Journal of accounting, auditing finance tập 28, số 01, 2013 1

Journal of Accounting, Auditing &
Finance
/>
Dedication
Journal of Accounting, Auditing & Finance 2013 28: 3
DOI: 10.1177/0148558X12472129
The online version of this article can be found at:
/>
Published by:


On behalf of:
Sponsored by The Vincent C. Ross Institute of Accounting Research, The Leonard N. Stern School of
Business

Additional services and information for Journal of Accounting, Auditing & Finance can be found at:
Email Alerts: />Subscriptions: />Reprints: />Permissions: />
>> Version of Record - Dec 14, 2012
What is This?

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Dedication

Journal of Accounting,
Auditing & Finance
28(1) 3
The Author(s) 2013
Reprints and permission:

sagepub.com/journalsPermissions.nav
DOI: 10.1177/0148558X12472129


This issue is dedicated to Professor Lee J. Yao, PhD who passed away on November 14,
2012 due to complications from cancer at the age of 54. He was an expert in interdisciplinary research on forensic accounting, finance and information systems. He published widely
on issues in these diverse topics, resulting in three books and two book chapters, and more
than forty articles in leading refereed journals. His papers have appeared in several leading
journals on Accounting, Finance, Electronic Markets, Accounting Information Systems,
Accounting Education, International Accounting, Information Management, Computer
Information Systems, and Quantitative Finance.

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Not All That Glitters Is Gold:
The Effect of Attention and
Blogs on Investors’ Investing
Behaviors

Journal of Accounting,
Auditing & Finance
28(1) 4–19
ÓThe Author(s) 2013
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0148558X12459606


Nan Hu1, Yi Dong2, Ling Liu1, and Lee J. Yao3

Abstract
This article investigates the relationship between a firm’s visibility in blogspaces, termed
blog exposure, and the cross-sectional stock returns. We show that blog exposure is fundamentally different from the traditional media coverage, and securities with low blog exposure earn higher returns than stocks with high blog exposure. We further illustrate that
such an effect is more prominent for stocks with low institutional ownership. Contrary to
traditional media coverage, the return premium associated with blog exposure cannot be
explained by either the illiquidity hypothesis or the investor recognition hypothesis based
on the rational-agent framework. Instead, our results suggest that blog effect can be attributed to the limited attention theory and cannot be arbitraged due to investors’ selfattribution and short-sale constraints. Our research points out the importance of blogs in
information dissemination, especially for the stocks with limited attention.
Keywords
blog, expected return, limited attention, self-attribution, short-sale constraints, word of
mouth communication

Introduction
In this article, we investigate whether a firm’s visibility within blogspaces, termed blog
exposure, can affect security pricing even if the blog itself does not supply any authentic
news. When controlling for environmental factors such as traditional media coverage and
analyst coverage, and various risk factors such as size, book-to-market (BM) ratio, beta,
and momentum, our results indicate that stocks with high blog exposure earn lower returns
than stocks with low blog exposure. These results are more pronounced among stocks with
lower institutional ownership. We also show that a portfolio composed of stocks with low
institutional ownership produces sustained future abnormal returns. Such returns peak
approximately 10 months after formation of the portfolio, and reverse sharply after that,
1

University of Wisconsin, Eau Claire, USA
University of International Business and Economics, Beijing, China
3
Loyola University, New Orleans, USA
2

Corresponding Author:
Yi Dong, No. 10, Huixin Dongjie, Chaoyang District, Beijing, China
Email:

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Hu et al.

5

displaying a long-term return reverse pattern. In addition, the abnormal returns of a portfolio composed of stocks with high institutional ownership meander around zero over the
next 12 months after formation of the portfolio. We examine three plausible explanations
for this return premium, two from the rational-agent framework (liquidity and investor recognition) and one from the behavior finance framework (limited attention with short-sale
constraints). Surprisingly, contrary to media coverage, for which the return premium can be
explained by liquidity and investor recognition based on the rational-agent framework
(Merton, 1987), the return premium of blog exposure cannot be explained by that framework. Instead, our findings show that a blog exposure premium originates from the joint
forces of biased disclosure of bloggers and limited attention of consumers. Such overvaluation becomes larger over time due to consumers’ biased self-attribution, while the shortsale constraints prevent such a premium from being arbitraged. Our results are most consistent with the notion in Hirshleifer, Lim, and Teoh (2009). In addition, our results show that
the blog exposure effect is not subsumed by traditional media exposure, as documented by
Fang and Peress (2009). However, without mainstream media (e.g., news) planting a seed
of discussion, blog exposure actually has no impact on security returns.
Understanding the relation between blog exposure and stock returns is very important
for the marketing community and the finance community because it demonstrates how a
firm’s marketing strategy within blogspaces can influence elements of its finance strategy,
such as cost of capital. As a form of word of mouth (WOM)1, blogs represent the fastest
growing medium of personal publishing and the newest method of individual expression
and opinion on the Internet.2 In 2004, blogs were a fairly new phenomenon with only 5
million bloggers worldwide (Wright, 2006). However, at the time of writing this article,
according to www.BlogPulse.com, there were more than 126 million blogs on the World

Wide Web. We believe blogs are playing a role that is as important as that of newspapers
because (a) information in a blog is not a simple reflection of what is covered by traditional
news. In fact, many blogs address topics that are not covered by the mainstream media at
all. Blogs might either lead or follow traditional news and (b) blogs disseminate information to a much broader audience faster and with in-depth analysis. In fact, compared with
other online media, blogs are viewed as more credible. In addition, compared with traditional sources, more than three quarters of respondents view blogs as moderate to very
credible.
This article adds to the growing body of studies of the valuation of online WOM literature (Antweiler & Frank, 2004; Tumarkin & Whitelaw, 2001; Wysocki, 1999). Our article
is also closely related to but distinct from Fang and Peress (2009)’s media coverage study
that shows that, by helping to reach a broad population of investors, mass media can alleviate information frictions and affect stock price even if it does not contain authentic news.
However, Fang and Peress focus on studying the impact of traditional media coverage,
measured by the number of newspaper articles about a firm on its stock returns. We concentrate on examining the influence of nontraditional media coverage—blog exposure—on
security pricing, while controlling for the traditional media coverage. Furthermore, our
results are distinct from Fang and Peress’ conclusions. The return premium of news coverage can be explained by the rational-agent framework (Fang & Peress, 2009). However,
such a return premium of blog exposure can only be explained by the joint forces of investors’ behavior and short-sale constraints.
Another branch of literature related to our study is the behavior finance literature that
recognizes that attention influences investors’ selling and purchasing behavior, and causes
asset pricing deviation from its fundamental value. The underlying reason is that investors

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


6

Journal of Accounting, Auditing & Finance

face a formidable search problem when buying a stock. Investors address such an issue by
limiting their choice sets (Barber & Odean, 2008) to those stocks that have recently caught
their attention (Odean, 1999). Because investors are overconfident (Daniel, Hirshleifer, &
Subrahmanyam, 1998; Odean, 1998) and biased toward self-attribution (Daniel et al.,
1998), stocks over bought heavily by individual investors will enjoy short-term positive

contemporaneous returns. These results emphasize the attention driven by traditional
media, such as securities mentioned in the news or securities that have gone through large
volume or price changes. Our study, however, focuses on the attention driven by nontraditional media, blogs.
One practical implication of our results is that a firm’s visibility within blogspaces,
regardless of whether the blog discussion is positive or negative, influences investors’ purchase decision. As a nontraditional media, blog discussions serve the role of disseminating
information that was traditionally covered by conventional information channels such as
analysts’ forecasts or newspapers. Such a role is especially important for stocks with limited attention. In fact, as we documented, because blog discussions do not affect the
expected stock returns when there is a lack of echo from the mainstream media, companies
should plant the seed of a discussion to foster the conversation within blogspaces.
Marketing managers of a firm can use blogs not only to communicate more efficiently with
its customers, partners, suppliers, and other stakeholders but also to work closely with
finance managers to lower the cost of capital by delivering information to a broader audience in a faster manner.
The rest of this article is organized as follows. In the next section, we discuss our data
collection processes, elaborate our variable definitions, and present our empirical results. In
the section ‘‘Explaining the Blog Visibility Effect,’’ we discuss three possible causes of the
blog exposure effect, and in the section ‘‘Conclusion,’’ we present our concluding remarks.

