conglomerate structure and capital market timing

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Conglomerate Structure and Capital

Market Timing

Xin Chang, Gilles Hilary, Chia Mei Shih, and Lewis H.K. Tam

We examine the effects of keiretsu structure on capital market-timing. Keiretsu groups offer

a hybrid structure between fully integrated conglomerates and stand-alone firms. We find that

past market conditions affect the capital structure ofkeiretsu firms more than they affect thecapital structure of unaffiliated firms. The decision to issue equity is more correlated with market

conditions forkeiretsu members than it is for unaffiliated firms. The stock returns ofkeiretsu firms

following the issuance of equity decrease with the size of the issuance. These results suggest that

keiretsu members time the issuance of equity more so than stand-alone firms.

The study of the optimal boundaries of the firm has been a central question in finance and

economics since the seminal work of Coase (1937). Among other things, prior research has

examined the effect of conglomerate structure on corporate financing behavior. The literature

relying on US evidence has suggested two advantages in terms of financing activities for fully

integrated conglomerates relative to focused stand-alone firms. First, conglomerates may have

better access to external capital markets, especially when capital market conditions are not

favorable (Dimitrov and Tice, 2006). Second, conglomerates may substitute their internal capital

markets for costly external markets (Williamson, 1975; Stein, 1997; Yan 2006; Yan, Yang, and

Jiao, 2010). We propose a third advantage for Japanese conglomerates. Conglomerate member

firms may time external capital markets more so than stand-alone firms.Japanese conglomerates, known as keiretsu, offer a hybrid structure between fully integrated

conglomerates and stand-alone firms. Although the member firms are separate legal entities that

are typically publicly traded and enjoy some degree of autonomy, they are closely related to

one another through a web of legal, economic, and personal relations. In addition, the member

firms are often associated with a main bank that is affiliated with the group. While this system

is a characteristic of Japan, it exists in comparable ways in other countries (e.g., South Korea).

This unusual structure has potential implications for the financial policy of its group members,

particularly in terms of their financing policies. More specifically, the valuation in equity markets

We thank Eric de Bodt, Bill Christie (editor), Sudipto Dasgupta, Armen Hovakimian, Chuan Yang Hwang, Jun-Koo Kang,

Clive Lennox, Laura Liu, Wei-Lin Liu, Peter McKay, Pascal Nguyen, Wei-Ling Song, Xueping Wu, Yishay Yafeh, An Yan, an

anonymous referee, and seminar participants at the 2006 International Banking and Finance Conference, the 2006 Asian

FA/FMA Conference, the 2006 FMA Annual Meeting, the 14th Conference on the Theories and Practices of Securities

and Future Markets, the 2007 EFA Annual Conference, the 2008 Asian FA Conference, Hitotsubashi University, Hong

Kong Baptist University, and Hong Kong Polytechnic University for their valuable comments on our manuscript. This

research was completed while Professor Hilary was on faculty at HKUST and HEC Paris. All remaining errors are ours.

Xin Chang is an Assistant Professor in the Division of Banking and Finance in the Nanyang Business School at Nanyang

Technological University, S3-B1A-17, Nanyang Avenue, Singapore 639798. Gilles Hilary is an Associate Professor in the

Department of Accounting at INSEAD, Fontainebleau, France, 77305. Chia Mei Shih, Citigroup and the Department of

Finance at the University of Melbourne, Melbourne, Australia, VIC, 3010. Lewis H.K. Tam is an Assistant Professor in

the Faculty of Business Administration at the University of Macau, Macau.

Financial Management Winter 2010 pages 1307 1338


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1308 Financial Management r Winter 2010

of keiretsu members in dissimilar industries could be different. Some member firms could be

overpriced while others are underpriced at the same time. The keiretsu members can cooperatively

time the market for the group as a whole by issuing equity for its overvalued members and avoiding

equity financing for its undervalued ones. While it could be argued that all firms have incentives

to take advantage of favorable market conditions if they can, keiretsu members should havegreater incentive to do so as they can reallocate the funds obtained in periods of hot market

sentiment to the keiretsu members that may make more productive use of these funds, even if

these members do not have the same facility to raise funds. By construction, this is not feasible

for stand-alone firms.1

Our empirical results are consistent with our expectations. We document a significant effect

of market timing on the capital structure of a general cross-section of publicly traded Japanese

firms. More importantly for our purposes, we find that the capital structure of firms that belong

to a conglomerate group is more affected by past market conditions than is the capital structure

of similar stand-alone firms. Keiretsu firms that timed the issuance of capital in the past have less

debt in their capital structure. The difference is both economically and statistically significant.

The change in the leverage ratio is also more sensitive to past stock returns for conglomerate

members than for stand-alone firms. Moreover, the decision of keiretsu firms to issue equity is

further correlated with market conditions than that of stand-alone firms. In a general cross-section

of firms, we find that stock returns following the issuance of equity decrease with the size of the

issue. Although this finding is not very statistically robust in a general cross-section of Japanese

firms, the correlation is stronger for firms that belong to a keiretsu than for stand-alone firms.

These results suggest that keiretsu members time the issuance of equity more than do stand-alone

firms, and that this timing behavior affects their capital structure to a greater degree.

We then consider the relationship among different members of keiretsu conglomerates and,

in particular, examine how firms use the proceeds from equity issuances. We find that firms

that time the market use the proceeds to repay their bank loans, thus having smaller amountsof bank loans on their balance sheets than firms that do not time the market. More importantly,

these effects are stronger for keiretsu firms than for stand-alone firms, suggesting that firms

use the proceeds of equity issues, which were raised when equity market conditions are good,

to repay outstanding bank loans. This strategy magnifies the effect of equity timing on capital

structure because, aside from the addition of equity to the firms balance sheet, good equity

market conditions enable the retirement of bank debt. Finally, we find that the timing activity of

a given keiretsu member affects the capital structure of other keiretsu members. Specifically, our

results indicate a positive association between the market timing activity of a keiretsu member

and the average loan-to-assets ratio of other keiretsu members of the same conglomerate. This

last result is also consistent with the idea that keiretsu firms redeploy capital within the group

after issuing overvalued capital through the main bank. In contrast to the situation in many other

settings, the benefits of the market timing of equity issuance in Japan may go to creditors (i.e.,

the main banks) and to other group members, rather than solely to existing shareholders.

Our research contributes to the literature in at least two ways. First, it improves our understand-

ing of conglomerates. Although the costs and benefits of external and internal markets have been

studied (Scharfstein and Stein, 2000; Khanna and Tice, 2001), the effects of the hybrid keiretsu

structure are much less understood. As noted above, we use this unique setting to investigate

1Our results are also consistent with the idea that greater market timing activity enables conglomerates to allocate internal

funds more optimally. This view is consistent with Yan Yang, and Jiao (2010) who find that investment in diversified

firms is less affected when external capital becomes more costly at the aggregate level. However, a thorough investigationof this issue is outside the scope of our study.


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Chang et al. r Conglomerate Structure and Capital Market Timing 1309

an advantage of conglomerates that has not been evidenced by the prior literature. Additionally,

in sharp contrast to the situation in the United States where banks are prohibited from holding

stock in the firms that they lend to and where external financing transactions are conducted on

an arms length basis, keiretsu members have privileged access to the main bank associated with

the group. This allows us to consider the effects of conglomerate structure and banking relation-ship on market timing. Thus, we investigate both the correlation between keiretsu members and

external equity markets and between keiretsu members and the main bank.

Second, our study offers new insights regarding the drivers of financing behavior. Leaving

aside their large size, considering the Japanese markets is helpful as it complements a recent body

of research exploring the market timing behavior of US firms. The literature has long suggested

that firms make securities issuance decisions based on the objective of maintaining either a target

capital structure (trade-off theory) or a financing hierarchy (the pecking order theory). A more

recent stream of research, however, observes the prevalence of establishing financing decisions

based on market conditions among US firms.2 In turn, it has been argued that past market timing

decisions have a persistent effect on the capital structure of a firm (Baker and Wurgler, 2002;

Huang and Ritter, 2009). However, the existence and effects of market timing remain controversial

among scholars (Hovakimian, 2005; Kayhan and Titman, 2007), and, to date, there have been few

studies examining the market timing hypothesis of capital structure outside the US market. Our

study contributes to filling this gap.

The rest of this paper is organized as follows. In Section I, we review the market timing

literature and elaborate our hypotheses. Section II details our market timing variables. Section III

describes the selection and properties of our sample. Section IV presents our main findings, while

Section V provides our conclusions.

