

with bankruptcy costs (Leland and Pyle 1977; Diamond, 1984; Gertner and Scharfstein, 1991;

Houston and James, 1996, Park, 2000; Denis and Mihov, 2003). Compared to banks, there is a

lack of incentive for public bondholders to monitor borrowers due to the diffusion of ownership

and free rider problems. Given the information role and the governance role that media may

play in the financial market, we hypothesize that firms with greater media coverage should

substitute away from more public debt financing and towards less bank loan borrowing.

To examine how media coverage affects firms’ debt financing, we employ RavenPack,

a leading provider of news analytics database, which covers all news articles and press releases

disseminated via Dow Jones Newswires. Specifically, we exclude corporate press released by

firms themselves. Our sample includes all the U.S. firms covered by Dow Jones Newswire and

have borrowed bank loans and/or issued bonds from 2001 to 2014. We find that firms with

more media coverage have significantly lower likelihood of having bank loans on their balance

sheets, but higher proportion of debt as bonds and notes. Our estimate suggests that an increase

of one standard deviation in total news coverage reduces bank loans usage by 9.6% of total

debt. In contrast, the usage of bonds and notes increases by 6.7% of total debt.

2 A few exceptional works are Jiang and Sun (2013), Gao, Wang, and Wu (2016), and Bushman, Williams, and

Wittenberg-Moerman (2017) and we will discuss them in Section 2.1.

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In our analysis, we scale the total number of relevant news articles by total assets to

mitigate the concern that larger firms tend to have more coverage. To address the concern that

firms may try to attract public attention in order to issue bonds versus borrow bank loans,

besides looking at total news, we also conduct our analysis by restricting to certain categories

of news (e.g., those related to earnings, revenue, and dividends) that are considered more

difficult for firms to manipulate (Solomon, 2012) and we label these news as “earnings news”.

The findings are also robust if we use the instrumental variable (IV) estimation method for

identification, where we construct two variables, i.e. (1) the newspaper shutdown in local area

and (2) an “exogenous” growth opportunity index and use them as instruments for media

coverage. To mitigate the concern that news may become duplicate as news media keeps

disseminating the same information, in our robustness tests, we also use alternative media

coverage measures where we only consider new story within a 24-hour time window across all

news stories in a particular package. Finally, another potential problem with our sample is that

certain types of firms may have access to only one debt market (either bank loan market or

public bond market), we therefore restrict the sample to $100 million to $700 million sized-

firms that are more likely to choose between public bonds and bank loans. We find all of our

results remain intact.

To understand the potential channels through which the media impacts firms debt

financing choices, we also examine the effects of media coverage in different information

environments (information channel) and governance structures (governance channel). We use

firm size and whether a firm is included in a major stock index as proxies for more information

transparency. We hypothesize that if the media plays a role in substituting other information

sources or information dissemination role, then we should observe that media coverage has a

stronger impact among firms with less information availability, which we term as the

information substitution hypothesis. On the other hand, if the media mainly complements other

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information sources and plays the information creation role, then the impact of media on the

reliance on bank debt will be stronger among firms with more information flows, which we

label as the information complementary hypothesis.

Consistent with the information complementary hypothesis, we find that the effects of

media coverage are concentrated among larger firms and firms that are members of major stock

indexes. Instead of providing more information for smaller firms and substituting for other

information sources, our findings indicate that the media complements to other information

sources. Media outlets pick out the newsworthy pieces and broadcast them widely. Such efforts

are by no means trivial because combining information through different sources can be time-

consuming and requires skills to pinpoint the relevant one. It also requires abundant public

information in the first place.

To examine how media coverage may affect firms’ debt financing through the corporate

governance channel, we employ two commonly used governance structures in the literature—

stock ownership concentration HHI index and whether a firm belongs to a relationship industry

as defined in Cremers, Nair, and Peyer (2008) and Bharath and Hertzel (2018). Existing studies

have shown that the existence of multiple large shareholders enhances external monitoring (e.g.

Maury and Pajuste, 2005). Therefore, firms with multiple large shareholders tend to have lower

external monitoring needs. Relationship industries refer to those have strong incentives to

establish and engage in long-term business relationships with their customers (Cremers, Nair,

and Peyer, 2008). As these firms have close ties with other stakeholders, more outside

monitoring may not be needed from the point of view of the equity holders, because these

stakeholders have an incentive and the expertise to monitor them. In other words, firms

belonging to “relationship” industries have less need for external monitoring. Our evidence

shows that the negative (positive) relationship between media coverage and bank loan (public

debt) is mitigated among firms with good governance, suggesting the governance role of the

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media. Overall, our evidence is consistent with the view that the media plays a complementary

role as information provider compared to banks and also serves a governance role in

substituting banks’ monitoring function.

The research design in this study needs to address several empirical challenges. First,

it is difficult to separate the information contents, which can also be disseminated by other

information providers, such as credit rating agents and analysts mentioned above, from the

additional effects caused by the media.3 Therefore, we control for information provided by both

credit rating agencies and analysts as both have the obligations to provide timely information

prior to major corporate events. However, it is well known that both information providers are

subject to potential conflicts of interest due to the fees collected from corporate clients.4 For

this reason, there is indeed a role for media outlets to disseminate timely negative information.

By looking at negative news, we can also address the second empirical challenge— the reverse

causality problem. Firms and other compromised information providers have incentives to

promote favorable information. Therefore, positive media coverage can be associated with

outside financing activities but the causality runs from financing activities to media coverage.

In contrast, negative news is more likely to be disseminated by the media; thus, we can attribute

the findings from negative news to the effects of media coverage.5

The third empirical challenge is related to the fact that it is difficult to obtain detailed

debt usage information for each firm. Rauh and Sufi (2010) point out the problem as follows:

“While the debt financial footnotes typically list each individual debt issue, there is

often insufficient information in the footnotes alone to categorize the issue. For

3 Engelberg and Parsons (2011) solve the problem by examining local investors’ trading with access to different

local media coverage of the same firms’ earnings announcements. 4 See Bolton, Freixas, and Shapiro (2012) for the conflicts of interest of credit rating agencies and Michaely and

Kent (1999) for that of analysts. Mao and Song (2018) show that analysts who are subject to conflicts of interest

can significantly delay the release of negative information. Kothari, Li, and Short (2009) provide excellent

discussions on the different incentives for disclosures by the management, analysts, and business press. Ahern

and Sosyura (2014) also show that bidders tend to manage media coverage to increase stock prices prior to

merger announcements. 5 The media also has potential for conflicts of interest due to advertisement incomes (e.g., Gurun and Butler,

2012). However, this tends to occur among local media outlets. Our sample firms are more likely covered by

national press; therefore, this should not be a concern.

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example, an issue labeled ‘9.5% notes due 2004’ could be medium-term notes, public

debt, term bank debt, or a private placement.”

Therefore, empirical work in this area typically relies on several databases to draw inferences

therefrom because each by itself is incomplete. We start with the debt structure information

provided by Capital IQ, from which we can categorize two major types of debt: (1) bank loans;

and (2) bonds and notes. However, there is no way that we can distinguish the quantities of

bonds and notes that are publicly issued from those that are privately placed using Capital IQ.

Nonetheless, this distinction is crucial for our purpose because privately placed debt is different

from publicly issued bonds. Private placement has less diffusive ownership, more covenants,

and provides higher yields due to its illiquidity.6 For this reason, we supplement our analysis

using the new issues databases provided by Thomson Reuters’s SDC Platinum. SDC

Platinum’s coverage of public bonds is rather comprehensive. However, its coverage of

privately placed debt is limited; for example, most of the interest spreads are missing because

it is privately negotiated.

To further understand how media coverage affects debt financing, we supplement our

analysis using new debt financing activities given different news sentiments. Debt structure,

which provides the composition of debt, is scaled by total debt, therefore, we don’t know

whether the size of debt pie, i.e., debt access, is affected by the media. We find that both the

level of media coverage and positive earnings news significantly increase the likelihood of

outside debt financing. However, negative earnings news significantly reduces debt access,

particularly bank loans and public bonds. Nevertheless, negative earnings news significantly

increases the likelihood of new privately placed debt. This finding is consistent with the

hypothesis on the governance role of the media and also consistent with the claim in Carey,

Post, and Sharpe (1998) that, in the private lending markets, banks tend to serve lower-risk

6 See Carey, Post, and Sharpe (1998) and Denis and Mihov (2003) for more discussions on the differences

between private debt and public bonds.

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borrowers, while non-bank financial institutions serve higher-risk borrowers. Finally, we also

show that when the markets are negative about firms’ future prospects, firms report more

negative discretionary accruals. This result suggests that media coverage also has accounting

implication in affecting firm debt structure.

Our study contributes to the literature in several ways. First, we add to the growing

literature that documents the media’s effects in the financial markets. While a vast majority of

the studies, which we review in the second section, focus on the equity markets, our study

examines the debt markets. Although there are several papers examining debt instruments, their

emphasis is on the media’s information role whereas our results imply that the media also

serves an indispensable role in reducing the information free-riding problem and filling this

gap for bondholders.

Second, the analysis of debt financing contributes to our understanding of how firms

choose financing instruments. Existing empirical studies have shown that firms’ debt structure

is determined by growth opportunities, credit quality, the level of asymmetric information, and

accounting quality. 7 Firms’ choices of debt can also be affected by external governance

pressure, the divergence between ownership and control, and social capital. We add to the list

by showing that media coverage mitigates the information production incentive problem

among bondholders and significantly impacts firms’ usage of debt instruments.

Third, we contrast the roles of different information providers in the financial system

in the spirit of Kothari, Li, and Short (2009) who discuss and present evidence in the equity

market by comparing information disclosed by management, analysts, and business press. In

our study, we focus on credit rating agencies and media outlets. We find that firms with more

media coverage tend to have significantly better ratings. News sentiments also predict

subsequent rating changes, Tobin’s q, and earnings management. In addition, although both

7 See Section 2.2 for the list of references.

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rating changes and news sentiments significantly affect pricing and some non-pricing terms of

debt, only news sentiments have impact on the choices of debt instruments. These findings

indicate that media coverage fills the void left by credit rating agencies.

The remainder of the paper proceeds as follows. Section 2 provides the literature review

and hypothesis development. Section 3 describes sample selection and the databases. Summary

statistics of key variables are also reported. Section 4 examines the relation between media

coverage and debt structure. Section 5 discusses the evidence of new debt financing activities.

Section 6 reports the analysis of earnings management. Finally, the concluding remarks are

provided in Section 7.

2. Literature Review and Hypotheses Development

2.1 Literature on the role of media in the financial markets

The important role played by the media in the financial markets has been largely

recognized. There is a growing body of literature that has examined the information role of the

media in the financial market (e.g. Tetlock, Saar-Tsechansky, and Macskassy, 2008; Kothari,

Li, and Short, 2009) and whether the media plays the role of information creation or

information dissemination in affecting investors (e.g. Bushee, Core, Guay, and Hamm, 2010;

Tetlock, 2011). Although the literature on media coverage in corporate finance is still

developing, a key finding is that media coverage helps firms to raise capital (Tetlock, 2015).

