Journal of Multinational Financial Management 11 (2001) 165 – 182
The effect of market returns, interest rates, and
exchange rates on the stock returns of Japanese
horizontal keiretsu financial firms
Timothy W. Koch
a,1, Andrew Saporoschenko
b,*
aUni
6ersity of South Carolina,Columbia,SC29208,USA bCollege of Business Administration,Uni
6ersity of Akron,Akron,OH44325-4803,USA Received 8 February 1999; accepted 21 February 2000
Abstract
This research empirically examines the sensitivity of individual and portfolio stock returns for Japanese horizontal keiretsu financial firms to unanticipated changes in market returns, interest rates (government bond returns), exchange rate changes, and nominal interest rate spread changes. Results indicate that the stock returns of keiretsu financial firms often exhibit significant negative responses to interest rate increases. Results also indicate that keiretsu financial firms have higher than average market risk but insignificant exposure to exchange rate changes. There is weak evidence that risk exposures, as measured by betas, are larger when keiretsu financial firm cohesion is greater. © 2001 Elsevier Science B.V. All rights reserved.
JEL classification:G20; G30
Keywords:Keiretsu; Japanese financial system; Return-generating model
www.elsevier.com/locate/econbase
1. Introduction
The Japanese horizontal (financial) keiretsu is an economic organization with significant influence on the health and development path of the Japanese economy
* Corresponding author. Tel.: +1-330-9726331; fax:+1-330-9725970.
E-mail addresses:[email protected] (T.W. Koch), [email protected] (A. Saporoschenko).
1Tel.: +1-803-7776748; fax:+1-803-7776876
as well as the world economy. A substantial portion of the financing for Japanese firms comes from keiretsu financial firms. Except for the three long-term credit banks (two of which have been nationalized), several small city banks, the large regional banks, the major securities brokers, and some consumer finance compa-nies, all Japan’s major financial institutions are members of the roku dai kigyo shudan or six large horizontal keiretsu. Thus, the measurement of keiretsu financial firm stock return sensitivities to various financial risk parameters can provide financial managers and regulators with additional information, including informa-tion on systematic market risk, in evaluating risks of financial relainforma-tionships.
The objectives of this study are fourfold. The first is to describe some of the key features of the financial keiretsu, which may affect the risk profiles of the keiretsu financial firms including reasons why the financial firms in a keiretsu may be viewed as a unified economic organization. The second is to examine the pricing of financial risks assumed by Japanese keiretsu financial firms, using the same method as Flannery and James (1984) and others, and thereby estimate the stock market reaction to market, interest rate, and foreign exchange exposure risk for Japanese horizontal keiretsu firms through stock return responses to market return, interest rate, and exchange rate innovations (shocks). As such, this study extends recent research by Chamberlain et al. (1997), Madura and Zarruk (1995), Kane et al. (1991), Pettway et al. (1988), who estimate return-generating models with different economic factors for different types of Japanese commercial banks. The third is to identify any systematic patterns of risk assumed across keiretsu for the same type of financial institution. For example, do the marine/fire (property/casualty) in-surance firms of all the keiretsu assume high levels of interest rate risk? Finally, the fourth is to estimate and compare the risk sensitivities for individual keiretsu firms and keiretsu-specific portfolios of financial firms. Examining the sensitivity of portfolio returns can identify patterns of risks assumed that may not be apparent by examining the sensitivities of individual firm returns. For example, a liability-sensitive city bank which transfers (sells) substantial amounts of long-term real estate loans to an affiliated trust bank or property/casualty insurer, can thus substantially lower its exposure to interest rate increases. Ideally, this exposure to interest rate increases would still be reflected in the response of the keiretsu portfolio returns to unexpected interest rate increases.
Review of the estimated sensitivity measures across keiretsu financial firms suggests that these financial firms have higher than average market risk (betas). Economically significant bond return (interest rate) sensitivity is exhibited by certain city banks, trust banks, trading companies, and property/casualty insurance firms. Significant bond return betas (interest rate risk exposure) are found for fourteen keiretsu financial firms. Measuring sensitivities using portfolios of keiretsu financial firms does not, in general, result in lower risk measures when compared to the relevant city bank. There is weak evidence that risk exposures as measured by betas are larger when keiretsu financial firm cohesion is greater.
T.W.Koch,A.Saporoschenko/J.of Multi.Fin.Manag.11 (2001) 165 – 182 167
portfolio stock return sensitivities. In Sections 3 and 4, we present the empirical methodology and results, respectively. Section 5 offers conclusions and comments.
2. The financial firms of a keiretsu as a form of corporate organization
2.1. Description of the horizontal keiretsu
A keiretsu is an informal grouping of Japanese firms tied together by significant cross-holdings of equity. Horizontal (financial) keiretsu, the largest and most prominent keiretsu, are grouped around a city bank. Keiretsu members tend to specialize in different businesses but often offer competing products or services. Fig. 1 summarizes the framework of a horizontal keiretsu, which has financial, indus-trial, and trading arms, using the Mitsubishi keiretsu as an example.