I. Previous Literature and Hypothesis Development

A. Industrial Organization in Japan

The corporate structure environment in Japan is very different from the one in the United

States. In particular, it is characterized by two elements that are not present in Anglo-Saxon

countries. The f irst is the existence of industrial groups known as keiretsu. Douthett, Jung, and

Kwak (2004) describe keiretsu members as having close financial and personal ties with one

another, as well as with their banks, through cross-shareholdings, credit holding, interlocking

corporate directorates, and a variety of business transactions.3 For example, Presidents Club

(shacho-kai) meetings are regularly scheduled among presidents of keiretsu firms (Douthett

and Jung, 2001). Although the firms in a keiretsu operate in different industries, they createreciprocal voting rights and form coalitions. In doing so, the firms in a keiretsu can credibly

threaten management with demotion or termination, thus ensuring cooperation among various

keiretsu members (Berglof and Perotti, 1994). While this system is characteristic of Japan, it

2For example, in a survey of 392 US-based chief financial officers, Graham and Harvey (2001) report that 70% believe

that one of the most important considerations for equity issuance is the extent to which the stocks of their companies

are overvalued or undervalued at a given point in time. This suggests that managers take advantage of a window of

opportunity by timing their equity issues.

3Keiretsu can be categorized into vertical (manufacturer-centered) and horizontal (bank-centered) groups. Keiretsu firms

are vertical if they are linked by customer-supplier relationships, and horizontal if they are linked by a main bank

system in which the main bank assumes a pivotal role in financing the investments of the keiretsu firms and readily helps

them out of financial distress should they require it. Following the prior literature (Weinstein and Yafeh, 1995; Wu andXu, 2005), we consider only horizontal groups and use the term keiretsu only in reference to these conglomerates.


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Table I. Summary Statistics

Data are collected from the Pacific-Basin Capital Markets (PACAP) database for the years 1977-2004. Thetable reports the summary statistics for the full sample and for subsamples classified by keiretsu affiliation,

where data on group affiliation come from Industrial Groupings in Japan. Total assets (A) is the book valueof total assets. Book leverage ratio (TDB) is the ratio of total liabilities to total assets. Market leverage ratio

(Lev) is total liabilities divided by (total assets book equity+market capitalization). Bank-loans-to-total-

assets (LOAN/A) and bank-loans-to-total-liabilities (LOAN/OLIB) ratios are self-explanatory and definedin the Appendix. Profitability (ROA) is defined as the return on assets, which is taken as the income from

operations divided by total assets. Market-to-book-ratio (MB) is defined as (total assets book equity +market capitalization) divided by total assets. Firm size (SIZE) is the natural logarithm of net sales in millions

of yen. Tangibility (TANG) is defined as net fixed assets divided by total assets. Financial institutional

ownership (FinOwn) and business corporation ownership are obtained from the PACAP database.

Full Stand-Alone KeiretsuSample Firms Members

Total assets (A) Mean 225 194 283

Median 55 49 75

Book leverage ratio (TDB) Mean 0.62 0.60 0.67

Median 0.65 0.61 0.70

Market leverage ratio (Lev) Mean 0.50 0.48 0.54

Median 0.50 0.48 0.54

Bank loans-to-total liabilities (LOAN/A) Mean 0.22 0.20 0.25

Median 0.18 0.15 0.22

Bank loans-to-other liabilities (LOAN/OLIB) Mean 12.5 12.5 12.5

Median 1.73 1.40 2.25

Firm size (SIZE) Mean 11.0 10.8 11.3

Median 10.9 10.8 11.2

Profitability (ROA) Mean 0.042 0.044 0.038

Median 0.038 0.040 0.035

Market-to-book ratio (MB) Mean 1.42 1.44 1.39

Median 1.23 1.23 1.24

Tangibility (TANG) Mean 0.26 0.27 0.26

Median 0.24 0.24 0.24Financial institutional ownership (FinOwn) Mean 31.5% 29.9% 34.5%

Median 31.0% 29.1% 35.0%

Business corporation ownership (BusOwn) Mean 30.1% 30.0% 30.4%

Median 26.1% 26.1% 26.1%

Number of firm years 40,136 26,387 13,749

Significant at the 0.01 level.Significant at the 0.05 level.

Building on this literature, Baker and Wurgler (2002) test the theory with their market timing

measure, the external finance weighted average market-to-book ratio (BWMB). This variable

takes a high value if external financing occurs in high market-to-book years. Baker and Wurgler

(2002) argue that BWMB should be inversely related to a firms leverage if the firm constantly

times the equity markets but does not rebalance its capital structure. Consistent with their predic-

tions, they find evidence of timing in the United States and the persistence of these timing effects

on capital structure. They suggest that current capital structures reflect the cumulative effect of

timing the equity markets in the past. Also consistent with this view, Huang and Ritter (2009)note the long-lasting influence of past financing transactions on leverage as firms revert very


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slowly to their optimal capital structure. Baker and Wurgler (2002) conclude that low leverage

firms are those that raised funds when their market valuations were high, as measured by the

market-to-book ratio, while high leverage firms are those that raised funds when their market

valuations were low.

Several studies (Hovakimian, 2005; Leary and Roberts, 2005; Kayhan and Titman, 2007; Liu,2005) have built on this approach to refine the initial timing measure. These studies argue that

Baker and Wurglers (2002) timing measure actually reflects information about the historical

market-to-book ratio, which is related to the firms growth opportunities. As firms with higher

growth opportunities desire lower leverage ratios, the negative correlation between Baker and

Wurglers (2002) measure and the debt ratio can be attributed to firms adjusting their debt ratios

toward a target, rather than to timing activity. To address this issue, Kayhan and Titman (2007)

propose a decomposition of Baker and Wurglers (2002) timing measure that incorporates the

average past market-to-book ratio (a measure of growth opportunities) and the covariance between

issuance activity and the market-to-book ratio (a measure of timing activity). They argue that

the covariance term better captures the market timing behavior of firms. Using this alternative

measure, Kayhan and Titman (2007) demonstrate that the covariance term is negatively related

to the change in debt ratios, but at a lower economic magnitude than that found by Baker and

Wurgler (2002).

Further research has sought to understand the drivers of this behavior and the cross-sectional

determinants of the importance of capital market timing for capital structure and issuance de-

cisions. For example, Chang, Dasgupta, and Hilary (2006, 2009) find that the capital structure

of transparent firms is less sensitive to stock market conditions and transparent firms are less

affected by market conditions when they issue equity than opaque firms. However, little research

has been conducted regarding the additional drivers of market timing and, in particular, the effects

of organizational structure and relationship banking. Based on our prior discussion, we predict

that market timing should be more important for keiretsu members than for unaffiliated stand-alone companies. More specifically, we expect the capital structure of keiretsu members to be

more affected by past market conditions than the capital structure of stand-alone f irms. Relatedly,

we expect that keiretsu firms should be more sensitive to stock market conditions when they issue

equity than stand-alone firms.

C. The Use of the Proceeds from Market Timing

We also consider how firms use the proceeds of their capital issuance. Given that business

segments in a traditional US style conglomerate can cross-subsidize each other through internal

capital markets, we expect keiretsu groups may behave in a similar fashion. Unlike business

segments in a conglomerate, keiretsu members do not share the same fully integrated internal

capital markets. Nevertheless, they can redeploy capital to other members of the group through

at least two channels. The first is for the overpriced keiretsu members to raise money from

equity markets and pay off the loans borrowed from the group affiliated banks. In this way, the

keiretsu member banks can reallocate loans to the underpriced keiretsu members. The second

is forkeiretsu members to cross-subsidize one another through direct equity investment. In this

case, the overpricedkeiretsu members can raise money from equity markets and transfer the funds

to the underpricedkeiretsu members by investing in the equity of the latter.

Both channels are possible and not mutually exclusive. However, while there is a possibility

that some redeployment is done through equity investment, we focus on the f irst channel for three

reasons. The first reason is that using debt to transfer capital within the group is usually easier thanusing equity. Loans can be easily repaid, but equity instruments are harder to issue and cancel.


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For example, a board decision is often required to modify firm capital, but no such decision is

typically needed when debt instruments are involved. Moreover, the buying and selling of equity

can have significant legal, regulatory, and accounting implications (e.g., the consolidation or not

of the investment), whereas changes in debt investments are much less likely to trigger these

effects. Second, debt financing is economically significant in Japan. Table I indicates that theaverage ratio of total liability to the market (book) value of assets in Japan is 50% (62%) for the

years 1977-2004.5 Over the same period, we estimated the corresponding average ratio of total

liability to the market (book) value of assets to be 41% (50%) for all American firms in Compustat.

Third, to be able to implement clean tests of the second channel, we would need a good measure

of the equity investment of each member in other group affiliates. Unfortunately, such level of

disaggregation is not available in our data set. In contrast, debt financing is typically concentrated

in Japan. The bond market is very small compared to the bank loan market (Pinkowitz and

Williamson, 2001), and at the firm level, loans are typically concentrated in the hands of the main

bank.

We expect keiretsu firms to utilize bank loans as an intermediary to redeploy the proceeds of

issuances that are opportunitistically timed to take advantage of favorable equity conditions. In

other words, when a keiretsu member issues overpriced equity, we expect it to use the proceeds

to pay back loans. The proceeds are then redistributed to other keiretsu members as new loans.