There are relatively fewer studies on the monitoring and corporate governance role of

the media. Existing research has shown that media can facilitate public monitoring through

insider trading disciplining or accounting fraud identification (e.g. Miller, 2006; Dai, Parwada,

and Zhang, 2015; Rogers, Skinner, and Zechman, 2016). Among them, studies by Dyck,

Volchkova, and Zingales (2008) and You, Zhang, and Zhang (2018) show that in Russia or

China the press plays the corporate governance role if firms are exposed to international media

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or non-state-controlled media. Furthermore, current studies on media coverage have focused

extensively on the equity market (e.g. Tetlock, 2007; Liu, Sherman, and Zhang, 2014;

Hendershott, Livdan, and Schurhoff, 2015). Evidence on the debt markets remains limited.

There are several exceptions. Bushman, Williams, and Wittenberg-Moerman (2017) show that

media coverage encourages outside, less informed syndicate participants to originate loans by

reducing information asymmetry among those lenders. Gao, Wang, and Wu (2016) document

that media coverage reduces firms’ cost of issuing bonds because it increases investor

recognition. Jiang and Sun (2013) find that the illiquidity of corporate bond market is reduced

when public news arrives. All three papers focus on the information role of the media. Our

work differs from the above-mentioned research in at least three respects: (1) we study firms’

debt structure, new debt access, and new debt choices; (2) we go beyond the information role

to examine the corporate governance role of the media; and (3) we provide a complete picture

of the effect of media coverage in the debt markets by examining price and non-price terms of

bank loans, public bonds, and privately placed debt.

2.2 Literature on debt structure and choice

The size of debt financing is enormous in the U.S., with $3200 billion of new bank

loans borrowed and $1,500 billion of corporate bonds issued in 2015.8 It is well documented

that U.S. firms vary greatly in their debt structure (Rauh and Sufi, 2010). The different types

of debt instruments can influence firms’ capital structure decisions (Faulkender and Petersen,

2006), hence the return that firms earn for their shareholders.

A firm’s debt structure is critically important because it determines whether a firm can

survive during a financial crisis or recession. A large body of research has investigated the debt

structure of firms and particularly why different firms choose to use different types of debts.

8 Data source: Federal Reserve Bank of St. Louis and Securities Industry and Financial Markets Association.

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Various financial theories have explained the benefits and costs of public debt versus private

debt (e.g. Diamond, 1984, 1991; Fama, 1985; Rajan, 1992; Park, 2000).

Empirical analyses also show that firms’ financial characteristics, such as growth

opportunities, credit quality, the level of asymmetric information, and accounting quality,

determine their debt structure (Houston and James, 1996; Denis and Mihov, 2003; Bharath,

Sunder, and Sunder, 2008; Li, Lin, and Zhan, 2019).9 Firms’ choice of debt type may also be

affected by external governance pressure (Bharath and Hertzel, 2018), the divergence between

ownership and control (Lin, Ma, Malatesta, and Xuan, 2013), state ownership (Boubakri and

Safffar, 2018) and social capital (Hasan, Hoi, Wu, and Zhang, 2017). However, research on the

impact of media coverage on debt structure has been relatively scarce. To our knowledge, our

study is the first to examine the governance role of the media in the debt markets.

2.3 Hypothesizing the information role and the governance role of media

The costly information-production models predict that bank debt is preferred over

public debt because the cost of producing information required for public debt financing is

higher than that for bank financing (Fama, 1985). Compared with public bondholders, banks

have at least two information advantages. First, banks have superior access to private

information that may not be known to the public (Fama, 1985). Second, compared to arm’s

length investors, banks use more resources to process vast amounts of information. A large

body of research has shown that by rebroadcasting the information, media draws attention to

certain stocks, lowers the searching costs, and improves market efficiency (e.g. Fang and Press,

2009; Fang, Peress, and Zheng, 2014). By examining the earnings announcement, Bushee,

Core, Guay, and Hamm (2010) argue that the business press increases information flow in the

market by creating new information by analyzing the implications of earning releases and

interpreting the management forecasts. Therefore, there are at least two mechanisms through

9 See Kale and Meneghetti (2011) for an extensive review of this topic.

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which the media could influence the firms’ information environment: information

dissemination and information creation.10 When the press facilitates information dissemination

and/or creates new information, it reduces the level of information asymmetry between the

borrowing firm and lenders. To the extent that media alleviates the information asymmetry

problem, we predicts a negative (positive) relationship between media coverage and bank loan

borrowing (public debt financing).

The moral hazard model of the public/private debt choice is built upon the notion that:

(1) shareholders of levered firms need to be monitored because they have incentives to engage

in activities that damage the debtholders, i.e., the shareholder-creditor conflict; and (2) banks

are more efficient and effective monitors than public lenders. There is a lack of incentive for

monitoring on the part of bondholders due to diffused ownership and free-rider problems

(Diamond, 1984, 1991). Even if the bondholders are willing to monitor, as Houston and James

(1996) point out, it could still be inefficient for them to do so due to the duplication of efforts.

In contrast, a widely held view on one of the potential benefits of bank borrowing is that it

provides efficient and costly monitoring services that can mitigate agency problems, whereas

these services are not available from the widely dispersed investors in the public market.

Existing research has shown that media can alleviate agency problems through disciplining

insider trading or identifying accounting fraud (Miller, 2006; Dai, Parwada, and Zhang, 2015;

Rogers, Skinner, and Zechman, 2016). In particular, Dyck, Volchkova, and Zingales (2008)

show that the press plays a corporate governance role among Russian firms when these firms

are exposed to international media. Therefore, the governance role of media suggests a

negative (positive) relationship between media coverage and bank loan borrowing (public debt

financing).

10 Note that in our main analysis, we will not differentiate between the information dissemination and creation

effect of media on debt choice. Our argument is that either function of the media will alleviate the information

asymmetry between the borrower and the lender. Nonetheless, we will attempt to conduct additional tests to

examine whether original news as compared to stale news will make any difference to our results.

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Taken together, we propose the following hypothesis: Holding everything else constant,

firms with greater media coverage rely less on bank loan borrowings and more on public bond

financing compared to other firms. It is worth to note that in our analysis, we do not try to

disentangle or separate the governance role from the information role of the media. Rather, we

are trying to document that media actually plays both the information role and the governance

role in affecting firms’ choice of debt financing.

3. Data, Sample Description, and Key Variables

The unit of our analysis is at the firm-year level. We obtain media coverage data from

RavenPack News Analytics (RP), a leading provider of news analytics data that cover all the

news articles and press releases disseminated via Dow Jones Newswires. It has over 22,000

online sources of financial news and opinion, and covers over 41,000 companies worldwide.

We use the RP database to determine the total number of news items for each firm over a one-

year period. As RP allows us to identify which publications lead the news cycle and how the

story developed in the media, throughout all of our analyses, we only use news items that are

not originated by firms themselves. Specifically, we use two media coverage measures as our

key measures. The first one is Total_News measured as the natural logarithm of the total

number of news items with a relevance score of 100 reported from RP scaled by the firm’s total

assets. Due to the concern that firms may try to attract public attention in order to issue bonds

rather than borrowing bank loans, we construct our second measure by restricting news items

to certain categories, that is, only those related to earnings, revenue, and dividends. These news

items are considered more difficult for firms to manipulate (Solomon, 2012). We use the

variable Earnings_News to denote this type of news and measure it as the natural logarithm of

the ratio of news items specializing in earnings evaluations with either positive or negative

sentiment * 100 to total news items for firm i in year t.

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We construct firm-level debt structure variables using Capital IQ. Capital IQ aggregates

debt structure into seven categories: (1) total commercial paper, (2) total revolving credit, (3)

total term loans, (4) total senior bonds and notes, (5) total subordinated bonds and notes, (6)

capital leases (including current portion), and (7) other borrowings. For our purpose, we focus

on items (2)-(5) because these are related to the three types of newly issued debt instruments—

bank loans, public bonds, and privately placed debt—that will be analyzed in greater detail in

Section 5. We aggregate items (2) and (3) and calculate the first main dependent variable used

in this subsection, i.e., Bank_Loans/Total Debt, measured as the ratio of total bank loans to

total debt for each firm i in year t, where total debt refers the summation of items (1) to (7).

The second key dependent variable is the sum of items (4) and (5). In particular, we measure

it as Bond & Notes/Total Debt which is the ratio of senior and subordinated bonds and notes

over total debt for each firm i in year t. We also construct dummy versions of these two

variables to indicate whether each firm i has bank loans or bonds and notes on its balance sheet

in year t.

We also use the Capital IQ sample to match with the Compustat database to ensure that

our sample includes all firms that have private and public debt information and accounting data

available. Using Compustat, we construct a vector of time-varying firm characteristics,

including firm size (Size), firm profitability (ROA), Leverage, Market-to-Book ratio, Sales

Growth, and Cash Flow. We then combine these data with the RP database. The media

coverage measures and all the control variables are lagged by one year so that post-event news

coverage is not counted in our sample.

As RP data became available in 2001, we use a sample period ranging from 2001 to

2014. Our primary sample contains 36,002 firm-year observations involving 5,226 firms in the

U.S. The average (median) firm size is about $5,606 ($246) million U.S. dollars, and the mean

(median) of total news per firm per year is 180 (81) pieces. The average news items specializing

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in earnings evaluations with either positive or negative sentiment to total news is around 13%

for each firm in each year. The average of Bank Loans/Total Debt ratio is about 0.43 and the

average of Bonds & Notes/Total Debt ratio is 0.41. In our primary sample, about 44% of firms

hold both bank loans and bonds and notes. The summary statistics of other variables can be

found in Table 1. The detailed definitions of all the key variables are presented in Appendix

Table 1.

To study the new debt issuance, we use the Securities Data Company (SDC) database

to obtain information on public bond issuance and private placement and Dealscan to obtain

information on bank loans. The new issues database of SDC Platinum provides detailed

financial information on newly issued securities. It covers more than 800,000 bond deals since

1970. Relying on the SDC database, we are able to construct two indicator variables,

New_Public_Bonds and New_Private_Placement, to indicate whether a firm has new

borrowings of public bonds or private bonds, where New_Public_Bonds

(New_Private_Placement) equals one if a firm has a new bond issuing (placed private debt) in

year t, and zero otherwise. We then construct the pricing and non-pricing terms of the newly

issued public bonds. In particular, we measure the price of public bonds, i.e.

Public_Bonds_Spread, as the natural logarithm of bond gross spread, where gross spread is the

interest spread in basis points and net of yield of treasury securities of comparable maturity.

For the non-pricing terms, we measure total issuance amount and the bond maturity, where

Public_Bonds_Amount is the natural logarithm of total amount of public bonds (in million $)

issued by firm i in year t, and Public_Bonds_Maturity is the natural logarithm of the bond

maturity in months. The pricing terms of private bond issuance are generally missing in SDC;

therefore, we are only able to capture the non-pricing terms of private bond issuance, i.e.

Private_Placed_Amount and Private_Placed_Maturity, which are measured similarly to public

bonds.

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Using Dealscan, we examine the bank loan information at the facility level. For new

borrowing of loans, we construct an indicator variable New_Bank_Loans that equals one if a

firm has a new loan facility borrowing in year t, and zero otherwise. We measure loan pricing

(Bank_Loans_Spread) using the all-in-drawn spread, that is, the interest spread over LIBOR

plus associated loan origination fees. For bank loans’ non-pricing terms, we use the loan

amount in million US dollars as measures of loan size (Bank_Loans_Amount) and the time to

maturity in months as measures of loan maturity (Bank_Loans_Maturity). We take the natural

logarithm of each of these three variables in performing regression analysis.