Data Collection and Empirical Results
Data Collection
In this article, blog visibility/exposure is defined as the extent to which a company’s products or services are discussed in blogspaces. We collect such a measurement from
www.BlogPulse.com using its conversation track tool. Our data collection is composed of
two steps. In Step 1, we identify the brand names of products or services associated with a
company by searching a company’s web site, reading its financial reports, or using
researchers’ domain knowledge. In Step 2, using the names identified in Step 1 as keywords, for each company, we retrieve its blog visibility over time using the conversation
tracker tool provided by www.BlogPulse.com. Of the total daily blogging activities traced
by www.BlogPulse.com, this measurement represents the percentage of the total blog conversation related to a particular firm, its products, or its services. It measures how (and
how much) a firm’s current customers, potential customers, competitors, industry peers,
and so on are talking about the products or services of the firm. Hence, it represents the
visibility of a firm within the overall blogspaces. Figure 1 shows one example of the blog
trend for Advance Auto Parts Inc.3

The data on a firm’s media exposure were collected from Factiva. Factiva is a database
of the Dow Jones and Reuters companies. It provides timely, domestic, and international
information, such as articles from the Dow Jones and Reuter’s newswires and The Wall
Street Journal. This information covers market data, firm and industry news, financial

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Hu et al.

7

Figure 1. Blog coverage data collection
Note: In this figure, we use Advance Auto Parts Inc. as an example to show how we collect the exposure of a firm within blogspaces using www.blogpulse.com

quotes, and newspaper articles. We collected and counted all the nonredundant news items
each day for the fiscal year of each company. Our analysts’ forecast data were collected
from the Institutional Brokers Estimates System. The company accounting data were
obtained from CompuStat, and the stock return data were from the Center for Research in
Security Prices (CRSP). For detailed variable definitions, please refer to the appendix.
Following Amihud (2002), we delete one firm with extreme illiquidity value. The final
sample contains 404 Standard & Poor (S&P) 500 firms with daily blog visibility and traditional media coverage information from March 1, 2006, to August 22, 2006. We give
detailed variable definitions in the appendix. Following Fang and Peress (2009), to minimize the noise of daily data, for each firm, we aggregate its daily blog visibility and
Factiva news to a monthly level to represent its blog visibility and media coverage, respectively. Table 1 shows the summary statistics of our key variables, including blog coverage
and media coverage. As we can see, only 1% of the stocks in our sample do not have
media coverage, but more than 25% of the stocks in our sample do not have blog coverage.
It seems that for the S&P 500 firms, and for this sample period, traditional media has
broader coverage in terms of the number of stocks discussed than blog conversations.
However, this does not necessarily mean that, in general, traditional news media has
broader coverage than blogs.

Table 2 shows the Pearson correlation among blog coverage, media coverage, and other
firm characteristics. We find blog coverage and media coverage are positively correlated,
and analysts, news, and blogs have the tendency to feature the same set of stocks. This is
reasonable because analysts, news, and blogs pay attention to large firms and well-known
firms. Furthermore, it seems that traditional media cares more about firms with a lower

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


8

Journal of Accounting, Auditing & Finance

Table 1. Summary Statistics of Blog Exposure and Media (Factiva) Coverage
Blog
M
SD
Skewness
Quintile
100% maximum
99%
95%
90%
75% Q3
50% median
25% Q1
10%
5%
1%
0% minimum

Factiva
0.40
1.22
5.66
11.89
7.38
1.90
0.80
0.22
0.04
0.00
0.00
0.00
0.00
0.00

M
SD
Skewness
Quintile
100% maximum
99%
95%
90%
75% Q3
50% median
25% Q1
10%
5%

1%
0% minimum

236.42
415.45
3.50
2,791.00
2,267.00
1,085.00
535.00
224.00
95.00
41.00
19.00
7.00
0.00
0.00

Note: See the appendix for detailed variable definitions.

institutional ownership, whereas bloggers do not differentiate between firms with high institutional ownership and those with low institutional ownership.

Blog Coverage and Short-Term Cross-Section of Stock Return
In this section, we study whether a firm’s exposure within blogspaces affects its security
pricing by regressing stock returns on blog coverage, with media coverage, beta, size, BM
ratio, and other risk factors controlled (Table 3, Model 1). For a robustness check, we take
an incremental approach by adding momentum, media coverage, percentage of institutional
ownership, and illiquidity one by one, and present the results in Models 2, 3, and 4 of
Table 3, respectively. To control for the potential confounding effect caused by the difference across months and industries, we also include month fixed effect and industry fixed
effect (two-digital standard industrial classification from CRSP) in all our models.

For Model 1, the coefficient of blog exposure is 20.0026 with t-value at 21.93, which
is negatively associated with the following month’s stock return. Therefore, stocks with
high blog exposure tend to have lower stock returns compared with the stocks with low
blog coverage. Such an impact is not subsumed when we add the traditional media exposure and other risk factors (Models 3 and 4). Furthermore, using Factiva as proxy for
media coverage, we found that media coverage has an insignificant coefficient regardless
of whether we exclude the blog exposure. The untabulated table shows that our results are
qualitatively the same if we exclude those firm-month observations in which there are earning announcements for the firm in that month. Therefore, we conclude that the blog effect
is not driven by month effect, industry effect, or earnings announcements.
Our media coverage results are different from those of Fang and Peress (2009).
However, in their article, they focus on four influential newspapers with large subscriptions. Our media coverage includes all the newspapers included in the Factiva database.
Another reason for the different results might lie in the sample selection. In their article,

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Hu et al.

9

Table 2. Pearson Correlation
Blog
1
2353
Factiva
0.1550
\0.0001
2,353
Size
0.1334
\0.0001

2,346
BM
20.1214
\0.0001
2,261
Momentum 20.0740
0.0003
2,352
Dispersion 0.0125
0.5513
2,266
IO
0.0088
0.6695
2,353
Coverage
0.1293
\0.0001
2,281
Illiquidity
20.0212
0.3043
2,353
Idiorisk
20.0038
0.8558

Factiva

Size

BM

Momentum Dispersion

IO

Coverage Illiquidity Idiorisk

Blog

1
2,353
0.5198
\0.0001
2,346
20.0383
0.0685
2261
20.0242
0.24
2,352
0.0050
0.8131
2,266
20.1053
\0.0001
2,353
0.3358
\0.0001

2,281
20.0369
0.0732
2,353
20.0725
0.0004

1
2,346
20.2583
\0.0001
2,259
0.0886
\0.0001
2,345
0.0076
0.7169
2,263
0.0614
0.0029
2,346
0.6064
\0.0001
2,278
20.2677
\0.0001
2,346
20.3569
\0.0001

1
2,261
20.1616
\0.0001
2,261
20.0384
0.0722
2,191
20.0905
\0.0001
2,261
20.1358
\0.0001
2,201
0.0348
0.0983
2,261
20.0612
0.0036

1
2,352
0.0218
0.3008
2,266
0.2115
\0.0001
2,352
0.0195
0.3528

2,281
20.0602
0.0035
2,352
0.1148
\0.0001

1
2,266
20.0500
0.0173
2,266
0.0012
0.9549
2,266
20.0399
0.0578
2,266
0.0035
0.8681

1
2,353
0.0353
0.0916
2,281
20.1454
\0.0001
2,353
0.0578

0.005

1
2,281
20.1501
\0.0001
2,281
20.0635
0.0024

1
2,353
0.2501
1
\0.0001

Note: BM = book to market; IO = institutional ownership. For detailed variable definitions, refer to the appendix.

they argue that the most significance of high media coverage, low return effect comes from
the small and illiquid firms. However, in our article, we use only S&P 500 firms, which are
all big firms.