If these conjectures are correct, we expect, for all firms, past market timing activity to have an

effect on the amount of loans outstanding and, more importantly for our purpose, that this effect

will be stronger for keiretsu firms than for stand-alone firms. In addition, we predict a positive

correlation between past market timing activity of a keiretsu firm and the loan-to-assets ratio of

the keiretsu firm members other than the one that is timing the external capital market.

II. Market Timing Variables

We use three main proxies for market timing (Timing). The first is that proposed by Baker and

Wurgler (2002): BWMB or the external finance weighted average market-to-book ratio,

BWMBt1 =

t1s=0

EFst1r=0

EFr

(MBs ) , (1)

where EFs andMBs denote the external financing (the sum of net debt and equity issued) and the

market-to-book ratio, respectively, at time s.6

For each firm-year, we calculate the summations ofEquation (1) from the first year that financial data are available to the end of the previous year.

Following Baker and Wurgler (2002), the assigned weight for each year is bounded below at zero.

In other words, ifEF is negative in a year, it is set to zero. BWMB takes on a high value if firms

raise external capital in the years in which their market-to-book ratio is high. To the extent that

a higher market-to-book ratio proxies for greater equity mispricing, Baker and Wurgler (2002)

argue that BWMB will be negatively related to leverage if firms issue equity in the year in which

the overvaluation occurred. This conjecture intuitively captures the notion of market timing.

5The market value of assets is defined as total assets minus the book value of equity plus the market value of equity.

6Following Baker and Wurgler (2002), we define the net debt and equity issuances using balance sheet data. The net

equity issuance is equal to the change in total equity minus the change in retained earnings, and the net debt issuance is

the change in total assets minus the net equity issuance. We define the market-to-book ratio as the quasi-market value of

assets divided by the total book value of assets The details of the variable definitions are included in the Appendix


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mispricing, only KTCOV really captures the timing intuition of Baker and Wurgler (2002). The

second term, KTMB, is simply the historical average market-to-book ratio, which proxies for

investment opportunities. As such, we use KTCOV as our second proxy for market timing.

To further address the concern that MB may proxy for investment opportunities rather than

misvaluation, we use a third measure of market timing based on the methodology developed byRhodes-Kropf, Robinson, and Viswanathan (2005), which captures stock misvaluation by filter-

ing out growth opportunities. Specifically, Rhodes-Kropf, Robinson, and Viswanathan (2005)

decompose the logarithm ofMB as follows:8

Ln(M B)it = mit bit

= mit v(i t, j t) F S E

+ v(it, j t) v(i t, j) T S E

+ v(it, j) bi t L RV

, (6)

where m and b are logarithms of the quasi-market value of assets (M) and the book value ofassets (B), respectively. The subscripts, i, j, andt, denote firm, sector, and time, respectively. The

first term, the firm-specific pricing error (FSE), is the difference between the market value and

the fundamental value v(it, j t) computed by using its firm-specific accounting multiples itand its sectorjmultiple jt measured at valuation yeart. The second term, the time-series sector

error (TSE), measures the difference between a firms fundamental value conditional on both

contemporaneous accounting principles and its value as implied by its accounting information

and long-run multiples. This term captures the misvaluation of the whole sector at time t as

v(it, j) measures sector-specific valuation, which does not vary over time. The third term,

LRV, concerns the difference between a f irms valuation based on long-run multiples and its book

value, and captures its set of investment prospects at time t.9

Having isolated the f irm-specific misvaluation from the other components of market valuation,such as industry-wide mispricing and fundamental growth prospects, we then define three new

external finance weighted average measures based on three components of the market-to-book

ratios, namely, 1) FSE, 2) TSE, and 3) LRV. To ensure that the averaged variables are positive,

8Unlike Rhodes-Kropf, Robinson, and Viswanathan (2005) who use net income as the measure of accounting profitability

to decompose the market-to-book equity ratio, we use operating income instead to decompose the market-to-book assets

ratio for consistency.

9More specifically, we estimate the model,

m it = 0jt + 1jt bit + 2jt ln(O I)++ 3jtI(O I


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we use the exponential forms of the three components and compute the following three weighted

averages:

BWFSEt1 =

t1s=0

EFst1r=0

E Fr

eFSEs . (7)

BWTSEt1 =

t1s=0

EFst1r=0

E Fr

eTSEs

. (8)

BWLRVt1 =

t1s=0

EFst1r=0

E Fr

eLRVs

. (9)

The three external f inance weighted average timing measures (BWFSE, BWTSE, andBWLRV),

which are based on three components of the market-to-book ratios, are comparable to those

in Kasbi (2007). Simple algebra indicates that Baker and Wurglers (2002) measure of market

timing (BWMB) can be written as the sum of BWFSE, BWTSE, and BWLRV. As FSE focuses

more on firm-specific mispricing than the market-to-book ratio does, we use the external fi-

nance weighted average firm-specific pricing error (BWFSE) as the third proxy for market

timing.

III. Sample and Summary Statistics

Our sample consists of Japanese firms in the Pacific-Basin Capital Markets (PACAP)

database from 1977 to 2004. Financial firms and firms with missing or negative book val-

ues of assets are excluded. We obtain the financial, stock market, and ownership struc-

ture data of the sample firms from PACAP and exclude firms that have missing data

on stock prices, equity issues, or debt issues. This results in a sample of approximately40,000 firm-year observations. All of the variables are winsorized at the 0.5% level on both

sides of the distribution to reduce the impact of outliers or wrongly recorded data on our

results.

Firms in the sample are partitioned according to their keiretsu affiliation (K). The keiretsu

classification is obtained from different versions ofIndustrial Groupings in Japan, a publication

by Dodwell Marketing Consultants and Brown & Company Ltd.10 Consistent with Weinstein and

Yafeh (1995) and Wu and Xu (2005), firms are classified as being affiliated with a keiretsu if

they are members of the six largest horizontal keiretsu groups, namely, DKB, Fuyo, Mitsubishi,

10We use the 1992/1993, 1996/1997, 1998/1999, and 2000/2001 editions to identify the keiretsu affiliation. As the

affiliations are stable over time, we use the 1992/1993 edition to identify keiretsu affiliation before 1993 and the2000/2001 edition for 2001 and after.


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Mitsui, Sanwa, and Sumitomo. This classification is largely a constant throughout our sample.

Of the 478 firms that were classified as keiretsu members in 1991, 433 (90.5%) were classi-

fied as members in 1996, 419 (87.7%) in 1999, and 404 (84.5%) in 2001. To further ensure

that our results are not driven by an endogenous relationship between financing decisions and

keiretsu membership, we reestimate the various measures and equations using only observa-tions for which the keiretsu membership has not changed over time, and all of the results still

hold.

Aside from identifying the members of a keiretsu, Industrial Groupings in Japan also evaluates

the degree of integration of the firms in the group and partitions the keiretsu members into

four groups based on their group integration. Among all criteria, equity ownership by other

group members is the most important variable in determining group integration.11 We create

an indicator variable for group affiliation (I) that equals one if a firm belongs to the two most

integrated categories reported in the Industrial Groupings in Japan and zero if it belongs to the

two least integrated categories.

Table I reports the descriptive statistics of the key variables for the whole sample and for

the two subsamples distinguished by keiretsu affiliation. The definitions of the variables are

included in the Appendix. We consider different alternative measures of financial leverage such

as the market leverage ratio (Lev), the book leverage ratio (TDB), the bank-loans-to-assets ratio

(LOAN/A), and the bank-loans-to-other-liabilities ratio (LOAN/OLIB). The market-to-book ratio

(MB), assets tangibility (TANG), profitability (ROA), and firm size (SIZE) are employed by Rajan

and Zingales (1995) and Baker and Wurgler (2002), who find them to correlate significantly with

leverage.12

The summary statistics indicate that firms belonging to a keiretsu are generally larger (in

terms of total assets and sales (SIZE)) than stand-alone firms and also have lower firm valuation

(MB) and profitability (ROA). They have more debt (Lev and TDB) in their capital structure

and use more bank loans (LOAN/A andLOAN/OLIB). Finally, keiretsu firms also have a higherpercentage of their ownership controlled by f inancial institutions (FinOwn) and industrial firms

(BizOwn) than stand-alone firms. This result is consistent with the finding of Douthett, Jung, and

Kwak (2004) that firms in keiretsu are linked by a main bank system through cross-shareholdings

among members. Further, an (untabulated) analysis reveals that financial institutional owner-

ship (FinOwn) is negatively related with the bank-loans-to-assets ratios of keiretsu members

in general (the correlation coefficient is 0.1). This suggests that the main bank controls the

core members of the keiretsu by the importance of their loans, rather than through direct equity

ownership.

Table II reports the correlation coefficients among the key variables. Most of the univariate

correlations for the control variables (TANG, ROA, MB, and SIZE) are reasonably low, sug-

gesting that multicollinearity is not an issue in our setting. In addition to the expected strong

correlation between the book leverage ratio (TDB) and the market leverage ratio (Lev), we find

a negative relationship between the market-to-book ratio (MB) and the market leverage ratio

(the association between the market-to-book ratio and the book leverage ratio is also negative,

but much less significant). As expected, our various measures of market timing are positively

correlated.

