Does Physical Distance Affect Bank-Firm Relationship ...

Does Physical Distance Affect Bank-Firm Relationship? - Empirical Analysis of Listed and Unlisted Bankrupt Firms

Tsuyoshi Mori, University of Tsukuba Cindy Yoshiko Shirata, University of Tsukuba, [email protected]

ABSTRUCT We relate physical distance to formation of relationships in corporate banking context. focus on the physical distance between firms and their main banks.

We

Because banks have strong

incentive to bail out corporate customers, bank staff visit distressed customers frequently in order to help them.

If firms are physically farther, it costs more for banks to travel.

Therefore, we hypothesized that, if the physical distance between bankrupt firms and banks became shorter, firms were more likely to reorganize.

In addition, we hypothesized that firms

were likely to reorganize if they had large debt, or if the size of the banks was smaller.

We

constructed a model using a dataset of unlisted bankrupt firms in Japan, and tested the model using a dataset of listed bankrupt firms. information systems.

We calculated the distance by geographical

The results supported our hypotheses.

The physical distance between

firms and their main banks can give firms extra information to business relationship.

Keywords: Bank, Business Relationship, Bankruptcy, Reorganization, Distance JEL Classification: J41

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I.

INTRODUCTION

Most Japanese firms have their main banks,1 and have heavily relied on them.

Main

banks have strong business relationship with the firms, and have played the role of the delegated monitors of the firms (Ogawa et al. 2007).

The relationships between

Japanese firms and their main banks are critical when the firms are in financial distress (Aoki & Patrick 1994). Main banks have closely monitored and relieved corporate customers (hereafter, we define “customers” of a bank as firms which borrow money from the bank) which face financial distress.2

Main banks would like such customers

to reorganize, because they are expected to recover more money in reorganization than in liquidation (Tsuruta et al. 2006).

Main banks have been penalized in Japanese

financial markets for not monitoring distressed firms (Aoki 2001).3

Distressed firms in better financial conditions do not always reorganize more successfully, however.

Shirata (2006) suggests that Japanese distressed firms filing

reorganization procedures in court are in worse financial conditions than those filing

1

White Paper on Small and Medium Enterprises in Japan (2007) indicates that 93.2% of Japanese firms have their main bank. Aoki (2001) indicates that monitoring is conceptually divided into three steps – ex ante, interim and ex post. In Anglo-American financial system, each monitoring step is separately delegated to specialized institutions. In Japanese main bank system, three monitoring steps are integrated and exclusively delegated to main banks. 3 Aoki (2001) discusses that such penalty is high enough for main banks to bail out temporarily unproductive (but recoverable) firms, but is not too high for main banks to help critically unproductive firms. 2

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liquidation procedures. Here we focus on the role of main banks. Efforts of main banks will help firms reorganize. Main banks provide customers not only finance, but also managerial advisory and even corporate executives and/or professional personnel (Aoki & Patrick 1994).

Which firms do main banks relieve?

In this paper, we relate physical distance to

formation of relationships in corporate banking context. distance between firms and their main banks.

We focus on the physical

We hypothesize that, if the physical

distance between firms and banks shortens in five years prior to their bankruptcy, firms will reorganize.

Berger et al. (2005) suggests that banks typically obtain information

of corporate customers through personal contact, because such information is not available in public.

Uchida et al. (2006) reveals that bank staff in Japan often visit

corporate customers so as to contact them in person.4

If customers are physically

farther, it takes longer for bank staff to travel. Bank staff consume their time which they could otherwise spend in pursuing other business chances. banks are very high.5

Opportunity costs for

Thus the physical distance affects the costs of banks.

Because

banks have strong incentive to bail out corporate customers (Diamond 1984), bank staff 4

Uchida et al. (2006) indicates that more than half of Japanese firms usually contact their main bank in person; the average of frequency for firm owners to physically meet bank loan officer is once a month and the median is twice a month; and 79% of firm owners meet bank loan officer at their companies. 5 In fiscal year 2009, personnel costs of 123 major banks in Japan accounted for 42% of their total expenses.

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visit distressed firms very frequently in order to help them.

Banks usually foresee

financial distress of customers at least five years prior to their bankruptcy (Ohmura et al. 2002).

A bank physically closer to distressed customers can help reorganize them with

less cost to travel.

Physical distance between firms and banks varies if banks or firms

relocate, or if firms switch their main banks.

For about half of bankrupt firms in Japan,

the physical distance to main banks had become either longer of shorter in five years prior to their bankruptcy (Mori & Shirata 2009).6

In this paper we will conduct two analyses.

Firstly, we construct a model to reveal the

possibility that a distressed firm will be reorganized (as opposed to be liquidated), using data of unlisted bankrupt firms in Japan.

A key explanatory variable in this model is

the difference in physical distance between firms and their main banks in five years prior to their bankruptcy.7

We investigate if shortened physical distance between firms

and banks leads to reorganization.

Secondly, we test the model constructed in the first analysis using data of listed bankrupt firms.

We investigate if the model works well for listed firms, and if the

6

Mori & Shirata (2009) indicates that, in 5 years prior to their bankruptcy, 36% of bankrupt firms in 2008 switched their main bank, and additional 15% of them had their main banks relocated. The distance changes if firms switch their main bank, or if the firms or their main banks relocate.

7

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difference in physical distance between firms and banks is an important explanatory variable. We calculate the distance by geographical information systems (GIS).

II. PREVIOUS LITERATURES Many literatures have highlighted the role of banks in corporate finance. delegated the task of costly monitoring of loan contract (Diamond 1984).

A bank is Firms with

credit rating toward the middle of the spectrum rely on bank loans (Diamond 1991). Banks make business relationship with current or potential corporate customers to gather “soft” information not available in public. on soft information of the firms.

Banks make credit decisions based

This lending technology, defined as relationship

banking (Boot 2000), is one of the most powerful technologies available for banks to reduce asymmetric information problems (Berger & Udell 2002).

Relationship

banking aims to resolve problems of assymetric information (Boot 2000), especially in lending smaller firms (Berger & Udell 2002).

Stein (2002) reveals how a bank gathers,

accumulates, and uses firsthand and qualitative information in its organization.

This

mechanism of credit decision in bank organization has been empirically tested in many countries (Berger et al. 2005; Bongini et al. 2007; Kano et al. 2006).