To contrast the effects from other information providers in the financial system, we

control for earnings surprises and credit rating changes. We download sample firms’ monthly

S&P domestic long-term issuer credit ratings (SPLTICRM) from Compustat. The credit ratings

are transformed to numerical numbers by assigning 0 to no rating, 1 to rating D or below, and

so forth. The highest credit rating AAA has a value of 22. The change in credit rating

(Ratings_Change) is calculated using the rating at the end of year t minus that of year t-1.

Earnings Surprise is estimated using the Institutional Brokers’ Estimate System (I/B/E/S)

detail history database from Thomson Reuters. We first obtain the one-year ahead earnings

forecasts (EPS, Fiscal Year 1) by all analysts. We keep the most recent forecasts by each analyst

within one year prior to the earnings announcements. The consensus is estimated using the

mean value of these forecasts. An earnings surprise is calculated as actual earnings minus the

mean forecast scaled by the absolute value of mean forecast. Earnings forecast dispersion is

the standard deviation of these forecasts scaled by the absolute value of mean forecast.

4. Media Coverage, Debt Structure, and Credit Ratings

4.1 Media coverage and debt structure

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We begin our analysis by examining the relation between media coverage and existing

debt structure on a firm’s balance sheet. Unlike new debt issuance, studying existing debt

structure provides a comprehensive picture of how firms use different debt instruments without

conditioning on the availability of new financing activities. Although the financing decisions

were made in the past, the fact that firms have not replaced them with other types of instruments

is an indication of continuing usage of a particular type of debt structure. Otherwise, firms

could refinance and modify the mix of debt structure. Therefore, this type of analysis provides

useful implications by showing the composition of debt structure that firms choose to adopt.

To examine the impact of media coverage on debt financing, we primarily use a panel

regression in which the unit of analysis is a firm-year observation and where we control for

both industry (𝜃𝑑) and year (𝜃𝑡) fixed effects. The year fixed effects control for all time-varying

influences that are common to all the firms each year, while the industry fixed effects condition

out all time-invariant industry characteristics. In particular, we estimate the following ordinary

least squares equation:

𝐷𝑒𝑏𝑡 𝐶ℎ𝑜𝑖𝑐𝑒𝑖𝑡 = 𝛽 ∙ 𝑀𝑒𝑑𝑖𝑎𝑖𝑡 + 𝛾′ ∙ 𝑋𝑖𝑡 + 𝜃𝑑 + 𝜃𝑡 + 𝜀𝑖𝑡. (1)

where 𝐷𝑒𝑏𝑡 𝐶ℎ𝑜𝑖𝑐𝑒𝑖𝑡 is one of the four measures of debt for firm i in year t (i.e.,

Dummy_Bank_Loans, Dummy_Bonds & Notes, Bank Loans/Total Debt, Bonds & Notes/Total

Debt). 𝑀𝑒𝑑𝑖𝑎𝑖𝑡 is one of the two media coverage measures, i.e. Total_News and

Earnings_News. Both variables measure the quantity of news available. The first one includes

all news items except for firms’ press releases scaled by a firm’s total assets. The second one

includes news items specializing in evaluations on firm earnings, revenue and dividends, with

either positive or negative sentiment over total news items. 𝑋𝑖𝑡 represents a vector of time-

varying firm traits: Size is the natural logarithm of the BHC’s total assets, ROA is the ratio of

net operating income over total assets, Leverage is the ratio of total debt over book value of

assets, Market_to_Book is the market value of equity plus book value of debt divided by book

18

value of total assets, Sales Growth is the percentage of annual growth in total sales, and Cash

Flow is the cash flows from operations deflated by average total assets. In seeking to assess the

impact of an intensification of media coverage on debt choice, we focus on estimating β. We

report heteroskedasticity-consistent standard errors that are clustered at the firm level.

Table 2 reports the estimation results on media’s impact on firm debt structure.

Columns (1) and (3) (columns (2) and (4)) show that total news (earnings news) negatively

affects bank loan borrowing, and by contrast, columns (5) and (7) (columns (6) and (8)) show

that total news (earnings news) positively affects public bond issuance. For example, in

columns (1) and (3), we find significantly negative coefficients on total news, which means

that firms with more news coverage have a lower tendency to use bank loans and a lower

proportion of bank loans on their balance sheets. To put this into economic magnitude, an

increase of one standard deviation in total news coverage, the estimate in column (3) indicates

a reduction in bank loan usage by 9.6% of total debt. This number is not only statistically

significant but also economically sizable. In contrast, the estimates in columns (5) and (7),

which analyze bonds and notes, show the opposite. For an increase of one standard deviation

in total news coverage, the usage of bonds and notes increases by 6.7% of total debt. Firms

with more media coverage do show better access to bonds and notes and rely less on bank

loans. In addition, when compare the control variables in Table 2, we find that firms of smaller

size tend to borrow bank loans and firms of larger size tend to issue corporate bonds, which is

consistent with the pecking order theory on debt issuance.

4.2 Instrumental variable estimation

Our baseline empirical results so far show a strong negative relationship between media

coverage and bank lending and a positive relationship between media coverage and bond and

note financing. However, reverse causality is a potential threat to reliable inferences in our

analysis because it may be possible that before making the debt issuance decision, firms choose

19

to attract media attention and disseminate more news. Also, despite including a series of firm

controls and industry and year fixed effects in the regressions, there are still concerns about the

omitted variable bias. If some unknown factors such as managerial characteristics are

correlated with both news coverage and debt financing choices, then our earlier results on the

impact of media coverage will be biased. Although it is extremely difficult to completely

eliminate endogeneity bias, in this section, we attempt to address the problem using the

instrumental variable approach.

The underlying assumption of the instrumental variables should be that they are highly

correlated with media coverage but do not directly affect firm decision on debt choice, except

through the media coverage channel. We use two instruments for media coverage. The first

one is newspaper shutdown, which is defined as the shutdown of metropolitan or local

newspapers in each firm’s headquarter state of each year. 11 We use this as an instrument for

firm’s media coverage as the shutdown of traditional media spread tool may induce exogenous

variation in news coverage for individual firms located in that area, meanwhile, local

newspapers closure is not expected to directly influence a firm’s choice of public versus private

debt. The second instrument is the state-level exogenous growth opportunity. Firms located in

a state with greater growth and investment opportunities are expected to be more appealing to

reporters, thus these opportunities should be highly correlated with individual firms’ media

coverage. However, state-level growth may be correlated with local variables such as firms’

debt choices; therefore, inspired by the work of Bekaert et al. (2005), we construct an

“exogenous” measure of a state growth opportunity by treating each state being composed of

a set of industries with each industry having time-varying growth opportunities, while

assuming that these growth prospects are reflected in the price to earnings (PE) ratios of a

11 These data are available at: https://en.wikipedia.org/wiki/List_of_defunct_newspapers_of_the_United_States

20

country’s (i.e. U.S.) industry portfolio.12 More specifically, we measure this variable using

𝐸𝑥𝑜𝑔𝑒𝑛𝑜𝑢𝑠 𝐺𝑟𝑜𝑤𝑡ℎ 𝑂𝑝𝑝𝑜𝑟𝑡𝑢𝑛𝑖𝑡𝑦𝑆 (∑ 𝑤𝑆𝐼 ∗ 𝑃𝐸𝐼𝐼 ) , where 𝑃𝐸𝐼 represents the industry PE

ratio in the U.S., and 𝑤𝑆𝐼 represents the state-level industry weight based on the industry’s

market capitalization. This measure has been proved to be valid and widely used in the finance

literature. We expect a positive association between the exogenous growth opportunity and

media coverage.

We apply a two-stage least squares (2SLS) estimation and present the results in Table

3. The first four columns in Table 3 report the second stage regressions where the dependent

variable in columns (1) and (2) is Bank Loans/Total Debt, and that in columns (3) and (4) is

Bonds and Notes/Total Debt. The key explanatory variables of media coverage are either

Total_News (Columns (1) and (3)) or Earnings_News (columns (2) and (4)). As shown by the

results, the coefficients on media coverage are still negative for Bank Loans/Total Debt and

positive for Bonds and Notes/Total Debt. These results not only continue to be statistically

significant, but also become slightly larger in terms of economic magnitude.

With regard to the validity of the instruments, we first present the first-stage results

from the IV regressions in columns (5)-(6) of Table 3. The positive coefficients on the measure

of the newspaper shutdown is interesting as it suggests that the closure of traditional media

causes firms overall media coverage to increase. This increase could be due to the entrance of

new media tools that fade out the traditional newspaper release. Meanwhile, the positive

coefficients on the measure of the state-level exogenous growth opportunity is consistent with

our expectation that firms’ media coverage is positively associated with the exogenous

economic growth opportunity at each state. Both instruments are significant at the 1% level in

12 The intuition behind is that a state’s PE ratio for a particular industry should be correlated to its country

counterpart given that all growth opportunities are competitively priced and absorbed by the U.S. capital

markets. Moreover, the weighting of industries within a particular state affects local GDP growth relative to the

country’s GDP growth (Bekaert et al., 2005; Bekaert et al., 2007).

21

the first-stage regressions, suggesting that we have powerful instruments that can easily pass

the relevance tests. The weak instrument tests further rejects the null hypothesis that our

instrument is irrelevant to the endogenous variable. Taken together, the IV results corroborate

our baseline findings that media coverage is negatively related to bank lending but positively

related to bond and note financing.

4.3 Sensitivity analysis

We conduct a series of sensitivity tests to verify the robustness of our results. Due to

space limit, we report these results in the Appendix tables. The first set of test is using novel

news as the measure of news coverage. Novel news is defined as a new story within a 24-hour

time window across all news stories in a particular package (Dow Jones, Web or PR Editions)

and with relevance score of 100 in RP. Due to the dissemination feature of news media, news

reports could be more duplicated over longer period of time. We use the fresher news to address

the concern that the media coverage effect is mainly due to the dissemination of the stale news

rather than creation of the new information. The results report in Appendix Table 2 are similar

to those using total news and earnings news.

Columns (1)-(4) of Appendix Table 3 report the second set of robustness tests in which

firms are restricted to those with total book value of equity between $100 million and $700

million U.S. dollars, because very large firms have good access to the bond markets and

routinely issue bonds through self-registrations or medium-term notes programs. Very small

firms may not have access to the bond market; therefore, there is no choice per se. By using

mid-sized firms, we confirm that our findings are not driven by including small and large firms.

In columns (5) and (6) of Appendix Table 3, we only use term loans to replace bank loans, i.e.

we do not include revolving credits, and the results continue to hold. Finally, we also remove

sample firms during the 2008 Financial Crisis and the following Great Recession (years 2008-

2010), and the findings remain robust. The results are not reported for brevity.

