The value of relationship banking Evidence from interbank

The value of relationship banking: Evidence from interbank liquidity crunch in China Yiyi Bai Cent. ER – Tilburg University Qing He School of Finance, Renmin University of China Liping Lu VU University Amsterdam Oct 8, City University of Hong Kong 1

Motivation ¯ The value of relationship banks is an important issue yet to be fully understood (Chava and Purnanandam, 2011; Ongena, et al. , 2003). ¯ Identification is a major concern. ¯ Traditional methods: firms with bank loans V. S. firms without bank loans -accounting performance - market reactions around financial crisis period (firm performance should be sensitive to unexpected shocks to bank’s ability to supply credit (Fama, 1980; King and Plosser, 1984; Chava and Purnanandam, 2011) 2

What we do Question: When banks suffer from exogenous liquidity shocks, 1)Do borrowers who have lending relationships with banks suffer? 2)Is lending relationship with suffering banks harmful to borrower’s performance? 3)Whether types of lenders and borrowers matters? üExogenous liquidity shocks: Interbank liquidity crunch in June 2013 in China serves as a natural experiment to evaluate the bankfirm relationship in the eyes of investors when banks suffer liquidity shock. üBank lending relationship: the largest lender of long-term loans in a firm is consider as its main bank

cont’d Findings: 1) Yes. Firms on average have negative CARs during interbank liquidity crunch 2) No! ¯Firms with lending relationships with banks outperform others in terms of firm CARs ® Lending relationships with local banks are associated with lower firm CARs ® A positive relation between firms’ CARs and banks’ CARs, as well as banks’ interbank liquidity position, when the bank is a big 4 bank or local bank.

Interbank liquidity crunch on June 20 th 2013

Background-Before Interbank liquidity crunch ¯ Chinese economy is slowing down ¯ The M 2/GDP ratio has been rising in recent years. Highest Money Supply M 2 in the world, supply of money has been plentiful. 6

Economic performance ¯ Blue line: quarterly growth rate (GDP) 7

M 2/GDP ratio ¯ Red line: China 8

New intuitional loans in each ¯ Red line: monthly growth rate; Blue line: total new institutional loans in a month. 9

¯ Financial resources are misallocated. ¯ Banks and nonbank financial institutions aggressively fund their lending or offer their loans (newly loans increased by 863 billion in June 2013, i. e. 28. 89% (monthly growth rate) ¯ The central banks aimed to maintain financial stability. (take most risks) 10

¯ Exogenous and Unexpected: ®Moral-hazard problem: PBOC always injected liquidity into the market whenever there is a dry-up, which created a moral-hazard problem. ®It’s a big challenge for Chinese economy ®New leadership: The new leadership came to power in March 2013, Premier Keqiang Li emphasized the financial reform: reinvigorate idle capital and allocate incremental capital more effectively (用 好 增 量 、 盘 活 存 量 ). a dramatic policy change from rapid growth to quality growth

Premier Keqiang Li ¯ Reinvigorate idle capital and allocate incremental capital more effectively 12

Securities daily 13

What happens ¯ Banks continue aggressive lending practices in June 2013 to meet the semi-annual performance goals ¯ the PBOC refused to inject additional funds to cover the liquidity shortfall (implying a dramatic policy change from rapid growth to quality growth) ¯ The central bank intended the cash crunch to serve as a warning to overextended banks ¯ Short-term, but with significant effect 14

Date 2013/6/5 Progress The bond issuance of Agriculture Development Bank of China failed to attract enough subscriptions. 2013/6/14 The issuance of treasury bonds failed to attract enough subscription. 2013/6/19 Premier Li expressed a determination for the financial reform by the government. The overnight rate increases to 7. 66%. 2013/6/20 The overnight rate hikes to 13. 44%. PBOC initiated the issuance of bills. A rumor flies that Bank of China was in default in the interbank market. 2013/6/21 PBOC supplied 50 billion RMB to ICBC. The overnight interbank interest rate decline to 8. 49%, i. e. a decrease about 500 basis points from the previous day. 2013/6/23 Several branches of the ICBC in Beijing and Shanghai closed unexpectedly. 2013/6/24 Shanghai composite index plummet by about 5%, and the stock prices of Ping An Bank, China Minsheng Bank, falls by around 10%. 2013/6/25 The PBOC suspended the issuance of bills and supplied liquidity for financial institutions. 2013/6/26 The overnight interbank interest rate further decreased to 5. 55%.

Literature ¯ Relationship banking can add value as it facilitates the information exchange and production (Ongena et al. , 2003) ¯ Firms will disclose more information and banks are also motivated to invest more in acquiring proprietary information (Ongena and Smith, 2000; Boot, 2000) ¯ Positive market reactions of bank loan announcements (James, 1987; Lummer and Mc. Connell, 1989) certification effect ¯ Firms that primarily relied on banks for capital suffered larger valuation losses during crisis period (Chava and Purnanandam, 2011)

Literature ¯ Small and less prestigious firms have more benefits from screening and monitoring services associated with bank loans (Solvin et al. , 1992). ¯ The quality, organizational structure, and the origin of lender also matter for market reactions (Slovin et al, 1988; Billet et al. , 1995; Ongena and Roscovan, 2013).