There are strong economic ties among members documented in the literature and listed in the center of Fig. 1. Specifically, firms own significant amounts of stock in other member firms. For example, banks commonly hold stock in firms to which they extend credit while borrowers, in turn, own stock in the banks. Keiretsu members also exchange personnel regularly, especially transferring employees of financial firms to become managing directors of industrial firms. Other ties include monthly meetings of the presidents’ council where top management of the keiretsu firms exchange information and coordinate some activities; substantial intra-group trade; and the pursuit of joint ventures by members (Hoshi, 1994). Firms may join or leave a keiretsu at management discretion but membership is typically very stable.
As noted with the Mitsubishi keiretsu in Fig. 1, several types of financial institutions are tied to each keiretsu. Cable and Yasuki (1985) document the standard framework where each group contains a commercial (city) bank, trust bank, trading company, and at least one insurance company so that a full range of financial needs can be met by group members: ‘‘The presidents’ council typically includes the top manager from the city bank, one life insurance company, one casualty insurance company, and one or more trading companies and is instrumen-tal in keeping the group members together.’’ The ‘‘leader’’ of a financial keiretsu is generally the city bank but there are no formal leadership roles.2
During the 1990s, there were six horizontal keiretsu that controlled a substantial portion of economic activity within Japan. Table 1 lists the main financial institu-tion members for each of these keiretsu. Though not often viewed explicitly as financial institutions, the trading companies assist in export and import transactions and finance. As part of a keiretsu, they supply substantial amounts of short-term credit to the industrial firm members. Because they have intimate knowledge of their clients’ export markets, sales prospects, and financing needs, they can often structure more effective credit agreements.
The Coase concept of a firm as a nexus of contracts enables a more effective understanding of the keiretsu form of corporate organization. The keiretsu may be viewed as a ‘‘super-firm’’ based on a nexus of mostly implicit contractual arrange-ments. Within a keiretsu, firms maintain long-term financial and real economic ties. According to Yoshimura and Anderson (1997), ‘‘When a company belongs to a keiretsu …, its most senior people may in some contexts consider the keiretsu to be the reference group.’’ The substantial, interlocking web of intra-keiretsu equity holdings strengthens these ties.
Gerlach (1992) argues that financial ties are the strongest link among keiretsu firms. Substantial financial relationships and close economic ties suggest that the financial firms of the keiretsu can be viewed as a unified economic structure. The recent financial scandals, such as the involvement of the Japanese yakuza in property lending and practices of sokaiya, further demonstrate the subtle behind the scene relationships within Japanese corporate governance that affect keiretsu business practices.3
2.2. The financial firms in a horizontal keiretsu as a unified economic organization
The effect of the keiretsu economic form on asset/liability management will not always be transparent because each major financial institution is a separate legal
2For instance, Mitsubishi Bank, Mitsubishi (trading) Corporation, and Mitsubishi Heavy Industries
are cited in Dodwell Marketing Consultants, Industrial Groupings in Japan (1992) as co-leaders of the Mitsubishi keiretsu.
3The yakuza are the organized criminal gangs of Japan, which recent revelations indicate were heavily
T
The component keiretsu firms for each stock return portfolioa
Group A
Keiretsu Group B
Per ID Name Type Per
Name Type 8404 Yasuda Trust trust 0.157 8404
Yasuda M/F p/c ins. 0.046 8755
0.049 Yasuda M/F p/c ins.
8755
8002 Marubeni Corp. trading 0.062
8315 Mitsubishi Bank city 8315 Mitsubishi Bank city 0.570
Mitsubishi (Group 2) 0.713
8359 Hacinjuni Bank regional 0.058 8405 Nippon Trust trust 0.016 8584 JACCS Corp. cons cr 0.018 Nikko Securities broker 0.030
Sumitomo (Group 3) 8318 Sumitomo Bank
Sumitomo Trust trust 0.190 8403 Sumitomo Trust trust 0.211 8403
Sumitomo M/F p/c ins. 0.025
8053 Sumitomo Corp. trading 0.054 Daiwa Securities broker 0.043 8601
8320 Sanwa Bank city 8320 Sanwa Bank city 0.684
Sanwa (Group 4) 0.838
Toyo Trust trust 0.108 8407 Toyo Trust trust 0.132 8407
Nippon M/F p/c ins. 0.024
8583 Nippon Shinpan consr cr 0.083
DKB city 0.816
8311 0.992
DKB (Group 5) 8311 Daichi Kangyo Bk city
0.008 8765 Taisei M/F p/c ins. 0.006 Taisei M/F
8765 p/c ins.