11In particular, the publication uses the ratio of the groups total interest in the firm to the total equity ownership of the

top 10 shareholders to classify keiretsu firms into four groups.

12As a robustness check, we measure firm size as the log of market capitalization instead of net sales and find that ourresults are essentially unchanged (untabulated).


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TableII.

Correlation

TablefortheKeyVariables

ThePearsonscorrelationcoefficientsfo

rthekeyvariablesarereported.BW

MB

,istheexternalfinanceweightedaveragemarket-to-bookratio

ofBakerand

Wurgler(2002).KTCOVisobtainedacc

ordingtoKayhanandTitmans(2007)decompositionofBWMB.

BWFSEistheexternalfinanceweighted

average

FSE,

wher

eFSEisthefirm-specificerrorofvaluationbyRhodes-Kropf,Robinson,

andViswanathan(2005).BWMB,

KTCOV,

andBWFSEarealldefinedusingthe

infor

mationfromYear1toYeart

1.The

keiretsudummy(K)isanindicatorofkeiretsuaffiliation.

Allofthe

othervariablesareasdefinedinTableI.

TDB

Lev

TANG

ROA

MB

SIZE

K

eiretsu

BWMB

KTCOV

BWFSE

Marketdebtratio(Lev)

0.7

6

Tang

ibility(TANG)

0.0

1

0.0

1

Profitability(ROA)

0.2

5

0.4

0

0.0

2

Market-to-book(MB)

0.1

4

0.6

1

0.0

3

0.33

Firm

size(SIZE)

0.1

6

0.1

5

0.0

5

0.05

0.0

7

Keiretsu

0.1

8

0.1

3

0.0

3

0.07

0.0

3

0.1

6

BWM

B

0.3

3

0.5

1

0.0

0

0.16

0.5

4

0.0

9

0.0

3

KTCOV

0.1

8

0.1

8

0.0

4

0.16

0.1

1

0.1

0

0.0

5

0.5

1

BWF

SE

0.1

0

0.3

3

0.0

9

0.15

0.5

0

0.0

0

0.0

2

0.7

1

0.2

5

Significantatthe0.0

1level.

Significantatthe0.0

5level.


13/33


IV. Empirical Results

A. The Effect of Market Timing and KeiretsuMembership on Capital Structure

1. The Sensitivity of Capital Structure to Past Equity Prices

We first consider the impact of market conditions on capital structure and how keiretsu af-

filiation affects this relationship. Our first test is based on the intuition proposed by Baker and

Wurgler (2002) and Kayhan and Titman (2007). Specifically, we estimate

Levi,t = a + 1Timingi,t1 + 2Ki,t1 + 3Timingi,t1 Ki,t1 + y Xi,t1 +IND+ i,t.

(10)

We regress the amount of debt in the contemporary capital structure (Lev) on measures of past

market timing (Timing), an indicator variable (K) that equals one if a firm belongs to a keiretsu

and zero otherwise, and the interaction term Timing K. We predict that the coefficient on themarket timing variables should be negative if the capital structure of Japanese firms is affected

by market timing activity, and should be more negative for keiretsu members than for stand-

alone firms.13 In other words, we expect both coefficients, 2 and3, to be negative. We also

include the same vector of control variables (X) employed by Rajan and Zingales (1995) and

Baker and Wurgler (2002), specifically, the market-to-book ratio (MB), assets tangibility (TANG),

profitability (ROA), and firm size (SIZE). Frank and Goyal (2009) find that MB, TANG, andROA

are the more reliable variables for explaining market leverage among a long list of factors

that affect financial leverage. The market-to-book ratio (MB) potentially proxies for investment

opportunities and should, according to the trade-off theory, be negatively related to leverage.

Asset tangibility (TANG) should be positively associated with leverage, given that tangible assetsindicate a better debt capacity. Profitability should increase the availability of internal funds

and, hence, reduce the need for debt financing. Therefore, ROA should be negatively related to

leverage. Following Frank and Goyal (2003) and Lemmon and Zender (2010), who f ind that large

firms are more reliant on debt financing than small companies due to greater debt capacity, we

expect firm size to be positively related to the leverage ratio. Finally, we also include a set of

industry indicator variables (IND) to control for industry effects.

Table III presents our results based on the estimation of Equation (10). We use three measures

of past market timing. In Column (1), we use the variable proposed by Baker and Wurgler (2002),

in Column (2), we focus on the variable proposed by Kayhan and Titman (2007), while in Column

(3), we consider the measure constructed based on the methodology proposed by Rhodes-Kropf,

Robinson, and Viswanathan (2005). In all of the reported specifications, the dependent variableis the market leverage ratio (Lev).

Throughout this paper, we estimate all of the specifications using a modified version of

the Fama and McBeth (1973) procedure, similar to that used by Baker and Wurgler (2002). 14

Specifically, we run the yearly regression based on the number of years since the first year that

13As a robustness check, we split the sample into keiretsu firms andnonkeiretsu firms, run separate regressions for the

debt ratios, and compare the coefficient of the market timing variable between the groups, but this approach leads to the

same conclusion as the one we report here.

14The exceptions are Table IV and VI, in which we consider the stock returns. In this case, we use both the traditional Fama

and MacBeth (1973) approach and a pooled sample approach in which we correct the standard errors of the estimated

coefficient for heteroskedasticity and the clustering of observations by both firm and period (Cameron, Gelbach, andMiller, 2006).


14/33


Table III. The Sensitivity of Capital Structure to Market Conditions

The dependent variable is the Market leverage ratio (Lev), which is defined as the ratioof total liabilities to themarket value of assets. Three market timing measures are used to explainLev.BWMB is the external finance

weighted average market-to-book ratio of Baker and Wurgler (2002). KTCOV and KTMB are obtainedaccording to Kayhan and Titmans (2007) decomposition of BWMB. BWLRE, BWTSE, andBWFSEare the

external finance weighted average LRV, TSE, andFSE, respectively, and LRV, TSE, and FSE are defined

according to Rhodes-Kropf, Robinson, and Viswanathan (2005). All of the other explanatory variables areas defined in Tables I and II. The explanatory variables are lagged one period relative to the dependent

variable. A constant term and industry dummies are included in the regressions, but not reported. The modelis estimated using Baker and Wurglers (2002) modified Fama and MacBeth (1973) procedure based on the

number of years since the IPO.

(1) (2) (3)Timing= BWMB Timing = KTCOV Timing = BWFSE

MB 0.091 0.088 0.087

(18.0) (18.8) (20.0)TANG 0.033 0.033 0.101

(1.8) (1.8) (9.7)

ROA 1.368 1.394 0.578

(23.2) (23.8) (9.4)

SIZE 0.012 0.011 0.012

(4.4) (3.9) (6.8)

K 0.109 0.053 0.016

(12.0) (10.5) (6.1)

Timing 0.085 0.105 0.120

(8.9) (8.2) (14.0)

K Timing 0.048 0.017 0.071

(

9.9) (

3.1) (

8.2)KTMB 0.088

(9.8)

BWTSE 0.127

(4.7)

BWLRV 1.138

(30.5)

Constant 0.821 0.837 0.790

(19.9) (19.2) (21.7)Observations 40,136 40,136 40,106

R2 0.47 0.47 0.60


there is stock price information on the firm in PACAP rather than using calendar years. The

reported coefficients are then based on the average value of the yearly coefficients, and the

t-statistics are calculated using the standard errors of the estimated coefficients. This approach

allows us to address the potential cross-correlation in error terms for firms of a similar age.

Our conclusions hold when we use the traditional Fama and MacBeth (1973) approach and a

pooled sample approach in which we correct the standard errors of the estimated coefficient for

heteroskedasticity and the clustering of observations by both firm and period (Cameron, Gelbach,

and Miller, 2006). This last specification simultaneously addresses the issues of serial correlationand that of the cross-correlation of error terms in a given period.


15/33


All of the columns indicate that the capital structures of Japanese firms are influenced by past

equity market conditions with BWMB, KTCOV, and BWFSE all being statistically significant.

The effect is also economically significant. For example, in untabulated regressions, we drop the

interaction terms with K to estimate the unconditional effect of the proxies for market timing. We

find the coefficients associated with BWMB, KTCOV, andBWFSE to be economically large. Anincrease in one standard deviation ofBWMB leads to a reduction of approximately 11.3% in the

average value ofLev, and the corresponding values forKTCOV andBWFSEare 11.6% and 5.3%.

These results suggest that the market timing theory of capital structure is not uniquely applicable

to the US setting. When we consider the interaction of K with the measure of market timing, we

observe that market timing is more important forkeiretsu members than for stand-alone f irms.

We reach this conclusion irrespective of the proxy for market timing used. The economic effect

is such that the effect of BWMB and BWFSE is increased by approximately 60%, whereas the

effect of KTCOV is increased by approximately 15%. The control variables have the expected

signs, although the statistical significance of TANG is low in the first two columns.