Blog Coverage and Long-Term Cross-Section of Stock Returns
In the previous section, we documented the short-term return premium associated with low
blog exposure firms. In this section, we study the long-term return impact of blog exposure.
Figure 2 represents the cumulative abnormal returns of stocks with different blog coverage,
starting from Month 1 after the portfolio formation. Each month, we sort our sample into
three groups according to their monthly blog exposure. Then, based on capital asset pricing
model, for each stock, we estimate its abnormal return in the subsequent 13 months. Low
blog curve (Figure 2) represents the average abnormal returns for the lowest blog coverage

group, whereas high blog curve represents those of the highest blog coverage group. Figure
2 shows that the highest blog coverage portfolio consistently has insignificant abnormal
returns starting from the 1st month after the formation of the portfolio to the next 13

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


10

Journal of Accounting, Auditing & Finance

Table 3. Blog Coverage, Media Coverage, and Stock Returns
Model 1
Intercept
Blog
Factiva
Beta
Size
BM
Momentum
Coverage
IO
Illiquidity
Adjust_R2

0.0116 (20.45)
20.0026 (21.93)
20.0026 (21.1)
0.0024 (1.72)
0.0042 (1.78)

.1119

Model 2
0.0295 (21.03)
0.0000 (20.87)
20.0028 (21.78)
0.0012 (0.70)
0.0040 (1.72)

.1107

Model 3
20.0043
20.0033
0.0000
0.0003
0.0021
0.0012
20.0323

(20.15)
(22.45)
(20.65)
(20.13)
(1.23)
(0.51)
(25.79)

.1253

Model 4
0.0041
20.0033
0.0000
0.0002
0.0019
0.0013
20.0311
20.0001
20.0034
20.0203
.1246

(20.12)
(22.39)
(20.65)
(20.06)
(0.87)
(0.56)
(25.36)
(20.25)
(20.68)
(20.95)

Note: BM = book to market; IO = institutional ownership; SIC = standard industrial classification. In this table, the
dependent variable is the following month’s stock return. The independent variables include traditional risk factors
and other firm characteristics that may affect expected stock returns. Our samples include 404 distinct firms. We
run pooled regression, controlling month fixed effect and industry fixed effect (2-digital SIC).
Nextmonthre5a 1 bbeta 3 beta 1 bsize 3 size 1 bBM 3 BM 1 bmomentum 3 momentum 1 bIO 3 IO 1 bcoverage 3

coverage 1 billiquility 3 illiquility 3 bblog 3 blog 1 bFactiva 3 Factiva:

Figure 2. Cumulative abnormal return
Note: CAPM = capital asset pricing model; CAR = coverage abnormal return. Figure 2 presents the
cumulative abnormal return of a high blog coverage sample and a low blog coverage sample starting
from Month 1 after the portfolio formation. Each month we sort our sample into two groups according to its monthly blog coverage. Then, based on CAPM model, for each stock, we estimate its cumulative abnormal return in the subsequent 13 months. The blue line represents the average CAR for
the low blog coverage group, whereas the red line plots that of the high blog coverage group.

months, and the low blog coverage portfolio enjoys a positive coverage abnormal return in
the following 13 months. In addition, there is a return reversal when the cumulative abnormal returns peak at the 10th month.

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Hu et al.

11

Our results are not driven by the return reversals among no-coverage stocks because the
abnormal returns associated with low blog visibility firms do not reverse until 10 months
after the portfolio formation, while a typical return reversal pattern among no-news losers
is short lived (Chan, 2003). Given that our blog coverage effect represents a long-term
effect, there must be a force to prevent traders from such arbitrage. So what are the driving
forces?

Explaining the Blog Visibility Effect
In this section, we examine three potential explanations of the return premium associated
with blog exposure: illiquidity, investors’ recognition, and bloggers’ limited attention and
short-sale constraints. The first two are based on the rational-agent framework, whereas the
third explanation originates from behavior finance literature.

Illiquidity Hypothesis
Fang and Peress (2009) document that stocks with no media coverage enjoy higher returns
than stocks with high media coverage. Furthermore, such a return premium is very stable
over time. They believe that the lack of liquidity explains why such an abnormal return
cannot be arbitraged. Hence, we first test whether blog visibility is similar to media coverage aroused due to illiquidity. According to the rational-agent framework, if the blog effect
represents an arbitrage opportunity, it can only be persistent in the situation where some
kind of impediment to trade prevents arbitrage. Hence, if the blog visibility effect is also
caused by illiquidity, we expect the blog effect to be most significant in the portfolios that
are composed of the most illiquid stocks.
We use multivariate regressions (either separate regressions for each portfolio or pooled
together) to study whether the illiquidity can explain the cross-sectional return differences
we document. Each month we sort our sample into three portfolios based on various illiquidity proxies proposed by previous literature including the Amihud (2002) illiquidity
ratio, bid/ask spread, trading price, and firm size. Stocks with the highest illiquidity ratio or
spread, or stocks with the lowest price or size, are the most illiquid ones. For each illiquidity proxy and each portfolio, the following month’s stock returns are regressed on blog coverage with other controlled factors that are known to affect the cross-section of returns,
such as beta, size, BM ratio, and momentum. To control for the potential heterogeneity
across months as well as industries, we run our regression by controlling month and industry effects.
Table 4 reports the blog effect of stocks sorted by different illiquidity proxies. Due to
space constraint, for each portfolio under different illiquidity measures, we report only the
coefficient before blog coverage and the corresponding t statistic. If the blog visibility
effect is caused by illiquidity, we expect the blog exposure effect to be most significant in
the portfolios composed of the most illiquid stocks, such as stocks with the highest illiquidity and spread or those with the lowest price or size. However, the results shown in Table 4
fail to support the illiquidity hypothesis. For example, with respect to the Amihud illiquidity ratio, blog effect is significant in the low illiquidity portfolio (para = 20.0068 and
t-value = 23.04) and the medium illiquidity portfolio (para = 20.0058 and t-value =
22.9), but not in the high illiquidity portfolio (para = 0.0022 and t-value = 0.67). This contradicts the illiquidity hypothesis that high impediments of trade should result in the most
significant blog exposure effect. Similar examples of evidence are found when we use

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012

12

Journal of Accounting, Auditing & Finance

Table 4. Illiquidity and the Blog Effect
Low

Illiquidity
Spread
Price
Firm size

Medium

High

Parameter

t-value

Parameter

t-value

Parameter

t-value

20.0068
20.0042

20.0040
0.0004

23.04
21.88
21.46
0.14

20.0058
20.0048
20.0079
20.0015

22.39
21.90
23.20
20.40

0.0022
20.0015
20.0065
20.0072

0.67
20.55
21.68
23.57

Note: In this table, each month we classified our sample into three portfolios according to their respective illiquidity proxies. We use the Amihud illiquidity ratio (Amihud 2002), bid ask spread, size of the firm, and price to proxy
for illiquidity. The following months’ stock returns are regressed on blog coverage, beta, size, BM ratio, and

momentum, controlling month fixed effect and industry fixed effect. For brevity, for each portfolio under different
illiquidity measures, we report only the coefficient before blog coverage and corresponding t statistic.

alternative illiquidity measurements. Using price or firm size to proxy for liquidity, we
observe that the blog exposure associated return premium is most significant in the portfolios composed of the most liquid stocks, for example, stocks with highest trading prices
(para = 20.0065 and t-value = 21.68) or stocks with the biggest firm sizes (para =
20.0072 and t-value = 23.57). In addition, we also use bid/ask spread to proxy for illiquidity because highly illiquid stocks often have a large bid/ask spread. The coefficients of
blog visibility for low and medium bid/ask spread portfolios are negative and significant
but not for the high bid/ask spread portfolio (para = 20.0015 and t-value = 20.55)—the
most illiquid stocks. Overall, our results show that the blog visibility effect disappears
among the most illiquid stocks, hence are contrary to the illiquidity theory that suggests
that the blog visibility effect should be the strongest for most illiquid securities.
To check the robustness of the above results, for each illiquidity proxy, we run a pooled
regression using all three portfolios. To be more specific, for each illiquidity proxy, we
first define one dummy variable to distinguish high illiquidity firms from low illiquidity
firms. For example, the dummy variable Rank for Amihud illiquidity is set to one if the illiquidity ratio of that firm is higher than the median Amihud illiquidity ratio, and zero otherwise. Then, in addition to the original independent variables, we also include an interaction
term between the blog coverage variable and the illiquidity dummy variable Rank to test
whether the blog exposure effect is more significant for stocks with higher illiquidity. Our
untabulated results show that consistent with the conclusions from running separate regressions, results using pooled regressions also demonstrate that illiquidity cannot explain the
blog exposure effect that we have documented.