22

4.4 Channel tests

In this subsection, we further investigate the potential channels through which the

media plays a role in the debt markets. There are different layers of information asymmetry

problems. The most serious type is that there is no publicly available information, such as a

small start-up private company, while the other extreme is that there is too much information,

such as a well-known high-tech multinational firm. Companies with access to the bond markets

are likely to tilt towards the end of too much information. However, there is still a wide

spectrum of information availability among these companies. If the function of media coverage

is to resolve the information asymmetry problem in the “small company” sense, i.e. adding

information to boost information availability, we should observe that the effect of media

coverage is stronger among firms with more serious information asymmetry problems. We test

this as the information substitution hypothesis. However, if the information function of the

media is to separate the wheat from the chaff, then we will find stronger media effects among

firms with less information asymmetry problems, i.e., the information complementary

hypothesis. We use two proxies for the level of information flow: large firm size and whether

the firm is a member of a major stock index, where Large Size is defined as a dummy variable

that equals one if a firm’s total assets are above the sample median of that year, and zero

otherwise, and Stock Index Member is defined as a dummy variable that equals one if a firm is

a major stock index member, including Dow Jones, Nasdaq 100, or S&P 500 indexes, and zero

otherwise. We include the interaction term of our media coverage measure with each of the

proxies (Earnings_News * Large Size or Earnings_News * Stock Index Member) into equation

(1) to test our conjecture.

Table 4 reports the results. As shown, the estimated coefficients on the interaction terms

are negative for bank loan borrowing (columns (1) and (3)) and positive for bond issuance

(columns (2) and (4)). These results are unanimously consistent with the role of “separating

23

the wheat from the chaff” and the information complementary hypothesis. Using large firm

size as an example, the effect of earnings news is mainly driven by firms with above-median

total asset size in the sample. This information function is very different from that for small

firms. It also points out the limitations of the media that, if there is not enough publicly

available information in the first place (i.e. not much chaff) and if the news is not significant

or eye-catching (i.e. no wheat to pick), it is unlikely for the media to play an information role.

Besides the information role of media, we also focus on media’s role in facilitating

monitoring and corporate governance. A widely held view on one of the potential benefits of

bank borrowing is that it provides efficient and costly monitoring services that can mitigate

agency problems, whereas these services are not available from the widely dispersed investors

in the public market. Therefore, firms with poor governance problems or more monitoring

needs may choose bank debt. However, when the external governance pressure, such as the

media scrutiny is imposed, the monitoring needs of the bank will be reduced, and thus the

bank’s debt demands will be lowered (Bharath and Hertzel, 2018). In other words, although

firms with monitoring needs tend to rely on private debt, media coverage is expected to reduce

this reliance.

To further understand the governance channel through which the media can make a

difference, we examine the interactions between media coverage and two corporate governance

proxies—stock ownership concentration and whether a firm belongs to a relationship industry.

Existing studies have shown that the existence of multiple large shareholders enhances external

monitoring (e.g. Maury and Pajuste, 2005). Therefore, firms with multiple large shareholders

tend to have lower monitoring needs. We define High_Ownership_HHI as an indicator variable

that equals one if a firm’s Herfindahl-Hirschman index of its institutional ownership

concentration is above the sample median level of that year, and zero otherwise, and we include

the interaction term of High_Ownership_HHI and earnings news into our baseline model.

24

Our second governance proxy is whether a firm is in a relationship industry as defined

by Cremers, Nair, and Peyer (2008).13 The idea of using this variable to proxy for corporate

governance is that firms in such an industry have close ties with stakeholders, and more outside

monitoring may not be needed in the equity holders’ view because these stakeholders have an

incentive to monitor these firms. To test the conjecture on the corporate governance role of

media, we include the interaction term of our media coverage measure with a relationship

industry indicator into the baseline regression (i.e. Earnings_News * Relationship Industry).

As shown in Table 5, the estimates on the interaction terms between earnings news and high

ownership concentration (relationship lending industry) are positive on bank loan borrowing

(columns (1) and (3)) and negative on bond issuance (columns (2) and (4)).This result is

consistent with our corporate governance hypothesis that the negative (positive) relationship

between media coverage and bank loan (public debt) is mitigated among firms with good

corporate governance, suggesting the governance role played by the media.

4.5 Media coverage and borrowers’ credit ratings

The media is not the only information provider in the financial system. In this

subsection, we examine its relation with borrowers’ credit ratings. We focus on credit ratings

because they are important for debt instruments. Credit rating agencies are also major

information providers in the financial markets. As a preliminary step, in columns (1) and (2)

of Appendix Table 4, we show that firms with more news coverage have a significantly higher

chance of having an S&P domestic long-term issuer credit rating. The findings in columns (3)

and (4) indicate that firms with more news coverage also have significantly better credit ratings.

We then turn into news sentiments to examine whether the tone of the news that drives actions

because either positive or negative news should be the most relevant types of news for access

13 To be more specific, these industries include SIC codes of 15, 16, 17, 34, 35, 36, 37, 38, 39, 42, 47,

50, 51, 55, 60, 61, 62, 63, 64, 65, 67, 75, 76, and 87.

25

to the debt markets. We specifically focus on two types of news sentiments—positive and

negative earnings news, where positive (negative) earnings news is defined as news items

specializing in earnings evaluations with positive (negative) sentiment. Both measures are

expressed as ratios of total news. In Table 6, we examine the validity of information content

disseminated by the media and analyze whether news sentiments predict subsequent rating

changes and firm value. Results in Table 6 show that firms experiencing more positive

(negative) earnings news have significant increases (declines) in credit ratings and firm value.

These results validate the information content conveyed by our news measures. Otherwise, if

the news is stale, we should not observe systematic relations. To prevent the possibility that

news sentiments only transmit the same level of information as that transmitted by credit rating

changes, we control for it in the remaining tables.14 We also control for analyst earnings

forecast dispersion. By doing so, we can attribute the effects of news sentiments to media

coverage.

5. News Sentiments and New Debt Financing Activities

We start this section by revisiting the analysis of debt structure by replacing total news

or earnings news with news sentiments. The results are reported in Table 7. Interestingly, the

results we find in Table 2 are driven by negative news sentiment as none of the estimated

coefficients on positive earnings news are significantly different from zero. However, the

estimates on negative earnings news indicate that firms experiencing more negative news

coverage are related to significantly less bank loan usage but more bond and note issues.

It is possible that firms experiencing negative news sentiments have different

characteristics from firms with positive sentiments. Our results are confounded if any of these

14 For the same reason, we also control for earnings surprises to take care of additional earnings information

related to analysts. However, there are many missing values for this variable, so we do not report the results for

brevity. The results are available upon request.

26

characteristics also simultaneously contribute to the decision on debt choice. To address this

concern, we use propensity score matching approach. In particular, we compute news sentiment

by taking the difference between number of negative news and positive news over a year and

consider a firm as negative-news-firm if it is covered by more negative news than positive news

over the year. We then match negative-news-firms with non-negative-news-firms using

propensity scores and then estimate the average debt ratio between these two groups of firms.

The propensity scores are estimated via a probit model. The dependent variable is the dummy

variable for negative-news-firm and the independent variables include firm size, profitability,

sale growth, leverage, cash-to-asset ratio, market-to-book ratio, credit rating change, and

industry and year fixed effects. The matching estimator is Kernel-based matching techniques.

The results reported in Table 8 show that negative-news-firms consistently have lower bank

loans/total debt ratio and higher bonds & notes/ total debt ratio than similar non-negative-news

companies.

Since Capital IQ groups both privately placed debt and public bonds together, to

understand which one is driving the above results, we now turn to new issuance activities.

Before we examine each types of new borrowing, we analyze the level and sentiments of the

media coverage on debt access. Because Table 7 only shows the proportion of debt type relative

to total debt, we do not know whether the size of the pie itself is affected. In Table 9, we find

that more media coverage indeed enhances debt access in general, particularly the positive

earnings news. The likelihood of new debt financing is significantly reduced upon the release

of negative earning news.

In Table 10, we analyze new bank loans, public bonds, and privately placed debt. In

columns (2), (4), and (6), we examine negative news sentiments, and the results indicate that

firms with more negative news coverage significantly reduce the likelihood of borrowing new

bank loans and issuing new public bonds, but increase the likelihood of privately placing new

27

debt. The findings are consistent with the claim that the media plays a governance role in the

sense that it affects corporate behavior towards debt choices. When firms with public bond

access experiencing negative media coverage, they resort to private placement of debt rather

than bank loans. This finding is consistent with the points put forth in Carey, Post, and Sharpe

(1998). They show that, in the private lending markets, banks tend to serve lower-risk

borrowers, while non-bank financial institutions serve higher-risk borrowers. They argue that

this type of lending specialization is likely driven by regulations and capital requirements that

limit banks’ risk-taking ability. In addition, relationship banking means that banks tend to work

with firms when they are in financial distress, rather than forcing firms into liquidation.

Therefore, ex ante, banks only serve medium to low-risk firms.

Although lending terms have been examined in several papers, such as Bushman,

Williams, and Wittenberg-Moerman (2017), we also examine the effects of news sentiments

on price and non-price terms of new borrowings in Table 11 to complement research in this

area. Panels A, B, and C present the findings of bank loans, public bonds, and privately placed

debt, respectively. All estimated coefficients on news sentiments have expected signs,

particularly for interest spread and debt amount analyses. Despite controlling for rating changes

and earnings forecast dispersion, positive (negative) news significantly reduces (increases)

interest spread for both bank loans and public bonds. We do not analyze interest spread for

privately placed debt because the information is largely missing in SDC Platinum.

Improvements in ratings significantly reduce loan and bond interest spreads while earnings

forecast dispersion have no effects on both.

Firms with positive news coverage are able to raise significantly more money with bank

loans and public bonds, but there is no effect on privately placed debt. On the other hand, firms

experiencing negative news coverage raise significantly more capital through private

placement of debt and less with bank loans and public bonds. Finally, news sentiments only

28

affect bank loan maturity but have no effect on the other two types of debt instruments. Results

in Table 11 are largely consistent with extant literature in this area.

6. News Sentiment and Managers’ Post-Financing Earnings Management

Earnings management can be deliberately used by managers who focus on a desired

level of reported earnings. The choice of discretionary process may reflect the market sentiment

because news sentiment conveys investors’ belief about the companies’ cash flow and

investment risks that may not be available from accounting data. Ali and Gurun (2009)

document that optimism in the capital markets encourages managers to conduct more earnings

management. This is because the excess value generated by market optimism could allow

managers to report discretionary accounting earnings to their favorable outcomes. In the

pessimistic periods, however, managers are more conservative and thus may conduct

downward discretionary practices. In this section, we examine how the news sentiment affects

managers’ post-financing decisions on earnings management. In particular, we analyze

whether the positive or negative news affect managers’ report on discretionary accruals upward

or downward.

To examine earnings management, we look at discretionary accruals based on three

different models, that is, the Modified Jones Model (Jones, 1991; Dechow et al. 1995), the

models by McNichols (2002), and by Kothari et al. (2005). In the Modified Jones Model,

discretionary accruals are estimated using the residuals from the following regression:

𝑇𝐴𝑡

𝐴𝑇𝑡−1= 𝛼1 (

1

𝐴𝑇𝑡−1) + 𝛼2 (

∆𝑅𝐸𝑉𝑡−∆𝑅𝐸𝐶𝑡

𝐴𝑇𝑡−1) + 𝛼3 (

𝑃𝑃𝐸𝑡

𝐴𝑇𝑡−1) + 𝜀𝑡, (2)

where t is the hypothesized year of earnings management, ΔRECt is net receivables in year t

less net receivables in year t−1, ΔREVt is revenues in year t less revenues in year t−1, PPEt is

gross property plant and equipment at the end of year t, and ATt−1 is total assets at the end of

year t−1.