We then reproduce this specification (Equation (10)) with only the keiretsu members and

examine the effect of group integration on the importance of market timing. We employ an

indicator variable for group affiliation (I) that equals one if a firm belongs to the two most

integrated categories reported in the Industrial Groupings in Japan and zero if it belongs to the

two least integrated categories. We find that the impact of market timing on capital structure

is stronger for closely affiliated keiretsu members (I= 1) than for loosely affiliated members

(I= 0). For example, untabulated results indicate that when we restrict our sample to keiretsu

members and substitute I for K, the interaction between I and our three measures of market

timing is significantly negative (with t-statistics ranging from 3.4 for I BWMB to 5.8 for

I BWFSE).

These results are also robust to alternative econometric specifications. For example, they hold

when we use the book leverage ratio (TDB) as the dependent variable, and also when we use asplit-sample approach and test the differences in coefficients among the groups. They hold when

we use firm (instead of industry) fixed effects, and also if we add an interaction between BWTSE

andK in the model reported in Column (3) (as BWTSE and BWFSE have a zero correlation by

construction). In this last case, KBWTSEis negative and significant (with a t-statistic equal to

2.7), whereas BWTSE remains significant (with a t-statistic equal to 4.5). We also find that

our various results hold for both the pre-1990 and post-1990 periods, suggesting that they are not

driven by a time-series effect but rather by cross-sectional differences.

2. The Sensitivity of Capital Structure to Stock Returns

We then examine the impact of stock returns on the change in capital structure. In other words,

we revisit our results from Table III but condition our analysis on the change in price rather than

the price level. To do so, we regress the change in the market leverage ratio on stock returns

over the same period. We consider three periods (from t 5 to t, from t 3 to t, and from

t 1 to t), and measure stock returns using CSR. CSR is the cumulative stock returns defined as

the holding-period return over the 12 months prior to the beginning of the current fiscal year. We

include the same explanatory variables as in Table III but use changes instead of levels (MB,

TANG, ROA, andSIZE). We also control for the impact of the target leverage ratio on

financing decisions using the approach outlined in Hovakimian, Opler, and Titman (2001) who

examine the debt-equity choice. Specifically, our estimation procedure involves two stages. In the

first stage, the debt-to-assets ratio is regressed on the vector of control variables (X) in Equation(10), together with the year and industry indicator variables. The purpose of this first stage is to


16/33


provide an estimate of each firms optimal or target leverage ratio. As the dependent variable in

the first-stage regression is, by definition, censored both below (by the value of zero) and above

(by the value of one), to obtain consistent estimates, we estimate the Tobit regression with this

double censoring. We then calculate the deviation from the target (DEVI) by subtracting the target

debt ratio at time t jfrom the actual debt ratio at time t j. In the second stage, we estimate thefollowing OLS regression:

Levi,[tj,t] = + 1CSRi,[tj,t] + 2Ki,t1 + 3CSRi,[tj,t] Ki,t1 + Xi,[tj,t]

+DEVIi,tj+

IND+ i,t. (11)

If Japanese f irms time their equity issuances, then we would expect a negative relation between

stock returns and the change in leverage ratio. Similarly, if keiretsu firms time their securities

issuances more than stand-alone firms, then we would expect a more negative relationship for

these firms. We use the traditional Fama and MacBeth (1973) approach and report the results inTable IV.

Consistent with the predictions, we find that CSR has a significantly negative impact on

the change in leverage ratio, with t-statistics ranging from 17.9 to 6.8. More importantly

for our purposes, this effect is more significant for keiretsu firms than for stand-alone firms

(the t-statistics associated with the interaction between K and CSR range between 3.9 and

3.2).15 These results are consistent with the results from the specifications in levels reported in

Table III.

B. Equity Issuance and Stock Returns

Having established that the effect of market conditions on capital structure is more significantfor firms with a keiretsu affiliation than for stand-alone firms, we next consider the related issue

of the effect of market conditions on equity issuance. We consider two tests to investigate this

issue.

1. Stock Price Run-Up and External Financing

Previous studies (Loughran and Ritter, 1997) find that there is a significant stock price run-up

before a seasoned equity offering. We revisit this issue by regressing an indicator variable for

equity issuance on past stock returns and other control variables. To test whether the correlation

between previous price run-ups and equity issuances documented in the US setting also exist inJapan, we again follow the approach by Hovakimian, Opler, and Titman (2001) (outlined above),

which explicitly controls for the impact of target leverage ratio on financing decisions. After

estimating the first-stage model, we use a probit regression that predicts a firms choice between

debt and equity issuance in a given year using the following equation:

P[Issuei,t = 1] = F(a + 1CSRi,t1 + 2Ki,t1 + 3CSR Ki,t1 + 4DEVIt1

+yYi,t1 + IND), (12)

15As robustness checks, we use the change in book leverage ratio ( TDB) as the dependent variable and control for the

change in market value of equity in the regressions and find that our results still hold (untabulated).


17/33


Table IV. The Sensitivity of Capital Structure to Stock Returns

The dependent variable is the j year (j= 1, 3, or 5) change in the leverage ratio, which is defined as theMarket leverage ratio (Lev) at time t minus Lev at t j. The change in the market-to-book ratio (MB),

change in asset tangibility (TANG), change in profitability (ROA), and change in firm size (SIZE) aremeasured over the same period. The initial deviation from the target is defined as Lev at t j minus the

target Lev at t j, where the target Lev is estimated by regressing Lev on MB, TANG, ROA, SIZE, Keiretsu

affiliation (K), year dummies, and industry dummies and taking the fitted value. The cumulative stockreturn (CSR) is the holding period stock return over the same period. Industry dummies are included in the

regressions but not reported. The model is estimated using the Fama and MacBeth (1973) procedure basedon calendar year.

(1) (2) (3)[t 1,t] [t 3,t] [t 5,t]

MB 0.025 0.042 0.051

(6.2) (9.1) (10.8)

TANG 0.135

0.020 0.008(7.9) (1.5) (0.4)

ROA 0.322 0.478 0.559

(18.8) (23.9) (23.3)

SIZE 0.091 0.108 0.101

(26.4) (25.1) (16.8)

Deviation from target (DEVI) 0.030 0.073 0.116

(8.4) (14.0) (19.4)

K 0.001 0.004 0.004

(0.9) (3.3) (2.0)Cumulated stock return (CSR) 0.136 0.110 0.094

(17.9) (8.9) (6.8)

K

CSR

0.011

0.014

0.014

(3.9) (3.4) (3.2)

Constant 0.005 0.016 0.053

(1.5) (2.5) (4.0)

Observations 40,136 35,749 32,051

R2 0.71 0.68 0.65

Significant at the 0.01 level.Significant at the 0.05 level.Significant at the 0.10 level.

where P stands for the probability of equity being issued, and Issue is an indicator variable thattakes the value of one if the net equity issued constitutes more than 1% of the total book value

of assets and zero if the net debt issued exceeds 1% of the total assets. Only issue years in which

a firm issues net debt or equity in excess of 1% of the book value of assets are considered;

years in which both are issued or neither is above the 1% cutoff are excluded from the regression

analysis.16 Following Baker and Wurgler (2002), we define the net equity issuance as the change

in total shareholder equity minus the change in retained earnings according to balance sheet items,

and net debt issuance as the change in total liability. Kis the previously defined indicator variable

forkeiretsu affiliation. As noted above, CSR is the cumulative stock returns over the 12 months

16We set the 1% cutoff for the issue size to mitigate the impact of extremely small issues on our results. As in Hovakimian,Opler, and Titman (2001), we replicate our tests using a 5% cutoff for the issue size, and our main results still hold.


18/33


prior to the beginning of the current fiscal year. K CSR represents the interaction between

these two variables. We expect a positive relationship between the probability of issuance and

past stock returns if firms are engaged in market timing. If firms belonging to a keiretsu time

the market to a greater extent, then the coefficient for the interaction term between the proxy

for keiretsu affiliation and past returns should be positive. Y represents a vector of the controlvariables. We include the variables used in the capital structure regressions (MB, TANG, ROA,

and SIZE), and also the set of control variables suggested by Hovakimian, Opler, and Titman

(2001), which comprises DMB, DROA, Maturity, and DEVI. DMB is an indicator variable that

takes the value of one if the market-to-book ratio is greater than one and zero otherwise. DROA

is an indicator variable that takes the value of one if the earnings before interest and taxes (EBIT)

are negative and zero otherwise. Maturity is the short-term debt to long-term debt ratio. DEVI

is equal to the difference between a firms lagged leverage ratio (Levt1) and its estimated target

leverage, estimated from the first-stage regression.17 Table V reports the results of the analysis

of the debt-equity choice conditional on past price behavior.