Investors’ Recognition Hypothesis
Fang and Peress (2009) posit that, in addition to illiquidity, the investor recognition theory
proposed by Merton (1987) can also explain the media coverage effect they observe. Under
this theory, investors are assumed to have incomplete information and are aware of only a
subset of the available stocks. For stocks with less investor recognition, investors with
incomplete information will require higher returns as a compensation for the undiversified
risk and market clearing. Therefore, we hypothesize that if blog coverage can increase a
stock’s recognition, then the blog effect should be more prominent for stocks with a low
degree of investor recognition. Several measures, including analyst coverage, idiosyncratic

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Hu et al.

13

Table 5. Investor Recognition and the Blog Effect

Idiorisk
Coverage
Expenditure
Investor base

Low parameter

t-value

Medium parameter

t-value

High parameter

t-value

20.0088
20.0012
0.0035

20.0040

23.60
20.42
0.23
21.47

20.0069
20.0037
20.0068
20.0061

23.18
21.31
22.33
22.50

0.0004
20.0077
20.0012
20.0016

0.16
23.37
20.39
20.53

Note: In this table, each month we sort our sample into three portfolios according to different investor recognition proxies, including analyst coverage, idiosyncratic risk, advertising expenditure, and the number of investors of
a stock. The following months’ stock returns are regressed on blog visibility, beta, size, BM ratio, and momentum
as well as month fixed effect and industry fixed effect. For brevity, for each portfolio under different illiquidity measures, we report only the coefficient before blog coverage and the corresponding t statistic.

risk, advertising expenditure, and the number of investors of a stock, are adopted as proxies
for investors’ recognition. Analyst coverage is selected because investors listen to analysts
to make investment decisions, and stocks with high analyst coverage are assumed to have a
high degree of investor recognition. Idiosyncratic risk is adopted as another investor recognition measure because it represents the imperfect diversification driven by a lack of investors’ recognition (AHXZ 2006). Furthermore, following previous literature (Grullon,
Kanatas, & Weston, 2004; Singh, Faircloth, & Nejadmalayeri, 2005), we develop two additional investor recognition measures: advertising expenditure and the number of common
shareholders of a security.
Following the methods in the illiquidity hypothesis test section, we examine whether the
blog effect can be explained by investors’ recognition using a separate regression approach
and a pooled regression approach. For the separate regression methods, again we sort our
sample into three portfolios in each month according to different investor recognition measures. Then, for each investor recognition proxy and each portfolio, following months’
returns are regressed on blog visibility while controlling for other factors that are known to
affect the cross-section of returns such as beta, size, BM ratio, and momentum (Table 5).
Similarly, as in the section ‘‘Illiquidity Hypothesis,’’ we also ran pooled regressions to
study the impact of investor recognition measures on stock returns and reach very similar
conclusions as when we followed a separate regression approach.
Table 5 presents the results of separate regressions for portfolios with different degrees
of investor recognition. Following Table 4, for each investor recognition proxy, we report
only the coefficient and t-value of blog coverage in each portfolio. As we can see, when
investor recognition is measured by idiosyncratic risk, the coefficient of blog visibility is
significant only for the low and medium idiosyncratic risk portfolios, but not for the high
idiosyncratic risk portfolio. In addition, the absolute value of the coefficient of the low
idiosyncratic risk portfolio is bigger than that of the medium idiosyncratic risk portfolio.
This indicates that the blog effect is more significant in portfolios with high investor recognition, failing to support the hypothesis that the blog exposure effect can be explained by
investors’ recognition. We reach a similar conclusion when we use analyst coverage, advertisement expenditure, and number of investors as proxies for investor recognition. The blog
effect is much more prominent for stocks with higher analyst coverage, medium advertisement expenditure, and a medium investor base. None of these evidences support the investor recognition hypothesis.

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012

14

Journal of Accounting, Auditing & Finance
0.518
0.516
0.514

low blog

0.512

high blog

0.51
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Figure 3. Percentage of buy over time
Note: This figure presents the percentage of buy transaction for firms with the low daily blog coverage during the subsequent 30 days after portfolio formation. We sort our sample into 2 portfolios
based on their daily blog coverage, and low (high) daily blog coverage firms include those whose daily
coverage is lower (higher) than the median blog coverage on that day. We also conduct a similar analysis by sorting our sample into 4 or 10 portfolios and reach a similar conclusion.

Limited Attention Hypothesis and Short-Sale Constraints
Based on the rational-agent framework, the illiquidity hypothesis and the investors’ recognition hypothesis are able to explain how news media coverage drives the cross-section of
stock returns (Fang & Peress, 2009). However, such a rational-agent framework cannot justify the blog exposure effect we have documented in the section ‘‘Data Collection and
Empirical Results.’’ Given that the blog exposure effect persists over time, there must be
other forces driving this return premium and preventing it from being arbitraged.
In this section, we propose that the limited attention theory (Hirshleifer et al., 2009)
offers an explanation for the cross-sectional return differences we document, while the
short-sale constraints sustain such return premium over time. From a disclosure prospective, blogs serve as a positively biased disclosure channel. Investors with limited attention
will selectively interpret the biased disclosure by assuming that no news is good news. This

will make investors net buyers of low blog exposure stocks. The biased interpretation behavior of investors on blog disclosure is consistent with the framework proposed by
Hirshleifer et al. (2009) about how investors interpret the disclosures of those firms with
limited attention. To make matters worse, investors with limited attention are overconfident
(Daniel et al., 1998) and biased toward self-attribution (Daniel et al., 1998). In other words,
individual investors believe they are better in assessing blog information (i.e., overconfident) and they selectively trust the messages in WOM and selectively validate their beliefs
(i.e., biased toward self-attribution). The overall net buying behavior will be sustained over
an even longer period of time.
Are investors net buyers of low blog exposure securities? To prove that consumers are more
likely to be net buyers of securities with low blog exposure, we estimate the percentages of
buy transactions out of the daily total number of transactions for a high blog exposure portfolio and a low blog exposure portfolio, respectively (Figure 3). We follow the buy and sell
classification algorithm proposed by Lee and Ready (1991), which is commonly used in
early literature, such as Easley, Kiefer, O’Hara, and Paperman (1996), and Easley, Hvidkjaer,
and O’Hara (2002). Following Lee and Ready (1991), a transaction is defined as a buy (sell)
if it is executed above (below) the midpoint of the bid and ask price. For trades on the bid/
ask midpoints, we use a ‘‘tick test’’ to determine whether it is a buy or sell. To be more specific, a trade is a buy (sell) if it is executed at a higher (lower) price than the previous trade.
For those trades that have the same price as the previous trade, we look at the historical price

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Hu et al.

