Bank-Firm Relationships and Security Analyst Activities:
Evidence from Japan*
Kentaro Koga†

Graduate School of International Corporate Strategy
Hitotsubashi University

Satomi Uchino
School of Commerce
Senshu University

ABSTRACTBanks play a critical role in corporate governance in many economies around the world.
This paper empirically compares the activities of security analysts (i.e., analyst coverage,
forecast accuracy and forecast agreement) between firms with and without close working
relationships with their banks in order to gain insights into how bank-firm relationships
affect the information environments for capital market investors. Close bank-firm
relationships signal the banks’ positive evaluation of the firms because the banks screen thefirms before entering or extending the relationships. Further, during the relationships, thebanks monitor these firms. Thus, capital market investors are less motivated to scrutinize
the firms, thereby demand less information. Investigating Japanese firms, we document that
security analysts’ forecasts are less accurate and less agreed (i.e., more dispersed) for thefirms with long-established relationships with banks. Likewise, analyst coverage, forecast
accuracy and forecast agreement are all lower for the firms with a larger amount of loans(i.e., private debt). The associations between bank-firm relationships and security analyst
activities hold after controlling for the potential correlated variables (i.e., capital market
financing, performance volatility, financial distress, cross-holding).
JEL Classification: M41; G21; G32
Key Words: Corporate Governance; Security Analyst; Bank; Japan
Data Availability: Data are publicly available from sources identified in the paper.

* This paper benefited from the discussions with Shin’ichi Hirota and the comments from Joanna Ho, Ole-Kristian Hope, Mingyi
Hung, Kevin Jackson, Linda Myers, Tom Omer, Jaime Schmidt, Jae Shin and especially Susan Krische, as well as the workshop
participants at AAA 2006 Midwest Region Meeting, Japan Accounting Association 2006 National Convention, Nomura
Securities Financial & Economic Research Center and Waseda University. We acknowledge Thomson Financial for the security
analyst activity data. Kazumi Koga, Simmi Rakalla, Ying Wang and Cailiang Zhang provided valuable research assistance.
† Corresponding Author. Address Hitotsubashi University 2-1-2 Hitotsubashi Chiyoda-ku, Tokyo 101-8439, JAPAN
Telephone +81(3)4212-3056 E-mail kkoga@ics.hit-u.ac.jp
Received May 7, 2012; accepted June 25, 2012
Copyright©2012 Research Institute for Economics & Business Administration – Kobe University.

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1. Introduction
This paper empirically compares the activities of security analysts between firms with andwithout close working relationships with their banks in order to gain insights into how bankfirm relationships affect the information environments for capital market investors. Accounting
research about bank-firm relationships remains scarce,1 despite the critical role that banks play incorporate governance in many economies around the world (Shleifer and Vishny 1997). A difficultyof investigating bank-firm relationships in a cross-country study, a common research design ininternational accounting, is their correlation with other institutions, such as the strength of legal
protection provided to capital market investors (Ali and Hwang 2000; Bushman and Piotroski2006). We, therefore, apply a single-country setting (specifically, Japan) to this study and control
for the cross-country variations in other institutions. This paper enhances the understanding of theassociation between the information environments and the institutions of corporate governance.
Shleifer and Vishny (1997) identify two fundamental institutions of corporate governancearound the world: legal protection provided to capital market investors; and large capital providers,such as banks. In many economies, especially in continental Europe (Rajan and Zingales 1995) andAsia (Cooke 1996), banks play a critical role in corporate governance. Firms mostly raise capital frombanks, which, in turn, assume the roles of screening and monitoring the firms (Boyd and Prescott
1986; Diamond 1991; Fama 1985; Hoshi et al. 1990b).
The corporate governance through the banks’ screening and monitoring differs in several ways
from the corporate governance by capital market investors. Because the banks that provide capitalto each firm are more concentrated than dispersed capital market investors, the firm can maintainclose working relationships with its banks and share information with them privately (Hoshi etal. 1990b; 1991; Kang and Stulz 2000). In contrast, the great number of capital market investors,
as well as regulation (e.g., Regulation Fair Disclosure in the U.S.), constrains the firm’s channel todisseminate information mostly to public disclosure. Sharing information privately with few banksis less costly for the firm than disclosing information publicly to many capital market investors.
Accordingly, the information asymmetry between a firm and banks is smaller than the information
asymmetry between the firm and capital market investors ( Jacobson and Aaker 1993). This
information advantage permits the banks to perform screening and monitoring roles (Bharath et al.
2008; Boyd and Prescott 1986; Diamond 1991). Also, the few banks suffer from a smaller free-rider
problem when monitoring the firm because it is easier to coordinate among a small number of banks(Diamond 1984).
On the other hand, banks have a different incentive from capital market investors. Banks are
motivated to monitor a firm when it performs poorly in order to prevent the firm from financial
distress, but less so during the periods of good performance. Financial distress diminishes thefirm’s capability to repay the banks’ loans. In contrast, capital market investors have an incentive
to monitor the firm regardless of its performance. Also, the banks may extend loans to less risky
but less profitable firms and may direct the firms to forego risky but profitable investment projects( Jensen and Meckling 1976). The banks’ screening and monitoring, therefore, affects the information
environments differently from the corporate governance by capital market investors.
Although close bank-firm relationships are globally prevalent, the accounting literature has
1

An exception is Choi (2004) who investigates the association between bank-firm relationships and accrual quality. Bushman andPiotroski (2006) also explores the effect of bank-firm relationships on accounting conservatism by using a country level proxy ofbank-firm relationships.

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not directly investigated how bank-firm relationships affect the information environments forcapital market investors, presumably because, in a cross-country study, the importance of bankfirm relationships in an economy is correlated with other institutions, such as the strength of legal
protection provided to capital market investors (Ali and Hwang 2000; Bushman and Piotroski 2006).
The strong correlation makes it difficult to disentangle the effects of bank-firm relationships.
We, therefore, apply a single-country setting (specifically, Japan) to this study and control for the
cross-country variations in other institutions. Although prior research has classified Japan as a codelaw country, where firms typically raise capital from banks (Ball et al. 2000; Cooke 1996; Rajan andZingales 1995), there is a variation in the closeness of a firm’s relationships with its banks. Whilesome Japanese firms have historically preserved close relationships with their banks (Cooke 1996),
others have maintained looser ties (Hoshi et al. 1991). This variation increases the power of statisticaltests on bank-firm relationships.
Specifically, this paper empirically compares analyst coverage, forecast accuracy and forecast
agreement between Japanese firms with and without close relationships with their banks. The security
analyst literature has interpreted the activities of security analysts as proxies of the information
environments for capital market investors (Lang et al. 2003). The information is rich when many
security analysts cover the firm with accurate and agreed (i.e., not dispersed) forecasts.
Japan provides an additional advantage in data collection. In their financial statements, Japanesefirms report information on their relationships with the banks. This paper, thus, has an advantage
in measuring the closeness of a firm’s relationships with its banks. We measure each firm’s extent ofbank relationships by three different variables: the affiliation with industrial groups called keiretsu
within which group banks fund group firms, the monetary amount of loans (i.e., private debt) andthe proportion of ownership by banks.
This paper contributes to the international accounting literature by documenting that bank-firm
relationships are an institution associated with security analyst activities in an economy where banks
play a critical role in corporate governance. Specifically, security analysts’ forecasts are less accurate
and less agreed (i.e., more dispersed) for firms affiliated with industrial groups. Among the firms
independent of industrial groups, the amount of loans is negatively associated with analyst coverage,
forecast accuracy and forecast agreement. Likewise, the ownership by banks is negatively associatedwith analyst coverage and forecast accuracy. The negative associations are generally stronger among
low performance firms, which the banks have a strong incentive to monitor. Consequently, theinformation environments for capital market investors deteriorate with close bank-firm relationships,
especially when the firms perform poorly.
This paper also contributes to the corporate governance literature by providing evidence thatthe banks’ screening and monitoring play roles in the associations between bank-firm relationships
and security analyst activities. Close bank-firm relationships can affect security analyst activities
in two ways. First, close bank-firm relationships signal the banks’ positive evaluation of the firms
because the banks screen the firms before entering or extending the relationships (Boyd and Prescott1986). Further, during the relationships, the banks monitor these firms (Diamond 1991; Hoshi et al.
1990b). Thus, capital market investors are less motivated to scrutinize the firms, thereby demand lessinformation. Second, bank loans substitute for financing from capital markets (Hoshi et al. 1990b;
1991), therefore, the firms reduce their public disclosures (Frankel et al. 1995; Healy et al. 1999; Langand Lundholm 2000). The associations between bank-firm relationships and security analyst activities
hold after controlling for capital market financing as well as other potential correlated variables (i.e.,
performance volatility, financial distress, cross-holding). The results indicate that the banks’ screening

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The Japanese Accounting Review, 2 (2012), 49-73

and monitoring play roles in the associations.
This paper’s two contributions shed light on how the corporate governance by a group of
capital providers (i.e., the banks’ screening and monitoring) affects another group’s information
environments (i.e., the information environments for capital market investors). Past studies haveshown that corporate governance mechanisms enrich information environments in general (e.g.,
Hope 2003; Lang et al. 2003). In contrast, this paper finds that the information environments forcapital market investors deteriorate when the banks’ screening and monitoring take place in closebank-firm relationships. The findings are relevant not only to international accounting but also innorthern America where banks have become an important source of capital (Bharath et al. 2008).
The remainder of this paper is organized as follows. The next section reviews the literature anddevelops hypotheses. Sections 3 and 4 describe the data and sample, respectively. Section 5 reports
the results of testing the hypotheses. The final section concludes the paper.

