The Effect of Bank Loan Dependence on Management
and Analyst Forecasts*
SAORI NARA†

MIKIHARU NOMA‡

School of Commerce,
MEIJI UNIVERSITY

Graduate School of Business Administration,
HITOTSUBASHI UNIVERSITY

ABSTRACTThis paper investigates whether and how dependence on bank loans affects
management and analyst forecasts in Japan, where indirect finance has played an
important role in financial arrangements for many years. We study 4,681 firm-year
observations listed on the Japanese equity market from 2001 to 2011, and we show thefollowing results. First, the dependence on bank loans increases management forecast
errors. Second, the dependence on bank loans increases analyst forecast errors anddispersions but decreases analyst coverage. Third, dependence on bank loans decreases
the value relevance of management and analyst forecasts. These empirical results showthat disclosure and information environment are not good for firms that depend onbank loans. Furthermore, management and analyst forecasts are less incorporated instock prices because investors rely on bank monitoring and do not utilize firms’ public
disclosures or other information, such as analyst forecasts, for those who depend onbank loan. Analyst coverage, analyst forecast accuracy, and forecast agreement are all
lower for firms having long term-established relationships with banks in Japan. By
contrast, our research contributes two things. First, we show that disclosure of firmsthat are dependent on bank loans are inadequate. More specifically, management
forecasts of firms that are dependent on bank loans are less accurate. Second, we reveal*

Acknowledgments:We appreciate the helpful comments of the discussants at the American Accounting Association
Annual Meeting (2014). This work was supported by JSPS KAKENHI Grant Numbers 16K17217, 15K03764.
†
Associate Professor, School of Commerce, Meiji University, Faculty Office Building 1125, Surugadai Campus, 1-1
Kanda-Surugadai, Chiyoda-ku, Tokyo 101-8301, Japan. Tel. +81-3-3296-2287, Fax. +81-3-3296-2287, E-mail:sarinara@meiji.ac.jp‡
Professor, Graduate School of Business Administration, Hitotsubashi University, National Center of Sciences 2-1-2
Hitotsubashi, Chiyoda-ku, Tokyo 101-8439, Japan. Tel. +81-3-4212-3108, Fax. +81-3-4212-3020, E-mail:mnoma@hab.hit-u.ac.jp
Received March 5, 2018; accepted January 25, 2019; available online March 27, 2019 (Advance publication by J-Stage)
DOI:10.11640/tjar.9.2019.01
Copyright©2019 Research Institute for Economics & Business Administration – Kobe University.

2

The Japanese Accounting Review, 9 (2019), 1-30

that management and analyst forecasts are less incorporated in stock prices in firmsthat are reliant on bank loans.
JEL Classification: M41; G21; G32
Keywords: Management Forecasts; Analyst Forecasts; Disclosure; Bank Loan; Bank
Monitoring; Value Relevance1. Introduction
This paper investigates whether and how the dependence on bank loans affects management
and analyst forecasts in Japan, where indirect finance has played an important role in financial
arrangements for many years. Jensen and Meckling (1976) argue that, once investment has takenplace, managers use funds without inhibition, such that the interest of outside investors became
dampened. When managers do not behave in ways desired by investors, those investors suffer
losses in the form of agency costs. To minimize agency costs, therefore, monitoring anddisclosures are necessary.
Monitoring is usually conducted by agents, such as banks, bond investors, and equity
investors. Among these, only banks can access firms’ private and public information. Therefore,
Fama (1985) note that banks have information advantages over other investors. Additionally,
Diamond (1984) state that bank monitoring is the most effective, because monitoring costs
associated with other investor types can overlap, creating critical sub-optimality. This suggeststhat investors can rely on bank monitoring only if bank monitoring works well.
When bank monitoring is weak for some reason, or when firms increase funds from capital
markets, public monitoring by investors (e.g., equity and bond holders) is required. Monitoring byinvestors is accomplished via firms’ disclosures. Healy and Palepu (2001) imply that disclosures,such as management forecasts and financial reporting, have various benefits for firms. Suchdisclosures can reduce the cost of capital, improve liquidity, increase analyst coverage, andameliorate information asymmetries. Thus, managers have an incentive to improve disclosures byenhancing voluntary disclosure protocols with mandatory disclosures, especially in firms
dependent on external finances (Lang and Lundholm 1993, 1997; Healy et al. 1999).
Public disclosures, on the other hand, require more work. Thus, it becomes an additional costfor managers. Therefore, disclosure items and their frequency and quality vary temporally andcross-sectionally between firms. Generally, managers who raise funds from public markets
diligently pursue disclosure objectives. By contrast, managers of firms that are dependent on bankloans and bank monitoring do not require public market funds. Thus, they have less incentive todisclose.
Additionally, some studies showed that information environments, such as analyticalenvironments, are not enough for firms that depend on bank loans. Koga and Uchino (2012)
investigate the role of firm analysts who have long-established relationships with banks, findingthat analyst coverage, analyst forecast accuracy, and forecast agreement (i.e., analyst forecast
dispersion) are all lower for firms that have long-term-established relationship with banks.
Because long-term relationships suggest good evaluation by banks as banks monitor firms during
the relationship. Therefore, investors do not require disclosure when they have less motivation toclosely examine the firms. Additionally, the researchers mention that sharing private information

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

3

with a few banks is less costly for firms compared to making full public disclosures.
We examine disclosures from firms that are dependent on bank loans, assuming that they
typically do not provide sufficient disclosures for two reasons. First, investors do not require
public information, because bank monitoring works well, and they rely on it. Second, the cost of
public disclosure is relatively expensive compared to sharing private information with a few banks.
When firms want to reduce disclosure, managers will reduce voluntary disclosures first. In Japan,
among voluntary disclosures, management forecasts are time-consuming and costly for managers
in terms of preparation and amendment. Therefore, management forecasts become the initial
targets for the scrap heap when the reduction of disclosures is prioritized. Therefore, we focus on
management forecasts especially its forecasting errors.
We also investigate the information environment more specifically the analysts, because wewish to test value relevance of both management and analyst forecasts. If investors do not requirein-depth information about firms that depend on bank loans, then security analysts have no needto cover them. However, if analyst do not provide enough cover, their forecast errors anddispersions will increase. Furthermore, if investors neither require nor reference public
information on bank-dependent firms and their analytical information, the information is lesslikely to be incorporated in stock prices.
For this paper, we study 4,681 firm-year observations, as listed on the Japanese equity market
from 2001 to 2011. We conduct three analyses. First, we investigate the management forecast
accuracy of firms that are dependent on bank loans. Next, we explore the relationship between thescale of bank loans and analyst forecast accuracy, analyst coverage, and analyst forecast dispersion.
Finally, we test the value relevance of management and analyst forecasts to understand the extent
to which this information is used in the stock market.
We focus on Japan for two reasons. First, indirect financing and the main-bank system bothplay a central role in Japan. The information environment, disclosure system, and firms’ behaviors
toward investors have changed over the past 15 years. However, many firms still depend on bankloans. Second, in Japan, more than 95% of listed firms disclosed annual management forecasts, as
of FY2012, because securities exchanges strongly recommend the disclosure of management
forecasts. Even firms that depend heavily on bank loans provide management forecasts, althougharguments can be made to suggest that they would do so reluctantly and sparingly, compared tocounterpart firms that are not beholden to banks in this way. Furthermore, we can apply
management forecasts in the context of Ohlson’s (2001) model to test value relevance. The
institutional settings, vis-à-vis Japanese firms, are suitable for such an exercise, because such firms
provide annual management forecasts, unlike firms in the United States.
Based on our three analyses, the following results are revealed. First, dependence on bankloans increases management forecast errors. Second, dependence on bank loans increases analyst
forecast error and dispersion but has little to do with analyst coverage. Third, dependence on bankloans decreases the value relevance of management and analyst forecasts. These empirical results
show that disclosure and information environment are not good for firms that depend on bankloans. Furthermore, management and analyst forecasts are less incorporated in stock prices,because investors do not refer to the information; they instead rely on bank monitoring for firmsthat depend on bank loans.
Koga and Uchino (2012) provide evidence that the information environments of firms withlong-established relationship with banks are less rich in Japan. By contrast, our research provides
two contributions. One is that we show that disclosure of firms dependent on bank loans are not

4

The Japanese Accounting Review, 9 (2019), 1-30

adequate. Moreover, the management forecasts of firms that are dependent on bank loans are lessaccurate. The other is that value relevance of both management and analyst forecasts are low insuch firms. Thus, management and analyst forecasts of firms that rely on bank monitoring are lessincorporated in stock prices.
We also explain the management forecasting system in Japan (Section 2) before reviewing
related literature and positing our hypotheses (Section 3). Our methodology and data are thendelineated (Section 4), followed by the results of our empirical analysis (Section 5). Finally, wesummarize our results and offer concluding remarks (Section 6).

2. Management Forecast Systems in Japan
Figure 1 shows the capital structure of firms in Japan and other key developed countries. The
proportion of bank loans therein is highest in Japan, followed closely by Germany. However,Japanese firms reduced the scale of bank loans throughout the 2000s and increased the scale ofexternal financing via the public market. Figure 2 shows the structure of debt and equity ofJapanese firms.1 In FY2003, the proportion of bank loans (equity) was 51.0% (43.1%). By
FY2012, this pattern had inverted, and bank loans had declined to 42.8%, compared to 51.8% forequity. Furthermore, from Figure 3, we find that the proportion of shares held by foreign investors
who could not access private information increased over this period.
Starting around year 2000, managers became increasingly convinced of the need forconstructive dialog with investors. Thus, disclosure systems began to change concomitantly withthis awareness. For example, disclosures of business risks, analysis of financial conditions,
corporate results, and corporate governance began in 2003. Quarterly disclosures commenced in
2004. Disclosure of managers’ testimonies began in 2005, and corporate governance and board
members’ compensation disclosures were enhanced in 2010. Furthermore, prompt disclosure,voluntary disclosure, and relations with investors were improved.
Among these disclosures, reinforcement of management forecasts was especially importantfor managers and investors because this information affects investors’ perspectives and stock prices.
In Japan, listed companies commonly provide the next year’s annual forecasts in a specific format
called “Kessan Tanshin”2 when announcing financial predictions. This disclosure of management
forecasts is a custom that started because of demand from a press club called the “Kabuto club.”
Subsequently, the Japanese Stock Exchanges Conference took over this custom; it now enforces
management forecast announcements. Owing to this effort, the management forecasting system
is widely diffused across Japan, taking a central role in disclosures.
In the management forecasts disclosure system, sales, recurring profit, and net income
forecasts are disclosed about participating firms. Specific descriptions regarding forecasts andforecasts of operating profit have been required since 2006. Additionally, forecasted information iscommonly disclosed in half-year periods and full business years. However, quarterly forecasts are
not disclosed, unlike in the United States.
When firms do not disclose forecasts, they instead should give prior notice to the Tokyo
Stock Exchange. Owing to this system, more than 95% of Japanese listed companies announced
1

We obtained these data from the Ministry of Finance, Japan. This is an annual survey assessing the real businessactivities of Japanese corporations. Methodological differences therein hinder comparisons with the data presented in
Figure 1.
2
A financial statement that is disclosed quarterly and annually, like 10-Q and 10-K in the U.S.