C Itoh and Co. trading 0.076 8001
8585 Orient Corp. leasing 0.093 8607 Kanaku Securities broker 0.009
Sakura Bank city 0.707
0.196 8401 Mitsui Trust trust 0.179 Mitsui Trust
Mitsui and Co. trading 0.083 8031
aPer indicates the percentage weighting for the firm stock return in each portfolio. The weights are based on the percentage of the book value of liabilities
entity with separate management and separately traded stock but with strong informal economic and social ties. However, there are several interrelated reasons why the keiretsu financial firms should be viewed as a unified economic structure. (1) Different financial institutions within a keiretsu may shift (sell) assets and/or liabilities to each other through implicit agreements more readily than to non-keiretsu firms. For instance, city banks may offer larger shares in more profitable
loan syndicates to property/casualty and life insurance companies, which are
members of the same keiretsu. Also, legal restrictions on asset/liability composition can probably be more easily evaded by shifting sales of restricted products to other members of the keiretsu, where the products are not restricted. For instance, the now disbanded jusen housing loan companies were formed by Japanese banks to evade restrictions on real estate lending.4
(2) Keiretsu financial institutions may maintain different asset/liability composi-tions than non-keiretsu financial institucomposi-tions. For instance, keiretsu city banks may make fewer short-term loans to small companies since keiretsu trading companies will provide trade finance to these companies.
(3) The more complete sharing of information (e.g., product market conditions, company financial conditions, technical back-office processing developments) may lower overall risk for keiretsu firms.5
(4) Three of the keiretsu have long histories of very close cooperation dating back
to the family-owned zaibatsu.6 This close cooperation may further encourage
coordinated management of activities between keiretsu firms. This cooperation is well-documented in historical accounts of keiretsu and zaibatsu.
(5) There is the possibility of concerted political influence by combined group members to achieve political favors.7 The Keidanren, of which Japanese banks are significant members, is one of the larger contributors to the ruling Liberal Demo-cratic Party in Japan.8 Its influence may benefit the members of a keiretsu as a whole. This political influence may be instrumental in delaying the entrance of competition by foreign financial institutions into Japanese domestic financial markets.
(6) There is complementary marketing of financial products among keiretsu
4The jusen were formed by the major Japanese banks in the 1970s to provide home mortgage loans
to Japanese consumers. They were liquidated by the Japanese government in 1996, due to the large number of unrecoverable real estate assets.
5For instance, among two Mitsui group major ‘‘co-operation schemes’’ are the Mitsui Information
Systems Conference and the Mitsui Inter-Business Research Institute.
6The zaibatsu is the pre-World War II predecessor to the keiretsu form of corporate organization. It
is distinguished from the keiretsu form by extensive single family ownership and member firms tending to be grouped as subsidiaries of a conglomerate type corporation.
7For instance, Dodwell Marketing Consultants, Industrial Groupings in Japan (1992) indicates that
the members of the Mitsubishi presidents’ council make decisions on the allocation of political contributions.
8The Keidanren, or Japanese Federation of Economic Organizations, is an influential association of
T.W.Koch,A.Saporoschenko/J.of Multi.Fin.Manag.11 (2001) 165 – 182 171
firms. For instance, insurance companies sell their products to employees of other keiretsu firms. This complementary marketing may allow for cheaper marketing costs and a more reliable source of revenue for keiretsu firms. As an example, Gerlach (1992) states that the employees of Japanese keiretsu commercial bank clients often have their wages automatically transferred to savings accounts at the keiretsu commercial banks.
3. Model specification and research method
3.1. The four factor return-generating model
The objective is to examine the stock return sensitivity of Japanese horizontal keiretsu financial firms to market returns, interest rates, an interest rate spread variable, and exchange rates. Except for life insurance companies, each of the major financial firms in the six keiretsu have separately traded stock. We thus estimate the Eq. (1) GARCH four factor return-generating model using weekly stock returns from January 14, 1986 through December 29, 1992, where Rjt is the
weekly return for financial firm j in week t including dividend reinvestment;
INMKTt is the innovation in the weekly return on the PACAP Japanese
value-weighted equity market proxy including dividend reinvestment; INBDt is the
innovation in the weekly return of the J.P. Morgan long-term government bond
index; INFXt is a weekly trade-weighted yen exchange rate return innovation,
estimated using data from the J.P. Morgan economics department; DSPDt is the
weekly, non-innovation change in the spread between the Japanese short-term
prime rate and the Japanese 3-month deposit rate; and o1jt is the error term
modeled as an AR(1)-GARCH(1,1) process.9
Rjt=b01+b11INMKTt+b21INBDt+b31DSPDt+b41INFXt+o1t−u1o1jt−1,
hjt=k+d1hjt−1+a1ot2−1,
ot=(hjt)(et), (1)
where hjt is the conditional variance for the jth financial institution and et is
distributed normally and independently with a mean of 0 and constant variance. The AR(1)-GARCH(1,1) volatility model is adopted for this study to model the error terms. The lag order of the independent terms is one in accordance
9Note that bond index returns will move in the opposite direction of interest rate changes. INBD is
with the principle of parsimony. In the estimation of the volatility model, ot is the
residual from a Yule-Walker estimation.10
The Eq. (1) mean model is estimated for each of the individual Japanese financial firm stock return time-series as well as various keiretsu stock return portfolios described later.11 The long-term bond return is used because the liquidity of the Japanese long-term government bond markets is much greater than that of the short-term government bond markets. No variables are orthogonalized in either model. Instead, innovations of the independent variables are calculated, in part, because the innovations provide a more powerful result based on a market efficiency rationale.12
This study uses a relatively long time-period when stock prices were less influenced by government regulators. Pettway et al. (1988) document an increase in market efficiency for daily Japanese bank stock returns during the period 1984 – 1986 compared to 1982 – 1983. The period chosen is also included within the 1985 – 1996 Japanese monetary regime identified by Cargill et al. (1997).