2. Literature and Hypotheses
2.1 Bank-Firm Relationship Literature
An extensive body of the finance literature investigates how bank-firm relationships affectcorporate governance and investment decisions. Theoretically, Diamond (1984) and Fama (1985)
view banks as financial intermediaries specializing in the acquisition of client-firm information. This
information advantage permits banks to perform a screening role (Boyd and Prescott 1986) and amonitoring role (Diamond 1991).
Many empirical studies documenting the banks’ monitoring role investigate Japanese firms.
Kaplan (1994), Kaplan and Minton (1994) and Kang and Shivdasani (1995; 1997) find that Japanesefirms with close working relationships with their banks experience higher levels of CEO turnover,director appointments and asset restructuring during the periods of poor performance than firms
without such ties. Banks monitor the firms through these activities.
Overall, the finance literature claims that banks assume the roles of screening and monitoring
the firms to which the banks provide capital. The banks fulfill this role with close working
relationships with the firms, which result in small information asymmetry (Hoshi et al. 1990b; 1991;
Jacobson and Aaker 1993).
2.2 Security Analyst Literature
The security analyst literature has documented the association between security analyst activities
and firms’ disclosure levels. In a cross-sectional study of U.S. firms, Lang and Lundholm (1996)
show that analyst coverage, forecast accuracy and forecast agreement are positively associated withthe analyst ratings of firms’ disclosures. Likewise, examining firms in 22 countries, Hope (2003)
finds a positive association between forecast accuracy and firms’ disclosure levels. Healy et al. (1999)
document an increase in analyst coverage and a decrease in forecast dispersion among the firms that
substantially improve the analyst ratings of firms’ disclosures.
Analyst coverage, forecast accuracy and forecast agreement, in turn, are negatively associatedwith the firms’ costs of capital (Gebhardt et al. 2001) and positively related with security prices (Langet al. 2003). In the context of corporate governance, Lang et al. (2004) find a positive valuation effectwhen security analysts cover firms characterized by the combination of concentrated ownership(i.e., weak internal governance) and weak investor protection by the government (i.e., weak external

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53

governance).
In summary, the security analyst literature maintains that analyst coverage, forecast accuracy
and forecast agreement are related with firms’ public disclosures as well as security analysts’ private
information acquisition (Lang et al. 2003). Furthermore, security analysts may serve the monitoringrole and substitute for other corporate governance mechanisms such as high quality auditors or strict
enforcement of accounting standards (Lang et al. 2004).
Chang et al. (2002) find that firms’ affiliation with industrial groups is associated with analyst
coverage and forecast accuracy in 15 emerging economies. Chang et al. use the group affiliation asa broad proxy for the lack of transparency. This paper differs from Chang et al. by explicitly linking
group affiliation to the construct of bank-firm relationships and employing two additional variables
to fully measure the construct: the amount of loans and the ownership by banks. The three measures
together draw a comprehensive picture of bank-firm relationships and their association with security
analyst activities.
2.3 Hypotheses
The bank-firm relationship literature characterizes close bank-firm relationships by the banks’
screening and monitoring roles (Kang et al. 2000) and the bank loans that substitute for the firms’
financing from capital markets (Hoshi et al. 1990b; 1991). Nevertheless, the firms with close bankrelationships may not always raise less capital from the markets. The markets react positively to theannouncements of new security issuance for the firms with close bank relationships because thebanks screen and monitor the firms (Kang and Stulz 1996). The same premium does not exist for
the firms without bank ties. The premium may encourage the firms with close bank relationships toaccess capital markets despite the bank loans. In fact, no study empirically documents that the extent
of bank-firm relationships is negatively associated with capital market financing.
Therefore, we develop hypotheses based on the banks’ screening and monitoring roles, without
taking into account the firms’ financing from capital markets. In the regression analyses, we control
for the firms’ capital market financing to isolate the association between bank-firm relationships andsecurity analyst activities through the banks’ screening and monitoring.
Close bank-firm relationships signal the banks’ positive evaluation of the firms because
the banks screen the firms before entering or extending the relationships. Further, during therelationships, the banks monitor these firms. Thus, capital market investors are less motivated to
scrutinize the firms, thereby demand less information. As such, the firms should publicly disclose lessand security analysts should make less effort in analyzing these firms. Consequently, fewer analysts
should cover the firms with close bank relationships, and their forecasts should be less accurate andless agreed (i.e., more dispersed).
H1: Analyst coverage, forecast accuracy and forecast agreement are negatively associated with theextent of bank-firm relationships.
Banks are motivated to monitor a firm when it performs poorly in order to prevent the firmfrom financial distress, but less so during the periods of good performance. Therefore, the negative
associations between analyst coverage, forecast accuracy and forecast agreement, and the extent ofbank-firm relationships should be stronger among low performance firms.

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H2: Analyst coverage, forecast accuracy and forecast agreement are associated with the extent of bankfirm relationships more negatively among low performance firms than high performance firms.
The research setting of Japan introduces an empirical tension in H2. As in many code-law
countries, banks in Japan can hold other firms’ equity. If banks behave as shareholders rather
than debtholders, banks have an incentive to monitor the firm regardless of its performance. We
conjecture, however, that banks’ interest as debtholders dominates that as shareholders because aJapanese bank’s ownership in each firm is not large; the commercial law does not permit a bank to
own each firm’s equity more than five percent. In an interview survey, bankers stress the importanceof debtholder interest over shareholder interest because “70 to 80 percent of a bank’s assets are loans
while the ownership of other firms’ equity represents no more than five percent” (Hirota 1999).

3. Data
3.1 Dependent Variables
We measure a firm’s information environment by three variables of security analyst activities:
analyst coverage, forecast accuracy and forecast agreement. Data for these variables are collected fromthe IBES Summary File.
We define the variables of security analyst activities according to Hope (2003), Lang andLundholm (1996) and Lang et al. (2003). Analyst coverage (COVER) is the number of analysts in
IBES who forecast the firm’s annual earnings.2 Forecast accuracy (ACCURACY) is the negative of
the absolute value of the median analyst forecast error, scaled by the beginning stock price.3 A highvalue of ACCURACY represents accurate forecasts. Forecast agreement (AGREE) is the negativeof the standard deviation of analyst forecasts, scaled by the beginning stock price. A high value of
AGREE indicates a high level of agreement (i.e., low level of dispersion) among forecasts.
Following Lang and Lundholm (1996), this paper computes analyst coverage (COVER),
forecast accuracy (ACCURACY) and forecast agreement (AGREE) as the average of each measureacross the 12 monthly reporting periods of the IBES Summary File during the firm’s fiscal year. 4
Furthermore, in the regression analyses, we logarithmically transform COVER (i.e., lnCOVER).
Because ACCURACY and AGREE contain a few outliers with extremely small values, thesevariables are winsorized at the first percentile, as in Lang et al. (2003).
3.2 Independent Variables
In Japan, firms can establish close working relationships with banks by three means. First, firms
can enjoy close bank relationships through the affiliation with industrial groups called keiretsu. At the
When IBES does not report any security analyst for the entire fiscal year, we interpret that COVER is missing. We also performthe regression analyses by setting the value of COVER to zero instead of missing. The results are similar to this paper’s findings.
When IBES does not report any security analyst for some months of the fiscal year, we interpret that none covered the firm in
those months and set COVER to zero in those months.
3
We perform the regression analyses scaling ACCURACY and forecast agreement (AGREE) by total assets per share. The results
are similar to this paper’s findings.
4
The original timing of the security analyst forecasts may influence the 12 month averages of ACCURACY and AGREE. For
example, if many security analysts forecast a firm’s annual earnings only later in a fiscal year, ACCURACY is likely to be high.
As such, we perform the regression analyses using only the data from the 11th month of fiscal year. The results are similar to thispaper’s findings.
2