29

McNichols (2002)’s model is based on Dechow and Dichev (2002)’s model, where

discretionary accruals are estimated using residuals from the following regression:

𝑇𝐴𝑡

𝐴𝑇𝑡−1= 𝛼1 (

1

𝐴𝑇𝑡−1) + 𝛼2 (

𝐶𝐹𝑂𝑡−1

𝐴𝑇𝑡−2) + 𝛼3 (

𝐶𝐹𝑂𝑡

𝐴𝑇𝑡−1) + 𝛼4 (

𝐶𝐹𝑂𝑡+1

𝐴𝑇𝑡) + 𝛼5 (

∆𝑅𝐸𝑉𝑡

𝐴𝑇𝑡−1) + 𝛼6 (

𝑃𝑃𝐸𝑡

𝐴𝑇𝑡−1) + 𝜀𝑡,

(3)

Where CFO represents cash from operations and the other variables are defied the same as

before.

Kothari et al. (2005) estimate a model that is similar to the Jones and Modified Jones

Models, except that it is augmented to include firm profitability measure ROAt. This approach

is designed to provide a comparison of the effectiveness of performance matching versus

including a performance measure in the accruals regression. To put it formally, discretionary

accruals are estimated using residuals from the following regression:

𝑇𝐴𝑡

𝐴𝑇𝑡−1= 𝛼1 (

1

𝐴𝑇𝑡−1) + 𝛼2 (

∆𝑅𝐸𝑉𝑡

𝐴𝑇𝑡−1) + 𝛼3 (

𝑃𝑃𝐸𝑡

𝐴𝑇𝑡−1) + 𝛼4 (

𝑅𝑂𝐴𝑡

𝐴𝑇𝑡−1) + 𝜀𝑡, (4)

Using these three accrual-based models, we estimate the signed abnormal accruals and

test how news sentiments affect managerial discretionary accruals. The results are reported in

Table 12. The dependent variables in Table 12 are discretionary accruals based on the above

mentioned three models: modified Jones model (Dechow et al. 2005) (columns 1-2),

McNichols et al. (2002)’s model, and Kothari et al. (2005)’s model. All these discretionary

accruals are estimated one year after the new debt issuance. The explanatory variables used are

the same as in Table 10. From Table 12, we find that when the news is negative, that is, when

the markets are negative about firms’ future prospects, firms report more negative discretionary

accruals. This is consistent with our previous conjecture that managers may become more

conservative when the news coverage is pessimistic. However, we do not find that firms

manage their earnings upward after the debt financing when the news is positive. This could

because the new debt constrains managers from using their skills to opportunistically report

favorable earnings results.

30

7. Conclusions

By analyzing the effect of media coverage on firms’ debt financing, we find that media

coverage reduces firms’ reliance on bank loans but increases amounts of public bonds & notes

on their balance sheets. We document two channels that 1) such effect is concentrated among

firms with less information asymmetry problems suggesting that the media complements other

information sources (information channel), and 2) such effect is mitigated among firms with

better corporate governance suggesting that the media substitutes for banks on firm monitoring

(governance channel). By examining debt structure, this paper provides evidence that the media

serves both a complementary information role and a governance role in the debt markets.

We supplement the above analysis using new debt financing and find that higher level

of media coverage enhances new debt financing, particularly, positive earnings news.

However, negative earnings news significantly reduces new debt access, particularly public

bonds and bank loans, but increases the usage of private placement. It suggests that firms

experiencing negative news coverage refrain from issuing public bonds. Our results of news

sentiment on price and non-price terms of new borrowings corroborate this finding, i.e.

controlling for rating changes and earnings forecast dispersion, we find that positive (negative)

news significantly reduces (increases) interest spread for both bank loans and public bonds;

while there is no effect on privately placed debt with positive news coverage, firms with

negative news coverage increases capital significantly through private placement of debt.

We also investigate credit ratings and firm value changes post-media-coverage. We

show that news sentiments predict subsequent rating changes and firm values. It is worth to

note that although both news sentiments and rating changes affect borrowing terms, only news

sentiments have impacts on the choices of debt instruments. That is, firms experiencing more

positive (negative) earnings news have significant increases (declines) in credit ratings and

31

firm value. Finally, we analyze news sentiment impacts on managers’ report on discretionary

accruals and show that managers become more conservative in earnings reporting when the

news coverage is pessimistic, but there is no evidence that firms manage their earnings upward

with positive news after the debt financing. This result suggests that the new debt may constrain

managers to use their skills opportunistically to report favorable earnings results.

32

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35

Figure 1. Media Coverage and Total Debt

This figure shows the trend of media coverage and total debt by year. Media coverage is the median

of the total number of news items covering each firm in each year, and total debt is the median of

the total amount of debt (in million $) borrowed by each firm in each year.

0

50

100

150

200

250

300

350

400

2000 2002 2004 2006 2008 2010 2012 2014

Year

Median Coverage and Total Debt

Total # of News Total Debt ($million)

36

Table 1. Summary Statistics

This table presents the summary statistics for the key variables used in the paper. The number of

observations is 36,002.

Variable Mean SD P25 Median P75

Total_News 6.07 3.04 3.70 5.39 8.65

Earnings_News 1.77 2.09 1.70 2.38 2.85

Positive Earnings News 0.34 2.78 0.15 1.56 2.23

Negative Earnings News 0.23 2.78 -0.25 1.31 2.12

Dummy_Bank_Loans 0.74 0.44 0 1 1

Dummy_Bonds & Notes 0.64 0.48 0 1 1

Bank Loans/Total Debt 0.43 0.4 0 0.33 0.88

Bonds & Notes/Total Debt 0.41 0.41 0 0.3 0.85

Size (in million $) 5606.32 48899.73 25.09 245.60 1420.81

Size (in log) 5.26 2.79 3.22 5.50 7.26

ROA 1.71 3.51 0.01 0.08 1.15

Sales Growth 0.09 0.2 -0.03 0.06 0.18

Leverage 0.88 1.30 0.13 0.31 0.82

Cash Flow 2.93 5.82 0.04 0.14 2.27

Market to Book 2.52 2.07 1.13 1.87 3.17

37

Table 2. Media Coverage and Debt Structure - Baseline

This table presents results of media coverage on debt structure. The sample includes all the U.S. firms that have either borrowed banks loans or

issued bonds or both during the sample period 2001-2014. The key media coverage variables are Total News (columns 1, 3, 5, and 7) and Earnings

News (columns 2, 4, 6 and 8), where Total News is equal to Ln(total number of news items for firm i in year t, scaled by firm i’s total assets);

Earnings News is equal to Ln(the ratio of news items specializing in earnings evaluations to total news for firm i in year t*100+0.01). The dependent

variables are Dummy_Bank_Loan (columns 1-2), Bank Loans/Total Debt (columns 3-4), Dummy_Bonds & Notes (columns 5-6), and Bonds &

Notes/Total Debt (columns 7-8). Detailed definitions of variables can be found in Appendix Table 1. Standard errors reported in parentheses are

heteroskedastic robust and clustered at the industry level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

(1) (2) (3) (4) (5) (6) (7) (8)

Model Logit OLS Logit OLS

Dep Var Dummy_Bank_Loans Bank Loans/Total Debt Dummy_Bonds & Notes Bonds & Notes/Total Debt

Total_News -0.2796*** -0.0316*** 0.0812*** 0.0228*** (0.0243) (0.0032) (0.0235) (0.0030) Earnings_News -0.0278*** -0.0069*** 0.0253*** 0.0037***

(0.0078) (0.0010) (0.0069) (0.0009)

Size -0.2434*** -0.0588*** -0.0917*** -0.0701*** 0.5755*** 0.5163*** 0.0862*** 0.0708***

(0.0173) (0.0073) (0.0023) (0.0011) (0.0178) (0.0088) (0.0022) (0.0010)

ROA 0.0074 0.0107** 0.0071*** 0.0072*** -0.0494*** -0.0492*** -0.0039*** -0.0040***

(0.0054) (0.0054) (0.0008) (0.0008) (0.0054) (0.0054) (0.0008) (0.0008)

Sales Growth 0.2388*** 0.2002*** 0.0721*** 0.0670*** -0.3293*** -0.3103*** -0.0889*** -0.0855***

(0.0686) (0.0684) (0.0099) (0.0099) (0.0713) (0.0712) (0.0094) (0.0094)

Leverage 0.2496*** 0.2647*** 0.0005 0.0023 0.4399*** 0.4357*** 0.0460*** 0.0448***

(0.0168) (0.0167) (0.0023) (0.0023) (0.0183) (0.0182) (0.0022) (0.0022)

Cash Flow -0.0678*** -0.0703*** -0.0075*** -0.0078*** -0.0050 -0.0044 0.0052*** 0.0054***

(0.0032) (0.0032) (0.0005) (0.0005) (0.0032) (0.0031) (0.0004) (0.0004)

Market_to_Book -0.0494*** -0.0655*** -0.0058*** -0.0076*** -0.0418*** -0.0378*** 0.0015 0.0028***

(0.0065) (0.0064) (0.0010) (0.0010) (0.0075) (0.0073) (0.0010) (0.0010)

Industry fixed effects yes yes yes yes yes yes yes yes

Year fixed effects yes yes yes yes yes yes yes yes

N 35994 35994 36002 36002 35888 35888 36002 36002

R-sq 0.1187 0.1152 0.2079 0.2068 0.2517 0.2518 0.3154 0.3146

38

Table 3. Media Coverage and Debt Structure – IV Estimation

This table presents results of media coverage on debt structure using the instrumental variable (IV) approach. The sample includes all

the U.S. firms that have either borrowed banks loans or issued bonds or both during the sample period 2001-2014. Columns 1-4 present

second-stage results where the key explanatory variables of media coverage are Total News (columns 1 and 3) and Earnings News

(columns 2 and 4). The dependent variables are Bank Loans/Total Debt (columns 1-2) defined as the ratio of a firm’s bank loans to total

debt, and Bonds & Notes/Total Debt (columns 3-4) defined as the ratio of a firm’s public/private bonds and notes to total debt. Columns

5-6 present first-stage results where the instrumental variables used are (1) Newspaper Shutdown, defined as the shut-down of

metropolitan or local newspapers in each firm’s headquarter state in year t, and (2) State Growth Opportunity, defined as in Bakaert et

al. (2005) and equal to ∑ 𝑤𝑆𝐼𝑡 ∗ 𝑃𝐸𝐼𝑡 where each state has a set of industries 𝑤𝑆𝐼𝑡 with each industry having time-varying growth

opportunities while assuming that these growth prospects are reflected in the price to earnings (P/E) ratios of the country’s industry

portfolio 𝑃𝐸𝐼𝑡. Detailed definitions of all the other variables can be found in Appendix Table 1. Standard errors reported in parentheses

are heteroskedastic robust and clustered at the industry level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%

levels, respectively.