3.2. Description of keiretsu portfolio regressions
We also test for sensitivity using portfolios of keiretsu financial firm stock returns.13In particular, we estimate Eq. (1) for two different portfolios formed from
10Yule-Walker estimation is used due to the inclusion of the additional lag 1 error term in the mean
model. A GARCH volatility model is proposed for this study due to the indication of time-varying heteroscedasticity for most of the stock return time series, based on Engle (1982) Lagrange multiplier tests, as well as the finding of time-varying heteroscedasticity by Engle and Ng (1993) for Japanese daily stock returns.
11The use of stock portfolios does not allow estimation of the complete effect of financial firm specific
asset/liability structures on risk. Thus, time-series of individual firm stock returns are used in some regression. For example, because individual banks may have either net asset or net liability duration structures, the stock response of a positive asset duration bank may cancel that of a bank with a net liability duration structure.
12An appendix available from the authors describes the derivation of the ARIMA innovations used
as independent variables. When available, only data previous to the forecast date are used to estimate new models each year and to calculate one-step ahead forecasts. For instance, a model is estimated for the period January 5, 1986 to January 1, 1988. A one-step ahead forecast is then estimated and an innovation calculated. The same model is used for the next one-step ahead forecast except the data period is moved up one week to cover the period January 12, 1986 to January 8, 1988. A new model is estimated beginning with the start of the next year. This procedure allows for a more accurate measurement of the innovations than the use of model residuals as the innovation measures. The use of residuals assumes that market participants use data which are not available to them in forecasting interest rate changes since the residuals are estimated using all the data. In the interest rate sensitivity literature reviewed, only Kwan (1991) and Saunders and Yourougou (1990), use a similar procedure to that used in this paper. Kwan allows his period of model identification to extend much longer into the past than the period, in which interest rate sensitivity is measured, which allows for the possibility of structural changes in his forecast models.
13Saunders and Yourougou (1990) use a portfolio approach to compare market betas and interest
T.W.Koch,A.Saporoschenko/J.of Multi.Fin.Manag.11 (2001) 165 – 182 173
firms in each keiretsu. The first portfolio includes only the city banks, trust
banks and property/casualty insurance companies (Group A). The second
portfo-lio includes all keiretsu financial firms that had enough valid stock returns over
the period of the study (Group B).14 All stock returns were weighted by the
combined market value of equity and book value of debt of each firm. Appendix A outlines the procedure used to generate the weighted-portfolio returns includ-ing an adjustment for cross-holdinclud-ings.
One difficulty with interpreting the keiretsu portfolio results is that large Japanese life insurance firms are mutuals and thus do not issue traded equity. This may influence the results of the portfolio regressions because any asset/ li-ability effects related to the life insurance firms may not be reflected fully in the portfolio stock returns. Komiya (1990) states that life insurance companies, while holding significant equity and loan portfolios in specific keiretsu firms, have had few life insurance employees transferred to these firms either as line employees or directors as compared to keiretsu city banks. Thus, they had little influence over the management of these firms. He also states that the relationship of life in-surance companies to a keiretsu is weak, based partially on their mutual form of corporate governance.
4. Results
4.1. Description of keiretsu portfolio weights
Table 1 provides the names of the financial institutions by keiretsu and type, and provides firm percentage weightings for each portfolio. The three keiretsu that have the most cohesive group ties (Mitsubishi, Sumitomo, and Mitsui) have trust banks whose assets are a larger percentage of total assets for the financial arms of the keiretsu.15 This indicates one area of greater risk exposure for the more cohesive groups, in that trust banks have recently been exposed to have some of the more severe asset quality problems and implicit guarantees of sup-port in times of financial distress exist between the keiretsu city banks and keiretsu trust banks. Note that property/casualty insurance and trading compa-nies comprise a much smaller percentage of keiretsu financial firm assets. Trading companies hold more real assets and liabilities with smaller balance sheet valua-tions.
14There were several small firms that are either joint ventures or with peripheral ties to the keiretsu
that had very few, if any, listed stock return data. These firms were not included in the portfolios. Due to their small size, they should have little influence on the results.