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center of each keiretsu are banks that extend loans to the firms within the group. At the same time,these banks own equity in the group firms (Sheard 1985).5
Finance and accounting studies on Japanese industrial groups identify each firm’s affiliation withthe six major keiretsu, Daiichi Kangyo, Fuyo, Mitsubishi, Mitsui, Sanwa and Sumitomo (Gramlich
et al. 2004; Hoshi et al. 1991; Kang et al. 2000). We employ Brown (2001)6 to distinguish groupand independent firms. Using a four-star scale, Brown rates each firm by how intensely the firm isaffiliated with one of the major keiretsu. Brown assesses the affiliation intensity by qualitative andquantitative factors, including historical background, sources and amounts of bank loans, ownershipand the appointment of board directors. Following Gramlich et al. (2004), this paper classifies afirm with three- and four-star ratings as a group firm pertaining to one of the six major keiretsu. The
indicator variable, GROUP, takes the value of one if the firm is affiliated with a keiretsu.7
Joining a keiretsu is not easy because keiretsu are exclusive groups of firms with long-establishedrelationships. An independent firm, however, can still establish close working relationships withbanks by taking out a substantial amount of loans from the banks. The banks also hold a significant
proportion of the firm’s equity. Even the banks that extend small amounts of loans to the firm
may still own its equity in order to strengthen the bank-firm relationships upon loan commitment
agreements. In a loan commitment agreement, the banks commit to offer a predetermined amountof loan when the firm requests it (Hirota 1999). The loan commitment agreement motivates thebanks to monitor the firm in order to maintain its capability to repay future loans. Upon establishing
the ownership, the banks often send directors to the firm’s board. These board directors facilitate theinformation flow between the banks and the firm (Hoshi et al. 1990a).
A large amount of bank loans and a large proportion of ownership by banks are the other means
for independent firms to establish close working relationships with banks. This paper employs two
additional measures to capture the closeness of a firm’s relationships with its banks: the monetaryamount of long-term loans,8 scaled by the beginning total assets (LOAN); and the proportion ofownership by banks (BNK_OWN).9 These measures follow Ely and Pownall (2002). We collect data
for LOAN from the Nikkei Financial Quest database. We measure BNK_OWN in 2000 using data
in Brown (2001) that reports the ownership of large Japanese firms by the 15 major banks. Althoughother banks may also own equity of these large firms, the aggregate ownership by the 15 majorbanks should capture each firm’s overall relationships with banks because the large firms maintainthe closest relationships with the 15 major banks. For BNK_OWN after 2000, we use Toyo Keizai(2002-2005), which contains data similar to Brown. Because Brown reports the ownership of only
For example, a Mitsubishi manufacturing firm borrows substantially from the Bank of Tokyo-Mitsubishi and the Mitsubishi Trustand Banking, all affiliated with the Mitsubishi group. Further, each Mitsubishi bank owns as much as five percent of equity in the
Mitsubishi manufacturing firm, the maximum proportion the commercial law permits.
6
Brown is the successor of Dodwell Marketing Consultants that published the same book in older editions. Ely and Pownall (2002)
and Gramlich et al. (2004) use the editions by Dodwell Marketing Consultants.
7
We perform the regression analyses using different keiretsu classifications. An alternative classification is the membership ofpresidents’ council in each keiretsu. The presidents of the core keiretsu firms meet monthly to discuss the keiretsu strategy. Presidents’council membership is unambiguous because each keiretsu explicitly defines core firms and only allows the presidents of those corefirms to attend. The results do not qualitatively change when we replace GROUP with the presidents’ council membership. We also
adopt the classification by Keiretsu No Kenkyu that earlier studies use (Hoshi et al. 1990a; Hoshi et al. 1991; Nakatani 1984) andconfirm that the results do not qualitatively change. We acknowledge Jun-Koo Kang and Kwangwoo Park for sharing their keiretsu
affiliation data based on Keiretsu No Kenkyu.
8Inclusion of short-term loans does not qualitatively change the results.
9
We perform the regression analyses by scaling BNK_OWN as well as foreign ownership (FRG_OWN) not by total equity but by
the book value of total assets. The results are similar to this paper’s findings.
5

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The Japanese Accounting Review, 2 (2012), 49-73

large firms, the sub-sample with BNK_OWN is smaller than the full sample.
We do not have any prediction about group firms’ amount of loans (LOAN) and proportionof ownership by banks (BNK_OWN) because the group affiliation, itself, should warrant close
relationships with the group banks regardless of the LOAN and BNK_OWN levels. Accordingly,
the regression analyses include the interactions between group affiliation (GROUP), and LOAN and
BNK_OWN, respectively, in order to isolate the effects of the amount of loans and the ownership bybanks among the independent firms.
3.3 Control Variables
The association between bank-firm relationships and the firm’s financing from capital markets
is ambiguous. Accordingly, we develop this paper’s hypotheses based on the banks’ screening andmonitoring roles, without taking into account the firms’ capital market financing. In the regression
analyses, we control for the firms’ capital market financing by including the total amount of capital(i.e., stocks and bonds) raised from the Tokyo Stock Exchange during the 36 months centeredaround the fiscal year, scaled by the beginning total assets (CAPITAL).
Hadlock and James (2002) find that firms raise capital from banks instead of markets when thefirms’ business environment is highly uncertain. The extent of uncertainty may also affect security
analyst activities. This paper controls for the extent of uncertainty using performance volatility, earnings
surprise and industry indicator variables. We measure performance volatility by the standard deviationof ROA (i.e., earnings before extraordinary items over total assets), over the preceding seven fiscal years(PERF_VOL)10; and earnings surprise by the absolute value of change in earnings before extraordinary
items from the prior fiscal year, scaled by the beginning stock price (SURPRISE). In addition, theregression analyses include 35 industry indicator variables based on two-digit Nikkei industry codes.
The firm experiencing temporary financial distress but with positive long-term prospects
choose banks over markets because the banks’ information advantage facilitates the banks toverify the firm’s state (Chemmanur and Fulghieri 1994). The financial distress may also affectthe security analyst activities (Behn et al. 2008). This paper controls for temporary financial
distress by the negative of current ratio (i.e., the ratio of current assets to current liabilities)(DISTRESS).
Japanese group firms own equity of other firms within the group. The cross-holding constrains
the trade volume of the firms’ shares, which, in turn, may discourage security analysts to makeeffort in analyzing these firms. The regression analyses control for the proportion of ownership byblock shareholders (BLC_OWN) reported in the financial statements.
This paper also incorporates additional control variables. First, this paper controls for firm
size by the market value of equity at the fiscal year beginning after logarithmic transformation(lnSIZE). Second, the regression analyses include a variable for the proportion of ownership byforeigners (i.e., non-Japanese) (FRG_OWN). Third, this paper incorporates the book value ofoutstanding bonds, scaled by the beginning total assets (BOND). Fourth, the regression analyses
include firm performance that should be related with the disclosure level (Lang and Lundholm
1993; Miller 2002). We measure firm performance by ROA after subtracting the industry median11
in each year (ROA). We also include year indicator variables.
All the control variables are computed from the data in the Nikkei Financial Quest database,10
11

We require more than five fiscal year data to compute PERF_VOL.
We identify 36 industries by two-digit Nikkei industry codes.

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the Tokyo Stock Exchange Monthly Statistics and Toyo Keizai’s Stock Price CD-ROM. We use
financial data from the consolidated financial statements.12 When a firm does not report consolidated
financial statements, however, we use the parent firm’s financial statements, assuming that the firm
did not have any subsidiary that requires financial statement consolidation.

4. Sample
This paper imposes a few criteria on the sample. First, we include the firm-years of those firms
listed on the Tokyo Stock Exchange First and Second Sections, and of which fiscal years end between
2000 and 2004. The period between 2000 and 2004 is relevant to this paper’s hypotheses, which wedevelop based on the banks’ screening and monitoring roles without taking into account the firms’
financing from capital markets. The stagnant Japanese capital markets during the sample period
discouraged firms from raising capital (Isoyama and Matsuura 2001). The sample period makes it
easier to control for the firms’ capital market financing. Second, the firm-years must have all thenecessary data. Third, we drop financial institutions from the sample because they, themselves, arecapital providers that are irrelevant to this study.
The selection criteria leave 4,956 firm-years to the full sample for analyst coverage (COVER)(Table 1, Panel A). There are 4,912 firm-years with data for forecast accuracy (ACCURACY). In theanalysis of forecast agreement (AGREE), we use 2,697 firm-years with non-missing AGREE.13 The
sub-sample with the ownership by banks (BNK_OWN) diminishes to 1,700, 1,698, and 1,062 firmyears for COVER, ACCURACY and AGREE, respectively.
Panel B of Table 1 presents the sample’s distribution across years. The full sample’s number offirm-years decreases from 1,185 in 2001 to 861 in 2002 because of the reduction in the firms that the
IBES Summary File covers.
Panel C reports descriptive statistics. The median analyst coverage (COVER) is 2.83, which issmaller than the 10.8 of the Japanese sub-sample in Hope (2003) with only 190 firm-years, but close
to 3.0 in Lang et al. (2003) with 1,880 firm-years. The median forecast accuracy (ACCURACY ),–0.016, is similar to Hope’s –0.013, but worse than Lang et al.’s –0.0031.14 The difference between
this study and Lang et al. should derive from the timing when forecast accuracy is measured; wemeasure ACCURACY as the average over the entire fiscal year, whereas Lang et al. measure forecast
accuracy in the 11th month of the fiscal year.15 The median forecast agreement (AGREE) of –0.007
resembles Lang and Lundholm’s (1996) –0.005 among U.S. firms.16
The full sample’s 19.2 percent is affiliated with one of the six major keiretsu (GROUP). The
levels of the amount of loans (LOAN) and the ownership by banks (BNK_OWN) are equivalent
to Ely and Pownall (2002) who study Japanese firms. The first and third quartiles of the amountof capital raised from the markets (CAPITAL) are zero. Most firms did not access the capital
markets, probably because of the stagnant Japanese markets. Little capital market financing
Exceptions are bank and foreign ownerships (BNK_OWN and FRG_OWN, respectively). We use the ownership data of theparent firms’ financial statements, which are the only sources of these data.
13
By definition, when less than two security analysts cover the firm, the value of AGREE is missing.
14
This paper’s median ACCURACY is worse than Land and Lundholm’s (1996) –0.008 among U.S. firms.
15
We perform the regression analyses using only the data from the 11th month of fiscal year. The results are similar to thispaper’s findings.
16
Neither Hope (2003) nor Lang et al. (2003) reports the forecast agreement among Japanese firms.
12

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Table 1: Sample

Panel A: Number of observations in the sample selection process
Sample selection process