15

Table 6. Short-Sale Constraints and the Blog Effect
Variable
Blog

Low IO parameter

t-value

Medium IO parameter

t-value

High IO parameter

t-value

20.0067

22.58

20.0036

21.40

20.0020

20.82

Note: IO = institutional ownership; BM = book to market. In this table, in each month, we sort our sample into
three portfolios according to their short-sale constraints proxy, the institutional ownership. Then the following
month’s stock returns are regressed on blog visibility, beta, size, BM ratio, and momentum, as well as month fixed
effect and industry fixed effect. For brevity, for each portfolio, we report only the coefficient and t-value of blog
coverage

until we find a change in the trade price. Following Lee and Ready, for the computation, we
match our trade prices with 5-s-old quotes. After classifying the buy and sell for each trade,

we cumulate the number of buys and sells in a day to get the aggregated daily number of
buy and sell. Then the daily percentage buy is defined as the total number of buy transactions
divided by the total number of all transactions on that day.
Figure 3 presents the percentage of buy transactions for firms with low daily blog coverage for the 30 days subsequent to portfolio formation. We sort our sample into 2 portfolios
based on their daily blog coverage. Low (high) daily blog coverage firms include those
whose daily coverage is lower (higher) than the median blog coverage on that day. We also
conduct a similar analysis by sorting our sample into 4 or 10 portfolios and reach a similar
conclusion. Our results show that, regardless of the magnitude of blog exposure, on average, there is an increase in the percentage of buy transactions after the portfolio formation,
which might be driven by the overall market situation. Furthermore, Figure 2 shows that,
compared with the high blog exposure portfolio, the low blog exposure portfolio has a
higher percentage of buy transactions.4 The untabulated mean difference comparison also
validates this conclusion (difference = 0.0022 and t-value = 3.99). In addition, this difference remains relatively stable over time after portfolio formation. In addition, the untabulated results show that most of the increase in the percentage of buy transactions
concentrates on small investors.
Short-sale constraints. In our previous section, we documented that because investors with
limited attention become net buyers of low blog exposure securities, stock prices go up.
How about institutional investors or other rational investors? Why do they not come in and
fix this ‘‘irrational’’ behavior? We believe that the short-sale constraints might be one of
the answers. Short-sale constraints can prevent the arbitrage from happening, hence making
the return pattern we observe last longer.5 We expect, if short-sale constraints can explain
the blog exposure effect, that effect should be concentrated in a portfolio composed of
stocks with the highest short-sale constraints. Following Asquith, Pathak, and Ritter (2005),
we use institutional ownership of a firm as a proxy for its short-sale constraints, and a firm
with high institutional ownership is treated as the one with low short-sale constraints.
Table 6 shows that the blog effect is significant only in low institutional ownership
groups (para = 20.0067 and t-value = 22.58), representing a high sale constraints situation. Such evidence supports our hypothesis that the blog effect we documented can be
explained by short-sale constraints.

Relationship Between Blog Exposure and Media Coverage
Our readers might question how securities with low blog exposure get attention. If nobody
ever talks about a stock, how can investors be aware of it? Hence, in this section, we study

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


16

Journal of Accounting, Auditing & Finance

Table 7. Relationship Between Factiva and Blog
Factiva
Blog

Low parameter

t-value

Medium parameter

t-value

High parameter

t-value

0.0001

0.03

0.00

20.9700

20.00426

22.1800

Note: We sort our sample into three portfolios in each month according to their media coverage, proxy by
Factiva. Then the following month’s stock returns are regressed on blog visibility, beta, size, BM ratio, and momentum as well as month fixed effect and industry fixed effect. For brevity, for each portfolio, we report only the coefficient and t-value of blog coverage.

where the attention comes from. We believe that blog exposure needs to be built on news
attention. Hence, we investigate whether our blog effect will be subsumed under the
recently documented media effect (Fang & Peress, 2009). We first sort our sample into
three portfolios according to their monthly media coverage, proxied by Factiva. Then for
each portfolio, we regress the following month’s stock returns on blog coverage, size, beta,
BM ratio, and momentum. Table 7 shows that, with traditional media coverage controlled,
the blog effect is significant only in high media coverage portfolios and is insignificant in
low and medium media coverage portfolios. Our interpretation is that blog conversations
are not a simple reflection of the information content of traditional media; hence, the blog
effect cannot be fully subsumed by it. As a result, to really force the market to listen to
blog conversations, mainstream media need to plant seeds to spark the discussion; otherwise, a simple blog conversation without a mainstream echo will have no market response.
Furthermore, even with heavy traditional media coverage, blog conversations will not be
buried and will still stimulate market responses.

Conclusion
In this article, we investigate the relation between blog coverage and the cross-sectional
stock returns. We show that blog coverage is different from the traditional media coverage
documented by previous literature (Fang & Peress, 2009). We find that high blog coverage
is associated with low stock returns, even when controlling for other risk factors and traditional media coverage. We further illustrate that such an effect is more prominent for
stocks with low institutional ownership and cannot be explained by either the illiquidity
hypothesis or the investor recognition hypothesis, which have been shown in explaining the

cross-sectional relation between media coverage and expected stock returns. Our interpretation is that the blog coverage effect is caused by the selective interpretation of investors
with limited attention on the blog posting. The abnormal returns associated with the blog
exposure effect are sustained over time and cannot be arbitraged within a short period of
time due to short-sale constraints. All these things make blogs an important information
dissemination channel.
However, we should carefully interpret our results because there may be some other
mechanism that might cause the same phenomena we documented. For example, if overall
blog contents are negative instead of positive, then ‘‘no news is good news’’ might actually
be a rational response in the blog coverage context. If investors with limited attention fail
to understand such a relation, then investors with limited attention are likely to be pessimistic about firms with low blog coverage compared with rational investors, which will lead to
undervaluation in the current period. If stocks with low blog coverage are undervalued in
the current period, subsequently they outperform stocks with high blog exposure. Even

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Hu et al.

17

though for our data set, our untabulated sentiment mining results rule out such an explanation,6 future research might look deep into this issue with new data set covering big,
medium, and small firms, and across a longer period of time to draw more insights.
Our study offers great insights as to the importance of the marketing activities of a firm
on its expected return. Our results justify the buzz value creation of marketing strategy on
firm’s valuation. Our study also has policy implications for government agencies, such as
the Securities and Exchange Commission because it brings to the forefront the effect and
importance of blog information in the market valuation of firms. This is especially important for those firms with many small and naı¨ve investors, who have limited channels to
access and limited capabilities to process/digest value-relevant information.

Appendix

Variable Definitions
Variable
Coverage

Beta
Blog
Book to market
Dispersion

Expenditure
Factiva
Idiorisk
Illiquidity
Investor base
Institutional
ownership (IO)

Momentum
Size

Spread
Volume

Definitions
Analyst coverage is from the summary statistics database of Institutional
Brokers Estimates System (I/B/E/S). It is based on the variable Numest
(number of estimates), which represents the total number of estimators
covering the company for the fiscal period (annual forecast only).
Used to control market risk, run capital asset pricing model regression using
previous 60-month data.

Monthly blog coverage, defined as a sum of daily blog coverage.
Log of (book value/market value).
Analyst forecast dispersion, from the summary statistics database of I/B/E/S
database. It is based on stdev/medest, where stdev represents the standard
deviation of the forecast and medest represents the median estimation of
forecast for the fiscal period (annual forecast only).
Advertising expense from CompuStat Merged Fundamental Annual File. It is
based on the variable XAD.
Monthly media coverage, defined as a sum of daily newspaper news, from
Factiva.
Idiosyncratic risk, follow AHXZ (2006).
Follow Amihud (2002) method.
Number of common shareholders (CSHR)/ordinary shareholders of a stock.
It is based on CSHR collected from CompuStat.
IO from Thomson Reuters. IO is defined as all the shares held by institutional
investors divided by shares outstanding. It is on quarterly basis. Each month,
we use the IO of previous quarter as the IO for that month. We also match
monthly IO with the nearest quarterly end IO. Qualitatively, both measures
result in similar results.
Previous 12-month cumulative return.
Log of market value, defined as PRC 3 SHROUT based on Center for
Research in Security Prices’ (CRSP) monthly file, where PRC is the closing
price and SHROUT is the common shares outstanding.
Monthly bid ask spread (ask high minus bid low).
Monthly trading volume based on CRSP monthly file; it is the sum of the
trading volumes during that month.