4.2. Indi6idual keiretsu financial firm regressions
Eq. (1) was first estimated for the individual financial firms and then for the two sets of portfolio returns.16 Table 2 presents the betas for the individual financial keiretsu firms.17 All the keiretsu firms have market betas that are significantly different from zero. Individual market betas range from a low of 0.43 to a high of 1.58. Because financial firms, in general, are highly levered, the market (equity) betas will be levered up to high levels. Still, 76% of the market betas are greater than one, indicating high systematic (market) risk for the financial firms in a keiretsu. Twelve out of 14 significant bond return betas are positive indicating a net asset duration interest rate risk exposure for the financial firms of a keiretsu. Only five out of spread change betas are significant; four with the expected negative sign because net interest income decreases with spread decreases. Only two of the exchange rate betas are significant indicating the possibility of confounding effects as changes in the value of foreign asset positions are cancelled by changes in the value of foreign liability positions. Changes in credit quality resulting from changes in the competitiveness of the Japanese industrial economy may also contribute to create a confounding effect via the exchange rate changes. The ARCH(0), ARCH(1), and GARCH(1) terms are typically highly significant for the keiretsu firms indicating the influence of past volatility shocks on current stock returns.
4.3. Patterns of betas across financial institution types
The city banks (three out of six) and the trust banks (five out of six) both exhibit a substantial number of significant bond (interest rate) return betas. The interest rate sensitivity of trust banks stock returns may be due, in part, to the large real estate holdings of trust banks. The trust banks were the most heavily exposed to the property market which exposed them to holding very long-term real estate asset portfolios due to the very illiquid property market after the 1990 stock market decline, thus causing a large positive duration gap not fully reflected on their balance sheets. Three out of seven property/casualty insurance companies also have significantly positive (at a 0.10-level or better) and economically large bond return
betas.18 Three out of seven trading companies also have economically large and
16Condition number analysis and simple correlation statistics were calculated to test for
multi-collinearity of the independent variables. Simple correlation statistics indicated the expected high correlation between the long-term and short-term interest rate measures. As would be expected, a yen/dollar exchange rate variable was significantly correlated with the trade-weighted yen exchange rate variable. No evidence of significantly multicollinearity was found since the values of the condition numbers were all well below 10. Condition number analysis allows for detection of collinearity between three or more independent variables, a relationship which simple correlation analysis would not detect. The minimum value of the condition index that indicates substantial multicollinearity is given by Belsley et al. (1980) to be between 10 and 30, based on empirical evidence. Also, inspection of the actual change and innovation data series indicates no exceptionally large values.
17The data time-series are adjusted so that all regressions have the same number of observations. 18The three property/casualty companies are Yasuda M/F (8755, Fuyo Group), Tokio M/F (8751,
T
Results of AR(1)-GARCH(1,1) MLE estimation for individual Japanese keiretsu firm stock return series using all innovation variables (non-orthogonalized) except an actual change spread measure and with a long-term interest rate return measurea
A(1) ARCH(0) ARCH(1) GARCH(1) R2
COID INTCPT INMKT INBD DSPD INFX LIKHD/OBS
Fuyo group
0.007 −0.145 0.001 0.859
0.212 0
1.117 0.4453 678.6 (346)
8317 (city) 0.006 −2.366
(0.3903) (0.0001) (1.000)
(0.0001) (0.0145)
(0.0011) (0.9644) (0.0001) (0.0001)
0.148 0.167 2.786E−5 0.126 0.869
−0.608 0.3324
8404 (trust) 0.001 0.810 0.364 621.8815 (346)
(0.0478) (0.7269) (0.0001)
(0.0001) (0.0077)
(0.4828) (0.3575) (0.0638) (0.0001) 0.187
−1.094 0.308 0.073 0.001 0.874
0.003 1.245 0 0.5043 625.4161 (346)
8755 (p/c)
8002 (trading) 0.002 1.002 0.059 0.001 0.199 0 0.4346 641.2616 (346)
(0.0001) (1.000)
(0.3323) (0.0186) (0.3491) (0.2431) (0.4059) (0.0001) (0.0160)
Mitsubishi group
−0.182
−0.235