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

FIGURE 1. COMPARISON OF CAPITAL STRUCTURE OF FIRMS
0%

20%

Japan
U.S.
England

15% 2%

32%
15%

30%

10%

Loan

29%

Other Liabilities

50%

Bond
Equity61%

14% 6%

20%

ItaliaCanada

12% 2%

37%

100%

60%

5% 9%

26%

80%

55%

10%

21%

14%

60%
21%

39%

Germerny
France

40%

51%
40%

16%

Reference: Bank of Japan. International comparison of flow of funds accounts (2003, P12).

FIGURE 2. CHANGES IN CAPITAL STRUCTURE FROM 2003 TO 2012 IN JAPAN
100%
90%
80%
70%
60%
50%

43.1% 44.0% 47.3% 48.8% 47.3% 47.9%
5.9%

5.9%

5.6%

5.7%

5.4%

5.7%

50.0% 49.5% 52.0% 51.8%

5.7%

5.4%

40%
30%
20%

5.2%

5.4%

51.0% 50.1% 47.0% 45.5% 47.3% 46.3%

44.4% 45.1% 42.8% 42.8%

2003

2009

10%
0%
2004

2005

2006

2007

LOAN

BOND

2008

2010

2011

2012

EQUITY

Reference: Ministry of Finance. Survey of corporations (Annually).

56

The Japanese Accounting Review, 9 (2019), 1-30

FIGURE 3. TRANSITION IN SHAREHOLDING RATIO BY SHAREHOLDER TYPE
(ON A SHARE-NUMBER BASIS)
60
50
40
30
20
10

Govt. & Local Govt.

Financial Institutions

Securities Companies

Business Corporations

Foreigners

Individuals

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

0

Reference: Japan Exchange Group. Share distribution survey.

management forecasts as of FY2012. Additionally, once firms disclose their forecasts, they arecompelled to revise them as necessary.3 When firms revise their forecasts, new forecasts andrationale for amendments are registered by the listed companies via TD net. 4 Thus, themanagement forecasts system in Japan treats disclosures as tantamount to mandatory.5

3. Related Literature and Hypotheses
3.1. Related literature
Monitoring is normally conducted by an agent (e.g., bank, bond investor, equity investor).
However, disclosure requirements are heterogeneous across agent types. Firms provide two typesof disclosure: public information that anyone can access and private information that limited
parties, such as banks, can access. The difference is thus in terms of the right to access private
information. Only banks can access all (public and private) information. This plausibly confers
advantages.
3

For example, if published forecasts of sales change ±10% or if published forecasts of operating profit, recurring profit,
and net income change ±30%, firms must amend their original forecasts.
4
A timely disclosure network operated by the Tokyo Stock Exchange.
5
The management forecasts disclosure system is amended from 2012. For example, it was approved to makemanagement forecasting private. However, the effect is limited and more than 95% of listed companies still announce
management forecasts as of FY2012. Additionally, our study period is from 2001 to 2011, and our result is
independent of the amendment.

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

7

Leftwich (1983) discusses how banks have the substantive ability to gather and handle
information compared to stock and bond investors. The corollary of this is that banks pay costs toobtain a comprehensive understanding of firms and to monitor them efficiently and effectively(Hoshi et al. 1990). The advantage of this for banks is that, by accessing firms’ private information,
they gain informational advantages over other investors. Diamond (1984) stated that bankmonitoring is the most effective type of monitoring, whereas there are issues in terms of bondsand equity, because monitoring costs can overlap among investors and aggregate inter-investor
costs, causing them to exceed accruable benefits.6 Additionally, Dass and Massa (2011) state thatstronger relationships between banks and firms that inducing better monitoring would lead tobetter corporate borrower governance.
Prior research indicates that firms’ attitudes toward disclosure change when bank monitoringis likely to also change in some way, perhaps via a corporate event (both the borrowers and bank).
When bank monitoring seems to weaken, the firm’s disclosure increases. Lo (2014) tests thedisclosure of firms when a banking relationship is threatened by a decline in the bank’s health,finding that borrowers increase their quality and informativeness of management forecasts after adecline of bank health. Chen and Vashishtha (2017) show that borrower disclosure increases
when their bank engages in mergers and acquisitions (M&A). Furthermore, they insist that theeffect is stronger when the borrower relies heavily on the bank, and when the M&A causes a largechange in the bank’s monitoring and borrowers’ financing. Hence, disclosure of firms decreases
when bank monitoring seems to strengthen. Vashishtha (2014) indicate that firms reduce
disclosures after they violate covenants, because shareholders delegate the monitoring to banks.
Additionally, firms’ attitudes regarding disclosure relate to the funding methods. Firms thatwill fund from public capital provide more disclosure. For example, Lang and Lundholm (1993)
show that ratings of analysts increased in firms that issued securities in the current or future
period. This meant that managers of those firms provided disclosure positively during the
preparation of financial arrangements. Additionally, regarding firms that issue equity, Lang andLundholm (1997) indicate that disclosure improves 6 months before the equity issue. These
trends are confirmed in the case of issuing bonds. Healy et al. (1999) indicate that firms having
experienced improved ratings from analysts are more likely to issue bonds. Moreover, regarding
the relation between debt and disclosure, Eng and Mak (2003) find that ownership structure
affect disclosure, and firms with lower debt had greater disclosure. They also conclude that
disclosure is inversely related to debt (Singapore).
The information environment is also influenced by bank-loan dependence. Koga and Uchino(2012) show that the bank–firm relationship influences the information environments of many
analysts. They find that analyst coverage, analyst forecast accuracy, and forecast agreement (i.e.,analyst forecast dispersion) are all lower for firms having long-term-established relationship withbanks. They insist that investors of firms that depend on bank loans do not require public
information, because bank monitoring works well. Additionally, they state that sharing private
information with a few banks is less costly for firms compared to making public disclosures. Thisis because firms must deliver public information to great number of investors through public
disclosure.

6Petersen and Rajan (1994) show that the cost of loans increases according to the number of banks from which thefirm borrows.

8

The Japanese Accounting Review, 9 (2019), 1-30

3.2. Hypotheses
Based on the literature, investors seem to rely on bank monitoring when it works well.
However, when bank monitoring changes, or, more specifically, when banking monitoring is likely
to decline, disclosure of firms increase. Firms are thus inactive for disclosure when theeffectiveness of bank monitoring is strong. Therefore, we research disclosures of firms that dependon bank loans. Additionally, as Koga and Uchino (2012) points out, demand for in-depth researchof analyst is low for firms dependent on banks loans. Thus, we also investigate the information
environment. Furthermore, to illuminate and explore these management and analyst forecasts, wetest the value relevance of both forecasts, and we investigate information usage levels in equity
markets.
We assume firms that rely on bank loans also rely on bank monitoring. Furthermore, weinvestigate their disclosure and information environments. Regarding disclosure, we investigate
the accuracy of management forecasts. Additionally, for information environments, we focus on
analysts, particularly in terms of forecast accuracy, coverage, and forecast dispersion.
3.2.1. Disclosure: management forecasts
There are two types of disclosures: mandatory and voluntary. When firms wish to reduce
disclosure, they first target voluntary disclosures, because mandatory disclosure are required by law.
Among voluntary disclosures, management forecasts are time-consuming and costly in terms ofpreparation and amendments. Thus, there is an inverse relationship between the quality of suchforecasts and dependence of bank loan.
Hypothesis1-1: Dependence on bank loans increases management forecast errors.
3.2.2. Information environment: analysts
To test the information environment, we investigate analyst forecast error, analyst coverage,
and analyst forecast dispersion. Koga and Uchino (2012) find that analyst coverage, analyst
forecast accuracy, and forecast agreement (i.e., analyst forecast dispersion) are all lower for firms
with long-term-established relationships with banks. Because investors do not require analyst
information when bank monitoring works well, and because they can rely on bank monitoring insuch firms, the following hypotheses apply.
Hypothesis2-1: Dependence on bank loans increases analyst forecast errors.
Hypothesis2-2: Dependence on bank loans decreases analyst coverage.
Hypothesis2-3: Dependence on bank loans increases analyst forecast dispersion.
3.2.3. Value relevance of management and analyst forecasts
If investors do not leverage management forecasts, the information is obviously lessincorporated in the stock price. Thus, we surmise the value relevance of management forecasts offirms, dependent on bank loans, decreases.
Hypothesis3-1: Dependence on bank loans decreases the value relevance of management
forecasts.

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

9

A similar argument can be made about analyst forecasts. Thus, we surmise the value
relevance of analyst forecasts of firms dependent on bank loans also decreases.
Hypothesis3-2: Dependence on bank loans decreases the value relevance of analyst forecasts.

4. Methodology and Data
We conduct three analyses. First, we investigate the relationship between the scale of bankloans and management forecast errors. Second, we investigate the relationship between the scaleof bank loans and analyst forecast errors, analyst coverage, and dispersions in analyst forecasts.
Third, we consider the relationship between the value relevance of these forecasts and the scale ofbank loans.
To conduct these analyses, we calculate LOAN in the first instance (Equation 1).
𝐿𝑂𝐴𝑁 =.