Full sample observations with data for group affiliation, loan amount and all thecontrol variables(Less) Brown (2001) does not cover (Missing bank ownership data)
Sub-sample observations with data for all the independent and control variables(Less) IBES does not coverSample for analyst coverage (COVER)(Less) Missing forecast accuracy data
Sample for forecast accuracy (ACCURACY)(Less) Missing forecast agreement data
Sample for forecast agreement (AGREE)

Full sample Sub-sample
Firm-years
7,023

2,067
4,956
44
4,912
2,215
2,697

4,808
2,215
515
1,700
2
1,698
636
1,062

Panel B: Distribution by year of the sample for analyst coverage (COVER)
Full sample Sub-sample
Firm(-year)s
1,115
412
1,185
426
861
298
887
288
908
276
4,956
1,700

Year in which fiscal year ends
2000
2001
2002
2003
2004
Total

Panel C: Descriptive statistics
Variable

MeanStandard
deviationFirst
quartile

MedianThird
quartile

4,956
4,912
2,697

4.93
–0.049–0.010

5.07
0.101
0.012

1.00
–0.043–0.0122.83–0.016–0.0077.33–0.006–0.004

4,956
4,956
1,700

0.192
0.102
0.083

–
0.120
0.054

–
0.010
0.043

–
0.066
0.077

–
0.150
0.119

4,956
4,956
4,956
4,956
4,956
4,956
4,956
4,956
4,956

0.016
0.019
0.019
–1.736
0.444
232
0.093
0.037
0.007

0.046
0.015
0.025
1.382
0.147
862
0.104
0.062
0.042

0.000
0.009
0.005
–1.988
0.337
16
0.015
0.000
–0.016

0.000
0.015
0.011
–1.369
0.422
41
0.057
0.000
0.000

0.000
0.024
0.024
–0.997
0.545
142
0.135
0.060
0.022

Firm-years

Dependent variable
COVER
ACCURACY
AGREE
Independent variable
GROUP
LOAN
BNK_OWN
Control variable
CAPITALPERF_VOL
SURPRISE
DISTRESSBLC_OWNSIZE
FRG_OWN
BOND
ROA(Continued )Koga and Uchino: Bank-Firm Relationships and Security Analyst Activities

59(-Continued )COVER:

Monthly average of the number of security analysts who forecast the firm’s annual earnings
during the fiscal year.
ACCURACY: Monthly average of the negative of the absolute value of median analyst forecast error
during the fiscal year, scaled by the beginning stock price.
AGREE:
Monthly average of the negative of the standard deviation of analyst forecasts during the
fiscal year, scaled by the beginning stock price.
GROUP:
Indicator variable with the value of one if the firm pertains to one of the six major keiretsu;
and zero otherwise.
LOAN:
Amount of long-term loans, scaled by the beginning total assets.
BNK_OWN: Proportion of ownership by banks.
CAPITAL:
Total amount of capital (i.e., stocks and bonds) raised from the Tokyo Stock Exchange
during the 36 months centered around the fiscal year, scaled by the beginning total assets.
PERF_VOL: Standard deviation of ROA (i.e., earnings before extraordinary items over total assets) overthe preceding seven fiscal years.
SURPRISE: Change in earnings before extraordinary items from the prior fiscal year, scaled by the
beginning stock price.
DISTRESS: The negative of current ratio (i.e., the ratio of current assets to current liabilities).
BLC_OWN: Proportion of ownership by block shareholders.
SIZE:
Market value of equity at the fiscal year beginning, in billion yen.
FRG_OWN: Proportion of ownership by foreigners (i.e., non-Japanese).
BOND:
Book value of outstanding bonds, scaled by the beginning total assets.
ROA:
ROA (i.e., earnings before extraordinary items over total assets) after subtracting industry
median in each year.

should limit its effect on security analyst activities and makes it easier to control for the firms’
capital market financing in the regression analyses.
Table 2 shows the correlations among the variables. Among the dependent variables, analyst
coverage (lnCOVER), forecast accuracy (ACCURACY ) and forecast agreement (AGREE)
are positively and significantly correlated with one another. The positive correlations endorsethat lnCOVER, ACCURACY and AGREE capture the same construct of the information
environments for capital market investors. The firms’ capital market financing (CAPITAL)
is positively and significantly correlated with group affiliation (GROUP) and the amount ofloans (LOAN), respectively. CAPITAL is not correlated with the ownership by banks (BNK_OWN) at a conventional level. The non-negative correlations are in line with our inference thatthe association between bank-firm relationships and the firms’ capital market financing is notnecessarily negative.

5. Results
5.1 Tests of H1
Ordinary least square regressions of a pooled sample across fiscal years may suffer from inflated
statistical significance because of the autocorrelation among multiple observations of the same
firm. Accordingly, we employ clustered standard errors17 (Petersen 2009) to assess the statisticalsignificance, which we report using a one-sided test. H1 hypothesizes that analyst coverage, forecast
accuracy and forecast agreement are negatively associated with the extent of bank-firm relationships.
17

We perform the regression analyses applying the Fama and MacBeth method to address the autocorrelation among multiple
observations in the same year. The results do not qualitatively change.

BOND
0.331
0.041
–0.120
0.181
0.287
0.140
0.543
–0.242–0.1280.285–0.290
0.399
0.164
–
–0.146

ROA
0.208
0.384
0.420
–0.118–0.337–0.026–0.0300.118
0.188–0.342
0.153
0.149
0.285
–0.177
–

GROUP
0.141
–0.029–0.103–0.123–0.1300.038–0.113–0.0500.135–0.107
0.152
0.040
0.136
–0.105LOAN–0.145–0.208–0.329
0.156
–
0.053
0.090
–0.113–0.0830.374–0.109–0.106–0.1830.165–0.228

BNK_OWN CAPITAL PERF_VOL SURPRISE DISTRESS BLC_OWN–0.090
0.347
0.006
0.040
–0.137–0.183–0.0350.132–0.066–0.095–0.2030.038–0.0050.012–0.054–0.080–0.281
0.075–0.1300.149–0.118–0.0370.181–0.1170.133
0.189–0.204–0.1330.647–0.183–0.032–0.179–0.1000.072–0.2760.016––0.216–0.0990.216–0.213–0.132
0.014
–
0.389
–0.3060.151–0.109
0.009
0.458
–
–0.196
0.086
0.084
0.076
–0.2020.169––0.120–0.307–0.0900.106
0.066–0.091
–
0.027
0.221
–0.093–0.019–0.0840.258–0.158
0.130
0.130
0.124
–0.232
0.096
0.098
0.397
–0.157–0.0710.211
0.258–0.062
0.023
0.177
0.212
–0.276
0.179

Table 2: Correlation

Note: Pearson product moment correlation beneath the diagonal and Spearman rank order correlation above the diagonal.
The numbers of firm-years included in the correlations with ACCURACY, AGREE and BNK_OWN are 4,912, 2,697 and 1,700,
respectively. The number of firm-years included in all the other correlations is 4,956.
For the measurements of variables, see Table 1 Panel C.

FRG_OWN
0.674
0.310
0.246
0.070
–0.240–0.092
0.229
0.095
0.105
–0.296–0.186
0.639
–
0.070
0.255

lnSIZE
0.811
0.363
0.307
0.162
–0.0970.008
0.393–0.126–0.048–0.067–0.275
–
0.547
0.383
0.154

lnCOVER
ACCURACY
AGREE
GROUP
LOAN
BNK_OWNCAPITALPERF_VOL
SURPRISE
DISTRESSBLC_OWN
lnSIZEFRG_OWN
BOND
ROA

AGREE
0.231
0.594
–
–0.116–0.2240.009
0.068–0.004–0.054–0.154
0.065
0.222
0.131
–0.0460.303

lnCOVER ACCURACY
lnCOVER–
0.345
ACCURACY
0.232
–
AGREE
0.191
0.553
GROUP
0.140
–0.015LOAN–0.141–0.174
BNK_OWN –0.052–0.033
CAPITAL
0.216
0.090
PERF_VOL–0.003–0.038SURPRISE
0.037–0.009
DISTRESS–0.118–0.133
BLC_OWN–0.174
0.041
lnSIZE
0.778
0.238
FRG_OWN
0.534
0.156
BOND
0.286
0.056
ROA
0.178
0.258