1.091 0.061 0.000 0.256 0.684 0.5054 711.5689 (346) 0.003
8315 (city) 0.301
(0.1071) (0.3526) (0.0001)
(0.0001)
(0.0947) (0.1823) (0.7888) (0.0047) (0.0001)
8402 (trust) 0.001 1.306 0.985 −0.887 0.231 0.175 2.447E−5 0.046 0.942 0.5183 603.9193 (346) (0.0156)
(0.2954)
(0.0017) (0.0001) (0.5773)
(0.6960) (0.0001) (0.0010) (0.2175)
8405 (trust) 0.004 1.328 −0.658 1.398 0.183 0.181 0.001 0.262 0.514 0.3124 515.8946 (346) (0.0012)
(0.0022)
(0.0250) (0.0001) (0.1460) (0.0001) (0.0524) (0.4806) (0.3543)
0.592 −0.522
8757 (p/c) 0.101 0.001 0.160 0.330 0.4806 645.5836 (346) (0.0858)
8058 (trading) 0.083 0.001 0.192 0.421 0.4588 650.3210 (346) (0.0025)
8359 (reg bk) 0.429 −0.234 1.817 −0.058 0.209 0.001 0.661 0 0.1921 716.3137 (346) (0.0001)
8584 (cons cr) 0.004 1.253 0.476 2.360 0.130 9.035E−5 0.086 0.878 0.4374 570.4457 (346)
(0.0001) (0.1123) (0.0001)
(0.0819) (0.1261) (0.8984) (0.0115) (0.1324) (0.0082) 0.059 0.169 −0.178 0.061 3.580E−5
0.198 0.912
0.000 1.485 0.6753 681.4888 (346)
8603 (broker)
0.535 0.257 9.318E−5 0.122 0.810
0.681 0.5877
8318 (city) 0.002 1.295 −0.078 676.3000 (346)
(0.5933) (0.0001)
(0.0023) (0.5800) (0.0001)
(0.0001) (0.0423)
(0.1406) (0.0014)
0.088
−1.801 0.245 0.162 0.000 0.457
8403 (trust) 0.001 1.339 0.823 0.5300 623.7500 (346) (0.2381) (0.0480) (0.0001)
8753 (p/c) 0.439 0.106 0.001 0.099 0.427 0.4598 619.3016 (346) (0.1016)
8053 (trading) 0.218 0.184 0.001 0.137 0.238 0.5631 708.5473 (346) (0.0085)
(0.0398) (0.0100)
(0.0919) (0.0001) (0.0799) (0.2144) (0.7212) (0.4790) 0.002 1.585 0.297 −0.002 0.049
8601 (broker) 0.473 0.000 0.118 0.695 0.6455 638.3886 (346)
(0.0001) (0.0001)
T
Table 2(Continued)
COID INTCPT INMKT INBD A(1) ARCH(0) ARCH(1) GARCH(1) R2 LIKHD/OBS
DSPD INFX
Sanwa group
8320 (city) 0.001 1.097 0.208 1.143 −0.099 0.094 0.000 0.169 0.658 0.5063 685.3344 (346) (0.0007)
8407 (trust) 0.901 −0.220 0.275 0.086 1.10108E−5 0.074 0.926 0.3431 625.3587 (346) (0.0001)
(0.1875)
(0.8663) (0.0001)
(0.4981) (0.0001) (0.6620) (0.0994)
0.003 1.047 −0.016 0.439 0.138
8754 (p/c) 1.470 0.000 0.257 0.629 0.4607 638.0288 (346)
(0.0001) (0.0001)
(0.0843) (0.9428) (0.0065) (0.1053) (0.0385) (0.0457) (0.0003) 1.192 0.281
0.003
8063 (trading) −0.078 0.369 0.031 0.001 0.154 0 0.4612 620.7295 (346) (0.0245)
8004 (trading) 0.163 3.353E−5 0.051 0.925 0.4218 648.3833 (346) (0.0248)
(0.2283)
(0.0053) (0.0001) (0.0001) (0.9067) (0.5417)
(0.4305) (0.9371)
8591 (leasing) 0.003 0.743 −0.042 0.869 0.115 0.147 0.002 0.080 0.066 0.1999 538.2194 (346) (0.6306) (0.6983) (0.0169)
(0.9132) (0.8891)
(0.0001) (0.0530)
(0.2248) (0.0651)
8583 (cons cr) 0.002 1.093 0.245 0.718 0.046 0.141 0.000 0.083 0.843 0.4285 620.0567 (346) (0.7899) (0.6546) (0.0205) (0.1780)
(0.3653) (0.0661)
(0.3071) (0.0001) (0.0001)
Dai-ichi Kangyo group
0.560
0.002 0.820 −1.533 −0.126 0.187 2.693E−5 0.148 0.856 0.4254 669.9640 (346) 8311 (city)
(0.0184) (0.0074) (0.0001)
(0.0001)
(0.2430) (0.4242) (0.1371) (0.0369) (0.0001) 0.426
8001 (trading) 0.107 0.001 0.246 0.236 0.5400 677.9889 (346) (0.0001)
8585 (leasing) 0.001 0.883 −0.117 −1.625 0.062 0.001 0.110 0.489 0.3777 645.9076 (346)
(0.6800) (0.3729) (0.1189)
(0.0001) (0.3737)
(0.5926) (0.0500) (0.1391) (0.0507)
8607 (broker) −0.001 0.823 0.047 1.165 −0.155 0.062 8.946E−6 0.115 0.894 0.3898 655.5574 (346) (0.3448) (0.1498) (0.2527) (0.0971)
8314 (city) 0.055 0.101 0.000 0.437 0.558 0.3788 662.9165 (346) (0.0001)
8401 (trust) 0.149 0.001 0.346 0.380 0.3783 598.0407 (346) (0.0001)
8752 (p/c) 0.001 0.142 0.5069 636.1751 (346)
(0.3068)
8031 (trading) 0.001 0.291 0.5001 671.0938 (346)
(0.1265) (0.0001)
(0.0040) (0.0001) (0.0588)
(0.2758) (0.1309) (0.6157) (0.0202)
aOBS refers to the number of observations according to the SAS GARCH estimation procedure.P-values are reported below the parameter estimates.R2is totalR-squared, i.e., 1−(sum of squares for
T.W.Koch,A.Saporoschenko/J.of Multi.Fin.Manag.11 (2001) 165 – 182 177
significantly positive bond return betas indicating their status as financial intermediaries.19
4.4. Keiretsu financial firm portfolio regressions