(1)

The denominator is the total amount of external financing, and the numerator is the total
amount of bank loans, where STborrow is short-term borrowings, CLTborrow is current portion of
long-term debt, LTdebt is long-term debt, TE is total equity, Cbond is current portion of bonds,
CPCB is current portion of convertible bonds, bond is bonds, and CB is convertible bonds. Koga
and Uchino (2012) use total assets for the denominator. However, we use the sum of the externalfinancing, because we want to clarify the impact of bank loans in external financing, except for the
impact of reserves and so on.
Next, we define our dependent variables: management forecast error (MFERR), analyst
forecast error (AFERR), analyst coverage (AN), and the standard deviation of analyst forecasts(DISPAF). MFERR, AFERR, AN, and DISPAF are calculated as follows.
𝑀𝐹𝐸𝑅𝑅 =||

𝐴𝐹𝐸𝑅𝑅 =

|

|

,

(2)

,

(3)

AN= the number of analysts providing annual earnings forecasts, and𝐷𝐼𝑆𝑃𝐴𝐹 =

,

(4)
(5)

where MF is the management forecasts of net income, ACT is the actual earnings of net
income, AF is the analyst forecasts of net income7, SDAF is the standard deviation of analyst net
income forecasts, and MVE is the market value of equity as of the end of the fiscal year (end of
March). All remaining variables, except MVE, are set at the end of June each year. When
MFERR and AFERR are large, the forecast accuracy is low. When DISPAF is high, consensus
among analysts is lacking, which could be a function of increasing information asymmetry anduncertainty.

7

We use average of analyst forecast.

10

The Japanese Accounting Review, 9 (2019), 1-30

4.1. Disclosure: management forecasts
To capture management forecast accuracy, we estimate a multiple regression model
incorporating an array of plausible control variables (Equation 6).
𝑀𝐹𝐸𝑅𝑅 = 𝛼 + 𝛼 𝐿𝑂𝐴𝑁
+𝛼 𝑀𝐹𝐸𝑅𝑅+ 𝛼 𝐿𝑂𝑆𝑆+ 𝛼 𝑀𝐹𝐸𝑅𝑅+ 𝛼 𝑆𝐺𝑅𝑂𝑊𝑇𝐻+𝛾 𝑌𝐷 ++ 𝛼 𝑙𝑛𝑀𝑉𝐸+ 𝛼 𝑂𝐹𝐹𝐸𝑅

𝛿 𝐼𝐷 + 𝜀 .

(6)LOAN is the ratio of bank loans to external financing. If the dependence on bank loans
increases management forecast errors, the coefficient of LOAN will be positive.
LOSS and SGROWTH are dummy variables for loss and sales growth over the previous year,
respectively. Irani (2000) find that financially distressed firms tend to announce optimistic
forecasts, whereas growing firms tend to announce pessimistic forecasts. Biased forecasts also
decrease forecast accuracy. Thus, we predict that the coefficient of both variables will be positive.
lnMVE is the natural logarithm of the market value of equity as of the end of previous year; it is a
proxy variable for firm size. According to Jaggi (1980), the management forecast error of large
firms tends to be small. We predict the coefficient will be negative. OFFER denotes income fromseasonal offerings. Frankel et al. (1995) and Irani (2000) test forecast optimism of firms that arelikely to access capital market. However, they did not find optimism in management forecasts.
Using Japanese data, Ota (2006) show that seasonal public firms tend to announce pessimistic
management forecasts, and biased forecasts decrease forecast accuracy. Thus, the coefficient
should be positive. MFERR indicates management forecast error. Based on Williams (1996), theaccuracy of management forecasts becomes an indicator of the believability of current
management forecasts for analysts. This is not a direct result of management forecasts, but webelieve previous forecast errors will affect current forecast errors. Thus, we invoke management
forecasts from the past 2 years and predict that the coefficient will be positive. Additionally, we
add year (YD) and industry (ID) dummies to control year and industry effects in an OLS
regression.

4.2. Information environment: analysts
4.2.1. Accuracy of analyst forecasts
To investigate the effect of LOAN on analyst forecasts, we estimate the following model.
𝐴𝐹𝐸𝑅𝑅 = 𝛼 + 𝛼 𝐿𝑂𝐴𝑁 + 𝛼 𝑙𝑛𝐴𝑁 + 𝛼 𝐸𝐺𝑅𝑂𝑊𝑇𝐻 + 𝛼 𝑙𝑛𝑇𝑉 + 𝛼 𝑂𝐹𝐹𝐸𝑅+𝛼 𝑙𝑛𝑀𝑉𝐸 +𝛾 𝑌𝐷 +

𝛿 𝐼𝐷 + 𝜀 .

(7)

LOAN is the ratio of bank loans to external financing. If dependence on bank loans increases
analyst forecast errors, then the coefficient of LOAN will be positive.
lnAN is the natural logarithm of analysts following the firm as of the end of June. Brown(1997) indicates that analyst forecasts error tends to be small when firms have sufficient analyst
coverage. We predict that the coefficient will be negative. EGROWTH represents earnings growthover the previous year. Cheong and Masum (2010) note that analysts invest resources inhigh-growth firms, where private demand for information is high, and that high-growth firms

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

11

disclose additional information to show their growth potential. This information enables analysts
to provide more-accurate information. Thus, the coefficient should be negative. lnTV is thenatural logarithm of the trading volume. Hayes (1998) argues that analysts’ research activities areaffected by how much commission they can earn. High trading volume becomes an incentive foranalysts to make optimistic forecasts. Thus, we predict that the coefficient will be positive.
OFFER represents income from seasonal offerings. Firms that issue or sell stock have an
incentive to issue optimistic forecasts (Ota 2006). Therefore, analyst forecasts are influenced bythese forecasts, and the forecast errors will increase. Thus, we assume that the coefficient will bepositive. lnMVE is the natural logarithm of the market value of equity as of the end of the fiscal
year. Brown (1997) show that analyst forecasts of larger firms are more accurate. Thus, we predictthe coefficient will also be negative. We again add year (YD) and industry (ID) dummies tocontrol the year and industry effects for OLS regression.
4.2.2. Analyst coverage
For analyst coverage, we follow Bhushan (1989) and Rock et al. (2000) and estimate thefollowing model.
𝑙𝑛𝐴𝑁 = 𝛼 + 𝛼 𝐿𝑂𝐴𝑁 + 𝛼 𝐹𝑂𝑅 + 𝛼 𝑃𝑒𝑟𝐹𝑂𝑅 + 𝛼 𝑅𝑆𝑄𝑀𝐾𝑇 + 𝛼 𝐿𝑂𝐵𝐼
+𝛼 𝑅𝑒𝑡. 𝑉𝑎𝑟. + 𝛼 𝑙𝑛𝑀𝑉𝐸 +𝛾 𝑌𝐷 +

𝛿 𝐼𝐷 + 𝜀 .

(8)LOAN is the ratio of bank loans to external financing. If analysts’ coverage of firms with low
dependence on bank loans tends to be small, the coefficient of LOAN will be negative.
Regarding other independent variables, we follow Bhushan (1989). FOR and PerFOR are thenumber of foreign shareholders and percentage of ownership by foreign shareholders, respectively.
Bhushan (1989) and Rock et al. (2000) indicate that the number of institutions holding shares in
the firm and percentage of a firm’s shares held them by increasing analyst coverage. We apply the
number of foreign shareholders and the percentage of foreign shareholders owing to data
restrictions. We suggest that if the needs from overseas investors are high, the demand for analysts’
research increases. Thus, analysts’ coverage increases. Therefore, we predict that both coefficients
will be positive. RSQMKT is the R2 of the regression between a firm’s return and the market’s
return. Bhushan (1989) and Rock et al. (2000) show that analyst coverage is positively associatedwith the relationship between a firm’s return and market return. When this is low, marginal
information acquisition costs increase and analysts avoid following such firms. We predict thatthe coefficients will be positive. LOBI shows the number of business domains. Bhushan (1989)
and Rock et al. (2000) posit that the number of a firm’s business domains affect the cost of
information acquisition. When the number of those domains increases, information acquisition
costs increase and analyst coverage decreases. Thus, the coefficient will be negative. Ret.Var.
indicates the variance of the total return of the firm. Bhushan (1989) shows that need for analysts’
research is an increasing function of the reliability of return. For firms where the return variability
is high, private information is more valuable for predicting future returns. Because return
variability leads to more analysts, we predict that the coefficient will be positive. lnMVE is thenatural logarithm of the market value of equity as of the end of the fiscal year. Bhushan (1989)
states that investors can generate larger profits from larger firms and that they can earn larger

12

The Japanese Accounting Review, 9 (2019), 1-30transaction fees from them. Therefore, the demand for research of larger firms increases, and its
analyst coverage will increase. Thus, we expect the coefficient to be positive. We again add year
(YD) and industry (ID) dummies to control for year and industry effects in this OLS regression.
4.2.3. Analyst forecast dispersion
To investigate the effect of LOAN on analyst forecast dispersion, we estimate the following
model.
𝐷𝐼𝑆𝑃𝐴𝐹 = 𝛼 + 𝛼 𝐿𝑂𝐴𝑁 + 𝛼 𝑙𝑛𝐴𝑁 + 𝛼 𝐸𝐷𝐼𝑆𝑃 + 𝛼 𝐿𝑂𝑆𝑆 + 𝛼 𝐸𝐺𝑅𝑂𝑊𝑇𝐻2+𝛼 𝑆𝐺𝑅𝑂𝑊𝑇𝐻2 + 𝛼 𝑙𝑛𝑀𝑉𝐸 + 𝛼 𝐵𝑀 +𝛾 𝑌𝐷 +

𝛿 𝐼𝐷 + 𝜀 .