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Japanese main banks positively help distressed customers (Yamori & Kondo, 2007). In Japan, the main bank of distressed firms has strong incentive to help them. Japanese smaller firms which eventually went into bankruptcy had decreased trade credit, but had increased bank debt, in five years prior to their bankruptcy (Ohmura et al. 2002).

The main bank influences the type of bankruptcy process firms choose.

Collaterals of loan weaken bank’s incentive to monitor borrowers (Manove et al. 2001), and lead to liquidation (Tsuruta et al. 2006).

Firms whose main bank is a megabank8

tend to choose liquidation over reorganization (Helwege & Packer 2003).

Physical distance has been related to formation of human relationships (Festinger et al. 1950; Latane et al. 1995).

In banking context, we can hypothesize that physical

distance between firms and their main banks is related to the depth of business relationships between them.

In relationship banking, bank staff visit corporate

customers frequently in order to monitor them.

Uchida et al. (2006) reveals that bank

staff in Japan often visit corporate customers so as to contact them in person.

Banks

have strong incentive to bail out corporate customers (Diamond 1984). Therefore, it is important to focus on distressed firms in order to investigate the impact of physical

8

Megabanks include City Bank, Long-Term Credit Banks, and Trust Banks which are members of Japanese Bankers Association.

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distance between banks and firms on bank-firm relationships. Although some banking literatures analyze the physical distance between firms and banks (Petersen & Rajan 2002; Degryse & Ongena 2005; Uchida et al. 2006), they do not focus on unsound firms. In addition, most banking literatures measure the distance by questionnaires to firms.9 Arikawa et al. (2007) study the accuracy of GIS to calculate the physical distance. Mori & Shirata (2010) measure the distance between bank branch and bank headquarter by GIS, to reveal the impact of distance on bank-firm relationship.

We calculate the

physical distance between firms and banks to evaluate bank-firm relationships. We consider our empirical setting to be uniquely suited to analyze relationship banking. In Japan, banks play relatively important roles in national economy.

III. BANKING SYSTEMS IN JAPAN It is important to consider national financial structures in analyzing relationship banking. 10

We identify key characteristics of banking systems in Japan, with

comparing to those in U.S. (TABLE 1)

INSERT TABLE 1 ABOUT HERE 9

Degryse & Ongena (2005) use software to measure distance. They studied one Belgian bank focusing on interbank competition. Berger & Udell (2006) insists that financial structures affect the feasibility and profitability of different lending technologies, which include relationship lending.

10

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First, Japanese banks are concentrated. 9,441 in U.S.12 U.S.14

The number of banks is 580 in Japan,11 and

The number of bank branches is 20,000 in Japan13 and 60,000 in

Therefore, the average number of branches per one bank is 34 in Japan, and 6

in U.S.15

The number of branch is decreasing in Japan,16 while increasing in U.S.17

Second, small and medium sized firms (SMEs) play relatively important roles in Japan. SMEs18 in Japan hire 56% of total domestic corporate employees (SMEs in U.S. hire 28%), and ship 48% of total manufacturing outputs in Japan.

Third, Japanese banks play prominent roles in corporate finance.

Yabushita &

Busimata (2006) indicates that, in Japan, the ratio of bank loan to total assets is 34% for SMEs and 19% for large firms (i.e. non-SMEs); in U.S., 22% for SMEs and 4% for large firms.19

Osano & Hori (2001) suggests that, in Japanese economic systems, main

banks often try to help struggling firms for fear that they are badly reputed by local

11

580 privately-owned banks existed in Japan as of 2007. According to Maechler & McDill (2003), 9,441 banks existed in U.S. as of 2002. According to Mori & Shirata (2010), 20,000 bank branches existed in Japan. 14 According to Spieker (2003), more than 60,000 bank branches existed in U.S. 15 According to Maechler & McDill (2003), the number of U.S. banks with total assets of $100 million or less is 4,893. There are few banks with total assets of ¥10 billion (roughly equivalent to $100 million) or less in Japan. The average size of assets is ¥100 billion ($ 1 billion) for Shinyo Kumiai (Credit Union), the smallest bank type in Japan. 16 According to Japanese Bankers Association and Shinkin Central Bank Research Institute, the number of bank branches in Japan was 21,796 in 2001, and was 19,137 in 2007, 17 Spieker (2003) points out that the number of banks has declined and the number of branches has increased. 18 Firms with 100 or less employees 19 According to Yabushita & Bushimata (2006), smaller firms in U.S. rely on capital more heavily (40% of total assets) than smaller firms in Japan (22%). Large firms in U.S. rely more on bonds (16% of total assets) than Japanese large firms (8%). 12 13

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business community. relationships.

In addition, Japanese firms and their main banks keep long term

The average years the transaction between a firm and a bank continues

is 30 years in Japan20 (Uchida et al. 2006) and 9 in U.S.21 (Berger et al. 2005) Hundreds of new banks or “de novo banks” have been chartered in U.S. every year,22 which facilitate alternative lending availability of firms.

In Japan, only two de novo

banks have been chartered for business transactions in recent ten years.23

On-line

banks in U.S. have already entered corporate loan markets, but none in Japan have.24

Fourth, new lending technologies like credit scoring or asset based lending are limited in Japan, compared to in U.S.

Credit scoring is a statistical approach to predicting the

probability that a credit applicant will default or become delinquent25 (Berger & Frame 2007).

Credit scoring has been popular in U.S. since 1990s,26 while not so familiar to

Japanese banks.27

Asset-based lending, popular in U.S., is not widely used in Japan.28

20

Uchida et al. (2006) suggests that the relationships between Japanese firms and banks last 27 years. Berger et al. (2005) suggests that average duration of such relationship in U.S. is 9 years. 22 De Young (2003) indicates that over a thousand new commercial banks were chartered in the U.S. between 1995 and 2003. 23 Two banks recently chartered in Japan - Shinginko Tokyo and Incubator Bank of Japan - are considered to be de novo banks. Aside from these two banks, a few on-line banks have been newly chartered but they focus on individual markets. 24 Sony Bank, a Japanese on-line bank, recently has announced to participate in syndicated loan. However, it does not plan to accept loan application directly from corporate borrowers. 25 Credit scoring evaluates applicants for “micro credits” under $250,000 (Berger & Frame, 2007). Note that credit scoring are not key lending technology for lending our sample firms – $300,000 capital or more - which typically owe banks far more than $250,000. Berger & Frame (2007) point out that some banks use “rules” to automatically accept or reject credit applicants and to set credit terms based on purchased credit scores, while other banks use credit scoring in conjunction with other lending technologies, including relationship banking, for credit decisions. 26 See Frame et al. (2001). 27 See Hasumi & Hirata (2008) 28 According to Sankei Biz (http://www.sankeibiz.jp/), the ratio of asset-based lending outstanding over total corporate debt in 2006 was 19.7% in U.S. and 0.1% in Japan. 21

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IV. HYPOTHESIS AND SAMPLE DATA Hypothesis In this paper we hypothesize that firms are more likely to reorganize if the physical distance to their main banks becomes shorter in five years prior to their bankruptcy. Main banks will help reorganize distressed corporate customers.