The results indicate that equity f inancing decisions of Japanese firms are sensitive to market

conditions, as the coefficients associated withMB andCSR are both significantly positive (with t-

statistics equal to 6.2 and 9.4, respectively). The economic significance ofCSR on the probability

of equity issue is that an increase of one standard deviation in CSR raises the probability of an

equity issue by 2.7% (to put things in perspective, the unconditional probability of an equity

issue is 15.4%).18 More importantly, firms with a keiretsu affiliation are more likely to issue

equity after a price run-up than stand-alone firms. The coefficient associated with the interaction

term between K andCSR is significantly positive with a t-statistic equal to 2.1. However, Ai and

Norton (2003) indicate that the interaction effect cannot be evaluated simply by looking at the

sign, magnitude, or statistical significance of the coefficient on the interaction term when the

model is nonlinear, but requires the computation of the cross derivative or cross difference. As

with the marginal effect of a single variable, the magnitude of the interaction effect depends on allof the covariates in the model. In addition, it can have different signs for different observations,

making simple summary measures of the interaction effect difficult to formulate. To address this

issue, we use the procedure proposed by Norton, Wang, and Ai (2004) and present in Figure 1

a graphical representation of the distribution of the marginal effect (Graph A) and its statistical

significance (Graph B). Graph A indicates that the effect is always positive (the untabulated

mean value is.023), whereas Graph B confirms that the effect is significant for nearly all cases

(the mean value of the z-statistic is 2.32). Finally, we note that the effect of keiretsu affiliation

is insignificant. These results are again consistent with the idea that Japanese firms time the

issuance of their equity and that this behavior is more pronounced forkeiretsu members.

As mentioned above, we exclude firm years in which both equity and debt are issued or neither

is issued because we focus on the debt-equity choice (conditional on issuance) in this regression.

17DMB, DROA, Maturity, andDEVI are included to be consistent with Hovakimian, Opler, and Titman (2001). DMB is

added to address the concern that an equity issue will dilute a firms book value per share if the market-to-book ratio

exceeds one. DROA is employed to capture the financing choice of loss-making companies. Debt maturity is included

to control for the transfer of wealth from equity holders to debtholders that can occur when new equity is issued. Myers

(1977) suggests that the transfer of wealth is larger in firms financed primarily with long-term debt. Our results also hold

if we use only MB, TANG, ROA, andSize as the control variables (as in our first series of tests).

18The marginal effect ofCSR on the probability of equity issue, valued at the mean value of CSR, is 0.058. In other words,

ifCSR increases 100% from the mean, then the probability of equity issue will increase by 5.8%. The standard deviation

ofCSR is 45.9%. Therefore, an increase of one standard deviation in CSR will lead to a 0.058 0.459 = 2.7% increase

in probability.


19/33


Table V. Market Conditions and Probability of Equity Issuance

The dependent variable is an indicator of equity issues (Issue) that is equal to one if the equity issued (E)is greater than 1% of the total assets, but the debt issued (D) is less than 1% of the total assets, and zero

if the debt issued (D) is greater than 1% of the total assets, but the equity issued (E) is less than 1%of the total assets. A probit model is estimated for the effect of the indicator on the explanatory variables.

The annual stock return (CSR) is the holding period stock return over the 12 months prior to the end of

fiscal yeart. Debt maturity is defined as short-term debt / (short-term debt+ long-term debt). The deviationfrom the target is Lev at t 1 minus the target leverage ratio. The target leverage ratio is the f itted value of

the regression where the leverage ratio is regressed upon the vector control variables employed by Bakerand Wurgler (2002), together with the year and industry dummy variables. All of the other explanatory

variables are as defined in Table I and are lagged one period relative to the dependent variable. A constant

term is included in the regressions but not reported. The z-statistics in parentheses are calculated from theHuber/White/sandwich heteroskedastic consistent errors, corrected for correlation across observations for

a given firm.

Equity vs. Debt Issuance

MB 0.118

(6.2)Dummy forMB > 1 0.495

(13.8)

TANG 0.149

(1.5)

ROA 1.966

(4.8)

Dummy forROA < 0 0.211

(3.9)

SIZE 0.012

(1.1)Debt maturity 0.349

(7.1)Deviation from target (DEVI) 0.711

(6.8)

K 0.012(0.4)

CSR 0.274

(9.4)

K CSR 0.098

(2.1)Constant 1.844

(

7.9)Observations 18,048Pseudo R2 0.05


Since this truncation could potentially generate a sample selection problem, in a robustness check,

we estimate a probit model with a nonrandom sample selection in which the financing policy is

modeled as a two-stage decision. In the first stage, a firm decides whether to raise external funds

or to use internal funds. In the second stage, firms that have chosen to raise external funds decide

whether they issue debt or equity. Following Chang, Dasgupta, and Hilary (2006, 2009) and Learyand Roberts (2010), we include variables in the first stage that are likely to affect the companys


20/33


Figure 1. The Marginal Effect of the Interaction Effect in Table V

The two charts are plotted using the procedure proposed by Norton, Wang, and Ai (2004). Chart A presentsa graphic representation of the distribution of the marginal effects of the interaction term, K CSR, inTable V. Chart B plots the statistical significance of the interaction term.


21/33


requirement for external funds. Untabulated results indicate that the estimated coefficients and

the t-statistics are very similar to the tabulated results.19

2. External Financing and Future Stock Returns

Recent studies by Fama and French (2008), Pontiff and Woodgate (2008), and McLean, Pontiff,

and Watanabe (2009) document the significantly negative impact of share issuance on future

stock returns. Pontiff and Woodgate (2008) determine that the share issuance effect on stock

return is statistically stronger than the predictability attributed to size, book-to-market ratio, and

momentum, and is hardly explained by risk-based explanations. McLean, Pontiff, and Watanabe

(2009) examine the share issuance effect for 41 counties and document a similar finding. In

particular, they find that the effect is stronger in countries with greater issuance activity, more

developed stock markets, and stronger investor protection. Their findings suggest that firms

facing lower issuing costs are more capable of timing the market, and that the market may fail to

correct mispricing immediately. If this is true, and ifkeiretsu members have a greater capacity to

time the market, the above-mentioned share issuance effect may be stronger for these firms than

for stand-alone firms. To examine this possibility, we relate the postoffering returns in year t to

the size of equity issue in year t 1 by estimating

F S R[t,t+n] = a + 1(Et/At1)+ 2Ki,t+ 3(Et/At1)

Ki,t+ y Zi,t1 + IND+ i,t, (13)

where FSR is the cumulative future stock returns subsequent to equity issues. We measure future

returns up to three years after an issuance. E/A is the size of the equity issue measured as

the amount of equity issued from t 1 to t scaled by the total assets at the beginning of the

period. K is our previously defined indicator variable and K E/A is the interaction between

the size of equity issue and K. If a firm times the equity market by issuing more equity when

equity is overvalued, then we would expect to observe a negative correlation between future stock

returns (FSR) and the size of equity issues (E) if the misvaluation is subsequently corrected. If

this relation is stronger for keiretsu firms than for stand-alone firms, then we would expect the

coefficient associated with KE/A to be negative. We also control for a vector of variables Z

that includes market capitalization as a proxy for size, the book-to-market ratio of equity, and the

holding-period stock return over the previous 12 months to capture the momentum effects. We

detail the definitions of these variables in the Appendix. We also include TANG and ROA as in

our previous specifications.

Table VI reports the analysis of the association between the size of equity issue (E/A) andfuture stock returns. The dependent variable is the future 12-month holding period stock return

measured from the end of fiscal year t to t+ k. We consider t+ 1, t+ 2, and t+ 3. Most

of our regressions are estimated using the modified version of the Fama and McBeth (1973)

specification based on IPO age. However, this approach is not appropriate in this specification

as the serial correlation and cross-correlation by period of the error terms are likely to be more

important issues than the cross-correlation by age of the firms. Therefore, instead of using

19We find that the statistic, which measures the correlation between errors terms of the equations in the two stages,

is equal to 0.09 and is significant at the 5% level. This suggests that the results given by the standard probit models

may be biased. However, given that the estimates using the two methods are qualitatively similar, we prefer to emphasize

the results from the simpler probit estimation. The results obtained using the two-stage probit model with the sampleselection is not tabulated, but is available upon request.


22/33


Table VI. Past Equity Issuance and Future Stock Returns

The dependent variable is the future 12-month holding period stock return (FSR) starting from the endof fiscal year t. The independent variables include a firms market capitalization (MKTCAP), book-to-

market ratio (BTM), stock beta (Beta), stock turnover (Turnover), annual stock return (CSR), book-leverageratio (TDB), equity issue size (E/A), the keiretsu dummy (K), year dummies, and industry dummies.

Beta is computed as the coefficient of the market return in the regression in which the weekly individual

stock returns are regressed upon the returns on the value weighted market index over a 52-week period.

Turnoveris defined as the mean value of monthly shares traded (volume) divided by shares outstanding over a

12-month period. Industry dummies are included in the regressions, but not reported. The model is estimatedusing OLS (Columns (1)(3)) and the Fama and MacBeth (1973) procedure based on calendar years

(Columns (4)(6)).