60
The Japanese Accounting Review, 2 (2012), 49-73

Koga and Uchino: Bank-Firm Relationships and Security Analyst Activities

61

5.2 Primary Results
Panel A of Table 3 presents the primary results for the full sample. Model 1 has analyst coverage(lnCOVER) as the dependent variable. Overall, the coefficient estimates on the control variables havethe expected sign. The coefficient estimate on group affiliation (GROUP) is not negative (0.051), notconsistent with H1. Chang et al. (2002) find a similar result in 15 emerging economies. Supporting
H1, the loan amount of independent firms (LOAN) is negatively (–0.302) associated with lnCOVERat the 0.05 level. The larger the independent firm’s loans, the fewer the security analysts who cover
the firm.
Models 2 and 3, which have forecast accuracy (ACCURACY ) and forecast agreement(AGREE) as the dependent variables, include analyst coverage (lnCOVER) to control for its
effects on ACCURACY and AGREE. In Model 2, the coefficient estimate on group affiliation(GROUP) is negative (–0.008) and statistically significant at the 0.05 level. The result supports
H1. Security analysts forecast group firms’ earnings less accurately by 0.008 of the stock price thanthose of independent firms. The magnitude is economically significant relative to the full sample’s
median ACCURACY of –0.016 in Table 1, Panel C. The result is in line with Chang et al. (2002).
The coefficient estimate on independent firms’ loan amount (LOAN) is also negative (–0.119) andstatistically significant at the 0.01 level, consistent with H1. In Panel C of Table 1, the standard
deviation of LOAN is 0.120. The coefficient estimate of –0.119 indicates that when an independent
firm’s LOAN increases by a standard deviation, the analyst forecasts become less accurate by 0.014 of
the stock price. Again, the magnitude is economically significant.
In Model 3 with forecast agreement (AGREE), the coefficient estimates on group affiliation(GROUP) and independent firms’ loan amount (LOAN) are negative (–0.001 and –0.011) andstatistically significant at the 0.1 and 0.01 levels, respectively. The results support H1.
Panel B reports primary results for the sub-sample with the ownership by banks. In Models
4 and 5, which have analyst coverage (lnCOVER) and forecast accuracy (ACCURACY ) as thedependent variables, the coefficient estimates on independent firms’ bank ownership (BNK_OWN)
are negative (–1.279 and –0.105) and statistically significant at the 0.01 and 0.1 levels, respectively.
The results for group affiliation (GROUP) and independent firms’ loan amount (LOAN) are largely
consistent with the full sample in Panel A.
In summary, the primary results show that group affiliation (GROUP) is negatively associatedwith forecast accuracy (ACCURACY ) and forecast agreement (AGREE). Likewise, independent
firms’ loan amount (LOAN) is negatively associated with analyst coverage (lnCOVER),
ACCURACY and AGREE. Furthermore, independent firms’ bank ownership (BNK_OWN) is
negatively associated with lnCOVER and ACCURACY. Collectively, these results support H1.
5.3 Results of Change Regressions
In the previous analyses, we run the regressions by using the levels of all the variables. The
persistency in dependent and independent variables over time, however, may have distorted
the primary results. Accordingly, we perform the analyses with the change in each dependent,
independent or control variable over three years.18,19 Because group affiliation (GROUP) does not

We winsorize the changes in forecast accuracy (ΔACCURACY ) and forecast agreement (ΔAGREE) at the first and 99th
percentiles.
19
Using the changes over two or four years does not qualitatively change the results.
18

The Japanese Accounting Review, 2 (2012), 49-73

62

Panel A: Full sample
Independent orcontrol variable

Table 3: Primary Results For Testing H1
Hypothesized
sign in H1

Intercept
GROUP

–

LOAN

–

GROUP*LOAN

Model 1[lnCOVER]†–1.128(0.00)***
0.051(0.83)–0.302(0.04)**
0.427(0.12)

lnCOVER–CAPITAL

1.122(0.00)***
1.651(0.31)
0.670(0.59)–0.007(0.58)
0.012(0.93)
0.502(0.00)***
1.192(0.00)***
0.877(0.00)***
0.913(0.11)
Included
Included
0.655
4,956

PERF_VOL
SURPRISE
DISTRESSBLC_OWN
lnSIZEFRG_OWN
BOND
ROA
IndustryYear
R2
Firm-years

Model 2

[ACCURACY]†–0.057(0.00)***–0.008(0.05)**–0.119(0.00)***
0.031(0.43)
0.007(0.00)***
0.093(0.00)***–0.419(0.01)***–0.100(0.30)–0.001(0.17)
0.042(0.00)***
0.008(0.00)***–0.036(0.08)*
0.013(0.68)
0.486(0.00)***
Included
Included
0.174
4,912

Model 3

[AGREE]†–0.013(0.00)***–0.001(0.09)*–0.011(0.00)***–0.014(0.07)*
0.001(0.31)
0.018(0.00)***–0.091(0.00)***–0.042(0.00)***–0.000(0.02)**
0.003(0.15)
0.002(0.00)***–0.009(0.02)**–0.008(0.13)
0.082(0.00)***
Included
Included
0.286
2,697

Note: † denotes dependent variable.
Coefficient estimates in the upper rows. p-values upon clustered standard errors in the lower rows inparentheses (one-sided if the sign is hypothesized, two-sided otherwise).
*, ** and *** denote statistical significance at the 0.1, 0.05 and 0.01 levels, respectively.
For the measurements of variables, see Table 1 Panel C.
(Continued )change, we exclude GROUP from the regressions and focus on independent firms’ loan amount(LOAN) and bank ownership (BNK_OWN). Accordingly, we limit the sample to independent firms.
In Panel A of Table 4 for the full sample, the results from the change regressions are similar tothe primary results; independent firms’ change in loan amount (ΔLOAN) is negatively associatedwith the changes in analyst coverage (ΔlnCOVER), forecast accuracy (ΔACCURACY) and forecast
agreement (ΔAGREE). In Panel B for the sub-sample with bank ownership, independent firms’change in bank ownership (ΔBNK_OWN) is negatively associated with ΔCOVER. In Model

Koga and Uchino: Bank-Firm Relationships and Security Analyst Activities

63(-Continued )Panel B: Sub-sample with bank ownership
Independent orcontrol variable

Hypothesized
sign in H1

Intercept
GROUP

–

LOAN

–

GROUP*LOAN
BNK_OWNGROUP*BNK_OWN–

Model 4[lnCOVER]†–1.234(0.00)***
0.113(0.89)–0.099(0.34)
0.235(0.44)–1.279(0.01)***–0.369(0.61)

lnCOVER–CAPITAL

1.539(0.02)**
4.312(0.05)**
2.158(0.03)**–0.007(0.80)–0.200(0.31)
0.516(0.00)***
1.462(0.00)***
1.045(0.03)**
0.289(0.75)
Included
Included
0.777
1,700

PERF_VOL
SURPRISE
DISTRESSBLC_OWN
lnSIZEFRG_OWN
BOND
ROA
IndustryYear
R2
Firm-years

Model 5
[ACCURACY]†–0.053(0.04)**–0.025(0.01)***–0.101(0.05)**
0.062(0.32)–0.105(0.09)*
0.145(0.13)
0.007(0.13)
0.164(0.01)***–0.631(0.03)**–0.361(0.04)**–0.001(0.74)
0.052(0.07)*
0.008(0.03)**–0.077(0.08)*
0.116(0.01)***
1.022(0.00)***
Included
Included
0.199
1,698

Model 6
[AGREE]†–0.016(0.00)***–0.004(0.02)**–0.020(0.03)**–0.003(0.83)
0.004(0.65)
0.010(0.43)
0.000(0.78)
0.034(0.00)***–0.095(0.05)**–0.014(0.59)
0.000(0.70)
0.008(0.07)*
0.003(0.00)***–0.013(0.04)**–0.003(0.79)
0.116(0.00)***
Included
Included
0.362
1,062

Note: † denotes dependent variable.
Coefficient estimates in the upper rows. p-values upon clustered standard errors in the lower rows inparentheses (one-sided if the sign is hypothesized, two-sided otherwise).
*, ** and *** denote statistical significance at the 0.1, 0.05 and 0.01 levels, respectively.
For the measurements of variables, see Table 1 Panel C.

12 with ΔAGREE, the coefficient estimate on ΔBNK_OWN is also negative (–0.019) though
not statistically significant at a conventional level (p value 0.11). Although the results from thechange regressions are somewhat weaker than the primary results, the persistency in dependent andindependent variables over time does not appear to distort the primary results.

The Japanese Accounting Review, 2 (2012), 49-73

64

Panel A: Full sample
Independent orcontrol variable

Table 4: Results of Change Regressions For Testing H1
Hypothesized
sign in H1

Intercept
ΔLOAN–

Model 7

[ΔlnCOVER]†–0.143(0.00)***–0.721(0.00)***ΔlnCOVER–ΔCAPITAL–0.446(0.11)
4.111(0.00)***
0.902(0.07)–0.024(0.26)–0.001(1.00)
0.428(0.00)***
1.075(0.00)***–0.119(0.63)–0.059(0.89)
Included
0.217
2,705

ΔPERF_VAR
ΔSURPRISEΔDISTRESSΔBLC_OWNΔlnSIZEΔFRG_OWN
ΔBOND
ΔROA
Year
R2
Firm-years

Model 8[ΔACCURACY]†
0.013(0.00)***–0.152(0.05)**
0.004(0.13)–0.006(0.84)
0.446(0.05)**–0.088(0.31)–0.005(0.09)*
0.029(0.23)
0.031(0.00)***
0.022(0.60)–0.064(0.06)*
0.283(0.00)***
Included
0.107
2,702

Model 9[ΔAGREE]†
0.003(0.00)***–0.016(0.00)***–0.001(0.44)–0.002(0.76)–0.008(0.81)–0.048(0.00)***–0.000(0.17)
0.004(0.14)
0.008(0.00)***
0.006(0.29)–0.005(0.36)
0.018(0.05)**
Included
0.241
1,267

Note: † denotes dependent variable.
Coefficient estimates in the upper rows. p-values upon clustered standard errors in the lower rows inparentheses (one-sided if the sign is hypothesized, two-sided otherwise).
*, ** and *** denote statistical significance at the 0.1, 0.05 and 0.01 levels, respectively.
Variables are the changes in each variable in Table 1 Panel C over thee years.
(Continued )5.4 Results of Two-Stage Least Square Regressions
An endogeneity problem may exist in analyzing analyst coverage (lnCOVER) and forecast
accuracy (ACCURACY) separately. Alford and Berger (1999) suggest lnCOVER and ACCURACY
are simultaneously determined. This paper applies two-stage least square regressions for lnCOVER
and ACCURACY to address the endogeneity problem. Similar to Alford and Berger’s (1999) model,
we estimate a system of equations that include the monetary amount of trade volume (VOLUME)
as an endogenous variable,20 and prior fiscal year’s extraordinary items scaled by the prior fiscal year
beginning total assets (EXTRA) as an exogenous variable.
20

VOLUME also addresses the potential correlated variable problem that bank ownership may capture the lack of liquidity insteadof bank-firm relationships.