(9)LOAN is the ratio of bank loans to external financing. If the dependence on bank loans
increases the analyst forecast dispersion, the coefficient of LOAN will be positive.
lnAN is the natural logarithm of the number of analysts. When analyst coverage is small,information asymmetry increases. Thus, we predict that the coefficient will be negative. EDISP is
the dispersion of net income over the past three years. When firms’ earnings are volatile, it
becomes difficult for analysts to forecast those earnings. Here, we predict that the coefficient will
be positive. LOSS is a dummy variable for losses. Following Choi and Ziebart (2004), firms withlosses tend to announce optimistic forecasts. Biased forecasts increase information symmetry andanalyst forecast dispersion. Therefore, we predict that the coefficient will be positive. EGROWTH
and SGROWTH are earnings growth and sales growth over the past year, respectively, indicating
financial risk. McLaughlin et al. (1998) suggest that high-growth-rate firms tend to be associatedwith greater information asymmetries. Thus, the higher the growth rate, the larger the dispersion
in analysts’ forecasts. Therefore, we expect the coefficient to be positive. lnMVE is the natural
logarithm of the market value of equity as of the end of the fiscal year. Bhushan (1989) and Rocket al. (2000) state that larger firms have superior information environments compared to smallerfirms. In that case, information asymmetry decreases with analysts’ forecast dispersion.
Additionally, Lang and Lundholm (1996) show that analyst forecast dispersion is inversely related
to firm size. Thus, the coefficient will be negative. BM is the book-to-market ratio. This is a proxyvariable for investment and growth opportunities, and it can be indicative of poor firm
performance. Poor-performing firms tend to hesitate to disclose their performance, andinformation asymmetry increases. Thus, the coefficient will be positive. We again add year (YD)
and industry (ID) dummies to control the year and industry effects for OLS regression.
All the above indicators are shown in Table 1.

4.3. Value relevance of management and analyst forecast
Ohlson (2001) state that firm value, including market value of equity and stock prices, isexpressed by three factors: book value of equity, net income, and earnings forecasts. Additionally,
Dechow et al. (1999) conduct an empirical analysis showing that analyst forecasts show value
relevance by regressing with the book value of equity, net income, and earnings forecast (i.e.,analyst forecasts).
Based on Ohlson (2001), showed that value relevance of management forecasts of firms
dependent on bank loans tend to be low (Equation 10).

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

TABLE 1. DEFINITION OF VARIABLES
Dependent variables
Variables
Description
MFERR
management forecast error
MFERRt = abs(Et -MFt )/MVEt-1
AFERR
analyst forecast error
AFERRt = abs(Et -AFt )/MVEt-1
lnAN
logarithmic of the number of analyst at the end of JunelnANt = ln(ANt )vDISPAF
standard variation of analyst forecas
DISPAFt = standard variation of analyst forecastt /MVEt-1
Independent variables
Variables
Description
weight of bank loan to external financing
LOAN(Short-term borrowings + Current portion of long-term debt + Long-term debt)/(Short-term borrowings + Current portion of long-term debt + Long-term debt + Total
equity + Current portion of bond + Current portion of convertible bond + bond +convertible bond)
LOSS
loss dummywhen E is negative 1, otherwise 0. Where E is net income
SGROWTHsales growth
SGROWTH = (St -St-1)/MVEt-1. Where S is sales
lnMVE
logarithmic of the market value of equity at the end of fiscal year (end of March)lnMVE = log(MVE)
OFFER
income from seasonal offering
OFFER = OFFER/MVE
management forecast error of t-1
MFERRt-1
MFERRt-1 = abs(Et-1-MFt-1)/MVEt-2
management forecast error of t-2
MFERRt-2
MFERRt-2 = abs(Et-2-MFt-2)/MVEt-3
EGROWTHearnings growth
EGROWTH = (Et -Et-1)/MVEt-1.Where E is net income
lnTV
logarithmic of the trading volume
lnTV = log(trading volume)FOR
the number of foreign shareholders
PerFOR
the percentage of ownership of foreign shareholders (%)
2
RSQMKT
the R from the market model regression of a firm’s return on the market return
LOBI
Ret.VarEDISP
EGROWTH2
SGROWTH2BM

number of business segment
estimated variance of a firm’s daily stock return
dispersion of net income of past three years
EDISP = EDISP/MVE
average earnings growth of past three years
average sales growth of past three years
book to market ratio
BM = B/MVE

13

14

The Japanese Accounting Review, 9 (2019), 1-30

𝑀𝑉𝐸

𝛼

𝛼 𝐵

𝛼 𝐿𝑂𝐴𝑁

𝛼 𝐸
𝛾 𝑌𝐷

𝛼 𝑀𝐹
𝛿 𝐼𝐷

𝛼 𝑀𝐹

𝐿𝑂𝐴𝑁

𝜀.

10

MVE is the market value of equity as of the end of the fiscal year. B is the book value ofequity, E is net income, MF is management forecasts regarding net income, and LOAN is theratio of bank loans to external financing. All variables except LOAN and dummy variables are
divided by the MVEt-1 for the purpose of size adjustment. We predict that the coefficient of MF
will be positive if management forecast is value relevant. However, if the dependence on bankloans decreases the value relevance of management forecasts, the coefficient of MF*LOAN will be
negative.
Similarly, we investigate the value relevance of analyst forecasts of firms that depend onbank loans. We thus show the model below (Equation 11).
𝑀𝑉𝐸

𝛼

𝛼 𝐵

𝛼 𝐿𝑂𝐴𝑁

𝛼 𝐸
𝛾 𝑌𝐷

𝛼 𝐴𝐹
𝛿 𝐼𝐷

𝛼 𝐴𝐹
𝜀.

𝐿𝑂𝐴𝑁
11

The definitions of MVE, B, E, and LOAN are shown in Equation 10. AF represents analyst
forecasts regarding net income. All variables except LOAN and dummy variables are divided by
MVE t-1 for the sake of size adjustment. We predict that the coefficient of AF will be positive if
analyst forecasts are value relevant. However, if dependence on bank loans decreases the value
relevance of analyst forecasts, the coefficient of AF*LOAN will be negative.
We add year (YD) and industry (ID) dummies to control year and industry effects for OLS
regression. Furthermore, we add regressions estimated with robust standard errors clustered byfirm and year. In this case, we regress the model without a year dummy. For OLS analysis, we
provide two results, one including a year dummy and the other without.

4.4. Descriptive statistics
Our data are acquired from Astra Manager, provided by QUICK Corp., a financial service
company of the Nikkei group in Japan. We use IFIS consensus of IFIS, a financial information
service vendor in Japan for analyst forecast, analyst coverage, and analyst forecast dispersion,
because QUICK does not provide analyst forecast dispersion. We study 4,681 firm-year
observations, excluding financial sector entities (e.g., bank, securities, and insurance) listed on theJapanese equity market from 2001 to 2011. Data were removed where the settlement period was
not March8 and where deviations from 12-month accounting occurred. Additionally, we use
consolidated financial data above non-consolidated financial data. Furthermore, we limit the
sample to that having analyst coverage for more than two, because we use analyst forecast
dispersion in our analysis.9 June end forecast data for management and analyst forecasts is used,

89

In Japan, the accounting period of most firms is the end of March.
The samples shift to place a disproportionate emphasis on large firms.

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

15

because management forecasts are announced from the end of April to mid-May in Japan. It
takes about one month for analysts to revise forecasts. We removed upper and lower extreme
values (+/−0.1%) for each variable to mitigate outlier effects.
Table 2 provides descriptive statistics for the data. Panel A pertains to all variables used in
our analysis. The minimum value of LOAN is 0.000, the median is 0.189, the mean is 0.23310, andthe maximum is 0.920. From these figures, around 23% external financing is not provided
through the public market. It is noteworthy that the maximum value of LOAN is 92%, indicatinga very high dependency on bank loans. The mean of EGROWTH is negative, indicating thatsample firms’ performance is negative and that they struggle with poor performance. The mean of
PerFOR is 18.620, and the median is 17.030. This indicates that the proportion of foreign
investors is less than 20% during the period.
Panel B shows descriptive statistics per year. In terms of LOAN, both the mean and medianin 2009 are high. Many firms braced for a rapid downturn in the economy and increased cash
holdings because of the financial crisis. Prior to the crisis, the Japanese equity market wasbooming, and various firms raised funds from the equity market. Thus, LOAN is relatively low
compared to other years. This is consistent with the market timing hypothesis of Baker and
Wurgler (2002), who noted that firms raise funds from equity market when their stock prices ishigh, but they chose internal financing or debt when the stock price is low. MFERR, AFERR, andDISPAF also substantively increase in 2008 during the financial crisis. This indicates thatmanagers and analysts did not expect the financial crisis, and uncertainty increased over this year.
Additionally, the median analyst coverage is consistently 5–6.
Panel C shows descriptive statistics per industry sector. We use the middle industry
classification of Nikkei. Electric machinery, chemicals, machinery, and service sectors aresubstantial. Panel D shows the correlation of these indicators. Correlations among lnAN, lnMVE,and FOR are high. Generally, large firms have significant analyst coverage and foreign investors.
Furthermore, MFERR and AFERR are strongly correlated because management forecasts
influence analyst forecasts, as indicated by Baginski and Hassell (1990) and Williams (1996).
Table 3 presents descriptive statistics by the weight of LOAN. We show the mean andmedian of MFERR, AFERR, AN, and DISPAF of five groups divided by the size of LOAN.
Regarding mean and median MFERR, the figures naively increase as the group number increases.
The difference between group 1 and group 5 is significant at the 1% level. Therefore,
management forecasts of the group where LOAN is high (low) are less (more) accurate.
For AFERR, both mean and median increase as the group number increases, and thedifference between group 1 and 5 is significant at the 1% level. Similar to the foregoing, thisindicates that analyst forecasts of the group where LOAN is high (low) are less (more) accurate.
For AN, both mean and median of group 5 are smaller than that of group 1. However, this is notstatistically significant. Additionally, we cannot observe a systematic trend in AN, because oursample is limited to firms in which analyst coverage is more than two.11 Both the mean and

10

The weight of loans is lower compared to Figure 2, because our sample shifts toward larger firms. We use firms inwhich analyst coverage is more than two.
11
When we include samples with more than one analyst coverage, the result is more robust, and analyst coverage offirms that depend on bank loans is smaller.