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012

18

Journal of Accounting, Auditing & Finance

Acknowledgments
We would like to thank the Editor-in-Chief Kashi R. Balachandran and an anonymous reviewer as
well as workshop participants at the 2010 American Accounting Association Annual Meeting for
helpful suggestions.

Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/
or publication of this article.

Funding
The author(s) received no financial support for the research, authorship, and/or publication of this
article.

Notes
1. Blog content is written by authors (also known as bloggers). It is a form of word of mouth.

2.
3.

4.

5.

6.

Typically, there are websites comprising blog posts that are organized into categories and sorted

in reverse chronological order. Most blogs allow readers to comment on individual blog
posts.—Adopted from Wright (2006).
www.blogpulse.com. BlogPulse is an automated trend discovery system for blogs. BlogPulse
applies machine-learning and natural-language processing techniques to discover trends in the
highly dynamic world of blogs.
We admit that there might be some noise in the blog exposure proxy, depending on the types
and the number of blogs www.BlogPulse.com covered. However, given the large number of
blogs www.BlogPulse.com covered, we believe the blog exposure as measured by
www.BlogPulse.com is representative of the overall discussion on blogspaces. Furthermore, each
security’s blog exposure as retrieved from www.BlogPulse.com might either underestimate or
overestimate the true volume of blog conversation, depending on the keywords we specify as
well the algorithm www.BlogPulse.com uses to identify the information related to a firm.
However, we believe the results of such an estimate noise are more likely to be biased against
our findings.
We should be aware that there is another mechanism that might result in the return difference
between the high blog exposure stocks and the low blog exposure stocks. Investors are net sellers
of the securities with high blog exposure. However, Figure 2 results rule out such a possibility.
If that were true, the portfolio composed of high blog exposure stocks should have big and prolonged negative returns, but that is not the case. The portfolio return of high exposure stocks is,
in fact, meandering around zero.
Following Fang and Peress (2009), in our article, we use investor recognition to refer to
the model of Merton (1987) under the more traditional rational-agent framework, whereas the
limited attention refers to the behavior financial model (e.g., Barber & Odean, 2008).
Even though one of the key assumptions of the investor recognition hypothesis (tested in the
section ‘‘Investors’ Recognition Hypothesis’’) is that investors know about only the subset
of the available stocks, Merton’s (1987) model is built on the rational-agent framework and is
different from the limited attention hypothesis we study here. In Merton’s model, attention
grabbing by itself will not influence an investor’s purchase decision, whereas in our case, it
does.
Our unreported sentiment mining results of the blog contents collected from LexisNexis database
show that overall blog contents are dominated by positive sentiments.

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Hu et al.

19

References
Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of
Financial Markets, 5, 31-56.
Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of Internet
stock message boards. Journal of Finance, 59, 1259-1294.
Asquith, P., Pathak, P., & Ritter, J. (2005). Short interest, institutional ownership, and stock returns.
Journal of Financial Economics, 78, 243-276.
Barber, B., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies, 21, 785-818.
Chan, W. (2003). Stock price reaction to news and no-news: Drift and reversal after headlines.
Journal of Financial Economics, 70, 223-260.
Daniel, K. D., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market
under- and overreactions. Journal of Finance, 53, 1839-1885.
Easley, D., Hvidkjaer, S., & O’Hara, M. (2002). Is information risk a determinant of asset returns?
Journal of Finance, 47, 2185-2221.
Easley, D., Kiefer, N. M., O’Hara, M., & Paperman, J. B. (1996). Liquidity, information, and infrequently traded stocks. Journal of Finance, 51, 1405-1436.
Fang, L. H., & Peress, J. (2009). Media coverage and the cross-section of stock returns. Journal of
Finance, 64, 2023-2052.
Grullon, G., Kanatas, G., & Weston, J. P. (2004). Advertising, breadth of ownership, and liquidity.
Review of Financial Studies, 17, 439-461.
Hirshleifer, D., Lim, S. S., & Teoh, S. H. (2009). Driven to distraction: Extraneous events and underreaction to earnings news. Journal of Finance, 63, 2287-2323.
Lee, C. M. C., & Ready, M. J. (1991). Inferring trade direction from intraday data. Journal of
Finance, 46, 733-746.

Merton, R. C. (1987). A simple model of capital market equilibrium with incomplete information.
Journal of Finance, 42, 483-510.
Odean, T. (1998). Are investors reluctant to realize their losses? Journal of Finance, 53, 1775-1798.
Odean, T. (1999). Do investors trade too much? American Economic Review, 89, 1279-1298.
Singh, M., Faircloth, S., & Nejadmalayeri, A. (2005). Capital market impact of product marketing
strategy: Evidence from the relationship between advertising expenses and cost of capital. Journal
of the Academy of Marketing Science, 33, 432-444.
Tumarkin, R., & Whitelaw, R. (2001). News or noise? Internet postings and stock prices. Financial
Analyst Journal, 57, 41-51.
Wright, J. (2006). Blog marketing. New York, NY: McGraw-Hill.
Wysocki, P. D. (1999). Cheap talk on the web: The determinants of postings on stock message boards
(Working Paper). Ann Arbor: University of Michigan.

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


The Role of Reputable
Auditors and Underwriters in
the Design of Bond Contracts

Journal of Accounting,
Auditing & Finance
28(1) 20–52
Ó The Author(s) 2013
Reprints and permission:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/0148558X11421673


Yun Lou1 and Florin P. Vasvari1

Abstract
The authors empirically test the certification hypothesis by studying the roles of reputable
auditors and bank underwriters in the design of bond contracts. The certification hypothesis suggests that reputable capital market intermediaries can credibly communicate inside
information to outside investors, thereby helping improve financing terms for firms that
raise external funding. Consistent with this hypothesis, the authors provide evidence that
reputable auditors and underwriters help corporate bond issuers obtain lower bond yields.
The effect of reputable auditors on the yields is greater than that of reputable underwriters
in terms of economic magnitude and significance, consistent with auditors’ multiple roles as
information intermediaries, monitors, and insurance providers. The authors also find that
the presence of reputable auditors and underwriters affects bonds’ nonpricing terms. Firms
that hire reputable auditors obtain longer term bonds, whereas those that engage reputable
underwriters can issue larger bonds. Taken together, the results suggest that reputable
auditors and underwriters have integral, but different, roles in the bond-issuing process.
Keywords
reputable auditor, reputable underwriter, bond terms, certification hypothesis
Several theoretical articles suggest that third-party information intermediaries can certify
the quality of security-issuing firms that face significant information asymmetries in capital
markets (i.e., the Certification Hypothesis). For instance, the models of DeAngelo (1981),
Beatty and Ritter (1986), Booth and Smith (1986), and Titman and Trueman (1986) examine how bank underwriters and auditors help resolve information asymmetries of issuing
firms. These theories argue that underwriters and auditors use their reputation capital as a
bonding mechanism to credibly certify the information about the future prospects of the
issuing firms, thereby helping improve firms’ financing terms when raising external financing. In this article, we empirically investigate the certification hypothesis in the primary
bond market by combining the role of auditors and underwriters. Specifically, we study the
roles of reputable auditors and bank underwriters in the design of bond contracts.