Koga and Uchino: Bank-Firm Relationships and Security Analyst Activities

65(-Continued )Panel B: Sub-sample with bank ownership
Independent orcontrol variable

Hypothesized
sign in H1

Intercept
ΔLOAN–

ΔBNK_OWN–

Model 10[ΔlnCOVER]†–0.237(0.01)***–0.693(0.05)**–1.062(0.03)**ΔlnCOVER–ΔCAPITAL–0.253(0.76)
3.175(0.33)
1.097(0.38)–0.092(0.15)–0.011(0.97)
0.325(0.00)***
1.310(0.00)***–0.599(0.26)–1.097(0.30)
Included
0.232
600

ΔPERF_VAR
ΔSURPRISEΔDISTRESSΔBLC_OWNΔlnSIZEΔFRG_OWN
ΔBOND
ΔROA
Year
R2
Firm-years

Model 11
[ΔACCURACY]†

0.021(0.04)**–0.004(0.48)–0.019(0.42)
0.018(0.01)***–0.168(0.19)
0.523(0.24)
0.157(0.40)–0.018(0.14)
0.061(0.41)
0.038(0.00)***–0.047(0.57)–0.009(0.90)
0.464(0.00)***
Included
0.141600

Model 12[ΔAGREE]†

0.004(0.00)***–0.020(0.10)*–0.019(0.11)–0.000(0.87)–0.005(0.58)
0.003(0.96)–0.022(0.46)
0.000(0.92)–0.000(0.99)
0.010(0.00)***–0.006(0.45)
0.009(0.44)
0.021(0.34)
Included
0.339
350

Note: † denotes dependent variable.
Coefficient estimates in the upper rows. p-values upon clustered standard errors in the lower rows inparentheses (one-sided if the sign is hypothesized, two-sided otherwise).
*, ** and *** denote statistical significance at the 0.1, 0.05 and 0.01 levels, respectively.
Variables are the changes in each variable in Table 1 Panel C over thee years.

Same as the primary results, in Table 5, Panel A for the full sample, group affiliation (GROUP)
is negatively associated with forecast accuracy (ACCURACY), and independent firms’ loan amount(LOAN) is negatively associated with both analyst coverage (lnCOVER) and ACCURACY. In
Panel B for the sub-sample with the ownership by banks, independent firms’ bank ownership (BNK_OWN) is negatively associated with lnCOVER and ACCURACY. Overall, endogeneity does notappear to distort the primary results.
5.5 Tests of H2
H2 hypothesizes that analyst coverage, forecast accuracy and forecast agreement are associatedwith the extent of bank-firm relationships more negatively among low performance firms than highThe Japanese Accounting Review, 2 (2012), 49-73

66

Table 5: Results of Two-Stage Least Square Regressions For Testing H1

Panel A: Full sample
Independent orcontrol variable

Hypothesized
sign in H1

Intercept
GROUP

–

LOAN

–

GROUP*LOAN[lnCOVER]†
2.429(0.00)***
0.060(0.91)–0.333(0.01)***
0.426(0.09)*

Model 13

[ACCURACY]†–0.031(0.00)***–0.007(0.08)*–0.125(0.00)***
0.029(0.30)
0.018(0.00)***–[lnVOLUME]†–4.870(0.00)***
0.165(0.00)***
0.749(0.00)***–0.172(0.61)
1.054(0.00)***
1.771(0.06)*––

lnCOVER–

ACCURACYCAPITAL

4.746(0.00)***
0.392(0.00)***
0.022(0.94)PERF_VOL–

SURPRISE––0.485(0.00)***–0.123(0.07)*

DISTRESS––BLC_OWN–

lnSIZE–lnVOLUME

BOND

0.545
(0.00)***
0.785(0.00)***

ROA

–

EXTRA

–

IndustryYear
Adjusted R2
Firm-years

Included
Included
0.567

FRG_OWN–––0.016(0.99)–
0.087(0.00)***–2.289(0.00)***
0.510(0.00)***
–

–

–

0.483
(0.00)***–0.052(0.33)
Included
Included
0.158
4,911

–
–
Included
Included
0.759

Note: † denotes dependent variable.
Coefficient estimates in the upper rows. p-values in the lower rows in parentheses (one-sided if the sign ishypothesized, two-sided otherwise).
*, ** and *** denote statistical significance at the 0.1, 0.05 and 0.01 levels, respectively.
VOLUME: Average trade volume of each month’s last trading day during the fiscal year, in billion yen.
EXTRA: Prior fiscal year’s extraordinary items, scaled by the prior fiscal year beginning total assets.
For the measurements of the other variables, see Table 1 Panel C.
(Continued )Koga and Uchino: Bank-Firm Relationships and Security Analyst Activities

67(-Continued )Panel B: Sub-sample with bank ownership
Independent orcontrol variable

Hypothesized
sign in H1

Model 14–[lnVOLUME]†–5.144(0.00)***
0.094(0.33)
0.692(0.02)**–0.051(0.87)–0.878(0.17)–0.708(0.41)
0.810(0.00)***
0.499(0.61)–––––2.923(0.22)––0.739(0.00)***–0.391(0.01)***

DISTRESS––BLC_OWN––

lnSIZE

–

–
–

–

BOND

0.643
(0.02)**
1.017(0.00)***

0.030(0.35)–2.154(0.00)***
0.643(0.00)***

–

–

ROA

–

–

EXTRA

–

IndustryYear
Adjusted R2
Firm-years

Included
Included
0.695

0.938
(0.00)***–0.057(0.54)
Included
Included
0.163
1,698

Intercept
GROUP

–

LOAN

–

GROUP*LOAN
BNK_OWNGROUP*BNK_OWN–[lnCOVER]†
2.256(0.00)***
0.161(0.99)–0.153(0.24)
0.202(0.46)–1.596(0.00)***–0.597(0.36)

lnCOVER–

ACCURACYCAPITAL

2.362(0.00)***
0.429(0.00)***
0.226(0.72)PERF_VOL–

SURPRISE

lnVOLUME

FRG_OWN[ACCURACY]†–0.021(0.22)–0.018(0.04)**–0.110(0.00)***
0.058(0.12)–0.102(0.06)*
0.117(0.19)
0.017(0.00)***––
Included
Included
0.841

Note: † denotes dependent variable.
Coefficient estimates in the upper rows. p-values in the lower rows in parentheses (one-sided if the sign ishypothesized, two-sided otherwise).
*, ** and *** denote statistical significance at the 0.1, 0.05 and 0.01 levels, respectively.
VOLUME: Average trade volume of each month’s last trading day during the fiscal year, in billion yen.
EXTRA: Prior fiscal year’s extraordinary items, scaled by the prior fiscal year beginning total assets.
For the measurements of the other variables, see Table 1 Panel C.

68

The Japanese Accounting Review, 2 (2012), 49-73

performance firms. In order to test H2, we create a rank variable, RANK, that represents the quintileof ROA (i.e., earnings before extraordinary items over total assets, after subtracting industry medianin each year). RANK takes the value of zero for the lowest ROA quintile and four for the highest
ROA quintile.21 H2 predicts that the interactions between RANK and each of group affiliation(GROUP), and independent firms’ loan amount (LOAN) and bank ownership (BNK_OWN),
are positively associated with security analyst activities. We present the results from the clustered
standard error regressions.22
Table 6, Panel A presents the results for the full sample. For parsimony, Table 6 reports
the coefficient estimates and the p-values only for the independent variables. In Models 15 to
17, the coefficient estimates on the interaction between performance rank (RANK) and groupaffiliation (GROUP) are not statistically significantly at a conventional level. In Models 16 and17, the interaction between RANK and independent firms’ loan amount (LOAN) is positively andstatistically significantly associated with forecast accuracy (ACCURACY ) and forecast agreement(AGREE) at the 0.01 level (0.070 and 0.008, respectively). These results support H2.
In Panel B’s Models 19 and 20, the interaction between performance rank (RANK) andindependent firms’ bank ownership (BNK_OWN) is positively and statistically significantly
associated with forecast accuracy (ACCURACY) and forecast agreement (AGREE) at the 0.05 level(0.113 and 0.015, respectively). In these models, the interaction between RANK and group affiliation(GROUP) is positively and statistically significantly associated with ACCURACY and AGREE at
the 0.05 and 0.1 levels (0.012 and 0.001), respectively, as well.23
In summary, the interaction between performance rank (RANK) and independent firms’
loan amount (LOAN) is positively associated with forecast accuracy (ACCURACY ) and forecast
agreement (AGREE). The interactions between RANK, and group affiliation (GROUP) and bankownership (BNK_OWN) are also positively associated with ACCURACY and AGREE in the subsample. Collectively, the results provide evidence to support H2.
5.6 Additional Analyses
Hadlock and James (2002) find that firms raise capital from banks instead of markets when
the firms’ business environment is highly uncertain. We control for the extent of uncertainty in the
regression analyses using performance volatility (PERF_VOL), earnings surprise (SURPRISE) andindustry indicator variables. If these variables do not effectively capture the uncertainty, however,
the regression analyses may suffer from an omitted correlated variable problem. Accordingly, we add
the control variables that Hadlock and James use to measure the extent of uncertainty: stock return
volatility, the book-to-market ratio of equity, the book value of property, plant and equipment, andfirm age. The results (untabulated) are similar to this paper’s findings; an omitted correlated variable
does not appear to distort the results.