Main banks usually

foresee financial distress of customers at least five years prior to their bankruptcy (Ohmura et al. 2002). However, it costs more for main banks to help reorganize distant firms.

Costs include opportunity costs of bank staff to travel to monitor firms,

as well as direct travel expenses (gas, taxi fares, train tickets, etc.).

Therefore, if the

physical distance between firms and their mains bank shortens, the cost of main bank to help reorganize those firms will decrease.

H1: Firms are more likely to reorganize if the physical distance between firms and their main banks becomes shorter in five years prior to their bankruptcy

Also, we hypothesize that firms are more likely to reorganize if they owe more debt. Japanese banks play prominent roles in corporate finance. Main banks are typically the largest creditors of the firms.

The larger the debt, the more efforts main banks will

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make to help reorganize the customers.29

H2: Firms are more likely to reorganize if the amount of debt is larger

In addition, we hypothesize that firms whose main bank is a megabank tend to choose liquidation over reorganization.

Helwege & Packer (2003) argues that megabanks will

help unsound customers well before they are badly distressed, and customers actually filing bankruptcy procedures are so hopeless that megabanks cannot but liquidate it. Alternatively, megabanks will not help reorganize a customer of a given size (in debt) as hard as smaller banks will, because they have more large customers than smaller banks.

H3: Firms are more likely to reorganize if their main bank is not a megabank

Sample Data In our analysis, we focus on two major bankruptcy procedures in court – Civil Rehabilitation and Liquidation.30 reorganization or liquidation.

Bankruptcy procedures in court are aimed at either In Japan, the most popular procedure aiming at

29

Usually unlisted firms do not disclose the amount of debt they owe to their main banks. Therefore, we assume that the amount of debt they owe is proportional to the amount of debt they owe to their main banks. 30 Bankruptcy cases in Japan are either processed in court or processed out-of-court. Out-of-court processes are generally for very small firms, and usually aimed at liquidation.

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reorganization is Civil Rehabilitation procedure. 31 Under Civil Rehabilitation Law, management at the period of bankruptcy does not have to move, similar to debtor in possession of Chapter 11 procedure in U.S. (Helwege & Packer 2003).32

The most

popular procedure in Japan aiming at liquidation is Liquidation procedure.33

This

procedure parallels Chapter 7 in U.S., in that an appointed trustee shuts down the firm, sells the assets, and turns the proceeds over to the creditors (Helwege & Packer 2003).

In our analysis we utilize one dataset of unlisted firms, and the other dataset of listed firms.

For dataset of listed firms, we use corporate credit information of Teikoku Data

Bank (TDB).34

The dataset includes firm name, address, date of bankruptcy, debt size,

sales, amount of capital, number of employees, main bank35 and branch, and type of bankruptcy.

We use 943 unlisted firms which went into bankrupt between 2004 and

2008 with paid capital of 30 million yen (300 thousand US dollar) or over.36

Please

note that our sample unlisted firms are not micro-sized, but middle-sized firms.

31

We

Civil rehabilitation procedures accounted for 59% of overall court process. The other two court reorganization procedures, Kaisha Kosei and Shoho Kaisha Seiri accounted for 2% and 0.2% of court processes, respectively. Debtors can file Civil Rehabilitation procedures when they have “a possibility of facing facts out of which a bankruptcy may arise” (Civil Rehabilitation Law, Article 21). Debtors do not have to have experienced the cause of bankruptcy like suspension of payment before they file Civil Rehabilitation Law. This procedure aims to rehabilitate debtors before they completely liquidate. The main bank will accept rehabilitation plan if the bank is more profitable in the plan than in firm liquidation (Tsuruta et al. 2006). 33 Liquidation process accounted for 34% of overall court processes. The other court liquidation procedure, Tokubetsu Seisan, accounted for 5%. 34 Teikoku Data Bank (TDB) is a major Japanese credit information company. In Japan, they are most widely referred as bankruptcy information, since no governmental statistics regarding corporate bankruptcy exist. 35 We define the bank listed first in bank column of the dataset as the main bank of the firm. 36 We do not include data of construction and real estate industries. Banks may have excessively lent to these industries beyond their ability of repayment, whereas have supplied less money to other industries after 1990s (Takesawa et al. 2005). 32

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investigate their main bank in five years prior to their bankruptcy37 to find if the firms switched their main bank.38 former main banks.39 Liquidation.40

672 out of 943 firms have information of both current and

We focus on firms which filed either Civil Rehabilitation or

Consequently, our final dataset of unlisted firms has 413 samples.

For the dataset of listed firm, we use annual securities reports.41 4,000 firms listed in Japanese stock exchanges.42

There are around

We pick up listed firms which filed

either Civil Rehabilitation or Liquidation between 2005 and 2009.

43

We obtain

annual securities reports of these 18 firms.

We utilize GIS to calculate the distance between firms and their main banks.

In order

to accurately calculate the physical distance between firms and their main banks, we use NAVITIME, a Japanese geographic information system44 (FIGURE 1).