Pooled OLS Adjusted for 2-wayClustering

Fama and MacBeth(1973)

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

[t, t+1] [t, t+2] [t, t+3] [t, t+1] [t, t+2] [t, t+3]Ln(MKTCAP) 0.025 0.056 0.079 0.020 0.038 0.058

(13.9) (14.6) (13.4) (1.8) (2.1) (2.1)

Ln(BTM) 0.072 0.161 0.204 0.058 0.106 0.148

(18.0) (19.3) (16.2) (3.9) (3.6) (3.7)

Beta 0.095 0.180 0.349 0.013 0.045 0.073

(17.9) (18.9) (24.5) (0.5) (1.4) (1.5)

Turnover 0.327 0.709 0.836 0.269 0.528 0.862

(5.5) (6.1) (5.0) (2.6) (3.9) (4.5)

CSR 0.053 0.106 0.117 0.055 0.078 0.079

(8.2) (9.1) (7.2) (2.5) (2.7) (2.6)

TDB 0.141 0.287 0.481 0.105 0.172 0.234

(9.1) (8.8) (9.5) (2.4) (2.5) (2.6)TANG 0.131 0.233 0.449 0.052 0.083 0.086

(7.0) (5.7) (7.1) (1.6) (1.8) (1.3)

ROA 0.632 1.096 2.491 0.385 0.469 0.589

(9.2) (7.6) (11.7) (2.6) (2.0) (2.2)

K 0.018 0.038 0.077 0.010 0.022 0.033

(3.8) (4.0) (5.3) (1.6) (1.6) (1.9)

E/A 0.267 0.900 1.272 0.278 0.225 0.389

(2.0) (3.9) (5.4) (1.6) (0.8) (1.0)

KE/A 0.590 0.775 1.024 0.176 0.569 1.454

(2.9) (2.3) (2.8) (0.8) (1.7) (2.3)

Constant 0.414 1.053 1.157 0.335 0.619 0.906

(16.3) (14.6) (11.8) (2.3) (2.6) (2.4)Observations 38,262 36,175 34,187 38,262 36,175 34,187

R2 0.04 0.07 0.10 0.16 0.18 0.19


the IPO age approach, we use a pooled specification with industry fixed effects and correct

for heteroskedasticity and the simultaneous clustering of observations by firm and by period

(see Columns (1)-(3)). As an alternative, we also use the traditional Fama and MacBeth (1973)approach (see Columns (4)-(6)).


23/33


We find that when we control for standard risk proxies,E/A is negatively related to future stock

returns.20 However, the negative effect of equity issuance on future stock returns is statistically

more significant in the pooled regressions (in which the t-statistics adjusted for the clustering of

observations both by firm and period range from 2.0 to 5.4) than in the Fama and MacBeth

(1973) approach (in which the t-statistics range from 0.8 to 1.6). More importantly, thisrelationship is more significant for firms with a keiretsu affiliation. The t-statistics for the

interaction between K andE/A are again higher in the pooled specification (with t-statistics

ranging from 2.3 to 2.9) than in the Fama and MacBeth (1973) specifications, but the effect

remains statistically significant in this case (at least when we consider t+ 2 and t+ 3). The

sensitivity of one-year future stock returns on equity issuance forkeiretsu firms is equal to0.857

(=0.267 0.590), which is more than twice as large as it is for stand-alone firms ( =0.267).

For a robustness check, we use the calendar-time portfolio approach to test the correlation

between equity issuance size and future stock returns. Mitchell and Stafford (2000) determine

that the pooled regression analysis of long-term stock returns leads to an inflation of t-statistics

as the cross-correlations among overlapping return series are not well accounted for. In addition,

Fama (1998) suggests that the calendar-time monthly portfolio approach is susceptible to the bad

model problem. He argues that monthly portfolio returns have better statistical properties than

long-term holding-period returns.

In each year, we rank all firms into quintiles based on the net equity issuance (defined using

the method in Baker and Wurgler 2002) from July of the year t 1 to June of the year t.21 We

form five portfolios and calculate equally weighted monthly returns for the period from July ( t)

to June (t+ 1). We form a hedge portfolio by selling Portfolio 1 (smallest offer size) and buying

Portfolio 5 (largest offer size), and calculate time-series average monthly returns. We find that

that Portfolio 5 underperforms Portfolio 1 by 0.22% per month (the t-statistic of the difference

equals 1.75). We then perform a similar analysis, but rank keiretsu firms and stand-alone firms

into quintiles separately. We find that, Portfolio 5 of the keiretsu firms underperforms Portfolio1 by 0.31% per month (the t-statistic equals 2.21). In contrast, Portfolio 5 of the stand-alone

firms only underperforms Portfolio 1 by 0.17% per month and the difference is not statistically

significant (the t-statistic equals 1.36).22 This result suggests that keiretsu firms are more likely

to time the market than stand-alone firms.

C. Use of the Proceeds from Equity Issuance

Our last two series of tests examines the use of the proceeds from external equity financing.

We first consider the effect of equity market timing on the different types of liability. If a firms

external financing decisions are mainly motivated by market timing considerations, then it is

likely that it will retain the cash or pay off some of its debt out of the proceeds, perhaps to free

up some capital at the main bank that can be redeployed within the group. To investigate this

possibility, we examine whether past market timing has a significantly negative impact on a firms

bank debt ratio.

20Similar results are obtained if we replace the book-to-market-equity ratio by the market-to-book-assets ratio in the

regression.

21For example, for a firm whose fiscal year-end is May, the net equity issuance measure covers the period from May

(t 1) to May (t), while for a firm whose fiscal year-end is December, the net equity issuance measure covers the period

from December (t 2) to December (t 1).

22The average returns of five portfolios of keiretsu firms are 1.07%, 1.06%, 1.06%, 0.98%, and 0.76%, respectively, for

Portfolios 1 to 5. In contrast, the average returns of five portfolios of stand-alone firms are 1.01% (Portfolio 1), 1.05%,1.02%, 0.94%, and 0.85% (Portfolio 5).


24/33


We estimate a regression that is similar to that in Equation (10), but use a different dependent

variable. In Equation (10), we consider the cumulative effect of the past issuance of capital (the

sum of debt and equity issues) on the overall leverage ratio. In this subsection, we examine the

cumulative effect of past equity market timing on the total bank-loans-to-assets ratio. We use

three main independent variables to measure equity market timing: 1) BWMB_E, 2) KTCOV_E,and 3) BWFSE_E, which are collectively denoted as ETiming. They are obtained by replacing

total external financing (EF) with net equity issues (E) in Equations (1), (5), and (7). For

instance, KTCOV_E is the covariance between equity issue (E) and MB, scaled by the time-

series average ofE. We also consider the interaction of these equity market timing measures

with K, our indicator variable for keiretsu membership. We control for the same vector X of the

control variables (MB, TANG, ROA, andSIZE) that we used in Equation (11)

LOAN/Ai,t = a + 1ETimingi,t1 + 2Ki,t1 + 3ETiming Ki,t1 + y Xi,t1

+ IND+ i,t, (14)

where LOAN/A is defined as bank loans divided by total assets. We expect a negative relationship

betweenLOAN/A and the equity market timing variables (ETiming) if firms use the proceeds from

equity issuances to repay their bank loans. More importantly for our purposes, we also expect

this negative association to be stronger for keiretsu firms than for stand-alone firms if keiretsu

firms are more likely to repay bank loans with the proceeds of equity offerings proceeds.

Past equity issues can be negatively related to the bank loan-to-assets ratio even if firms do not

use the proceeds of equity issuance to actively pay off their bank loans, but instead hoard the cash

or invest in new projects. In this case, the bank loan-to-assets ratio drops due to the increase in the

asset base, rather than a reduction in the amount of outstanding loans. To address this potential

concern, we estimate the following model.

(LOAN/OLIB)i,t = a + 1E Timingi,t1 + 2Ki,t1 + 3E Timing Ki,t1 + y Xi,t1

+IND+ i,t, (15)

where LOAN/OLIB is defined as bank loans divided by other nonbank liabilities. This regression

focuses on the type, rather than the amount, of debt financing. We expect the effect of market

timing to be stronger on bank loans than on other liabilities. More importantly, we expect this

asymmetric effect to be stronger for keiretsu firms than for stand-alone firms.

Table VII reports the results from the regressions of another two measures of leverage ratio

on the market timing variables. We estimate the regressions in terms of level, which allows us

to consider the cumulative effects of market timing on the amount of bank loans on the balance

sheet. Columns (1) and (2) considerBWMB_E as a measure of equity market timing, Columns

(3) and (4) consider KTCOV_E, and Columns (5) and (6) consider BWFSE_E. We use LOAN/A

as the dependent variable in Columns (1), (3), and (5), and LOAN/OLIB in Columns (2), (4), and

(6).