16

The Japanese Accounting Review, 9 (2019), 1-30

TABLE 2. DESCRIPTIVE STATISTICS AND BIVARIATE CORRELATIONS
Panel A: Descriptive statistics
N
Dependent variables
MFERR
4,681
AFERR
4,681
lnAN
4,681
vDISPAF
4,681
Variables of loan dependance
LOAN
4,681
Control variables
LOSS
4,681
SGROWTH
4,681
lnMVE
4,681
OFFER
4,681
MFERRt-1
4,681
MFERRt-2
4,681
EGROWTH
4,681
lnTV
4,681
FOR
4,681
PerFOR
4,681
RSQMKT
4,681
LOBI
4,681
Ret.Var
4,681
EDISP
4,681
EGROWTH2
4,681
SGROWTH2
4,681
BM
4,681

Min.

1st

Median

Mean

3rd

Max.

0.000
0.000
0.693
0.000

0.005
0.005
1.099
0.002

0.014
0.013
1.792
0.004

0.033
0.033
1.672
0.006

0.033
0.033
2.303
0.007

0.668
0.632
2.833
0.146

0.000

0.052

0.189

0.233

0.373

0.920

0.000
-4.250
8.623
0.000
0.000
0.000
-1.040-5.854
4
0.610
0.000
1.000
9.469
0.020
-0.594-2.3170.082

0.000-0.03311.209
0.000
0.005
0.005
-0.014
1.141
104
9.860
0.079
2.000
29.3657.670-0.012-0.030
0.515

0.000
0.041
12.067
0.000
0.013
0.012
0.005
2.456
205
17.0300.202
3.000
36.57138.690
0.001
0.024
0.739

0.024
0.020
12.150
0.004
0.028
0.029
-0.0022.303
258
18.6200.225
3.225
38.454671.400-0.004
0.020
0.808

0.000
0.127
13.048
0.000
0.030
0.029
0.021
3.646
353
25.6600.345
4.000
45.255208.030
0.011
0.090
1.020

1.000
2.614
16.594
0.337
0.605
0.597
0.604
7.374
1,619
68.5400.746
13.00092.03766892.840
0.244
1.388
2.792

AFERR
mean median
0.046
0.025
0.027
0.014
0.024
0.012
0.017
0.009
0.021
0.010
0.013
0.007
0.020
0.009
0.087
0.053
0.041
0.021
0.024
0.012
0.035
0.015

mean
5.922
6.832
6.379
6.505
6.394
6.466
6.624
6.849
6.725
6.908
6.625

Panel B: Descriptive statistics by year
LOAN
MFERR
FY
N
mean median mean median
2001
384
0.254
0.209
0.047
0.026
2002
386
0.248
0.204
0.028
0.014
2003
406
0.251
0.191
0.025
0.012
2004
428
0.240
0.190
0.019
0.010
2005
424
0.227
0.177
0.021
0.011
2006
468
0.216
0.170
0.014
0.007
2007
495
0.212
0.162
0.020
0.009
2008
485
0.215
0.174
0.086
0.049
2009
418
0.253
0.219
0.042
0.019
2010
411
0.235
0.201
0.025
0.013
2011
376
0.222
0.180
0.035
0.015

AN
median
5.000
6.000
5.500
5.000
5.000
5.000
5.000
6.000
6.000
6.000
6.000

DISPAF
mean median
0.006
0.003
0.006
0.004
0.007
0.005
0.004
0.003
0.004
0.003
0.003
0.002
0.004
0.003
0.006
0.004
0.013
0.007
0.007
0.005
0.008
0.005

(continued on next page)Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

17

TABLE 2. (continued)
Panel C: Descriptive statistics by industry
Industry
Num of Obs.
Fishery
16
Mining8Construction
215
Foods
200
Textiles&Apparel
109
Pulp & Paper
58
Chemicals
470
Pharmaceuticals
199
Petroleum27
Rubber33
Glass&Ceramics
89
Steel
105
Nonferrous Metals
172
Machinery
Electric Machinery
Automobiles & Auto parts
Shipbuilding

454
744
248
28

Industry
Transportation Equipment
Precision Instruments
Other Manufacturing
Trading Companies
RetailOther Financial Services
Real Estate
Railway&Bus
Land Transport
Marine Transport
Air Transport
Warehousing
Communications

Num of Obs.
5
124
141
212
59
60
118
71
80
32
13
43
91

Electric PowerGas
Services

90
32
335

Note: This classification is the Nikkei Middle Industry Classification.

(continued on next page)

median DISPAF increase as the group number increases, and the difference between group 1 and
5 is significant at the 1% level. This result indicates that analyst forecast dispersion of the groupwhere LOAN is high is larger than the group where LOAN is low.

450.296 0.156
0.293 0.153
0.018 -0.019
0.212
0.251
0.202 0.035-0.061 0.062-0.052 0.056-0.021 0.070
0.381 0.226
0.259 0.215-0.055 0.056
0.217 0.390
0.097 0.087-0.015 -0.152
0.164 0.127
0.069 0.353
0.306 0.167
0.348 0.308-0.251 -0.001-0.142 0.026
0.299 -0.013

8-0.143-0.1440.728-0.1160.048-0.0270.011-0.034-0.168-0.136
0.056
0.596
0.898
0.510
0.163
0.299
-0.240
0.375
0.097
0.005
-0.3537-0.033-0.020-0.036-0.225-0.049-0.235-0.0230.081-0.1680.011
0.331-0.021-0.106-0.006-0.029-0.019-0.123-0.1060.325
0.563-0.164

6
0.144
0.148
0.038
0.408
0.033
-0.158-0.0290.002
0.167-0.002-0.171
0.053
0.040
0.010
0.077
-0.0040.152
0.089-0.159-0.1080.116

9
100.048 0.211
0.052 0.198-0.007 -0.041
0.042 0.417
0.121 0.225
0.017 0.252
0.013 -0.330-0.015 -0.144
0.057-0.0100.001 0.251
0.027 -0.144-0.082 0.113-0.055 -0.065
0.018 -0.099-0.021 0.106
0.042 0.021
0.080 0.340-0.066 0.232
0.069 -0.292
0.107 -0.158-0.173 0.249
0.308
0.103
-0.079-0.075
0.145
0.021
0.129
0.413
-0.065-0.144
0.121

11
0.097
0.096
-0.043
0.190
0.229
0.013
-0.073-0.105
0.066
0.224

12
13
-0.132 0.096-0.122 0.093
0.000 0.438-0.269 0.122-0.012 0.335-0.301 0.046
0.348 -0.086
0.062 0.536
0.017 0.031-0.502 0.120
0.433 0.123-0.0050.047
0.001 0.684
0.017 0.361
0.048 0.319
0.024 0.368-0.138 0.007
0.132 0.506
0.370 -0.026
0.047 -0.098-0.165 -0.063

TABLE 2. (continued)

0.638
0.298
0.327
-0.0930.475-0.051-0.110-0.18414-0.030-0.031
0.699
0.012
0.098
0.036
-0.0730.873-0.003-0.033-0.043-0.0070.546

15
16-0.039 0.085-0.033 0.089
0.476 0.194-0.044 0.128-0.125 0.132
0.016 0.080-0.003 -0.113
0.504 0.176
0.015 -0.017-0.077 0.109-0.046 0.133
0.008 -0.028
0.326 0.327
0.580 0.292
0.233
0.245
0.098 0.161-0.051 0.177
0.183 0.313
0.008 -0.182
0.006 -0.178-0.193 0.042

21-0.030-0.020-0.038-0.143-0.054-0.092
0.551
0.004
0.021
-0.210-0.2770.023-0.153-0.083-0.017-0.223-0.037-0.117-0.1600.378
0.113-0.270 -0.094-0.141 -0.255 0.453
0.061 0.109 -0.324 -0.222

20-0.170-0.161-0.010-0.319-0.076-0.249
0.331
0.100
0.000
-0.517-0.2350.429-0.053-0.021-0.012-0.1940.023-0.2870.014-0.0200.234
0.011-0.005-0.061

19
0.101
0.100
0.164
0.134
0.173
0.010
-0.044
0.184
0.055
0.090
0.274
0.162
0.200
0.263
0.106
0.118
0.091
0.033

18
0.207
0.206
-0.054
0.303
0.167
0.204
-0.205-0.235
0.079
0.339
0.130
-0.241-0.002-0.067-0.0370.178-0.01717-0.001-0.001
0.211
0.018
0.368
-0.004-0.041
0.315
0.007
0.021
0.017
0.010
0.354
0.343
0.125
0.209

Note: The upper-right triangular matrix is the Pearson correlation coefficient, and thebottom-left triangular matrix is the Spearman’s rank-order correlation.

Panel D: Bivariate correlation matrix
12
3
1 MFERR
0.988 -0.029
2 AFERR
0.915-0.0303 lnAN-0.053 -0.070
4 vDISPAF
0.343 0.352 0.131
5 LOAN
0.198 0.186 -0.009
6 LOSS
0.116 0.132 0.038
7 SGROWTH 0.026 0.024 -0.044
8 lnMVE-0.177 -0.194 0.743
9 OFFER
0.015 0.015 -0.012
0.263 0.247 -0.076
10 MFERRt-1
0.159 0.153 -0.085
11 MFERRt-2
12 EGROWTH -0.029 -0.047 0.006
13 lnTV
0.101 0.085 0.483
14 FOR-0.061 -0.074 0.763
15 PerFOR-0.074 -0.081 0.491
16 RSQMKT
0.106 0.100 0.178
17 LOBI
0.003 -0.008 0.189
18 Ret.Var
0.290 0.296 -0.060
19 EDISP
0.180 0.171 0.359
20 EGROWTH2 -0.145 -0.157 -0.026
21 SGROWTH2 -0.048 -0.052 -0.037
22 BM
0.277 0.289 -0.227
22
0.272
0.270
-0.2400.285-0.0360.136-0.190-0.362-0.0270.247
0.093-0.179-0.060-0.194-0.1990.043-0.0680.109
0.014-0.239-0.15918
The Japanese Accounting Review, 9 (2019), 1-30

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

19

TABLE 3. DESCRIPTIVE STATISTICS BY THE SIZE OF LOAN
low
1
0.020

←
2
0.025

LOAN
3
0.036

→
4
0.036

high
5
0.048

difference
1-5
statistic-0.028 -10.204 ***Median

0.009

0.012

0.014

0.016

0.021

-0.012 -12.367 ***

Mean

0.020

0.025

0.035

0.035

0.048

-0.028 -10.038 ***Median

0.009

0.012

0.013

0.015

0.021

-0.012 -11.498 ***

Mean

6.760

6.388

6.610

6.639

6.464

0.296

1.534

Median

6.000

5.000

5.000

6.000

5.000

1.000

1.282

DISPAF Mean

0.004

0.005

0.006

0.007

0.009

-0.005 -11.316 ***MedianNum of Obs.