1

London Business School, London, England

Corresponding Author:
Yun Lou, London Business School, Regent’s Park, London NW1 4SA, England
Email:

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Lou and Vasvari

21

Auditors and underwriters are important information intermediaries in the bond market,
a market that has received little attention so far despite the fact that it provides the most
significant source of external financing to U.S. firms.1 Auditors play a role in certifying
that the accounting information provided in the bond prospectuses by issuing firms is accurate and prepared in accordance with Generally Accepted Accounting Principles. In addition to the certification function, auditors have a monitoring role, which they fulfill by
reporting potential errors in financial statements and violations of covenants set in bond
contracts. Auditors also bear legal liability for accounting irregularities that occur in the
reports of the firms they audit and, under certain conditions, provide bond investors with a
means to indemnify their losses (e.g., Dye, 1993; Mansi, Maxwell, & Miller, 2004).2 These
monitoring and insurance roles complement auditors’ certification role and potentially
make auditors more relevant information intermediaries to bondholders than underwriters.
Although underwriters certify information about the future prospects of issuing firms and
use their extensive distribution networks and selling expertise to help issuing firms place
bond securities, their liability is limited to situations where negligence is proven. Hence,
underwriters typically do not provide insurance against investment losses. Furthermore,
they have a limited monitoring role after a bond is issued.3
High-quality bond issuers are likely to signal their type by seeking certification from
reputable auditors and underwriters. Auditors and underwriters develop reputation capital
by repeatedly entering into the market and providing credible information about the issuing
firms. As a result, the value of their reputation capital likely exceeds even the largest possible one-time gain that could be obtained from certifying falsely. Rational investors should

understand these incentives and thus provide capital under more favorable terms to the
firms certified by intermediaries with reputation capital at stake.
To test these arguments in the bond market, we first construct reputation proxies for
auditors and underwriters. We designate an auditor as a reputable auditor if its market
share based on the clients’ sales is the largest in the industry and outpaces the rest of auditors by at least 10% (Dunn & Mayhew, 2004; Palmrose, 1986). We define reputable auditors at the industry level because the prior literature shows that industry expertise possessed
by auditors can affect managers’ earnings management behavior and reduce information
asymmetry between firms and investors (e.g., Almutairi, Dunn, & Skantz, 2009; Balsam,
Krishnan, & Yang, 2003). As most issuers in the bond market hire large auditors, our focus
is only on companies audited by the big four/five auditors. We define an underwriter as
reputable if its market share, as captured by the bond volume advised in the whole bond
market, persistently ranks among the top five underwriters in the past 3 years.
Consistent with the certification hypothesis, we find that hiring reputable auditors
reduces bond issuance yields by 35 basis points, which is both statistically and economically significant. This decrease in bond yields translates into annual interest savings of
US$65,450 for the average bond issue in our sample. Reputable underwriters also help
issuers lower the yields by 19 basis points, a significantly weaker effect than that of reputable auditors. The greater impact of hiring reputable auditors is consistent with auditors’
multiple roles as information intermediaries, monitors, and insurance providers.
We further examine whether reputable auditors and underwriters provide value with
respect to nonpricing terms of bond contracts, such as bond maturity and size.4 Debt maturity plays an important role in reducing agency costs associated with asset substitution and
improving the efficiency of monitoring by lenders (Leland & Toft, 1996; Stulz, 2000).
Short bond maturities may reduce agency costs by subjecting managers to more frequent
monitoring by investors and rating agencies (e.g., Datta, Iskandar-Datta, & Raman, 2005).

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


22

Journal of Accounting, Auditing & Finance

However, hiring a reputable auditor may provide an alternative monitoring mechanism to

reduce these costs because reputable auditors are incentivized to monitor issuing firms’
financial reporting continuously to maintain their reputation in the industry. As a result,
issuing firms with reputable auditors may potentially borrow from bondholders for a longer
period compared with those with ordinary auditors. Consistent with our conjecture, we find
evidence that hiring reputable auditors, on average, lengthens bond maturities by 2.54
years, a statistically significant effect. We do not find a strong substitution effect between
the presence of reputable underwriters and bond maturity. This could be explained by the
nature of the underwriters’ job. Underwriters are responsible for marketing and selling
bonds; however, once the bonds are issued, they do not have any monitoring role.
Finally, we examine the impact of reputable auditors and underwriters on bond size,
which is an indicator of the issuing firm’s repayment ability. If an issuing firm has a higher
level of tangible assets and/or is able to generate larger future cash flows, it can borrow more
debt. To the extent that reputable auditors and underwriters can certify the accuracy of tangible assets and the ability to generate cash flows to pay the debt back, issuing firms with reputable auditors and underwriters may be able to borrow a larger amount than those with
ordinary auditors and underwriters. In addition to the certification effect, the size of a bond
issue also depends on underwriters’ marketing and selling abilities. Reputable underwriters
have extensive distributional networks and superior selling power, which allow them to place
larger bond issues. Consistent with these arguments, we find evidence that reputable underwriters have a strong positive effect on the size of the bond. Specifically, hiring reputable
underwriters increases the actual offering amount by 13.73% relative to the average offering
amount in our sample. We do not find a similar result for reputable auditors, suggesting that
these information intermediaries play a different role in the bond-issuing process.
Our article makes two significant contributions to existing knowledge on the value of
auditors and underwriters in the bond market. First, we bridge two disconnected strands of
literature by testing the certification hypothesis simultaneously for reputable auditors and
underwriters in the bond market. Although Mansi et al. (2004) and Ahmed, Rasmussen,
and Tse (2008) have explored the role of auditors in reducing bondholder–shareholder conflicts, reputable underwriters were not considered as additional intermediaries in the analysis. By combining the certification role of reputable auditors with that of reputable
underwriters, we highlight different roles played by these important capital market intermediaries with respect to the structuring of public debt financing.
Second, our article contributes to the growing body of literature that examines more
detailed aspects of debt contracts (e.g., Brockman, Martin, & Unlu, 2010; Qian & Strahan,
2007). Previous studies in the bond or syndicated loan market attempt to understand the
drivers of a single, contractual dimension (typically the bond yield or the loan spread).5 We

provide unique evidence on the effects of reputable auditors and underwriters on the bond
maturity and size. As a result, this study sheds light on the role of information intermediaries with reputation capital on the nonpricing terms of bond contracts and examines the
joint role of auditors and underwriters in a richer setting than other articles that focused on
the equity market.
The remainder of the article proceeds as follows: Section titled ‘‘Related Literature and
Hypotheses’’ provides a discussion of related literature and formalizes our hypotheses, section titled ‘‘Data and Research Design’’ describes empirical strategies and data and is followed by the ‘‘Results’’ section that presents the main results, section titled ‘‘Sensitivity
Analyses and Additional Tests’’ offers some robustness checks, and the final section titled
‘‘Conclusion’’ concludes the article.

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Lou and Vasvari

23

Related Literature and Hypotheses
The certification hypothesis is derived from the literature on the use of reputation capital to
guarantee product quality (Klein & Leffler, 1981). As an extension to this theoretical literature, DeAngelo (1981) shows that when incumbent auditors earn client-specific quasi rents,
auditors with a greater number of clients have more to lose by failing to report a discovered
breach in a client’s accounting system. The higher the value placed by large auditors on
their reputation, the better is the quality of their audits. Consistent with this argument, the
model of Titman and Trueman (1986) finds that firms that hire high-quality auditors
receive greater valuations when securities are issued. Similarly, Booth and Smith (1986)
model underwriter reputation as a bonding mechanism to solve the information problems
between issuing firms and investors, and find that underwriter reputation is formed either
through a premium price charged for quality assurance or the objective of maintaining
long-term profits through repeated entries into the market. The models of Chemmanur and
Fulghieri (1994) or Beatty and Ritter (1986) provide similar arguments by showing that
investment banks’ reputations are achieved by adopting stringent evaluation standards.