Like Google

37 We obtain data of the former main bank from CD-Eyes of Tokyo Shoko Research (TSR). We add former main bank information to our data when firm name and address matched. Sometimes firm name or address changed in five years. In such cases we also use name of president, industry and date of foundation as alternative matching keys. If two of these keys match with the same or very similar firm name, we add prior main bank information to our data. 38 If the main bank five years prior to firms’ bankruptcy is different from that immediately before their bankruptcy, we regard that the firm switched their main bank. We regard merged banks as the same bank, if the merger occurred in five years prior to bankruptcy. 39 Both TDB and TSR directly contacts companies to obtain main bank information, so we assume that information of these two sources are consistent. Both define bank name listed first in correspondent bank column in their data as main bank. 40 We also exclude firms whose current or former main bank was a governmental bank. Governmental banks have roles very different from those of privately-managed banks. They are exceptionally distant from customers. On the average, governmental banks and non-governmental banks were 64Km and 16 Km away from customers at the time of bankruptcy, respectively. 41 Disclosure documents under Financial Instruments and Exchange Act. All firms listed in stock exchanges in Japan must submit securities reports to Financial Services Agency. 42 TSE (Tokyo Stock Exchange), OSE (Osaka), NSE (Nagoya), SSE (Sapporo), FSE (Fukuoka), and JASDAQ. TSE, OSE and NSE have two general market sections, and others have one general market section. In addition, each of six exchanges has an emerging market section: Mothers, Hercules, Centrex, Ambitious, Q-Board, and NEO, respectively. 43 See 36. 44 We use the website of Navitime Japan. Navitime Japan is a firm specialized in geographic information software. Its products

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maps, which are frequently used in U.S. (FIGURE 2), NAVITIME accurately calculate the driving distance between two locations in Japan.

The software calculates the

distance based on actual driving route in accordance with traffic regulation.

We use

addresses of the firms and the branches of the main banks45 to calculate the physical distance between them.

We obtained the distance immediately before their bankruptcy,

and the distance five years prior to their bankruptcy.46

We subtract the former from the

latter to obtain the difference in distance in five years prior to the bankruptcy of firms.

INSERT FIGURE 1 ABOUT HERE


V. EMPILICAL ANALYSIS We conduct two analyses: 1) analysis of unlisted firms, and 2) analysis of listed firms. The number of listed firms is much smaller compared to that of unlisted firms. Therefore, we first build our model in analysis 1), and test the model in analysis 2).

are widely used in web and mobile phone application. Authors have tried multiple software products and find that it shows traffic route in Japan most accurately. http://www.navitime.com 45 For listed firm dataset, we use TDB data to specify branch name of the main bank. 46 We use Nikkin Kinyu Nenpo (Nikkin) to obtain address of closed or relocated bank branches.

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Analysis of unlisted firms First, we analyzed 414 unlisted firms which filed either Civil Rehabilitation procedures or Liquidation procedures.

TABLE 2 summarizes the data.

(DEBT) is 1,936 million yen (approximately 19 million USD).

The average debt size The average distance

between firms and their main banks (FBD) was 4.3 km (2.7 miles).

The difference in

physical distance in five years prior to bankruptcy (FBDDIF) is almost zero on the average, but ranges between -1,100 km and 200 km (-670 miles and 120 miles). MEGA suggests 39% firms have main banks categorized as megabanks. 47

Firm

financial positions were calculated by SAF model developed in Shirata (2003), a popular model in Japan to predict bankruptcy.

SAF accounts for both financial

condition and managerial performance of firms.48

More than 75% of the firms have

SAF values below .7, which shows clear signs of bankruptcy.49

REORG shows 61%

filed Civil Rehabilitation procedures, and the rest filed Liquidation procedures.

INSERT TABLE 2 ABOUT HERE

47 48

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See 8. Shirata (2003) developed SAF model that foresee corporate bankruptcy. SAF index is calculated by the following equation: SAF = 0.01036X1 + 0.02682X2 – 0.06610X3 – 0.02368X4 + 0.70773 X1: retained earnings over total asset = average retained earnings/average gross asset *100 X2: earnings before tax over total asset = EBIT/average gross asset*100 X3: inventory turnover period = average inventory*12/sales X4: interest expense over sales = interest payable/sales*100 Shirata (2003) reports that a firm is expected to go bankrupt if SAF value of the firm is 0.7 or less.

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TABLE 3 compares liquidated firms (i.e. Liquidation) and reorganized firms (i.e. Civil Reorganization).

The average debt of reorganized firms is 2,630 million yen (26

million USD), more than double of the average debt of liquidated firms (1,194 million yen or 12 million USD). liquidated firms.

FBDDIF is negative in reorganized firms and positive in

On the average, the difference in distance between firms and main

banks is negative in reorganized firms (-.3) and positive in liquidated firms (+.1).

In

five years prior to firms’ bankruptcy, the distance between reorganized firms and their main banks shortened, and the distance between liquidated firms and their main banks lengthened.

MEGA suggests that 41% of main bank in liquidated firms were

megabanks, whereas 38% in reorganized firms were megabanks.

Firms whose main

bank was a megabank chose liquidation over reorganization.


Next, we developed a model to reveal the probability a bankrupt firm filing court process would choose reorganization, as opposed to liquidation.

Because the

dependent variable REORG is binomial, we constructed the following probit regression equation form:

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REORG= p (z);50 z = f (LogDEBT, LogFBD, LogFBDDIF, LogBHD, AGE, MEGA, SAF, SWITCH) … equation (1)

The dependent variable, REORG, is 1 if the firm filed Civil Rehabilitation procedures and 0 if it filed Liquidation procedures. determine firms’ bankrupt type.

Independent variables for equation (1)

Debt size variable is LogDEBT.51

Firm financials

variable is SAF, the SAF value of the distressed firm in accordance with Shirata (2003). LogFBD is the distance between firms and their main bank branch.52

LogFBDDIF is

the positive difference in the distance between firms and their main bank in five year prior to their bankruptcy; 53 i.e. if the physical distance between firms and banks lengthened (shortened), LogFBDDIF is positive (negative).

LogBHD is the distance

between the account bank branches of the firms and headquarter of the bank. the firm age when the firm went bankrupt.

50

p(z) =

xp(

AGE is

MEGA is 1 if main bank is a megabank,

)du

51

Common logarithm of debt size in million yen, as of bankruptcy date 52 Common logarithm of (1 + distance in kilometers between firms and their main bank at the point of their bankruptcy) 53 LogFBDDIF equals to log (1 + FBDDIF) if the change is positive; -log (1 + (-FBDDIF)) if the change is negative; 0 if the change is 0. FBDDIF is in kilometers, and obtained by subtracting the distance five year prior to firms’ bankruptcy from the distance immediately before their bankruptcy

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and 0 otherwise.54 SWITCH is 1 if the firm had switched its main bank in 5 years prior to their bankruptcy, and is 0 otherwise.

TABLE 4 shows the result of our probit regression analysis.

Coefficients of

LogFBDDIF were significantly negative at 1% level, in accordance with H1.