0.003
930

0.003
937

0.004
934

0.004
937

0.006
943

-0.003 -14.468 ***MFERR Mean
AFERR
AN

Note: The sample includes 4,681 firm-year observations that were listed on the Japanese equity
market from 2001 to 2011. We show the management forecast error (MFERR), the analyst
forecast error (MFERR), the number of analysts (AN), and the standard deviation of analyst
forecast (DISPAF). Samples are divided into five portfolios by LOAN size. LOAN is calculated asfollows.
𝐿𝑂𝐴𝑁 =,(1)

where STborrow represents short-term borrowings, CLTborrow is the current portion of
long-term debt, LTdebt is the long-term debt, TE is the total equity, Cbond is the current portionof bonds, CPCB is the current portion of convertible bonds, bond is bond, and CB is theconvertible bond.
MFERR, AFERR, AN, and DISPAF are calculated as follows.
𝑀𝐹𝐸𝑅𝑅 =(

𝐴𝐹𝐸𝑅𝑅 =

(

AN

)

)

,

,

= the number of analysts providing an annual earnings forecast,

𝐷𝐼𝑆𝑃𝐴𝐹 =

,

(2)
(3)
(4)
(5)

where MF is the management forecast regarding net income, ACT represents actual earnings
regarding net income, AF is the analyst forecast, SDAF is the standard deviation of analyst
forecast, and MVE is the market value of equity as of the end of the fiscal year. Management
forecast, analyst forecast, and the number of analysts are as of June of each year. *** indicatessignificance at the 1% level.

20

The Japanese Accounting Review, 9 (2019), 1-30

5. Results
5.1. Disclosure: Management forecasts
Table 4 presents the results of the regression with LOAN regarding disclosure. In this analysis,
we show the results of the clustered-standard error regression with to OLS analysis. For OLS
analysis, we provide two results. One is the result that includes the year dummy. The other is theresult that does not include the year dummy. When we focus on the result of management
forecasts (MFERR), the coefficient of LOAN is positive and is statistically significant for every
equation. This suggests that dependence on bank loans increases management forecast errors.
After including other variables that are conceivable determinants of our dependent variables, we
confirm the robustness of the foregoing five portfolio analyses.
Three control variables are statistically significant. The coefficient of LOSS is predicted to bepositive, and the coefficients of lnMVE and MFERRt-1 are negative and positive, respectively.
These results are consistent with our expectations and the extant literature.

5.2. Information environment: analysts
Table 5 presents the results of the regression with LOAN regarding analysts. When we focus
on the results of analyst forecast error (panel A), the coefficient of LOAN is positive andsignificant. This result indicates that dependence on bank loans increases analyst forecast error.
This is consistent with our hypothesis and previous analyses. The coefficient of lnAN is positive incontrast to our prediction because our sample is limited to firms in which analyst coverage ismore than two (Table 3). Additionally, EGROWTH is negative. The coefficients of lnTV andlnMVE are positive and negative, respectively. These results are consistent with previous research.
In the analysis of analyst coverage (Panel B), the coefficient of LOAN is negative but
insignificant in OLS analysis with year dummy and the clustered-standard error regression. This
may also relate to our sample selection, as we mentioned above. PerFOR, Ret.Var., and lnMVE are
predicted to be positive and statistically significant for every equation. These results are consistent
with our expectations and the results of Bhushan (1998) and Rock et al. (2000).
In terms of analyst forecasts dispersion (Panel C), the coefficient of LOAN is positive andsignificant, indicating that dependence on bank loans increases the dispersion of analyst forecasts.
The results are consistent with our prediction and the previous portfolio analysis results. Analyst
forecast dispersion is used as a proxy variable for information asymmetry and future uncertainty(Barron and Stuerke 1998; Richardson 2000). Therefore, we can say information asymmetry anduncertainty increases in firms that depend on bank loans. Seven of the remaining eight
independent variables are significant. The coefficient of lnAN is positive compared to our
prediction. This could be a function of sample selection bias. As we mentioned before, we limited
our study to firms that have an analyst coverage more than two. We think that analysts of firms
with enough coverage do not delegate to other analysts. Thus, various forecasts are made.
Furthermore, the coefficient of lnMVE is negative. Additionally, the coefficients of EDISP, LOSS,
and BM are positive and significant. The coefficient of EGROWTH is negative and significant.

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

21

TABLE 4. REGRESSION RESULTS: DISCLOSURE
MFERR[3] clustered-standard
error regression
Estimate t value
0.113
3.470 ***

[1] OLS analysis[2] OLS analysis(Intercept)

Estimate t value
0.115 13.425 ***Estimate t value
0.113 11.574 ***

LOAN

0.039

8.708 ***

0.042

7.639 ***

0.042

4.450 ***

LOSS

0.038

7.236 ***

0.040

4.537 ***

0.040

6.330 ***

SGROWTH-0.001-0.494

0.006

1.431

0.006

0.600

-0.006-3.760 ***

0.047

0.760

0.134

3.440 ***

-0.014-0.290lnMVE-0.005-8.389

OFFER

0.054

1.499

MFERRt-1

0.150

7.791

MFERRt-2

0.025

1.432

***

***

-0.006-8.852

0.047

0.685

0.134

4.344

-0.014-0.691******year dummy

Yes

No

NA

industry dummy

Yes

Yes

Yes

2

Adj. R
2

R

0.235

0.122

0.122

0.243

0.129

0.129

Note: Table 4 shows the results of the regression analysis of Equation (6). The sample includes
4,681 firm-year observations that were listed on the Japanese equity market from 2001 to 2011.
𝑀𝐹𝐸𝑅𝑅 = 𝛼 + 𝛼 𝐿𝑂𝐴𝑁

+ 𝛼 𝐿𝑂𝑆𝑆+ 𝛼 𝑀𝐹𝐸𝑅𝑅+ 𝛼 𝑆𝐺𝑅𝑂𝑊𝑇𝐻+ 𝛼 𝑀𝐹𝐸𝑅𝑅++ 𝛼 𝑙𝑛𝑀𝑉𝐸

𝛾 𝑌𝐷 ++ 𝛼 𝑂𝐹𝐹𝐸𝑅

𝛿 𝐼𝐷 + 𝜀(6)

LOAN is the weight of bank loan to external financing, LOSS is the loss dummy (when net
income is negative, it is 1; otherwise, it is 0), SGROWTH is sales growth, lnMVE is thelogarithm of the market value of equity as of the end of the fiscal year, OFFER is income fromseasonal offering, MFERR is the management forecast error, YD is the year dummy, ID is theindustry dummy. [1] is the result of OLS analysis applying White’s standard error. [2] is theresult of OLS without year dummy analysis applying White’s standard error. [3] is the result ofa the clustered-standard error regression using standard errors clustered at the firm and year
level. *** indicates significance at the 1% level.

22

The Japanese Accounting Review, 9 (2019), 1-30

TABLE 5. REGRESSION RESULTS: ANALYSTS
Panel A: Analyst forcast error
AFERR[3] clustered-standard
error regression
Estimate t value
0.200
3.490 ***

[1] OLS analysis[2] OLS analysis(Intercept)

Estimate t value
0.197
15.040 ***Estimate t value
0.200
14.654 ***

LOAN

0.036

6.810 ***

0.032

5.613 ***

0.032

3.580 ***

lnAN

0.010

5.488 ***

0.011

5.781 ***

0.011

2.190 **

EGROWTH-0.059-3.899 ***-0.060-4.342 ***-0.060-4.170 ***

lnTV

0.004

6.503 ***

0.005

7.682 ***

0.005

2.280 **

OFFER

0.075

1.180

0.069

0.956

0.069

1.110

-0.016-3.220 ***lnMVE-0.014-11.693***-0.016-12.384***

year dummy

Yes

No

NA

industry dummy

Yes

Yes

Yes

2

0.241

0.131

0.131

0.249

0.138

0.138

Adj. R
2

R

Panel B: Analyst coveragelnAN[3] clustered-standard
error regression
Estimate t value-2.531 -8.140 ***[1] OLS analysis[2] OLS analysis(Intercept)

Estimate t value-2.885 -21.183 ***Estimate t value-2.531 -20.195 ***LOAN-0.051-0.085-2.235 **-0.085-1.110

0.000

3.981

***

0.000

1.610

0.005

5.839

***

0.005

3.030 ***

FOR

-1.329

0.000

1.422
***

PerFOR

0.007

7.502

RSQMKT

0.084

1.807 *

0.082

1.934 *

0.082

1.020

LOBI

0.000

0.026

0.002

0.412

***

Ret.Var

0.003

4.275

lnMVE

0.351

30.170 ***

0.002

0.200

0.005

8.386

***

0.005

4.020 ***

0.322

29.231 ***0.322

11.490 ***year dummy

Yes

No

NA

industry dummy

Yes

Yes

Yes

2

Adj. R
2

R

0.613

0.601

0.617

0.604

0.601
0.604
(continued on next page)Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

23

TABLE 5. (continued)
Panel C: Analyst forecast dispersion
DISPAF[3] clustered-standard
error regression
Estimate t value
0.013
5.800 ***

[1] OLS analysis[2] OLS analysis(Intercept)