Taken together, these theories imply that reputation capital can provide capital market
intermediaries such as auditors and underwriters with incentives to commit to honest information production on the firms they serve.
By providing more accurate information, these information intermediaries allow outside
investors to make more precise estimates of firm values and better investment decisions.
As intermediaries with reputation capital at stake can be adversely and materially affected
if their information certification proves false, investors may accept less protection on the
securities issued by firms hiring these intermediaries. Therefore, we hypothesize that both
auditors and underwriters with reputation concerns play certification roles that help reduce
issuers’ cost of debt or relax the nonpricing terms of their debt contracts.
Empirical studies have examined the certification roles of auditors and underwriters separately. Pittman and Fortin (2004) and Mansi et al. (2004) find that the cost of debt is
lower for firms with larger auditors. Ahmed et al. (2008) show that industry audit specialists help firms reduce the cost of capital, both equity and debt. Empirical evidence in
finance, however, finds that reputable underwriters obtain lower yields and charge higher
fees (e.g., Fang, 2005). However, auditors and underwriters have integral, but different,
roles in the bond-issuing process, and ignoring either in empirical analyses can lead to
imprecise inferences of their respective contributions.
The theoretical model developed by Balvers, McDonald, and Miller (1988) provides guidance for our empirical analysis of auditors and underwriters in the bond market. The
model shows that investment banks with reputation concerns are more likely to select highreputation auditors as a signal of their own quality, and together, they reduce the underpricing of initial public offerings of equity issues. The model also predicts that highly reputable auditors and underwriters have divergent effects on underpricing—as the reputation
effect of one intermediary increases, the effect of the other diminishes. We expect these
findings to apply to the bond market, as well, for several reasons. First, auditors provide
assurance that firms’ financial statements are prepared in accordance with Generally
Accepted Accounting Principles, whereas underwriters assist firms in documenting, marketing, and selling securities. Hence, the information content of both certification roles can
differ, with auditors verifying accounting information before and after a bond is issued, and
underwriters affirming to general future prospects about bond issuers.

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


24

Journal of Accounting, Auditing & Finance

Second, auditors are at high risk for litigation and play an additional insurance role by
indemnifying investors against disclosures of false accounting information. In recent years,
the litigation against auditors has grown dramatically, both in frequency and cost.6 The passage of the Sarbanes-Oxley Act further expanded the legal responsibility of auditors, requiring them to report on the adequacy of client firms’ internal control over financial reporting.
In addition, auditors—especially those with reputation capital at stake—incur indirect costs
from litigation, such as loss of reputation capital. If investors recognize the relatively high
litigation costs associated with reputable auditors in the event of failure to detect accounting irregularities, they may place more value on the certification role of reputable auditors
than on that of reputable underwriters. Therefore, given the differences in the certified
information content and exposed litigation costs that exist between auditors and underwriters, we expect reputable auditors to play a stronger role in reducing the cost of debt than
reputable underwriters.
Although the theoretical predictions about the certification effect on the credit spreads
are clear, inferences about certification’s role on the negotiated nonpricing bond terms are
less straightforward. Debt maturity is one of the main nonpricing terms of a bond contract
and is well regarded as an ex-post monitoring device. For example, Leland and Toft (1996)
argue that short-term debt reduces or even eliminates the agency costs associated with asset
substitution. Also, Stulz (2000) illustrates that short-term debt provides creditors with an
extremely powerful tool to monitor the borrowing firm’s management. Managers with
higher stock ownership choose a larger proportion of short-maturity debt, thereby committing to more frequent monitoring (Datta et al., 2005). Auditors, too, play a role in monitoring issuing firms’ financial reporting systems. In particular, auditors with a reputation
concern have stronger incentives to assure the quality of financial reporting throughout the
period when debt is outstanding. Given reputable auditors’ incentives to facilitate ex-post
monitoring of issuers, one could expect either a substitution or a complementary effect
between the presence of reputable auditors and the negotiation of a shorter debt maturity.
In contrast, the underwriters’ main role is to assist borrowers only at issuance; they have
no responsibility to monitor borrowers after issuance. As a result, we do not expect an association between the presence of reputable underwriters and bond maturity.
Another important nonpricing term of a bond contract is the size of the bond issue. The
size is associated with default risk—the larger the bond, the greater the pressure on its
issuer’s repayment ability. To the extent that reputable auditors and underwriters reduce the
inherent uncertainty associated with the measurement of default risk at issuance, one would
expect an increase in the bond sizes of issuers with these types of intermediaries. The size
of a bond issue is also a function of the distributional networks and selling abilities of the

underwriter. Reputable underwriters have extensive distributional channels, strong relationships with institutional and individual investors as well as superior marketing and selling
abilities, all of which facilitate the issuance of larger amounts of debt (Fang, 2005). Taking
this into account, we expect that reputable underwriters potentially play a more important
role in increasing the size of the bonds issued when compared with reputable auditors.

Data and Research Design
Proxies of Reputable Auditors and Underwriters
To capture the reputation concerns of auditors and underwriters, we measure their reputation capital based on the magnitude of their respective market share. This is consistent with

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012


Lou and Vasvari

25

the theoretical argument that if an information intermediary, such as an auditor or underwriter, engages in quality cutting, this information disseminates faster if the intermediary
has a large market share (e.g., Klein & Leffler, 1981). Furthermore, with a large market
share, the expected long-term fee premium from information intermediaries’ reputation is
also likely to exceed the short-term benefits that could be obtained by misinforming investors. Therefore, the market share reflects the income stream at stake, and larger auditors or
underwriters have more to lose from a damaged reputation.
We measure a reputable auditor’s market share using the total sales audited by an auditor within an industry (Dunn & Mayhew, 2004; Palmrose, 1986). We focus on the certification role of auditors specializing in a particular industry because they are associated with
high-quality audits (Craswell, Francis, & Taylor, 1995; Krishnan, 2003). Becoming an
industry specialist requires a significant investment in training and time to establish a solid
reputation. Also, industry audit specialists have a large market share, as their expertise is
recognized and they are sought out within the industry. As a result, consistent with
DeAngelo’s (1981) argument, they have more to lose if they fail to detect frauds in their
clients’ audits.
We define an industry as all firms with the same two-digit primary Standard Industry
Classification (SIC) code in the Compustat universe.7 We designate an auditor as a reputable auditor if its market share is the largest in the industry and outpaces the rest of auditors

by at least 10%. The 10% cutoff supports our inferences on the qualitative differences in
auditors’ reputations in a particular industry. In checking for robustness, we also confirm
that using a 15% or 5% cutoff does not alter the robustness of our results. Furthermore, we
validate this measure by investigating the association between the presence of industry
audit specialists and the accounting and governance risks of the firms that hire them.8
Although auditors provide services for the universe of public firms and are pressed to differentiate themselves through industry specialization, underwriters in the debt market, which
is not as competitive as the equity market, tend to focus on multiple segments. For instance,
Yasuda (2005) documents that underwriters’ bank relationships with borrowers have a positive and significant impact on their bond underwriting business.9 Therefore, we use the
market share based on the underwriter’s volume in the whole bond market to identify reputable underwriters. We define an underwriter as reputable if its market share persistently ranks
among the top five underwriters in the past 3 years.10 The intuition behind this measure is
that an underwriter with a large market share will not imperil its reputation for the sake of
short-term profits. Underwriters with a large market share extract economic rents on reputation from their clients (Fang, 2005). Moreover, they are repeat players, and the poor performance of a bond not only damages their reputation in the bond market but could also affect
their businesses in other areas, such as bank lending, equity underwritings, or Mergers &
Acquisitions Advisory services. In robustness checks (see section titled ‘‘Sensitivity Analyses
and Additional Tests’’), we also present results using the top eight underwriters and classifying reputable underwriters based on the number of bonds they place.

Regression Specifications
This section presents the regression specifications concerning the effects of reputable auditors and underwriters on the bond terms. To examine the certification roles of reputable
auditors and underwriters on bonds’ spreads, we estimate the following regression (we
present the computation of all variables in Appendix A):

Downloaded from jaf.sagepub.com at Taylor's University on December 22, 2012