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

Second Stage Second Stage First Stage

Dep Var Bank Loans/Total Debt Bonds & Notes /Total Debt Total_News Earnings_News

Total_News -0.1075*** 0.0874**

(0.0385) (0.0373)

Earnings_News -0.0090** 0.0072**

(0.0036) (0.0030)

Size -0.1408*** -0.0698*** 0.1280*** 0.0704*** -0.8226*** 0.1264***

(0.0242) (0.0041) (0.0247) (0.0050) (0.0028) (0.0044)

ROA 0.0068*** 0.0073*** -0.0037* -0.0041** 0.0681*** 0.0230***

(0.0023) (0.0020) (0.0022) (0.0021) (0.0018) (0.0027)

Sales Growth 0.0804*** 0.0670*** -0.0959*** -0.0851*** -0.1585*** -0.3661***

(0.0170) (0.0189) (0.0218) (0.0215) (0.0256) (0.0453)

Leverage -0.0028 0.0024 0.0488*** 0.0446*** 0.2961*** 0.1632***

(0.0106) (0.0098) (0.0092) (0.0087) (0.0049) (0.0080)

Cash Flow -0.0069*** -0.0078*** 0.0046*** 0.0054*** 0.0659*** 0.0221***

39

(0.0017) (0.0015) (0.0013) (0.0013) (0.0011) (0.0017)

Market_to_Book -0.0012 -0.0073*** -0.0024 0.0026 0.0434*** 0.0128***

(0.0033) (0.0026) (0.0029) (0.0018) (0.0028) (0.0039)

Newspaper Shutdown 0.0687*** 0.0349**

(0.0104) (0.0153)

State Growth Opportunity 0.0593*** 0.8564***

(0.0064) (0.0049)

Weak Instruments Test

(Cragg-Donald Wald F

value) 228 3923

228

3923 - -

Industry fixed effects yes yes yes yes yes yes

Year fixed effects yes yes yes yes yes yes

N 35931 35931 35931 35931 35931 35931

R-sq 0.1954 0.2067 0.3073 0.3149 0.8998 0.2901

40

Table 4. Information Channel

This table presents results of media coverage on debt structure and media coverage interacting

with information transparency proxies. The sample includes all the U.S. firms that have either

borrowed bank loans or issued bonds or both during the sample period 2001-2014. The key media

coverage variable is Earnings News, which is equal to Ln(the ratio of news items specializing in

earnings evaluations with either positive or negative sentiment to total news for firm i in year

t*100+0.01), where total news is defined as total number of news items with a relevance score of

100 but excluding corporate press releases for firm i in year t. Information asymmetry proxies used

include Large Size and Stock Index Member with higher values indicating less information

asymmetry. Large Size is a dummy variable that equals one if a firm’s total assets are above the

sample median of that year, and zero otherwise. Stock Index Member is a dummy variable that

equals one if a firm is a major stock index member, including Dow Johns, Nasdaq 100, or S&P

500 indexes, and zero otherwise. Detailed definitions of all the other variables can be found in

Appendix Table 1. Standard errors reported in parentheses are heteroskedastic robust and clustered

at the industry level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels,

respectively.

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

Dep Var

Bank

Loans/Total

Debt

Bonds &

Notes /Total

Debt

Bank

Loans/Total

Debt

Bonds &

Notes /Total

Debt

Earnings_News -0.0030 -0.0034 -0.0063*** 0.0030*

(0.0025) (0.0033) (0.0021) (0.0016)

Earnings_News * Large Size -0.0100*** 0.0194***

(0.0026) (0.0028)

Earnings_News * Stock Index

Member

-0.0250*** 0.0341***

(0.0069) (0.0088)

Stock Index Member -0.0764** 0.0663

(0.0354) (0.0403)

Firm controls yes yes yes yes

Industry fixed effects yes yes yes yes

Year fixed effects yes yes yes yes

N 36002 36002 36002 36002

R-sq 0.2115 0.3228 0.2155 0.3248

41

Table 5. Corporate Governance Channel

This table presents results of media coverage on debt structure and media coverage interacting

with corporate governance proxies. The sample includes all the U.S. firms that have either

borrowed bank loans or issued bonds or both during the sample period 2001-2014. The key media

coverage variable is Earnings News, which is equal to Ln(the ratio of news items specializing in

earnings evaluations with either positive or negative sentiment to total news for firm i in year

t*100+0.01), where total news is defined as total number of news items with a relevance score of

100 but excluding corporate press releases for firm i in year t. Corporate governance proxies used

include High_Ownership_HHI and Relationship_Industry. High_Ownership_HHI is an indicator

variable that equals one if a firm’s Herfindahl-Hirschman index of its institutional ownership

concentration is above the sample median level of that year, and zero otherwise. Relationship

Industry is an indicator variable that equals one if a firm belongs to the following industries with

SIC codes of 15, 16, 17, 34, 35, 36, 37, 38, 39, 42, 47, 50, 51, 55, 60, 61, 62, 63, 64, 65, 67, 75,

76, and 87 (see Cremers, Nair, and Peyer, 2008). Detailed definitions of all the other variables can

be found in Appendix Table 1. Standard errors reported in parentheses are heteroskedastic robust

and clustered at the industry level. *, **, and *** indicate statistical significance at the 10%, 5%,

and 1% levels, respectively.

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

Dep Var

Bank

Loans/Total

Debt

Bonds &

Notes /Total

Debt

Bank

Loans/Total

Debt

Bonds &

Notes /Total

Debt

Earnings_News -0.0140*** 0.0140*** -0.0121*** 0.0090***

(0.0035) (0.0031) (0.0029) (0.0027)

Earnings_News * High

Ownership_HHI 0.0103*** -0.0142***

(0.0029) (0.0034)

Earnings_News * Relationship

Industry 0.0077** -0.0077**

(0.0032) (0.0034)

Firm controls yes yes yes yes

Industry fixed effects yes yes yes yes

Year fixed effects yes yes yes yes

N 35192 35192 36002 36002

R-sq 0.2126 0.3226 0.2071 0.3149

42

Table 6. The Information Content of News Sentiments

This table presents the results of news sentiments on credit rating changes and firm value, where

news sentiments are measured using Positive_Earnings_News (column 1) and

Negative_Earnings_News (column 2). Positive_Earnings_News (Negative_Earnings_News) is

defined as the ratio of news items specializing in earnings evaluations with positive (negative)

sentiment to total news for firm i in year t. For both measures, we multiple them by 100 before

taking the natural logarithm. The sample includes all the U.S. firms that have either borrowed bank

loans or issued bonds or both during the sample period 2001-2014. The dependent variable

Ratings_Change is defined as the credit rating score at the end of year t+1 minus that of year t.

The credit rating score is coded from 0 to 22 with 0 representing no rating, 1 representing D or

below, and 22 representing the highest S&P rating AAA. Tobin’s Q is used as a proxy of firm

value. Detailed definitions of all the other variables can be found in Appendix Table 1. Standard

errors reported in parentheses are heteroskedastic robust and clustered at the industry level. *, **,

and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

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

Dep Var Ratings_Change Tobin’s Q (t+1)

Positive Earnings News 0.0049*** 0.0305***

(0.0013) (0.0036)

Negative Earnings News -0.0067*** -0.0053**

(0.0015) (0.0025)

Size -0.0122*** -0.0088*** -0.0470*** -0.0306**

(0.0023) (0.0019) (0.0142) (0.0134)

ROA 0.0072*** 0.0068*** 0.0467*** 0.0473***

(0.0013) (0.0013) (0.0088) (0.0090)

Sales Growth 0.0725*** 0.0678*** 0.3124*** 0.3417***

(0.0243) (0.0231) (0.0351) (0.0368)

Leverage -0.0079* -0.0068 -0.0972*** -0.0971***

(0.0043) (0.0043) (0.0188) (0.0186)

Cash Flow -0.0008 -0.0007 0.0149*** 0.0154***

(0.0009) (0.0009) (0.0028) (0.0029)

Market_to_Book 0.0108*** 0.0112*** 0.1742*** 0.1782***

(0.0022) (0.0021) (0.0080) (0.0082)

Ratings_Change 0.0281*** 0.0303***

(0.0064) (0.0066)

Industry fixed effects yes yes yes yes

Year fixed effects yes yes yes yes

N 36002 36002 35981 35981

R-sq 0.0174 0.0180 0.9579 0.9570

43

Table 7 News Sentiments and Debt Structure

This table presents the results of news sentiments on debt structure, where news sentiments are

measured using Positive_Earnings_News (columns 1 and 3) and Negative_Earnings_News

(columns 2 and 4). Positive_Earnings_News (Negative_Earnings_News) is defined as the ratio of

news items specializing in earnings evaluations with positive (negative) sentiment to total news

for firm i in year t. For both measures, we multiple them by 100 before taking the natural logarithm.

The sample includes all the U.S. firms that have either borrowed bank loans or issued bonds or

both during the sample period 2001-2014. Detailed definitions of all the other variables can be

found in Appendix Table 1. Standard errors reported in parentheses are heteroskedastic robust and

clustered at the industry level. *, **, and *** indicate statistical significance at the 10%, 5%, and

1% levels, respectively.

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

Dep Var Bank Loans/Total Debt Bonds & Notes/Total Debt

Positive Earnings News 0.0001 -0.0003

(0.0012) (0.0014)

Negative Earnings News -0.0093*** 0.0061***

(0.0020) (0.0017)

Size -0.0715*** -0.0704*** 0.0718*** 0.0710***

(0.0043) (0.0039) (0.0047) (0.0049)

ROA 0.0073*** 0.0066*** -0.0043** -0.0038*

(0.0020) (0.0019) (0.0021) (0.0020)

Sales Growth 0.0681*** 0.0537*** -0.0859*** -0.0767***

(0.0195) (0.0197) (0.0228) (0.0225)

Leverage 0.0016 0.0032 0.0455*** 0.0445***

(0.0102) (0.0099) (0.0088) (0.0086)

Cash Flow -0.0079*** -0.0077*** 0.0055*** 0.0054***

(0.0015) (0.0015) (0.0013) (0.0013)

Market_to_Book -0.0078*** -0.0081*** 0.0029 0.0031

(0.0027) (0.0027) (0.0019) (0.0019)

Ratings_Change 0.0056 0.0028 -0.0031 -0.0013

(0.0043) (0.0043) (0.0042) (0.0043)

Forecast Dispersion 0.0097 0.0156* -0.0189** -0.0228***

(0.0092) (0.0086) (0.0073) (0.0074)

Industry fixed effects yes yes yes yes

Year fixed effects yes yes yes yes

N 36002 36002 36002 36002

R-sq 0.2058 0.2091 0.3145 0.3159

44

Table 8. Media Coverage and Debt Structure – Propensity Score Matching Estimation

This table presents the relationship between media coverage and debt structure using propensity

score matching approach. The sample includes all the U.S. firms that have either borrowed bank

loans or issued bonds or both during the sample period 2001-2014. The propensity scores are

estimated via a probit model - the dependent variable is the dummy variable for negative-news-

firm and the independent variables include firm size, profitability, sale growth, leverage, cash-to-

asset ratio, market-to-book ratio, credit rating change, and industry and year fixed effects. The

matching estimator is Kernel-based matching techniques.

Variable Treated Controls Difference t-statistic

Bank Loans/Total Debt 0.428 0.461 -0.032 6.14

Bonds & Notes /Total Debt 0.413 0.396 0.017 3.24

45

Table 9 Media Coverage and Debt Access

This table presents the logit regression results of media coverage on debt access. The dependent

variable equals one if a firm has issued either bank loans, public bonds, or placed private debt in

year t, and 0 otherwise. The sample includes all the U.S. firms that have either borrowed bank

loans or issued bonds or both during the sample period 2001-2014. Detailed definitions of all the

other variables can be found in Appendix Table 1. Standard errors reported in parentheses are

heteroskedastic robust and clustered at the industry level. *, **, and *** indicate statistical

significance at the 10%, 5%, and 1% levels, respectively.