We perform the regression analyses replacing RANK with actual ROA. The results are similar to this paper’s results.
We perform the regression analyses with two-stage least square for lnCOVER and ACCURACY. The results do not qualitatively change.
23
In Models 16 and 17 for the full sample, the coefficient estimates on the interaction between RANK and GROUP are notstatistically significant at a conventional level. We attribute the different results between the full and sub-samples to whether
BNK_OWN appears in the models. When we exclude BNK_OWN from the regression in the sub-sample, the coefficientestimates on the interaction become statistically insignificant at a conventional level, as in the full sample.
21
22

Koga and Uchino: Bank-Firm Relationships and Security Analyst Activities

Panel A: Full sample

Independent or control
variable69

Table 6: Results for Testing H2
Hypothesized
sign in H2 (H1)Intercept

Model 15[lnCOVER]†–1.172(0.00)***

Model 16
[ACCURACY]†–0.073(0.00)***

Model 17
[AGREE]†–0.017(0.00)***

GROUP

(–)

0.044
(0.70)–0.013(0.10)*–0.001(0.29)RANK*GROUP

+

0.009
(0.40)

0.003(0.22)–0.000(0.53)

LOAN(–)–0.410(0.04)**–0.216(0.00)***–0.025(0.00)***RANK*LOAN+

0.127(0.12)

0.070(0.00)***

0.008(0.01)***GROUP*LOAN

0.810(0.05)**

0.088(0.20)–0.011(0.43)RANK*GROUP*LOAN–0.246(0.23)–0.019(0.47)

0.001(0.82)

0.054(0.00)***

0.012(0.00)***

0.002(0.00)***CAPITALPERF_VOL
SURPRISE
DISTRESSBLC_OWN
lnSIZEFRG_OWN
BOND
IndustryYear

lnCOVERCAPITALPERF_VOL
SURPRISE
DISTRESSBLC_OWN
lnSIZEFRG_OWN
BOND
Industry
Year
0.204
4,912

lnCOVERCAPITALPERF_VOL
SURPRISE
DISTRESSBLC_OWN
lnSIZEFRG_OWN
BOND
Industry
Year
0.302
2,697

RANK

Other control
variables
included

R2
Firm-years

0.659
4,956

Note: † denotes dependent variable.
Coefficient estimates in the upper rows. p-values upon clustered standard errors in the lower rows inparentheses (one-sided if the sign is hypothesized, two-sided otherwise).
*, ** and *** denote statistical significance at the 0.1, 0.05 and 0.01 levels, respectively.
RANK: Quintile rank of ROA (i.e., earnings before extraordinary items over total assets, after subtracting
industry median in each year).
For the measurements of the other variables, see Table 1 Panel C.

(Continued )The Japanese Accounting Review, 2 (2012), 49-73

70(-Continued )Panel B: Sub-sample with bank ownership
Independent or control variable

Hypothesized
sign in H2 (H1)Intercept
GROUP

(–)

RANK*GROUP

+

LOAN

(–)

RANK*LOAN+GROUP*LOANRANK*GROUP*LOAN
BNK_OWN(–)

RANK*BNK_OWN+

GROUP*BNK_OWN
RANK*GROUP*BNK_OWN
RANK

Other control
variables
included

R2
Firm-years

Model 18
Model 19[lnCOVER]† [ACCURACY]†–1.267–0.059(0.00)***(0.05)**
0.140–0.043(0.84)(0.02)**–0.0140.012(0.59)(0.04)**–0.410–0.188(0.16)(0.02)**
0.185
0.064
(0.19)(0.04)**
0.764
0.079
(0.14)(0.43)–0.326–0.011(0.21)(0.80)–0.430–0.327(0.33)(0.04)**–0.4480.113(0.86)(0.04)**–1.4350.416(0.25)(0.08)*
0.568–0.159(0.32)(0.05)**
0.036
0.006
(0.43)(0.28)CAPITALlnCOVERPERF_
CAPITALVOLPERF_
SURPRISE
VOL
DISTRESSSURPRISE
BLC_
DISTRESSOWN
BLC_
lnSIZEOWN
FRG_
lnSIZE
OWN
FRG_
BOND
OWN
Industry
BOND
Year
Industry
Year
0.778
0.205
1,700
1,698

Model 20
[AGREE]†–0.015(0.00)***–0.006(0.02)**
0.001(0.08)*–0.029(0.07)*
0.010(0.11)–0.006(0.77)–0.002(0.81)–0.028(0.13)
0.015(0.05)**
0.052(0.09)*–0.021(0.06)*
0.001(0.36)
lnCOVERCAPITALPERF_
VOLSURPRISE
DISTRESSBLC_
OWN
lnSIZE
FRG_
OWN
BOND
Industry
Year
0.368
1,062

Note: † denotes dependent variable.
Coefficient estimates in the upper rows. p-values upon clustered standard errors in the lower rows inparentheses (one-sided if the sign is hypothesized, two-sided otherwise).
*, ** and *** denote statistical significance at the 0.1, 0.05 and 0.01 levels, respectively.
RANK: Quintile rank of ROA (i.e., earnings before extraordinary items over total assets, after subtracting
industry median in each year).
For the measurements of the other variables, see Table 1 Panel C.

Koga and Uchino: Bank-Firm Relationships and Security Analyst Activities

71

6. Conclusion
This paper empirically compares the activities of security analysts between firms with andwithout close working relationships with their bank. This paper contributes to the international
accounting literature by documenting that bank-firm relationships are an institution associated withsecurity analyst activities in an economy where banks play a critical role in corporate governance. This
paper also contributes to the corporate governance literature by providing evidence that the banks’
screening and monitoring play roles in the association between bank-firm relationships and security
analyst activities.
The implications of this paper extend beyond these contributions. The corporate governanceliterature has classified the corporate governance models around the world into stakeholder andshareholder governance (e.g., Ahmadjian and Robbins 2005; Ball et al. 2000; Ball et al. 2003). No
study to date, however, has empirically investigated how the corporate governance models relate to
the information environments, presumably because the international differences in security analysts’tasks, experiences and resources (Barniv et al. 2005) make a direct comparison difficult.
This paper has implications as to which corporate governance model leads to richer information
environments. Bank-firm relationships are a key institution that distinguishes stakeholder andshareholder governance (Rajan and Zingales 1995). Accordingly, it is reasonable to assimilate thefirms with close bank relationships with stakeholder governance and the firms without such ties
with shareholder governance. Under this interpretation, this paper’s findings imply that capital
market investors under stakeholder governance face poorer information environments than undershareholder governance. Certainly, this interpretation is not conclusive; bank-firm relationships are
not the only institution distinguishing stakeholder and shareholder governance. Still, this paper
provides evidence to infer reasonably that stakeholder governance results in poorer information
environments than shareholder governance.
This paper has implications to standard setters as well. The International Accounting Standards
Committee has developed the International Accounting Standards (IAS)24 in order to generatecomparable financial information across countries (Ashbaugh and Pincus 2001), thereby leveling theinformation environments. This paper, however, documents that the information environments ofthe firms with close bank relationships are poorer than the firms without such ties under the same
Japanese GAAP. Accordingly, the findings imply that the IAS alone may not sufficiently level theinformation environments (Ball et al. 2003), given the variation in bank-firm relationships.
There are caveats to this paper. In particular, the findings’ external validity may be limited
because we investigate only Japanese firms. Nevertheless, there is evidence suggesting that bank-firm
relationships are relevant in other countries as well. Choi (2004) documents among U.S. firms thatclose bank-firm relationships are associated with low accrual quality. Also in the U.S., James (1987)
and Lummer and McConnell (1989) find that stock prices respond positively to the announcementsof bank loan agreements, especially, renewals, indicating that bank-firm relationships transmit
information to capital markets. These studies suggest that bank-firm relationships are relevant evenin northern America, where banks may not play as important a role. This paper investigates Japanesefirms because the extent of bank-firm relationships varies significantly in Japan. A study in Japan
should increase the power of statistical tests on bank-firm relationships.
24

IAS were succeeded by International Accounting Standards Board’s International Financial Reporting Standards in 2001.