The results of the portfolio regressions using keiretsu financial firm stock returns are presented in Table 3. For ease of comparison, the regression results for the appropriate keiretsu city bank are also included in Table 3. Few substan-tial differences are noted between the keiretsu city bank sensitivities and the sensitivities for the relevant keiretsu portfolios. The Fuyo and Mitsubishi portfo-lio stock returns exhibit significant and positive sensitivity to the long-term inter-est rate measure while the relevant city bank stock returns do not exhibit significant sensitivity to this variable. As evidenced in Table 2, the increased interest rate sensitivity is probably due to the interest rate sensitivity of the relevant keiretsu property/casualty insurance companies. The Fuyo keiretsu prop-erty/casualty insurance firm, Yasuda M/F, has a significant bond return beta of 0.874, while the Fuyo city bank has an insignificant bond return beta of 0.212. The Mitsubishi keiretsu property/casualty firm, Tokio M/F, has a significant bond return beta of 0.592, while Mitsubishi city bank has an insignificant interest rate beta of 0.301.
Using different classifications of keiretsu member cohesion such as attendance at multiple presidents’ councils, cross-holdings of equity, and exchange of corpo-rate directors, the Dai-ichi Kangyo, Fuyo, and Sanwa keiretsu are cited at the low end of group cohesion by Sheard (1994), Berglof and Perotti (1994) and Dodwell Marketing Consultants, Industrial Groupings in Japan (1992). The Mit-subishi, Sumitomo, and to a lesser extent, Mitsui keiretsu are thus cited on the high-end of group cohesion. Interestingly, none of the firms in the Sanwa keiretsu exhibit significant bond return (interest rate) sensitivity (Table 2). Only the Dai-ichi Kangyo keiretsu firms exhibit a similar pattern though the Dai-ichi Kangyo city bank has a significant bond beta. Also, examination of the Group B regressions indicates that the Sumitomo, Mitsui, and Mitsubishi keiretsu all have market betas larger than the Dai-ichi Kangyo, Sanwa and Fuyo keiretsu. A question for further study is if risk-taking by individual financial firms increases with increased keiretsu cohesion.
The volatility persistence of the portfolio regressions is similar to that of the
relevant city bank regressions. The adjusted R2s typically increase for each
keiretsu group as the regressions proceed from the regression for the individual keiretsu city bank to the portfolio regressions with the least number of firms to the regressions with the most firms in a portfolio. This is probably a result of an increase in fit, as a portfolio becomes closer to the market portfolio.
19The three trading companies are Marubeni Corp. (8002, Fuyo Group), Sumitomo Corp. (8053,
T
Results of AR(1)-GARCH(1,1) MLE estimation for Japanese keiretsu financial firm stock return portfolios using all innovations except for the spread variablea
INFX
INTCPT INMKT INBD DSPD A(1) ARCH(0) ARCH(1) GARCH(1) Likelihood/ GROUP
GRP1A −0.17863 0.000357 1.04630 0 742.461
(1.000)
GRP1B −0.1785 0.000322 1.06548 0 758.213
(1.0000)
(0.0001) (0.5318)
(0.0001) (0.0001) (0.0001) (0.0003) (0.0001)
(0.0001) (0.6169)
8317 0.006042 1.11701 0.21204 −2.36565 0.00689 −0.1448 0.000607 0.85917 0 678.597 (1.000)
0.002056 1.11473 0.42296 −0.49819 −0.14213 0.01535 0.000066 0.25995 748.764
GRP2A 0.70156
(0.0001) (0.0001)
(0.1669) (0.0210) (0.1998) (0.6197) (0.8272) (0.0118) (0.0001) (0.5917) 0.24499
−0.39919 −0.08764 −0.04678 0.000032 0.35267
GRP2B 0.002137 1.10159 0.73996 807.897
(0.5221)
GRP3A 0.61686 0.05665 0.16722 0.000850 0.18278 0 705.289
(1.000)
GRP3B 0.55857 0.08112 0.15088 0.000782 0.09602 0 731.923
(1.000)
8318 0.2568 0.000093 0.12183 0.81002 676.3
(0.0001)
GRP4A 0.001797 1.10151 0.12994 0.74336 −0.07703 0.07817 0.000141 0.16715 0.69329 727.353
(0.5725) (0.5719) (0.2542) (0.5555)
8320 0.09395 0.000218 0.16903 0.65766 685.334
T
Table 3 (Continued)
INFX
INTCPT INMKT INBD DSPD A(1) ARCH(0) ARCH(1) GARCH(1) Likelihood/ GROUP
TotalR2
DKB(Daichi Kangyo)group
0.55433 −1.51669
0.81758 −0.12471
0.001868
GRP5A 0.18521 0.000026 0.14784 0.85602 672.481
(0.0001)
GRP5B 0.13883 0.000021 0.16332 0.83948 735.57
(0.0001)
−1.53258 −0.12591 0.85563
0.56016 0.1865
GRP6A −0.04837 0.11382 0.01276 0.000142 0.54081 0.47492 713.312 (0.0001)
GRP6B 0.00398 0.000143 0.54928 0.45104 736.206
(0.0001)
aVariables are unorthogonalized and the long-term interest rate measure is used.P-values are reported below the parameter estimates. The results for the
5. Conclusions and issues for further study
Japanese horizontal keiretsu financial firms exhibit large market risk exposure, and, in general, exhibit significant exposure to interest rate increases. The Fuyo and Mitsubishi keiretsu portfolios exhibit significant negative (positive) sensitivity to the long-term bond return (interest rate) innovation while the relevant city bank stocks do not exhibit significant sensitivity. The increased portfolio sensitivity is likely due to the keiretsu property/casualty insurance firm’s stock sensitivity to bond return innovations. There is little evidence of lowered risk (lower market betas, bond return (interest rate) betas, exchange rate betas, or spread betas) for the portfolios of keiretsu firms when compared to the individual firms including keiretsu city banks.