Estimate t value
0.014
9.569 ***

Estimate t value
0.013
9.584 ***

LOAN

0.008

13.471 ***0.008

10.616 ***0.008

6.050 ***

lnAN

0.002

7.904 ***

0.002

9.317 ***

0.002

6.430 ***

EDISP

0.000

6.825 ***

0.000

3.907 ***

0.000

2.480 **

LOSS

0.017

24.462 ***0.018

7.883 ***

0.018

7.540 ***

EGROWTH-0.028-9.599 ***-0.032-6.019 ***-0.032-2.010 **SGROWTH-0.001-1.716 *-0.001-1.172***lnMVE-0.001-9.305BM

0.003

9.417 ***

-0.001-0.940-0.001-9.840***-0.001-5.660 ***0.004

8.723 ***

0.004

7.780 ***

year dummy

Yes

No

NA

industry dummy

Yes

Yes

Yes

0.373

0.364

0.364

0.380

0.370

0.370

2

Adj. R
2

R

Note: Table 5 shows the results of regression analysis of equations (7), (8), and (9). The sample
includes 4,681 firm-year observations that were listed on the Japanese equity market from 2001
to 2011.
𝐴𝐹𝐸𝑅𝑅 = 𝛼 + 𝛼 𝐿𝑂𝐴𝑁 + 𝛼 𝑙𝑛𝐴𝑁 + 𝛼 𝐸𝐺𝑅𝑂𝑊𝑇𝐻 + 𝛼 𝑙𝑛𝑇𝑉 + 𝛼 𝑂𝐹𝐹𝐸𝑅 + 𝛼 𝑙𝑛𝑀𝑉𝐸+

𝛾 𝑌𝐷 +

𝛿 𝐼𝐷 + 𝜀(7)𝑙𝑛𝐴𝑁 = 𝛼 + 𝛼 𝐿𝑂𝐴𝑁 + 𝛼 𝐹𝑂𝑅 + 𝛼 𝑃𝑒𝑟𝐹𝑂𝑅 + 𝛼 𝑅𝑆𝑄𝑀𝐾𝑇 + 𝛼 𝐿𝑂𝐵𝐼 + 𝛼 𝑅𝑒𝑡. 𝑉𝑎𝑟.
+ 𝛼 𝑙𝑛𝑀𝑉𝐸 +𝛾 𝑌𝐷 +

𝛿 𝐼𝐷 + 𝜀(8)

𝐷𝐼𝑆𝑃𝐴𝐹 = 𝛼 + 𝛼 𝐿𝑂𝐴𝑁 + 𝛼 𝑙𝑛𝐴𝑁 + 𝛼 𝐸𝐷𝐼𝑆𝑃 + 𝛼 𝐿𝑂𝑆𝑆 + 𝛼 𝐸𝐺𝑅𝑂𝑊𝑇𝐻2+ 𝛼 𝑆𝐺𝑅𝑂𝑊𝑇𝐻2 + 𝛼 𝑙𝑛𝑀𝑉𝐸 + 𝛼 𝐵𝑀 +𝛾 𝑌𝐷 +

𝛿 𝐼𝐷 + 𝜀(9)

LOAN is the weight of bank loan to external financing, MFERR is the management forecast
error, lnAN is the logarithm of the number of analysts as of the end of June, EDISP is thedispersion of net income of the past three years, LOSS is the loss dummy, EGROWTH2 is theaverage earnings growth of the past 3 years, SGROWTH2 is the average sales growth of the past
three years, lnMVE is the logarithm of the market value of equity as of the end of the fiscal year,
BM is the book-to-market ratio, lnTV is the logarithm of the trading volume, OFFER is income
from seasonal offering, FOR is the number of foreign shareholders, PerFOR is the percentage ofownership of foreign shareholders (%), RSQMKT is the R2 from the market model regression of

24

The Japanese Accounting Review, 9 (2019), 1-30

a firm’s return on the market return for year t. LOBI is the number of business segments, Ret.Var.
is the estimated variance of a firm’s daily return, EDISP is the dispersion of net income of thepast three years, LOSS is the loss dummy (when net income is negative, it is 1; otherwise, it is 0),
BM is the book-to-market ratio, YD is the year dummy, ID is the industry dummy. [1] is theresult of OLS analysis applying White’s standard error. [2] is the result of OLS without year
dummy analysis applying White’s standard error. [3]is the result of the clustered-standard error
regression using standard errors clustered at the firm and year level. ***,**, and * indicate
statistical significance at the 1%, 5% and 10% levels, respectively.

5.3. Value relevance of management and analyst forecasts
Panel A of Table 6 provides descriptive statistics of variables, which we apply to value
relevance analyses. Panel B provides correlations, and panels C and D provide regression results.
When we focus on panel B, there is a strong correlation between management forecasts andanalyst forecasts. Baginski and Hussell (1990) and Williams (1996) state that management
forecasts influence analyst forecasts. Additionally, correlations between MF*LOAN and MF andbetween AF*LOAN and AF are high.
Panel C delineates the results of regression analysis on management forecasts. When we
focus on MF*LOAN, coefficient values are negative and statistically significant. Thus, it would
appear that the dependence of bank loans decreases the value relevance of management forecasts.
The result is consistent with our hypothesis. Additionally, management forecasts of firms that
depend on bank loan are less accurate, and the forecast is not informative to investors. This might
be one of the reasons that management forecasts of firms that depend largely on bank loans areless incorporated into the market value of equity.
With respect to other variables, the coefficient of MF is positive and significant. This
indicates that management forecasts have value relevance. Nara and Noma (2012) test the value
relevance of management and analyst forecasts in Japan, indicating the value relevance of both
forecasts. Our results are consistent with their findings. Additionally, the coefficient of B is
positive and significant. Furthermore, the coefficient of E is negative and significant. This does
not match intuition. However, Dechow et al. (1999) state that the coefficient is negative.
Panel D provides regression results on analyst forecasts. When we focus on AF*LOAN, allcoefficients are negative and statistically significant. From this analysis, the dependence on bankloans decreases the value relevance of analyst forecasts. Additionally, the dispersion of analyst
forecasts for these firms tends to be large, and future uncertainty is large. This means that analyst
forecasts are not useful for the equity market. This might be one of the reasons that analyst
forecasts of firms that depend largely on bank loans are less incorporated into the market value ofequity.
With respect to other variables, the coefficient of AF is positive and significant. This
indicates that analyst forecasts have value relevance. The result is consistent with Dechow et al.
(1999) and Nara and Noma (2012). The coefficient of B is positive, whereas E is negative. Both,however, are significant to both analyses.

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

25

TABLE 6. VALUE RELEVANCE ANALYSES
Panel A: Discriptive statistics
MVE
B
Min.
0.619
0.082
1st
0.929
0.515
Median
1.016
0.739
Mean
1.049
0.808
3rd
1.138
1.020
Max.
2.185
2.792

E
-0.882
0.025
0.043
0.034
0.061
0.210

MF
-0.299
0.036
0.050
0.051
0.066
0.207

AF
-0.223
0.037
0.052
0.053
0.068
0.187

LOAN
0.000
0.052
0.189
0.233
0.373
0.920

LOAN*MF LOAN*AF-0.170-0.127
0.002
0.002
0.007
0.007
0.013
0.013
0.019
0.019
0.150
0.147

LOAN*MF LOAN*AF

Panel B: Correlations
MVE
B
E
MF
AF
LOAN
LOAN*MF
LOAN*AF

MVE

B

E

MF

AF

LOAN

1.000
0.293
-0.112
0.135
0.098
0.087
0.114
0.090

1.000
-0.161
0.006-0.032-0.036-0.014-0.033

1.000
0.452
0.525
-0.109
0.206
0.258

1.000
0.959
0.107
0.671
0.652

1.000
0.092
0.635
0.664

1.000
0.673
0.672

1.000
0.979

1.000

Panel C: Regression results: Management forecasts[1] OLS analysis(Intercept)
B
E

Estimatet value

0.936

68.446 ***0.087

11.646***-5.988

***

8.641

***
**

5.824

-1.123

3.529

0.144

-0.315

MF

1.709

VIF

MF*LOAN-1.193-2.547

LOAN
year dummyindustry dummy

0.114
Yes
Yes

3.947 ***

2

adj. R2R[3] clustered-standard error
regression[2] OLS analysis

1.462
1.424
3.547

Estimatet value

Estimatet value

0.860

79.823 ***0.860

21.490 ***0.144

21.091***0.144

2.400 **

-8.893***-0.377-3.960 ***10.628***1.470

2.000 **

-3.350***-1.123-3.010 ***6.219 ***

0.144

-0.377
1.470

0.401

No
Yes
0.146

0.407

0.152

2.820 ***
NA
Yes
0.146
0.152

(continued on next page)26

The Japanese Accounting Review, 9 (2019), 1-30

TABLE 6. (continued)
Panel D: Regression results: Analyst forecasts

1.461

Estimate
0.857
0.147

t value
78.364 ***21.615 ***[3] clustered-standard error
regression
Estimatet value
0.857
21.080 ***0.147
2.510 **

1.541

-0.395-8.894 ***-0.395-3.580 ***

3.698

1.454

10.455 ***1.454

2.040 **

-1.297-3.857***-1.297-4.000 ***

6.727

***

0.158

3.040 ***

[1] OLS analysis[2] OLS analysis(Intercept)
B

Estimate
0.931
0.091

t value
71.153 ***12.255 ***E-0.364-6.588 ***

AF

1.793

10.389 ***-1.334-3.142

***

4.499

***

AF*LOAN
LOAN
year dummyindustry dummy
2adj. R
2

R

0.123
Yes
Yes
0.401

VIF

5.844
3.607

0.407

0.158
No
Yes
0.141

NA
Yes
0.141

0.148

0.148

Note: Table 6 shows the results of the value relevance analysis regarding equations (10) and (11).
The sample includes 4,681 firm-year observations that were listed on the Japanese equity
market from 2001 to 2011.
𝑀𝑉𝐸 = 𝛼 + 𝛼 𝐵+𝛼 𝐸+

𝑀𝑉𝐸 = 𝛼 + 𝛼 𝐵

𝛾 𝑌𝐷 ++𝛼 𝐸++ 𝛼 𝑀𝐹 + 𝛼 (𝑀𝐹 × 𝐿𝑂𝐴𝑁 ) + 𝛼 𝐿𝑂𝐴𝑁
𝛿 𝐼𝐷 + 𝜀 .