If the

physical distance between firms and banks shortened, firms were more likely to reorganize. Coefficients of LogFBD were negative, but not statistically significant. Difference in the physical distance better explained bankrupt type than the physical distance itself did.

Coefficients of LogDEBT were statistically significant at 1% level.

This is in accordance with H2; firms with more debt filed reorganization, Coefficients of MEGA were negative and were statistically significant.

Firms whose main bank

was not a megabank tend to reorganize, which supported H3. were not statistically significant.

Coefficients of SAF

Financial figures of bankrupt firms were not related

to whether the firm chose reorganization or liquidation. Coefficients of SWITCH or MOVE were not statistically significant, either. variables in equation (1).

Model I in TABLE 4 uses all the

We narrowed variables in order to improve our model, to

found that model III is the best. Model III is in accordance with H1, H2 and H3.

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Other banks include Regional Banks, Second Regional Banks, Shinkin Banks, and Credit Unions.

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The

following equation (2), representing model III in TABLE 4, is our final model.

REORG= p(z) ; z = -3.2708 + 1.1344* LogDEBT – 0.3454*LogFBDDIF - 0.3694*MEGA … equation (2)


We tested if the difference in physical distance between firms and banks is a key variable in our model.

Model II and IV, alternatives model which omitted

LogFBDDIF from model I and III respectively, were worse than original models because of higher AIC and lower r-squared.

Therefore, LogFBDDIF, or the difference

in physical distance between firms and their main banks, is essential in our model.

We

calculated probability of reorganization, or p(z), of 414 bankrupt unlisted firms based on equation (2).


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FIGURE 3 displays distributions of p(z) values in liquidated and reorganized samples. It is clear that reorganized firms have higher possibility to reorganize than liquidated firms.

Independent variables in equation (2) – LogDEBT (debt size), LogFBDDIF

(difference in distance between firms and their main banks), and MEGA (main bank type) had been known prior to the bankruptcy of these firms. Therefore, with equation (2), we can predict whether a distressed firm will reorganize or liquidate.

Analysis of listed firms Next, we analyzed listed firms. unlisted firms do.

In general, fewer listed firms go bankrupt than

Because stocks of listed firms are publicly traded, stock exchanges

impose strict standards including financial and disclosure requirements.

We focused

on companies which filed either Civil Rehabilitation or Liquidation.

TABLE 5

indicates 18 sample firms, of which 14 filed reorganization and 4 filed liquidation.


We identified the main bank immediately before the bankruptcy of listed firms based on information in annual securities reports. There is no formal or legal definition of main bank, although agreed between firms and banks (Aoki & Patrick 1994). Annual securities reports do not explicitly disclose the main bank. 35

Aoki and Patrick (1994) indicated that the relationship between distressed firms and main bank is characterized by 1) banks’ lending to firms, 2) cross shareholding, and 3) banks’ provision of information and management resources for firms. Therefore, we identified main bank by three standards: 1) the largest lender among banks, 2) the largest shareholder among banks, and 3) existence of board members who were ex-members of the bank. We specified one main bank which satisfied the most of three standards.55 We also specified main bank five years prior to the bankruptcy of firms using annual securities reports in the corresponding years. We also obtained main bank information from TDB56 to verify main bank information. We concluded that the main bank was verified if the main bank identified by annual securities reports and that of TDB matched.

55 56

Banks sending auditors only were considered to satisfy 50% of board member standard. We used data of TDB Corporate Yearbook.

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TABLE 6 shows our results.

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In 10 listed firms (shaded in

TABLE 6) out of 18, both the main bank immediately before their bankruptcy, and the main bank five years prior to their bankruptcy were verified.


Next, we investigated if the model we have constructed in analysis of unlisted firms is applicable to listed firms.

We plugged data of 10 firms in equation (2).

distribution of listed firm is shown in FIGURE 4.

The

We observed a clear difference in

p(z) values between liquidated firms and reorganized ones. Our model enables us to forecast if a distressed firm would file Liquidation or Civil Rehabilitation procedures.


We tested if the difference in physical distance between firms and their main bank contributes to our model.

TABLE 7 compares p(z) values of our final model III in

TABLE 4 with p(z’) values of an alternative model IV in TABLE 4 that omitted LogFBDCHD variable. In our final model, the difference in p(z) values between the average of liquidated firms and that of reorganized ones is 25%.

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There was a big

difference in probability values between liquidated firms and reorganized ones. In alternative model, the difference in p(z’) values between the average of liquidated firms and that of reorganized ones is only 11%.

The difference in physical distance between

firms and their main bank greatly contributes to our model.


VI. CONCLUSION In this paper, we tested the hypothesis that the physical distance between firms and banks affect business relationship.

We investigated if firms are more likely to

reorganize when the physical distance between firms and main banks shortens in five years prior to their bankruptcy.

We found supports of our hypothesis.

Firms

becoming closer to their main banks were more likely to reorganize. In addition, we found that firms were more likely to reorganize if they have larger debt, or if their main bank was not a megabank.

We utilized geographic information systems to accurately

measure the distance. Our hypothesis is robust, because it is supported both in unlisted and listed firm samples.

We found that the physical distance between firms and their

24

main banks can give firms extra information to business relationship.

Limitations of our paper are as follows. collaterals.

We did not consider loan contract terms like

We did not consider competing banks other than main banks of the firms.

International researches will be necessary to test if the physical distance between firms and banks effects on business relationship in other countries than Japan.