The role of trading companies within the Japanese financial system and the keiretsu form of economic organization has only been peripherally analyzed even though the Bank of Japan has regularly included trading companies when making discount loans to manage the Japanese money supply. The large Japanese trading companies provide substantial amounts of short-term credit. How does this as-sumption of the financing function by trading companies affect the risks of the city and other banks in the keiretsu?
Recently, several trading companies announced that they would be entering the securities business. On one dimension, there is substantial, documented cooperation between keiretsu financial firms. On another dimension, there are elements of intra-keiretsu competition – for instance, keiretsu banks have also entered the securities business. Also, inter-keiretsu mergers of financial firms have been pro-posed. Will these cross (corporate) cultural mergers succeed? Further analysis of the equilibrium between competition and cooperation between the financial firms in a keiretsu is required.
The reasons for the economically large interest rate sensitivity of the property/ ca-sualty insurance companies attached to the more cohesive keiretsu groups (Sumit-omo and Mitsubishi groups) and the significant interest rate sensitivity of three trading companies also merit further investigation.20 Were the property
/casualty companies taking more risk on the asset-side of their balance sheet since their insurance premium liabilities were highly regulated and cartelized so that a uniform industry premium schedule pertained? What risks are the trading companies taking both on-balance sheet and off-balance sheet including foreign currency and futures speculation?21
Another question for future study would ask if there is any overt or covert shifting of assets between the financial firms in a keiretsu and thus intra-keiretsu shifting of risk. The previously mentioned shifting of a substantial portion of
20The bond return beta for the property/casualty company of the other old-line keiretsu (former
zaibatsu) group, Mitsui, also had a large positive coefficient with a significance level of 0.1040.
21The large losses of the Sumitomo trading company in copper futures speculation would be an
T.W.Koch,A.Saporoschenko/J.of Multi.Fin.Manag.11 (2001) 165 – 182 181
property lending to the jusen housing loan corporations (which were established by the major Japanese banks) from the major Japanese commercial banks after Ministry of Finance restrictions on bank property lending is a case in point. Analysis of these questions should be of value in creating more efficient regulatory structures.
Acknowledgements
The authors would like to thank the International Economics Department and the Bond Index Group of J.P. Morgan Bank for providing Japanese market data.
Appendix A. Method used to generate the portfolio weights for keiretsu firms
The following procedure was used to obtain portfolio weightings for the keiretsu financial firm stock returns. First, the market value of equity (last share price quoted in fiscal year 1991 times number of common stock shares outstanding as of fiscal year end 1991) and the book value of liabilities (at fiscal year end 1991) were obtained from the PACAP database. Second, to avoid double counting equity, the percentage of intra-keiretsu equity ownership as of 1991 was obtained from the Dodwell Marketing Consultants, Industrial Groupings in Japan (1992). This per-centage of equity ownership was subtracted from the market value of the firm that owned the equity. For example, Mitsubishi Bank owned 3.2% of Mitsubishi Trust. Thus, 3.2% of the market value of Mitsubishi Trust was subtracted from the market value of Mitsubishi Bank equity to arrive at Mitsubishi Bank’s adjusted market value of equity. The market value of Mitsubishi Bank’s equity was also adjusted for other equity stakes not included in the example. Third, the book value of liabilities and adjusted market value of equity were added to create a total assets figure. Fourth, the weights for the weekly returns of each firm in a portfolio were calculated as the percentage of firm’s total assets to the sum of total assets for all the firms in a portfolio.
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