(10)+ 𝛼 𝐴𝐹 + 𝛼 (𝐴𝐹 × 𝐿𝑂𝐴𝑁 ) + 𝛼 𝐿𝑂𝐴𝑁𝛾 𝑌𝐷 +

𝛿 𝐼𝐷 + 𝜀 .

(11)where MVE is the market value of equity as of the end of the fiscal year, B is the book value ofequity, E is the net income, MF is the management forecast, LOAN is the weight of bank loan
to external financing, AF is the analyst forecasts, YD is the year dummy, ID is the industry
dummy. All variables except LOAN and dummy variables are divided by the 𝑀𝑉𝐸
for thepurpose of size adjustment. [1] is the result of OLS analysis applying White’s standard error. [2]is the result of OLS without year dummy analysis applying White’s standard error. [3] is theresult of the clustered-standard error regression using standard errors clustered at the firm andyear level. *** indicates significance at the 1% level. ** indicates significance at the 5% level.

6. Conclusion
This paper investigates whether and how the dependence on bank loans affects management
and analyst forecasts in Japan, where indirect finance has played an important role in financial
arrangements. We study 4,681 firm-year observations listed in the Japanese equity market from2001 to 2011. We conduct three analyses and obtain the following results. First, dependence onbank loans increases management forecast errors. Second, dependence on bank loans increases

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

27

analyst forecast errors and dispersion but has little to do with analyst coverage. Third, dependence
on bank loans decreases the value relevance of management and analyst forecasts.
These empirical results show that management forecasts of firms that depend on bank loans
are not accurate. Because there is not a sufficiently strong incentive for managers to disclose, they
are not keen to assign resources to constructing management forecasts. We believe that investors
do not require public information, because they can easily rely on bank monitoring. Furthermore,
the cost of public disclosure is expensive, compared to private information provided to a few
banks. The empirical results pertaining to analyst forecasts indicate that information environmentis insufficient. We believe that there is no strong demand for analysts’ in-depth research byinvestors. Because investors do not need to closely examine firms that depend on bank loans, thestock prices do not fully incorporate that information.
Our empirical result has two limitations. First, our empirical result does not consider the
possibility of reverse causality. There is potential that poor disclosure prevents a rise in the capital
market. The other is the endogeneity problem. There may be a fundamental characteristic offirms that affects the level of the weight of bank-loan dependence and management forecasts.

REFERENCES
Baginski, S. P., and J. M. Hassell. 1990. The Market interpretation of management earnings
forecasts as a predictor of subsequent financial analyst forecast revision. The Accounting
Review 65 (1): 175-190.
Baker, M., and J. Wurgler. 2002. Market timing and capital structure. Journal of Finance 57 (1):1-32.
Bank of Japan. 2003. International comparison of flow of funds accounts. Available at:https://www.boj.or.jp/research/brp/ron_2003/data/ron0312a.pdf (last accessed August 7,2013).
Barron, O. E., and P. S. Stuerke. 1998. Dispersion in analysts' earnings forecasts as a measure of
uncertainty. Journal of Accounting, Auditing and Finance 13 (3): 245-270.
Bhushan, R. 1989. Firm characteristics and analyst following. Journal of Accounting and Economics
11 (2-3): 255-274.
Brown, L. 1997. Analyst forecasting errors: Additional evidence. Financial Analysts Journal 53 (6):81-88.
Chen, Q., and R. Vashishtha. 2017. The effects of bank mergers on corporate information
disclosure. Journal of Accounting and Economics 64 (1): 56-77.
Cheong, C. S., and M. A. Masum. 2010. Financial analysts' forecast accuracy: Before and after the
introduction of AIFRS. Australasian Accounting Business and Finance Journal 4 (3): 65-81.
Choi, J., and D. Ziebart. 2004. Management earnings forecasts and the market's reaction topredicted bias in the forecast. Asia Pacific Journal of Accounting and Economics 11 (2): 167-192.
Dass, N., and M. Massa. 2011. The impact of a strong bank-firm relationship on the borrowing
firm. The Review of Financial Studies 24 (4): 1204-1260.
Dechow, P. M., A. P. Hutton, and R. G. Sloan. 1999. An empirical assessment of the residual
income valuation model. Journal of Accounting and Economics 26 (1-3): 1-34.

28

The Japanese Accounting Review, 9 (2019), 1-30

Diamond, D. 1984. Financial intermediation and delegated monitoring. The Review of Economic
Studies 51 (3): 393-414.
Eng, L. L., and Y. T. Mak. 2003. Corporate governance and voluntary disclosure. Journal of
Accounting and Public Policy 22 (4): 325-345.
Fama, E. F. 1985. What’s different about banks? Journal of Monetary Economics 15 (1): 29-39.
Frankel, R., M. McNichols, and P. Wilson. 1995. Discretionary disclosure and external financing.
The Accounting Review 70 (1): 135-150.
Hayes, R. 1998. The impact of trading commission incentives on analysts’ stock coveragedecisions and earnings forecasts. Journal of Accounting Research 36 (2): 299-320.
Healy, P., A. Hutton, and K. Palepu. 1999. Stock performance and intermediation changes
surrounding sustained increases in disclosure. Contemporary Accounting Research 16 (3):485-520.
Healy, P., and K. Palepu. 2001. Information asymmetry, corporate disclosure, and the capital
markets: A review of the empirical disclosure literature. Journal of Accounting and Economics
31 (1-3): 405-440.
Hoshi, T., A. Kashyap, and D. Scharfstein. 1990. Asymmetric Information, Corporate Finance, andInvestment. Chicago, IL: University of Chicago Press.
Irani, A. 2000. Determinants of bias in management earnings forecasts. Accounting Enquiries 10(1): 33-86.
Jaggi, B. 1980. Further evidence on the accuracy of management forecasts vis-a-vis analysts'
forecasts. The Accounting Review 55 (1): 96-101.
Japan Exchange Group. 2016. Share distribution survey. Available at:https://www.jpx.co.jp/markets/statistics-equities/examination/01.html (last accessed May 9,2017).
Jensen, M. C., and W. H. Meckling. 1976. Theory of the firm: Managerial behavior, agency costs
and ownership structure. Journal of Financial Economics 3 (4): 305-360.
Koga, K., and S. Uchino. 2012. Bank-firm relationships and security analyst activities: Evidence
from Japan. The Japanese Accounting Review 2: 49-73.
Lang, M., and R. Lundholm. 1993. Cross-sectional determinants of analysts’ ratings of corporate
disclosures. Journal of Accounting Research 31 (2): 246-271.
Lang, M., and R. Lundholm. 1996. Corporate disclosure policy and analyst behavior. The
Accounting Review 71 (4): 467-492.
Lang, M., and R. Lundholm. 2000. Voluntary disclosure and equity offerings: Reducing
information asymmetry or hyping the stock? Contemporary Accounting Research 17 (4):623-662.
Leftwich, R. 1983. Accounting Information in private markets: evidence from private lending
agreements. The Accounting Review 58 (1): 23-42.
Lo, A. K. 2014. Do declines in bank health affect borrowers’ voluntary disclosures? Evidence frominternational propagation of banking shocks. Journal of Accounting Research 52 (2): 541-581.
McLaughlin, R., A. Safieddine, and G. K. Vasudevan. 1998. The information content of corporate
offerings of seasoned securities: An empirical analysis. Financial Management 27 (2): 31-45.

Nara and Noma : The Effect of Bank Loan Dependence on Management and Analyst Forecasts

29

Ministry of Finance. 2014. Survey of corporations. Available at:https://www.e-stat.go.jp/stat-search/files?page=1&layout=datalist&toukei=00350600&tstat=000001047744&cycle=8&year=20131&month=0&tclass1=000001047749&file_type=0&res
ult_back=1&second2=1 (last accessed October 1, 2014).
Nara, S., and M. Noma. 2012. Forecast accuracy and value relevance by firm size -In comparison ofmanagement forecast and analyst forecast. Working Paper. Hitotsubashi University.
Ohlson, J. 2001. Earnings, book values and dividends in equity valuation: An empiricalperspective. Contemporary Accounting Research 18 (1): 107-120.
Ota, K. 2006. Determinants of bias in management earnings forecasts: Empirical evidence fromJapan. In N. Gregoriou, and M. Gaber (Eds.), International Accounting: Standards, Regulations,and Financial Reporting (pp. 267-294). Burlington, MN: Elsevier Press.
Petersen, M. A., and R. G. Rajan. 1994. The benefits of lending relationships: Evidence fromsmall business data. The Journal of Finance 49 (1): 3-37.
Richardson, V. R. 2000. Information asymmetry and earnings management: Some evidence.
Review of Quantitative Finance and Accounting 15 (4): 325-347.
Rock, S., S. Sedo, and M. Willenborg. 2000. Analyst following and count-data econometrics.
Journal of Accounting and Economics 30 (3): 351-373.
Vashishtha, R. 2014. The role of bank monitoring in borrowers’ discretionary disclosure: Evidence
from covenant violations. Journal of Accounting and Economics 57 (2-3): 176-195.
Williams, P. 1996. The relation between a prior earnings forecast by management and analyst
response to a current management forecast. The Accounting Review 71 (1): 103-115.

30

The Japanese Accounting Review, 9 (2019), 1-30

Erratum
The Effect of Bank Loan Dependence on Management and Analyst Forecasts
By Saori Nara and Mikiharu Noma
Volume 9, P 29.
Page
Now reads in part
1 (footnotes)
E-mail : mnoma@hab.hit-u.ac.jp
Even number pages The Japanese Accounting Review, 9(2019), 1-30
The publisher regrets these errors.

Should readE-mail : mnoma@hub.hit-u.ac.jp
The Japanese Accounting Review, 9(2019), 1-29