25

REFERENCES Aoki, M. (2001). Information, Corporate Governance, and Institutional Diversity: Competitiveness in Japan, the USA, and the Transitional Economies. Oxford: Oxford Univeresity Press. Aoki, M., & Patrick, H. (1994). The Japanese Main Bank System - Its Relevance for Developing and Transforming Economics. New York: Oxford University Press. Arikawa, M., Konomi, S., & Ohnishi, K. (2007). Navitime: Supporting Pedestrian Navigation in the Real World. IEEE Pervasive Computing 6 , 21-29. Berger, A. N., & Frame, S. W. (2007). Small Business Credit Scoring and Credit Availability. Journal of Small Business Management 45 , 5-22. Berger, A. N., & Udell, G. F. (2006). A More Complete Conceptual Framework for SME Finance. Journal of Banking & Finance 30 , 2945-2966. Berger, A. N., & Udell, G. F. (2002). Small business credit availability and relationship lending: the importance of bank organization structure. The Economic Journal 112 , F32-F53. Berger, A. N., Miller, N. H., Petersen, M. A., Rajan, R. G., & Stein, J. C. (2005). Does function follow organization form? Evidence from the lending practices of large and small banks. Journal of Financial Economics 76 , 237-269. Bongini, P., Di Battista, M. L., & Zava, E. (2007). The Value of Relationship Lending: Small Banks in an Era of Consolidation. Economic Notes by Banca Monte dei Paschi di Siena SpA 36 , 209-230. Boot, A. W. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9 , 7–25. Degryse, H., & Ongena, S. (2005). Distance, Lending Relationships, and Competition. The Journal of Finance 60 , 231-266. DeYoung, R. (2003). De Novo Bank Exit. Journal of Money, Credit, and Banking 35 , 711-728. Diamond, D. (1984). Financial intermediation and delegated monitoring. Review of Economic Studies 51 , 393-414. Diamond, D. (1991). Monitoring and reputation. Jourual of Political Economy 99 , 689-721. Festinger, L., Schachter, S., & Back, K. (1950). Social pressures in informal groups : a study of human factors in housing. New York: Happer. Frame, S. W., Srinivasan, A., & Woosley, L. (2001). The Effect of Credit Scoring on Small-Business Lending. Journal of Money, Credit, and Banking 33 , 813-825. Helwege, J., & Packer, F. (2003). Determinants of the choice of bankruptcy procedure in Japan. Journal of Financial Intermediation 12 , 96–120. Kano, M., Uchida, H., Udell, G. F., & Watanabe, W. (2006). Information Verifiability, Bank Organization, Bank Competition and Bank-Borrower Relationships. RIETI Discussion Paper Series 06-E-003 , 1-33. Latané, B., Liu, J. H., Nowak, A., Bonevento, M., & Zheng, L. (1995). Distance Matters: Physical Space and Social Impact. Personality and Social Psychology Bulletin 21 , 795-805. Maechler, A. M., & McDill, K. M. (2003, November). Dynamic Depositor Discipline in U.S. Banks. FDIC Working Paper 2003-07 , 1-33.

26

Manove, M. A., Padilla, J., & Pagano, M. (2001). Collateral versus project screening: a model of lazy banks. RAND Joumal of Economics 32 , 726-744. Mori, T., & Shirata, Y. C. (2009). Business Relationship versus Physical Distance between Firms and Banks - Empirical Analysis of Main Bank on Japanese Bankrupt Firms. Proceedings of 21st. Asian Pacific Conference on International Accounting Issues . Ogawa, K., Sterken, E., & Tokutsu, I. (2007). Multiple Bank Relationship and the Main Bank System: Evidence from a Matched Sample of Japanese Small Firms and Main Banks. RIETI Discussion Paper Series 07-E-027 , 1-29. Petersen, M. A., & Rajan, R. G. (2002). Does Distance Still Matter? The Information Revolution in Small Business Lending. The Journal of Finance 57 , 2533-2570. Small and Medium Enterprise Agency. (2007). White Paper on Small and Medium Enterprises in Japan. Tokyo: Japan Small Business Research Institute. Spieker, R. L. (2003). Bank Branch Growth Has Been Steady – Will It Continue? FDIC Future of Banking Study , 1-19. Stein, J. C. (2002). Information Production and Capital Allocation: Decentralized versus Hierarchical Firms. The Journal of Finance 57 , 1891-1921. Uchida, H., Udell, G. F., & Watanabe, W. (2006). Bank Size and Lending Relationships in Japan. RIETI Discussion Paper Series 06-E-029 , 1-34. Uchida, H., Udell, G. F., & Yamori, N. (2006). Loan Officers and Relationship Lending. RIETI Discussion Paper Series 06-E-031 , 1-30. (References below are originally written in Japanese) Hasumi, R., & Hirata, H. (2008). Credit Scoring and Management of Financial Institusions. JCER Discussion Paper 116 , 1-26. Mori, T., & Shirata, Y. C. (2010). Analysis of Firms Switching Main Bank: Empirical Study of Business Relationship Focusing on Physical Distance. Journal of the Japan Society for Management Information 19 , Forthcoming. Mori, T., & Shirata, Y. C. (2010). Does Distance Explain Business Relationship? - Empirical Analysis of Bankrupt Firms. The Journal of Business Analysis 26 , 30-38. Ohmura, K., Kusumi, M., Mizukami, S., & Shiogai, K. (2002). Financial Characterstics of Bankrupt Companies, and Lending Activities of Financial Institutions. Discussion Paper on Economic Watch and Policy Analysis 02-5 , 1-29. Osano, H., & Hori, K. (2001). Financial Relationships and Cross Shareholdings between Main Bank adn Firm. Ritsumeikan University Research Center for Finance, Research Paper Series No. 01-003 , 1-32. Shirata, Y. C. (2006). Realities of Reorganized Firms. JICPA Journal 18 , 112-118. Takesawa, Y., Matsuura, K., & Hori, M. (2005). Lending Market for SMEs after 1990s by Prefectures and Industries: What Were the Problems? Toyo University Economic Review 30(2) , 17-36. Tsuruta, D., Xu, P., & Yuan. (2006). Relationship between Banks and Firms, and Choice of Legal Settlement Procedures of SMEs. 4th National Conference of Japan Law and Economics Association, Presentation Summary , 290-313. Yabushita, S., & Bushimata, T. (2006). Introduction to Small Business Finance. Tokyo: Toyo Keizai Shinposha. Yamori, N., & Kondo, K. (2007). The Development of Promotion Plan in Regional Finance and Realities of Relationship Banking. Study on Financial Structure 29 , 37-44.

27

TABLE 1 : BANKING SYSTEMS IN JAPAN AND IN U.S.

Banks

Number of banks Number of bank branches

Japan

U.S.

580

9,441

20,000

60,000

34

6

Average number of branches per bank Firms

Share of employees in SMEs

56%

28%

Firm-Bank

Bank loan / total assets - SMEs

34%

22%

Bank loan / total assets - large firms

19%

4%

30

9

relationships

Duration of firm-bank relationships, in years Number of newly chartered banks in 10 years New lending technologies

2

Credit scoring Asset-based lending/ Total corporate debt

*

1000+

Introduced

Popular

0.1%

20%

* banks entering into corporate loan markets

TABLE 2: DESCRIPTION – UNLISTED FIRMS unlisted firms (n=414) Variables DEBT FBD FBDDIF BHD

Description in mil. yen distance from firm to main bank branch (km) difference in FBD in 5yrs prior to bankruptcy (km) distance from bank bch. to headquarter (km)

Mean

Min.