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

Dep Var New Debt Issuance

Total News 0.2918***

(0.0556) Earnings News 0.0554**

(0.0231) Positive Earnings News 0.1008***

(0.0172)

Negative Earnings News -0.0309**

(0.0135)

Size 1.1719*** 0.9901*** 0.9602*** 0.9995*** (0.0413) (0.0213) (0.0220) (0.0214)

ROA 0.0847*** 0.0826*** 0.0792*** 0.0817*** (0.0132) (0.0133) (0.0132) (0.0133)

Sales Growth -0.3927** -0.3413** -0.4587*** -0.4072** (0.1572) (0.1580) (0.1605) (0.1602)

Leverage 0.3342*** 0.3172*** 0.3160*** 0.3208*** (0.0370) (0.0370) (0.0367) (0.0369)

Cash Flow -0.0289*** -0.0260*** -0.0267*** -0.0257*** (0.0075) (0.0075) (0.0075) (0.0075)

Market_to_Book 0.0292** 0.0432*** 0.0355** 0.0422***

(0.0144) (0.0142) (0.0143) (0.0142)

Ratings_Change 0.0129 0.0066 -0.0033 -0.0063 (0.0486) (0.0489) (0.0488) (0.0491)

Forecast Dispersion -0.1581 -0.1525 -0.1416 -0.1211

(0.1137) (0.1137) (0.1135) (0.1141)

Industry fixed effects yes yes yes yes

Year fixed effects yes yes yes yes

N 15597 15597 15597 15597

R-sq 0.5479 0.5462 0.5477 0.5462

46

Table 10 News Sentiments and New Borrowings

This table presents the logit regression results of news sentiments on the choice of new borrowings. The sample includes all the U.S.

firms that have either borrowed new bank loans or issued new bonds or both during the sample period 2001-2014. The dependent

variables are New_Bank_Loans (columns 1-2), New_Public_Bonds (columns 3-4), or New_Private_Placement (columns 5-6), where

New_Bank_Loans is an indicator variable that equals one if a firm has a new bank loan borrowing in year t, and zero otherwise;

New_Public_Bonds is an indicator variable that equals one if a firm has a new bond issuing in year t, and zero otherwise; and

New_Private_Placement is an indicator variable that equals one if a firm has placed private debt in year t, and zero otherwise. Firm

controls include ROA, Sales Growth, Leverage, Cash Flow, and Market_to_Book. Detailed definitions of all the other variables can be

found in Appendix Table 1. Standard errors reported in parentheses are heteroskedastic robust and clustered at the industry level. *, **,

and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

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

Dep Var New Bank Loans New Public Bonds New Private Placement

Positive Earnings News 0.1021*** 0.0833*** 0.0883 (0.0168) (0.0269) (0.0646)

Negative Earnings News -0.0287** -0.0390** 0.1382**

(0.0132) (0.0190) (0.0621)

Size 0.8333*** 0.8722*** 0.8941*** 0.9189*** 0.3814*** 0.4146*** (0.0199) (0.0190) (0.0260) (0.0253) (0.0722) (0.0809)

Market_to_Book -0.0003 0.0063 0.0872*** 0.0910*** -0.0165 0.0006

(0.0140) (0.0139) (0.0162) (0.0161) (0.0725) (0.0657)

Ratings_Change -0.0045 0.0090 -0.1447 -0.1308 -0.0461 0.0051

(0.0926) (0.0934) (0.1329) (0.1335) (0.1648) (0.1606)

Earnings Surprise -0.0289 -0.0306 -0.0155 -0.0214 -0.3238 -0.4314

(0.0492) (0.0495) (0.0571) (0.0577) (0.6772) (0.6879)

Firm controls yes yes yes yes yes yes

Industry fixed effects yes yes yes yes yes yes

Year fixed effects yes yes yes yes yes yes

N 15597 15597 15597 15597 10781 10781

R-sq 0.4869 0.4849 0.4253 0.4248 0.1823 0.1861

47

Table 11 News Sentiments and Borrowing Price and Non-Price Terms

This table presents the results of news sentiments on borrowing price and non-price terms of bank loans, public bonds, and privately

placed debt in Panels A, B, and C, respectively. The sample is conditional on new debt financing activities available for analysis from

all the U.S. firms that have either borrowed bank loans or issued bonds or both during the sample period 2001-2014. Interest rate (spread)

is not available for privately placed debt. All the price and non-price terms are expressed in natural logarithm. Firm controls include

ROA and Leverage. Detailed definitions of all the other variables can be found in Appendix Table 1. Standard errors reported in

parentheses are heteroskedastic robust and clustered at the industry level. *, **, and *** indicate statistical significance at the 10%, 5%,

and 1% levels, respectively.

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

Dep Var Bank_Loans_Spread Bank_Loans_Amount Bank_Loans_Maturity

Panel A: Bank Loans

Positive Earnings News -0.0194*** 0.0254*** 0.0046** (0.0021) (0.0044) (0.0021) Negative Earnings News 0.0246*** -0.0129*** -0.0038**

(0.0018) (0.0039) (0.0019)

Maturity 0.1052*** 0.1061*** 0.1539*** 0.1546*** (0.0073) (0.0073) (0.0147) (0.0147) Amount -0.1093*** -0.1094*** 0.0355*** 0.0356***

(0.0037) (0.0037) (0.0034) (0.0034)

Size -0.0933*** -0.1048*** 0.4875*** 0.4996*** -0.0295*** -0.0272*** (0.0035) (0.0034) (0.0059) (0.0056) (0.0033) (0.0032)

Ratings_Change -0.0237*** -0.0187*** -0.0058 -0.0076 0.0365*** 0.0358***

(0.0058) (0.0058) (0.0124) (0.0124) (0.0059) (0.0060)

Forecast Dispersion 0.0913*** 0.0722*** 0.0765*** 0.0878*** 0.0276** 0.0306**

(0.0125) (0.0125) (0.0266) (0.0268) (0.0128) (0.0129)

Loan purposes yes yes yes yes yes yes

Firm controls yes yes yes yes yes yes

Industry fixed effects yes yes yes yes yes yes

Year fixed effects yes yes yes yes yes yes

N 17807 17807 19940 19940 19940 19940

R-sq 0.5206 0.5231 0.4254 0.4247 0.2387 0.2386

48

Table 11 (Continued)

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

Dep Var Public_Bond_Interest Spread Public_Bond_Amount Public_Bond_Maturity

Panel B: Public Bonds

Positive Earnings News -0.0821*** 0.1687*** -0.0192 (0.0083) (0.0147) (0.0117) Negative Earnings News 0.0449*** -0.0359*** 0.0001

(0.0060) (0.0106) (0.0081)

Maturity 0.2274*** 0.2288*** 0.1575*** 0.1560*** (0.0134) (0.0135) (0.0211) (0.0215) Amount 0.0582*** 0.0462*** 0.0965*** 0.0925***

(0.0096) (0.0095) (0.0129) (0.0127)

Size -0.2376*** -0.2485*** 0.2240*** 0.2408*** -0.1201*** -0.1205*** (0.0101) (0.0103) (0.0176) (0.0180) (0.0140) (0.0141)

Ratings_Change -0.0406*** -0.0321** -0.1020*** -0.1090*** 0.0148 0.0142

(0.0142) (0.0144) (0.0253) (0.0259) (0.0199) (0.0200)

Forecast Dispersion 0.3065*** 0.2983*** 0.1827*** 0.1560** -0.0502 -0.0433

(0.0399) (0.0404) (0.0689) (0.0704) (0.0539) (0.0543)

Firm controls yes yes yes yes yes yes

Industry fixed effects yes yes yes yes yes yes

Year fixed effects yes yes yes yes yes yes

N 3240 3240 3676 3676 3676 3676

R-sq 0.6015 0.5964 0.6045 0.5914 0.1773 0.1766

49

Table 11 (Continued)

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

Dep Var Private_Placed_Amount Private_Placed_Maturity

Panel C: Private Placement

Positive Earnings News -0.0118 -0.0026 (0.0173) (0.0120) Negative Earnings News 0.0604*** -0.0011

(0.0204) (0.0145)

Maturity -0.1659** -0.1608** (0.0755) (0.0746) Amount -0.0801** -0.0794**

(0.0364) (0.0369)

Size 0.1840*** 0.1464*** 0.0228 0.0227 (0.0309) (0.0326) (0.0225) (0.0235)

Ratings_Change -0.1884* -0.2124** 0.1403* 0.1422*

(0.1073) (0.1062) (0.0745) (0.0747)

Forecast Dispersion 0.0811 0.0896 0.3190 0.3093

(0.3256) (0.3170) (0.2256) (0.2222)

Industry fixed effects yes yes yes yes

Year fixed effects yes yes yes yes

N 429 429 429 429

R-sq 0.3485 0.3631 0.3051 0.3050

50

Table 12. News Sentiments and Earnings Management

This table presents the results of news sentiments on firms’ earnings management, where news sentiments are measured using

Positive_Earnings_News (column 1) and Negative_Earnings_News (column 2). Positive_Earnings_News (Negative_Earnings_News)

is defined as the ratio of news items specializing in earnings evaluations with positive (negative) sentiment to total news for firm i in

year t. For both measures, we multiple them by 100 before taking the natural logarithm. The sample includes all the U.S. firms that have

either borrowed bank loans or issued bonds or both during the sample period 2001-2014. The dependent variable are discretionary

accrual based on modified Jones model (Jones, 1991; Dechow et al. 1995), discretionary accrual based on McNichols et al. (2002)’s

model, and discretionary accrual based on Kothari et al. (2005)’s model. Firm controls include ROA, Sales Growth, Leverage, Cash

Flow, and Market_to_Book. Detailed definitions of all the other variables can be found in Appendix Table 1. Standard errors reported

in parentheses are heteroskedastic robust and clustered at the industry level. *, **, and *** indicate statistical significance at the 10%,

5%, and 1% levels, respectively.

(1) (2) (5) (6) (7) (8)

Dep Var EM-Modified Jones (t+1) EM – McNichols Model (t+1) EM – Kothari Model (t+1)

Positive Earnings News 0.0003 0.0006 0.0003

(0.0007) (0.0006) (0.0007)

Negative Earnings News -0.0028*** -0.0025*** -0.0022***

(0.0007) (0.0006) (0.0008)

Size 0.0073*** 0.0076*** 0.0039** 0.0044** 0.0076*** 0.0078***

(0.0024) (0.0022) (0.0018) (0.0017) (0.0026) (0.0024)

Market_to_Book -0.0014* -0.0015* -0.0010 -0.0010 -0.0016** -0.0017**

(0.0008) (0.0008) (0.0008) (0.0009) (0.0007) (0.0008)

Ratings_Change 0.0025 0.0021 0.0023 0.0020 0.0035 0.0032

(0.0035) (0.0035) (0.0033) (0.0032) (0.0035) (0.0034)

Firm controls yes yes yes yes yes yes

Industry fixed effects yes yes yes yes yes yes

Year fixed effects yes yes yes yes yes yes

N 23556 23556 22143 22143 23097 23097

R-sq 0.0515 0.0529 0.0440 0.0453 0.0462 0.0471

51

Appendix Table 1. Variable Definitions

This table presents variable definitions of the key variables used in the paper.

Variable Name Definition

Panel A: Media Coverage Variables

Total_News Ln(total number of news items with a relevance score of 100

but excluding corporate press releases for firm i in year t, scaled

by firm i’s total assets in billion $). The news package includes

Dow Jones, Web and PR Editions.