The results confirm that bank loans are negatively related to all three measures of market

timing activity, irrespective of whether we scale the amount of bank loans by assets or by other

liabilities. In untabulated regressions, we omit K and its interactions with the three measures

of the timing variables to estimate the unconditional effect, and find it to be economically

significant. For example, an increase of one standard deviation in KTCOV_E leads to a reductionof 8.7% in the average value of LOAN/A. More pertinently, the amount of bank loans is more


25/33


Table VII. The Impact of Market Timing on Bank Loans

The dependent variable is 1) the bank-loans-to-total-assets ratio (LOAN/A) or 2) the bank-loans-to-total-liabilities ratio (LOAN/OLIB). All control variables are as defined in Table I and are lagged one period

relative to the dependent variable. Industry dummies and the constant term are included in the regressionsbut not reported. The model is estimated using Baker and Wurglers (2002) modified approach of the Fama

and MacBeth (1973) procedure, which is based on the number of years since firms enter PACAP.

ETiming= BWMB_E ETiming= KTCOV_E ETiming= BWFSE_E

(1) (2) (3) (4) (5) (6)LOAN/A LOAN/OLIB LOAN/A LOAN/OLIB LOAN/A LOAN/OLIB

MB 0.023 0.052 0.015 0.067 0.021 0.148

(4.4) (2.3) (2.8) (3.1) (3.3) (4.4)

TANG 0.147 0.449 0.142 0.487 0.213 0.092

(14.4) (6.8) (13.4) (6.7) (37.9) (1.9)

ROA 1.302 5.642 1.300 5.456 0.896 3.507

(19.4) (15.3) (20.0) (15.8) (16.7) (9.7)SIZE 0.008 0.157 0.007 0.147 0.006 0.144

(4.4) (15.9) (4.0) (15.8) (4.5) (22.4)

K 0.108 0.670 0.044 0.260 0.026 0.145

(28.2) (25.8) (26.3) (21.7) (10.5) (9.3)

ETiming 0.048 0.196 0.029 0.055 0.133 0.375

(9.5) (6.8) (9.2) (2.4) (6.4) (4.2)

K ETiming 0.041 0.276 0.024 0.207 0.089 0.667

(18.7) (17.8) (7.4) (6.4) (5.2) (6.5)

KTMB 0.060 0.445

(7.9) (9.1)

BWTSE_E 0.489 2.593

(5.8) (7.0)BWLRV_E 0.598 3.058

(8.5) (10.1)Constant 0.506 4.323 0.499 4.395 0.507 4.074

(16.5) (17.8) (15.2) (17.3) (14.0) (19.2)

Observations 36,916 36,657 37,057 36,796 37,057 36,796

R2 0.25 0.18 0.24 0.16 0.13 0.07


negatively related to past market timing activity for keiretsu firms than for stand-alone firms.

In fact, the coefficients associated with the interactions between K and the three equity market

timing measures are significantly negative in all six columns (with t-statistics ranging from

5.2 to 18.7). The economic effect is also significant. For example, the sensitivity of keiretsu

members to KTCOV_Eis approximately twice the sensitivity of stand-alone firms. In untabulated

regressions, we focus on keiretsu members and examine whether the degree of group affiliation

affects the market timing effect on bank loans. The results indicate that the effect is stronger for

closely affiliated members (I= 1) than for loosely affiliated members (I= 0). In the regressions

in which LOAN/A is the dependent variable, I BWMB_E, I KTCOV_E, andI BWFSE_Eare all negative, but only the first two variables are significant (with t-statistics equal to7.1 and


26/33


3.0), whereas IBWFSE_Eis insignificant. The interaction terms are generally not significant

in the regression in which LOAN/OLIB is the dependent variable.

We also perform regression analysis to investigate the change in the alternative leverage ratios,

similar to the one reported in Table IV. In Table VIII, we estimate the regressions in terms of

changes over a five-year period, which allows us to directly examine how firms use the proceedsfrom their equity issuances. We keep the same control variables as in Table VII, but we use

them to measure changes over a five-year period, rather than level. Columns (1) and (2) consider

BWMB_Eas the measure of market timing, Columns (3) and (4) consider KTCOV_E, Columns

(5) and (6) consider BWFSE_E, and Columns (7) and (8) consider CSR. We use LOAN/A as the

dependent variable in Columns (1), (3), (5), and (7), and the ratio of LOAN/OLIB in the other

columns.

The results confirm our findings from the level specification in Table VII. Across all columns,

they indicate that firms that issue equity when equity prices are high reduce their bank leverage

(the t-statistics are significant in all of the columns except Column (4)). In addition, this effect is

stronger forkeiretsu firms and the interaction between K andETiming is significant in all of the

specifications except Column (4) (with t-statistics ranging from 1.0 to 5.6).

Our last series of tests consider the effect of market timing on the keiretsu members other than

the firm timing the capital market. To do so, we reestimate Model (10), but instead of using the

firm leverage as a dependent variable, we use the average bank-loans-to-assets ratio (average

Loan/A) for all the other firms that belong to the same keiretsu group. We only include keiretsu

firms in these regressions. We include conglomerate indicator variables to control for the potential

conglomerate fixed effects on average bank loan ratios and our usual firm control variables. If,

as we predict, keiretsu firms use the proceeds of their timed issuances to redeploy capital to other

members through loans, our different measures of market timing of a firm should be positively

associated with the average loan-to-assets ratio of other companies in the same group.

Table IX reports our results. The results are consistent with our expectations. They indicatethat our different measures of past market timing are all positively associated with the average

bank-loans-to-assets ratio of other firms in the group. We reach similar conclusions if we use the

average leverage ratio (Lev) instead of the average loan ratio (untabulated results). This finding is

also consistent with the notion that the proceeds of overvalued issuances are redeployed through

the group using bank loans as a vehicle.

V. Conclusion

The literature relying on US evidence has suggested the existence of two advantages in terms of

financing activities for the fully integrated conglomerates relative to focused stand-alone firms in

their financing. First, conglomerates may have better access to external capital markets (Dimitrov

and Tice, 2006). Second, conglomerates may substitute their internal capital markets for costly

external markets (Stein, 1997; Yan, 2006). We propose a third advantage for the conglomerate

structure. Japanese conglomerates offer a hybrid structure between fully integrated conglomerates

and stand-alone firms. This unusual structure implies that some members could be overpriced

while others could be underpriced at the same time. The keiretsu members can then cooperatively

time the market for the group as a whole by issuing equity for its overvalued members and

avoiding equity financing for its undervalued members. The proceeds from these overvalued

issuances of capital could then be redeployed throughout the group, for example, through loansoffered by the main bank of the group.


27/33


Tab

leVIII.TheImpactofMarketTimingonChangesinBankLoans

Thedependentvariableisthefive-yearc

hangeinthebank-loans-to-total-assetsratio(LOAN/A)orthefive-year

changeinthebank-loans-to-total-liabilitiesratio

(LOA

N/OLIB).Thechangesincontrolvariablesandallequitytimingmeasuresaremeasuredbetweent5

andt.Allcontrolledvariablesare

measuredin

five-yearchanges.

Industrydummiesan

dtheconstanttermareincludedin

theregressionsbutnotreported.T

hemodelisestimatedusingBakerandWurglers

(2002)modifiedapproachofFamaandMacBeth(1973),whichisbasedon

thenumberofyearssincefirmsenterPACAP.

ETiming=

BWMB

_E

ETiming=

KTCOV

_E

ETiming=B

WFSE

_E

ETiming=

CSR

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

LOAN/A

LOAN/OLIB

LOAN/A

LOAN/OLIB

LOAN/A

LOAN/OLIB

LOAN/A

L

OAN/OLIB

MB

0.0

05

0.0

02

0.0

05

0.0

00

0.0

07

0.0

01

0.0

22

0.1

32

(2.0

)

(0.1

)

(1.9

)

(0.0

)

(3.0

)

(0.1

)

(3.9

)

(4.6

)

TANG

0.0

44

0.2

29

0.0

44

0.2

32

0.0

36

0.2

10

0.0

39

0.3

28

(2.3

)

(1.4

)

(2.4

)

(1.5

)

(1.8

)

(1.3

)

(2.5

)

(

2.3

)

RO

A

0.5

03

1.0

03

0.4

94

0.9

66

0.4

97

1.1

22

0.4

41

0.7

33

(15.1

)

(5.5

)

(15.2

)

(5.3

)

(15.7

)

(5.4

)

(14.4

)

(

4.8

)

SIZ

E

0.0

30

0.2

52

0.0

31

0.2

51

0.0

30

0.2

51

0.0

16

0.1

62

(5.6

)

(7.4

)

(5.5

)

(7.3

)

(5.9

)

(7.3

)

(2.4

)

(

3.3

)

K

0.0

30

0.1

03

0.0

03

0.0

04

0.0

07

0.0

22

0.0

06

0.0

21

(4.7

)

(2.2

)

(1.4

)

(0.2

)

(2.8

)

(1.0

)

(2.0

)

(

0.8

)

ETim

ing[t

5,

t]

0.0

09

0.0

49

0.0

15

0.1

03

0.0

07

0.0

82

0.0

33

0.1

69

(5.4

)

(2.8

)

(1.9

)

(1.6

)

(2.2

)

(2.3

)

(5.3

)

(

4.9

)

K

ETiming[t

5,

t

conglomerate structure and capital market timing

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