1st Q.

Median

3rd Q.

Max.

1,936

100

891

1,799

3,990

63,533

4.3

0.0

1.4

3.0

7.4

1,081.2

-0.1 -1,079.5

0.0

0.0

0.0

198.1

13.9

0.0

3.1

11.1

47.5

1,803.3

AGE

firm age, in years

36.4

3

22

36

50

99

MEGA

1 if bank is mega; 0 if not

0.39

0

0

0

1

1

SAF

index by Shirata(2003)

0.32

-25.55

0.29

0.54

0.69

1.43

SWITCH

1 if bk swithced ; 0 if not

0.27

0

0

0

1

1

REORG

1 if civil rehab.;0 if liq.

0.61

0

0

1

1

1

LogDEBT

log(DEBT)

3.29

2.00

2.95

3.26

3.60

4.80

LogFBD

log(1+FBD)

0.72

0.00

0.38

0.61

0.93

3.03

LogFBDDIF

log(1+FBDDIF) if FBDDIF>0;

-0.06

-3.03

0.00

0.00

0.00

2.30

LogBHD

log(1+BHD)

1.17

0.00

0.61

1.08

1.69

3.26

-log(1-FBDDIF) otherwise

28

TABLE 3

COMPARSON OF UNLISTED SAMPLE FIRMS

liquidated firms: REORG=0 (n=160) Mean DEBT (mil. yen)

Min.

1st Q.

Median

3rd Q.

Max.

1,194

100

600

1,199

2,328

27,164

FBD (Km)

4.1

0.0

1.2

2.9

7.3

1081.2

FBDDIF (Km)

0.1 -1079.5

0.0

0.0

0.0

63.7

BHD (Km)

14.1

0.0

3.0

9.6

49.7

1803.3

AGE (Year)

36.3

6

21

37

51

86

MEGA

0.41

0

0

0

1

1

SAF

0.23

-25.55

0.31

0.60

0.72

1.43

SWITCH

0.28

0

0

0

1

1

LogDEBT

3.08

2.00

2.78

3.08

3.37

4.43

LogFBD

0.71

0.00

0.34

0.59

0.92

3.03

LogFBDDIF

0.03

-3.03

0.00

0.00

0.00

1.81

LogBHD

1.18

0.00

0.60

1.03

1.71

3.26

1st Q.

Median

3rd Q.

Max.

reorganized firms: REORG=1 (n=254) Mean DEBT (mil. yen)

Min.

2,630

100

1,300

2,432

5,598

63,533

4.4

0.0

1.5

3.3

7.4

544.6

FBDDIF (Km)

-0.3

-533.8

0.0

0.0

0.0

198.1

BHD (Km)

13.8

0.0

3.2

13.2

44.0

1365.5

AGE (Year)

36.5

3

23

35

49

99

MEGA

0.38

0

0

0

1

1

SAF

0.37

-3.76

0.29

0.51

0.67

1.23

SWITCH

0.26

0

0

0

1

1

LogDEBT

3.42

2.00

3.11

3.39

3.75

4.80

LogFBD

0.73

0.00

0.40

0.63

0.92

2.74

-0.11

-2.73

0.00

0.00

0.00

2.30

1.17

0.00

0.62

1.15

1.65

3.14

FBD (Km)

LogFBDDIF LogBHD

29

TABLE 4:

MODELS OF BANKRUPT TYPE CHOICE – UNLISTED FIRMS

Dependent Variable= REORG # of sample =414

I

II

III

IV

-3.223 ***

-3.046 ***

-3.271 ***

-3.110 ***

Independent Variables (Intercept)

(0.531) LogDEBT AGE LogFBD LogFBDDIF

1.185 ***

(0.520) 1.122 ***

(0.162)

(0.158)

-0.005

-0.005

(0.004)

(0.004)

-0.105

-0.016

(0.148)

(0.141)

-0.364 ***

MEGA LogBHD SWITCH

AIC Psuedo R-squared

(0.156)

1.087 *** (0.153)

(0.119)

0.030

0.034

(0.051)

(0.049)

-0.399 ***

1.134 ***

(0.487)

-0.345 ***

(0.119) SAF

(0.498)

-0.373 **

(0.146)

(0.145)

0.019

-0.003

(0.092)

(0.092)

0.066

0.029

(0.161)

(0.159)

500.74 0.126

-0.369 ***

-0.343 **

(0.141)

(0.139)

508.48

493.36

500.53

0.108

0.121

0.105

Standard errors are in parentheses. ***, **, * denotes statistically significant at the 1%, 5%, 10% level.

30

TABLE 5 No.

LISTED BANKRUPT FIRM SAMPLES

Firm Name

Bankrupt

Bankrupt

Listed

DEBT

Type

Year

Market

(mil.yen)

1 HONMA GOLF CO., LTD.

Civil Rehab.

2005

JASDAQ

30,572

2 SANBISHI CO.,LTD.

Civil Rehab.

2005

NSE 2nd

9,500

3 zecoo Inc.

Liquidation

2005

Mothers

2,266

4 Adv. Tech. & Systems Co., Ltd.

Civil Rehab.*1

2006

Hercules

14,045

5 QUIN LAND Co.,Ltd.

Civil Rehab.*1

2007

Hercules

20,300

6 IXI Co., Ltd.

Civil Rehab.

2007

TSE 2nd

11,923

7 NIWS Co. HQ Ltd.

Civil Rehab.

2008

TSE 2nd

40,800

8 Fuji Biomedix Co.,Ltd

Civil Rehab.

2008

Centrex

22,992

9 TAIYO KOGYO CO.,LTD.

Civil Rehab.

2008

TSE 2nd

14,833

10 Genesis Technology Inc.

Civil Rehab.

2008

TSE 2nd

11,264

11 Produce Co.,Ltd.

Civil Rehab.

2008

JASDAQ

7,401

12 TransDigital Co., LTD.

Civil Rehab.

2008

JASDAQ

2,600

13 S.E.S.CO.,LTD.

Civil Rehab.*1

2009

JASDAQ

14,273

14 KOSUGI SANGYO Co.,Ltd.

Liquidation

2009

TSE 2nd

9,793

15 Cyber Firm Inc.

Liquidation

2009

Hercules

7,900

16 Nakamichi Machinery Co., Ltd.

Civil Rehab.