72

The Japanese Accounting Review, 2 (2012), 49-73

REFERENCES
AHMADJIAN, C. L., AND G. ROBBINS. 2005. A clash of capitalisms: Foreign shareholders and corporate restructuring in 1990s Japan.
American Sociological Review 70 ( June): 451-471.
ALFORD, A. W., AND P. G. BERGER. 1999. A simultaneous equations analysis of forecast accuracy, analyst following, and trading
volume. Journal of Accounting, Auditing and Finance 14 ( July): 219-246.
ALI, A., AND L.-S. HWANG. 2000. Country-specific factors related to financial reporting and the value relevance of accounting data.
Journal of Accounting Research 38: 1-21.
ASHBAUGH, H., AND M. PINCUS. 2001. Domestic accounting standards, international accounting standards, and predictability ofearnings. Journal of Accounting Research 39 (December): 417-434.
BALL, R., S. P. KOTHARI, AND A. ROBIN. 2000. The effect of international institutional factors on properties of accounting earnings.
Journal of Accounting and Economics 29 (February): 1-51.
BALL, R., A. ROBIN, AND J. S. WU. 2003. Incentives versus standards: Properties of accounting income in four East Asian countries.
Journal of Accounting and Economics 36 (December): 235-270.
BARNIV, R., M. J. MYRING, AND W. B. THOMAS. 2005. The association between the legal and financial reporting environments andforecast performance of individual analysts. Contemporary Accounting Research 22 (Winter): 727-758.
BEHN, B. K., J.-H. CHOI, AND T. KANG. 2008. Audit quality and properties of analyst earnings forecasts. The Accounting Review 83(March): 327-349.
BHARATH, S. T., J. SUNDER, AND S. V. SUNDER. 2008. Accounting quality and debt contracting. The Accounting Review 83 ( January):
1-28.
BOYD, J. H., AND E. C. PRESCOTT. 1986. Financial inermediary-coalitions. Journal of Economic Theory 38 (April): 211-232.
BROWN. 2001. Industrial Groupings in Japan. Fourteenth Edition. Tokyo, Japan: Brown & Company.
BUSHMAN, R. M., AND J. D. PIOTROSKI. 2006. Financial reporting incentives for conservative accounting: The influence of legal andpolitical institutions. Journal of Accounting and Economics 42 (October): 107-148.
CHANG, J. J., T. KHANNA, AND K. G. PALEPU. 2002. Analyst activity around the world. Working Paper, University of Pennsylvania,
Philadelphia, PA.
CHEMMANUR, T. J., AND P. FULGHIERI. 1994. Reputation, renegotiation, and the choice between bank loans and publicly traded
debt. The Review of Financial Studies 7: 475-506.
CHOI, W. 2004. Debtor-creditor relationships and accrual quality. Working Paper, Michigan State University, East Lansing, MI.
COOKE, T. E. 1996. The influence of the keiretsu on Japanese corporate disclosure. Journal of International Financial Management andAccounting 7 (September): 191-214.
DIAMOND, D. W. 1984. Financial intermediation and delegated monitoring. Review of Economic Studies 51: 393-414.
DIAMOND, D. W. 1991. Monitoring and reputation: The choice between bank loans and directly placed debt. Journal of Political
Economy 99 (August): 689-721.
ELY, K. M., AND G. POWNALL. 2002. Shareholder- versus stakeholder-focused Japanese companies: Firm characteristics andaccounting valuation. Contemporary Accounting Research 19 (Winter): 615-636.
FAMA, E. F. 1985. What's different about banks? Journal of Monetary Economics 15 ( January): 29-39.
FRANKEL, R., M. MCNICHOLS, AND G. P. WILSON. 1995. Discretionary disclosure and external financing. Accounting Review 70( January): 135-150.
GEBHARDT, W. R., C. M. C. LEE, AND B. SWAMINATHAN. 2001. Toward an implied cost of capital. Journal of Accounting Research
39 ( June): 135-176.
GRAMLICH, J. D., P. LIMPAPHAYOM, AND S. G. RHEE. 2004. Taxes, keiretsu affiliation, and income shifting. Journal of Accounting
and Economics 37 ( June): 203-228.
HADLOCK, C. J., AND C. M. JAMES. 2002. Do banks provide financial slack? The Journal of Finance 57 ( June): 1383-1419.
HEALY, P. M., A. P. HUTTON, AND K. G. PALEPU. 1999. Stock performance and intermediation changes surrounding sustained
increases in disclosure. Contemporary Accounting Research 16 (Fall): 485-520.
HIROTA, S. I. 1999. Ginkochuushinno kinyushisutemuwa henkashiteikunoka?: Intabyukekkawo chuushinnisita kousatsu (Is thebank-oriented system in Japan changing?: Interview evidence). In Japanese. Waseda Commercial Review 383: 143-168.
HOPE, O.-K. 2003. Disclosure practices, enforcement of accounting standards, and analysts' forecast accuracy: An international
study. Journal of Accounting Research 41 (May): 235-272.
HOSHI, T., A. KASHYAP, AND D. SCHARFSTEIN. 1990a. The role of banks in reducing the costs of financial distress in Japan.
Journal of Financial Economics 27 (September): 67-88.
HOSHI, T., A. KASHYAP, AND D. SCHARFSTEIN. 1990b. Bank monitoring and investment: Evidence from the changing structureof Japanese corporate banking relationships. In Asymmetric Information, Corporate Finance, and Investment, edited by R. G.
HUBBARD. Chicago, IL: University of Chicago.
HOSHI, T., A. KASHYAP, AND D. SCHARFSTEIN. 1991. Corporate structure, liquidity, and investment: Evidence from Japaneseindustrial groups. The Quarterly Journal of Economics 106 (February): 33-60.

Koga and Uchino: Bank-Firm Relationships and Security Analyst Activities

73

ISOYAMA, T., AND H. MATSUURA. 2001. Koseina shijoe: Kawaru desukuloja (Towards fair markets: Changes in disclosures). In
Japanese. Nihon Keizai Shimbun, June 14, 2001, 16.
JACOBSON, R., AND D. AAKER. 1993. Myopic management behavior with efficient, but imperfect, financial markets: A comparisonof information asymmetries in the U.S. and Japan. Journal of Accounting and Economics 16 (October): 383-405.
JAMES, C. 1987. Some evidence on the uniqueness of bank loans. Journal of Financial Economics 19 (December): 217-235.
JENSEN, M. C., AND W. H. MECKLING. 1976. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journalof Financial Economics 3 (October): 305-360.
KANG, J.-K., AND A. SHIVDASANI. 1995. Firm performance, corporate governance, and top executive turnover in Japan. Journal of
Financial Economics 38 (May): 29-58.
KANG, J.-K., AND R. M. STULZ. 1996. How different is Japanese corporate finance?: An investigation of the information content of
new security issues. The Review of Financial Studies 9: 109-139.
KANG, J.-K., AND A. SHIVDASANI. 1997. Corporate restructuring during performance declines in Japan. Journal of Financial
Economics 46 (October): 29-65.
KANG, J.-K., A. SHIVDASANI, AND T. YAMADA. 2000. The effect of bank relations on investment decisions: An investigation ofJapanese takeover bids. Journal of Finance 55 (October): 2197-2218.
KANG, J.-K., AND R. M. STULZ. 2000. Do banking shocks affect borrowing firm performance? An analysis of the Japaneseexperience. The Journal of Business 73 ( January): 1-23.
KAPLAN, S. N. 1994. Top executive rewards and firm performance: A comparison of Japan and the United States. Journal of Political
Economy 102 ( June): 510-546.
KAPLAN, S. N., AND B. A. MINTON. 1994. Appointments of outsiders to Japanese boards: Determinants and implications for
managers. Journal of Financial Economics 36 (October): 225-258.
LANG, M., AND R. LUNDHOLM. 1993. Cross-sectional determinants of analyst ratings of corporate disclosures. Journal of Accounting
Research 31 (Autumn): 246-271.
LANG, M. H., AND R. J. LUNDHOLM. 1996. Corporate disclosure policy and analyst behavior. The Accounting Review 71 (October):
467-492.
LANG, M. H., AND R. J. LUNDHOLM. 2000. Voluntary disclosure and equity offerings: Reducing information asymmetry or hyping
the stock? Contemporary Accounting Research 17 (Winter): 623-662.
LANG, M. H., K. V. LINS, AND D. P. MILLER. 2003. ADRs, analysts, and accuracy: Does cross listing in the United States improve afirm's information environment and increase market value? Journal of Accounting Research 41 (May): 317-345.
LANG, M. H., K. V. LINS, AND D. P. MILLER. 2004. Concentrated control, analyst following, and valuation: Do analysts matter mostwhen investors are protected least? Journal of Accounting Research 42 ( June): 589-623.
LUMMER, S. L., AND J. J. MCCONNELL. 1989. Further evidence on the bank lending process and the capital-market response to bankloan agreements. Journal of Financial Economics 25 (November): 99-122.
MILLER, G. S. 2002. Earnings performance and discretionary disclosure. Journal of Accounting Research 40 (March): 173-202.
NAKATANI, I. 1984. The economic role of financial corporate groupings. In The Economic Analysis of the Japanese Firm, edited by M.
AOKI. Amsterdam, The Netherlands: Elsevier Science.
PETERSEN, M. A. 2009. Estimating standard errors in finance panel data sets: Comparing approaches. The Review of Financial
Studies 22: 435-480.
RAJAN, R. G., AND L. ZINGALES. 1995. What do we know about capital structure? Some evidence from international data. The
Journal of Finance 50 (December): 1421-1460.
SHEARD, P. 1985. Main banks and structural adjustments in Japan. Working Paper, Australia-Japan Research Center.
SHLEIFER, A., AND R. W. VISHNY. 1997. A survey of corporate governance. The Journal of Finance 52 ( June): 737-783.
TOYO-KEIZAI. 2002-2005. Japan Company Handbook. Tokyo, Japan: Toyo Keizai.