Earnings_News Ln(the ratio of news items specializing in earnings evaluations

with either positive or negative sentiment to total news for firm

i in year t*100+0.01), where total news is defined as total

number of news items with a relevance score of 100 but

excluding corporate press releases for firm i in year t.

Positive Earnings News Ln(the ratio of news items specializing in earnings evaluations

with positive sentiment to total news for firm i in year t

*100+0.01), where total news is defined as total number of

news items with a relevance score of 100 but excluding

corporate press releases for firm i in year t.

Negative Earnings News Ln(the ratio of news items specializing in earnings evaluations

with negative sentiment to total news for firm i in year t

*100+0.01), where total news is defined as total number of

news items with a relevance score of 100 but excluding

corporate press releases for firm i in year t.

Panel B: Debt Financing Variables

Dummy_Bank_Loans A dummy variable that equals one if a firm has bank loans

outstanding in year t, and zero otherwise.

Bank Loans/Total Debt The ratio of bank loans to total debt in year t, where total debt

include bank loans, bonds & notes, and other debt items, such

as leases.

Dummy_Bonds & Notes A dummy variable that equals one if a firm has public/private

bonds and notes outstanding in year t, and zero otherwise.

Bonds & Notes /Total Debt The ratio of public/private bonds and notes to total debt in year

t, where total debt include bank loans, bonds & notes, and other

debt items, such as leases.

New_Bank_Loans An indicator variable that equals one if a firm has borrowed

new bank loans in year t, and zero otherwise.

New_Public_Bonds An indicator variable that equals one if a firm has issued new

public bonds in year t, and zero otherwise.

52

New_Private_Placement An indicator variable that equals one if a firm has placed

private debt in year t, and zero otherwise.

New_Debt_Issuance An indicator variable that equals one if a firm has borrowed

new bank loans, or has issued new public bonds or placed new

private debt in year t, and zero otherwise.

Bank_Loans_Spread The natural logarithm of bank loan spread, where loan spread

is measured as the all-in-drawn spread above LIBOR.

Bank_Loans_Maturity The natural logarithm of bank loan maturity where maturity is

measured in months.

Bank_Loans_Amount The natural logarithm of bank loan amount where the amount

is measured in million U.S. dollars.

Public_Bonds_Interest

Spread

The natural logarithm of bond interest spread in basis points

and net of yield of treasury securities of comparable maturity.

Public_Bonds_Maturity The natural logarithm of public bond maturity where maturity

is measured in months.

Public_Bonds_Amount The natural logarithm of public bond amount where the amount

is measured in million U.S. dollars.

Private_Placed_Maturity The natural logarithm of privately placed debt maturity where

maturity is measured in months.

Private_Placed_Amount The natural logarithm of privately placed debt amount where

the amount is measured in million U.S. dollars.

Panel C: Firm Level Variables

Size The natural logarithm of total assets.

ROA Net operating income over total assets.

Leverage Total debt over total assets.

Market_to_Book Market value of equity plus book value of debt divided by book

value of total assets.

Sales Growth The percentage of annual growth in total sales.

Cash Flow Cash flows from operations deflated by total assets.

Dummy_Rating A dummy variable that equals one if a firm has an S&P credit

rating, and zero otherwise.

S&P_Rating Defined as Ln (1+ credit rating) where credit rating is coded

from 0 to 22 with 0 representing no rating, 1 representing D or

below, and 22 representing the highest S&P rating AAA.

Ratings_Change Credit rating score at the end of year t minus that at year t-1.

Credit rating score is coded from 0 to 22 with 0 representing

no rating, 1 representing D or below, and 22 representing the

highest S&P rating AAA.

Forecast Dispersion Defined as the standard deviation of analyst earnings forecasts

(one-year ahead EPS) issued by analysts scaled by the absolute

value of mean forecasts. We only include the most recent

forecast by each analyst within a year prior to the earnings

announcement.

Earnings Surprise Defined as actual earnings at the end of year t minus mean

forecasts (one-year ahead EPS) issued by analysts scaled by the

absolute value of mean forecasts. We only include the most

53

recent forecast by each analyst within a year prior to the

earnings announcement.

Panel D: Instrumental Variable and Channel Test Variables

State Growth Opportunity Defined as in Bakaert et al. (2005) and equal to ∑ 𝑤𝑆𝐼𝑡 ∗𝑃𝐸𝐼𝑡 where each state has a set of industries 𝑤𝑆𝐼𝑡 with each

industry having time-varying growth opportunities while

assuming that these growth prospects are reflected in the price

to earnings (P/E) ratios of the industry portfolio 𝑃𝐸𝐼𝑡.

Large Size A dummy variable that equals one if a firm’s total assets are

above the sample median of that year, and zero otherwise.

Stock Index Member A dummy variable that equals one if a firm is a major stock

index member, including Dow Jones, Nasdaq 100, or S&P 500

indexes, and zero otherwise.

High_Ownership_HHI An indicator variable that equals one if a firm’s Herfindahl-

Hirschman index of its institutional ownership concentration is

above the sample median level of that year, and zero otherwise.

Relationship Industry An indicator variable that equals one if a firm belongs to the

following industries with SIC codes of 15, 16, 17, 34, 35, 36,

37, 38, 39, 42, 47, 50, 51, 55, 60, 61, 62, 63, 64, 65, 67, 75, 76,

and 87 (see Cremers, Nair, and Peyer, 2008).

54

Appendix Table 2. Novel News and Debt Structure

This table presents the relationship between novel news and debt structure. The sample includes

all the U.S. firms that have either borrowed bank loans or issued bonds or both during the sample

period 2001-2014. The key explanatory variable Novel News is defined as a new story within a 24-

hour time window across all news stories in a particular package (Dow Jones, Web or PR Editions)

and with relevance score of 100 in RP. The dependent variables are Dummy_Bank_Loan (column

1), Bank Loans/Total Debt (column 2), Dummy_Bonds & Notes (column 3), and Bonds &

Notes/Total Debt (column 4). Detailed definitions of all the other variables can be found in

Appendix Table 1. Standard errors reported in parentheses are heteroskedastic robust and clustered

at the industry level. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels,

respectively.

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

Dep Var

Dummy_Bank

_Loans

Bank Loans

/Total Debt

Dummy_Bonds

& Notes

Bonds & Notes

/Total Debt

Novel News -0.2731*** -0.0345*** 0.0616** 0.0220***

(0.0260) (0.0033) (0.0243) (0.0032)

Size -0.2461*** -0.0944*** 0.5635*** 0.0862***

(0.0190) (0.0025) (0.0186) (0.0023)

ROA 0.0122** 0.0076*** -0.0503*** -0.0043***

(0.0054) (0.0008) (0.0054) (0.0008)

Sales Growth 0.2326*** 0.0709*** -0.3242*** -0.0879***

(0.0687) (0.0099) (0.0712) (0.0094)

Leverage 0.2525*** 0.0006 0.4391*** 0.0458***

(0.0168) (0.0023) (0.0183) (0.0022)

Cash Flow -0.0678*** -0.0075*** -0.0048 0.0052***

(0.0032) (0.0005) (0.0032) (0.0004)

Market_to_Book -0.0501*** -0.0057*** -0.0407*** 0.0016

(0.0065) (0.0010) (0.0075) (0.0010)

Industry fixed effects yes yes yes yes

Year fixed effects yes yes yes yes

N 35994 36002 35888 36002

R-sq 0.1181 0.2081 0.2516 0.3152

55

Appendix Table 3. Additional Robustness Tests

This table presents the results of media coverage on debt structure using an alternative sample (columns 1-4) or an alternative dependent

variable (columns 5-6) for sensitivity tests. The sample includes all the U.S. firms that have either borrowed bank loans or issued bonds

or both during the sample period 2001-2014, except in columns 1-4, the sample is restricted to firms with total book value of equity

between $100 million and $700 million U.S. dollars. The dependent variables are Bank Loans/Total Debt (columns 1-2) defined as the

ratio of a firm’s bank loans to total debt, Bonds & Notes/Total Debt (columns 3-4) defined as the ratio of a firm’s public/private bonds

and notes to total debt, and Term Loans/Total Debt (columns 5-6) defined as the ratio of a firm’s term loans to total debt. Firm controls

include ROA, Sales Growth, Leverage, Cash Flow, and Market_to_Book. Detailed definitions of all the other variables can be found in

Appendix Table 1. Standard errors reported in parentheses are heteroskedastic robust and clustered at the industry level. *, **, and ***

indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

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

Dep Var Bank Loans/Total Debt Bonds & Notes /Total Debt Term Loans/Total Debt

Total_News -0.0292** 0.0326*** -0.0203***

(0.0126) (0.0116) (0.0041)

Earnings_News -0.0054** 0.0048** -0.0064***

(0.0027) (0.0023) (0.0013)

Size -0.1204*** -0.0964*** 0.1436*** 0.1171*** -0.0523*** -0.0380***

(0.0108) (0.0099) (0.0199) (0.0151) (0.0064) (0.0059)

Firm controls yes yes yes yes yes yes

Restricted sample yes yes yes yes no no

Industry fixed effects yes yes yes yes yes yes

Year fixed effects yes yes yes yes yes yes

N 13733 13733 13733 13733 36002 36002

R-sq 0.1400 0.1390 0.2812 0.2797 0.2054 0.2055

56

Appendix Table 4. Media Coverage and Borrowers’ Ratings

This table presents the relation between media coverage and borrowers’ credit ratings. The sample

includes all the U.S. firms that have either borrowed bank loans or issued bonds or both during the

sample period 2001-2014. The dependent variables are Dummy_Rating (columns 1-2), which is

equal to 1 if a firm has an S&P credit rating, and 0 otherwise, and S&P_Rating (columns 3-4),

which is defined as Ln (1+ credit rating) where the credit rating is coded from 0 to 22 with 0

representing no rating, 1 representing D or below, and 22 representing the highest S&P rating

AAA. Detailed definitions of all the other variables can be found in Appendix Table 1. Standard

errors reported in parentheses are heteroskedastic robust and clustered at the industry level. *, **,

and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

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

Dep Var Dummy_Rating S&P_Rating

Total_News 0.3633*** 0.0970***

(0.0371) (0.0186)

Earnings_News 0.0828*** 0.0081*

(0.0119) (0.0044)

Size 1.8681*** 1.6083*** 0.4806*** 0.4168***

(0.0321) (0.0193) (0.0134) (0.0104)

ROA 0.0251*** 0.0243*** -0.0044 -0.0048

(0.0076) (0.0075) (0.0051) (0.0051)

Sales Growth -0.9493*** -0.8781*** -0.3142*** -0.3038***

(0.1023) (0.1028) (0.0435) (0.0431)

Leverage 0.6708*** 0.6576*** 0.1138*** 0.1091***

(0.0240) (0.0240) (0.0161) (0.0156)

Cash Flow 0.0038 0.0084* -0.0132*** -0.0124***

(0.0044) (0.0044) (0.0041) (0.0040)

Market_to_Book -0.0336*** -0.0162 0.0191*** 0.0248***

(0.0106) (0.0104) (0.0060) (0.0065)

Industry fixed effects yes yes yes yes

Year fixed effects yes yes yes yes

N 34984 34984 35018 35018

R-sq 0.5546 0.5530 0.5471 0.5452