2009

SSE

7,654

17 SILVER OX Inc.

Liquidation

2009

TSE 1st

5,700

18 APRECIO CO.,LTD.

Civil Rehab.

2009

Centrex

2,200

*1: filed liquidation later

31

TABLE 6

MAIN BANK INFORMATION OF LISTED FIRM SAMPLE immediately before bankruptcy

No.

Firm Name

Annual

Reports

five years prior to bankruptcy

TDB

Largest

Largest

Board

Main

Main

Lender

Share

Member

Bank

Bank

1

HONMA GOLF CO., LTD.

SMBC

SMBC

n/a

SMBC

SMBC

2

SANBISHI CO.,LTD.

n/a

UFJ

n/a

UFJ

UFJ

3

zecoo Inc.

Hokkoku

n/a

Hokkoku

Hokkoku

Hokkoku

4

Adv. Tech. & Systems Co., Ltd.

MTB

n/a

n/a

MTB

5

QUIN LAND Co.,Ltd.

n/a

Resona*1

6

IXI Co., Ltd.

STB

n/a

7

NIWS Co. HQ Ltd.

MUFG

8

Fuji Biomedix Co.,Ltd

9

Annual MB

TDB

Largest

Largest

Board

Main

Main

Lender

Share

Member

Bank

Bank

MB

Sakura

Sakura

n/a

Sakura

Sakura

yes

n/a

Tokai

n/a

Tokai

Tokai

yes

n/a

n/a

Hokkoku*1

Hokkoku

Hokkoku

yes

BTM

BTM

n/a

n/a

BTM

BTM

yes

Resona

SMBC

SMBC

n/a

n/a

SMBC

SMBC

yes

n/a

STB

STB

Y

STB

n/a

n/a

STB

STB

yes

n/a

n/a

MUFG

MUFG

yes

n/a

n/a

n/a

n/a

MTB

Saitama

n/a

Saitama

Saitama

Saitama

yes

Saitama

n/a

Saitama

Saitama

SMBC

TAIYO KOGYO CO.,LTD.

MUFG

MUFG

n/a

MUFG

MUFG

yes

BTM

BTM

n/a

BTM

BTM

yes

10

Genesis Technology Inc.

n/a

n/a

n/a

n/a

MUFG

n/a

n/a

n/a

n/a

n/a

yes

11

Produce Co.,Ltd.

n/a

Daishi

Daishi

Daishi

Daishi

n/a

n/a

Daishi

Daishi

Daishi

yes

12

TransDigital Co., LTD.

Resona

n/a

n/a

Resona

Mizuho

Resona

n/a

n/a

Resona

UFJ

13

S.E.S.CO.,LTD.

MCorp.

MinamiN

n/a

MCorp.

MCorp.

yes

MCorp.

MCorp.

MCorp.

MCorp.

MCorp.

yes

14

KOSUGI SANGYO Co.,Ltd.

Mizuho

Mizuho

Mizuho*1

Mizuho

Mizuho

yes

Mizuho

Mizuho

Mizuho

Mizuho

Mizuho

yes

15

Cyber Firm Inc.

Resona

n/a

Okinawa*1

Resona

MUFG

SMBC

n/a

Okinawa*1

SMBC

SMBC

yes

16

Nakamichi Mach. Co., Ltd.

Hokuyo

Hokuyo

n/a

Hokuyo

Hokuyo

yes

Hokuyo

Hokuyo

n/a

Hokuyo

Hokuyo

yes

17

SILVER OX Inc.

SMBC

SMBC

n/a

SMBC

SMBC

yes

SMBC

SMBC

n/a

SMBC

SMBC

yes

18

APRECIO CO.,LTD.

SMBC

n/a

n/a

SMBC

SMBC

yes

n/a

Mizuho

Mizuho

Mizuho

SMBC

MUFG/ Resona

*1 auditors only

32

Yes

Reports

Y Y

yes

TABLE 7 No.

PROBABILITY OF REORGANIZATION FOR LISTED FIRMS

Firm Name

3 zecoo Inc.

REO

Log

RG

DEBT

Log

ME

FBD

-GA

-DIF

Z*1

p(z)

z'*2

p(z')

0

3.36

2.63

0

-0.37

35%

0.54

70%

14 KOSUGI SANGYO

0

3.99

0.43

1

0.74

77%

0.89

81%

17 SILVER OX Inc.

0

3.76

0.54

1

0.43

67%

0.63

74%

Average of liquidated firms (REORG=0) [a]

60%

75%

1 HONMA GOLF CO., LTD.

1

4.49

0.00

1

1.45

93%

1.42

92%

2 SANBISHI CO.,LTD.

1

3.98

0.00

1

0.87

81%

0.87

81%

6 IXI Co., Ltd.

1

4.08

0.34

1

0.87

81%

0.98

84%

9 TAIYO KOGYO CO.,LTD.

1

4.17

0.30

1

0.99

84%

1.08

86%

11 Produce Co.,Ltd.

1

3.87

0.40

0

0.98

84%

1.10

86%

13 S.E.S.CO.,LTD.

1

4.15

0.00

1

1.07

86%

1.06

86%

16 Nakamichi Mac. Co., Ltd.

1

3.88

0.00

0

1.14

87%

1.11

87%

Average of reorganized firms (REORG=1) [b]

85%

86%

[b] - [a]

25%

11%

*1: z= -3.271+ 1.134*log(DEBT) -0.345*log(FBDDIF) -0.369*MEGA *2: z'= -3.110+ 1.087*log(DEBT)

-0.343*MEGA

[model III in TABLE4]

[model IV in TABLE4]

33

FIGURE 1

CALCULATION OF DISTANCE IN JAPAN (NAVITIME)

FIGURE 2: CALCULATION OF DISTANCE IN U.S. (Google Map)

34

FIGURE 3

DISTRIBUTION OF PROBABILITY FOR UNLISTED FIRMS

3 rd qtr .81 Overall Mean .61

3rd qtr .65

Mean .69

Mean .50

1s t qtr .57

1s t qtr .40

Liquidated

FIGURE 4

Reorganized

DISTRIBUTION OF PROBABILITY FOR LISTED FIRMS 100% 90% 80% 70%

probability of reorganization P(z)

60% 50% 40% 30% 20% 10% 0% (1)

0

LIQUIDATION

1

REORGANIZATION

REORG

35

2