Hypotheses ¯ Hypothesis 1: Cumulative abnormal returns (CARs) of the listed firms during the liquidity crunch are negative ¯ Hypothesis 2: CARs of firms without lending banks are significantly lower than those of firms with lending relationships. ¯ Hypothesis 3: Firms’ relationship bank type matters for CARs. (i. e. local bank lenders V. S. non-local bank lenders, Big 4 bank lenders V. S. non-big 4 bank lenders) ¯ Hypothesis 4: Firm’s CARs are larger if their relationship banks experience higher CARs

Data and Methodology ¯

Bank classifications ¯ 4 state-owned banks (i. e. national banks), 12 jointstock banks (i. e. regional banks), and other small and medium-sized banks including city / rural commercial banks, urban / rural credit cooperatives, rural cooperative banks, village-town banks (i. e. local banks). ¯ 4 state-owned banks that dominate the Chinese banking sector as Big 4 banks, i. e. Industrial and Commercial Bank of China, China Construction Bank, Bank of China, and Agricultural Bank of China. 20

Market shares of Chinese Banks ¯ Big 4 state-owned Banks account for 44% of total credit (2013) 21

Identification: Lending Relationship with Banks TOP 5 lendings. ICBC is the largest lender of long-term loans for firm 600023 Firm ID Year Bank Name (Branch) 600023 China Construction Bank 2012 Corporation Zhijiang Branch in Hangzhou city Bank Name (Headquarter) Start date Finish date Interest Amount 2011 Amount 2012 (mil CNY) CCB 2010/10/21 2029/06/27 5. 895 2, 300 ICBC 2011/02/01 2026/11/27 5. 895 2, 250 2, 080 ICBC 2008/01/03 2023/12/10 5. 895 1, 060 CDC 2012/08/30 2032/08/30 5. 895 960 600 Bo. Comm 2012/04/28 2025/04/28 5. 895 960 440 Industrial & Commercial 600023 2012 Bank of China, Zhejiang Branch 600023 Industrial & Commercial 2012 Bank of China, Leqing Branch China Development Bank 2012 Corporation, Zhejiang Branch Bank of Communications 600023 2012 Co. Ltd, Zhejiang Branch • Average share largest long-term lender among top 5 long-term loans is 53%. • Average share top 5 long-term loans among all long-term loans is 51%. • Average share top 5 long-term loans among total debt is 34%.

Firm-bank relationship Our sample consists of all firms traded on the Chinese stock market in 2013. 2, 355 firms, including 42 financial firms and 2, 313 nonfinancial firms Mean Std. Dev Obs Bank 0. 873 0. 334 1021 Local bank 0. 416 0. 493 1021 Big 4 bank 0. 077 0. 267 1021

Market reactions around cash crunch( all non-financial firms) Mean Std. Dev # of obs. CAR[-1, 1] -0. 003*** (0. 002) 2, 313 CAR[-2, 2] -0. 008*** (0. 000) 2, 313 CAR[-3, 3] -0. 008*** (0. 000) 2, 313 CAR[-5, 5] -0. 004* (0. 082) 2, 313 CAR[-1, 0] -0. 000 (0. 936) 2, 313 CAR[0, 1] -0. 004*** (0. 000) 2, 313

CARs by firm-bank relationship Bank = 1 Bank = 0 Mean Std. Dev. Difference T-test Big 4 bank = 1 Big 4 bank = 0 Mean Std. Dev. Difference T-test Local bank = 1 Local bank = 0 Difference T-test Mean Std. Dev. CAR [-1, 1] -0. 001 (0. 001) -0. 009 (0. 004) 0. 008** 0. 000 (0. 002) -0. 004 (0. 002) 0. 004 -0. 010 (0. 004) -0. 002 (0. 001) -0. 009* CAR [-2, 2] -0. 008 (0. 002) -0. 019 (0. 006) 0. 011* -0. 005 (0. 003) -0. 012 (0. 003) 0. 007* -0. 025 (0. 006) -0. 008 (0. 002) -0. 017** CAR [-3, 3] -0. 012 (0. 002) -0. 022 (0. 006) 0. 011 -0. 011 (0. 004) -0. 014 (0. 003) 0. 003 -0. 028 (0. 007) -0. 012 (0. 002) -0. 016* CAR [-5, 5] -0. 016 (0. 003) -0. 035 (0. 007) 0. 019** -0. 015 (0. 004) -0. 020 (0. 004) 0. 005 -0. 031 (0. 009) -0. 017 (0. 003) -0. 014* CAR [-1, 0] 0. 001 (0. 001) -0. 004 (0. 003) 0. 005* 0. 003 (0. 002) -0. 001 (0. 001) 0. 003* -0. 008 (0. 003) 0. 001 (0. 001) -0. 009** CAR [0, 1] -0. 002 (0. 001) -0. 009 (0. 003) 0. 007** -0. 002 (0. 002) -0. 004 (0. 001) 0. 002 -0. 005 (0. 003) -0. 003 (0. 001) -0. 003

Determinants of CARs (1) (2) (3) (4) (5) (6) CAR[-1, 1] CAR[-2, 2] CAR[-3, 3] CAR[-5, 5] CAR[-1, 0] CAR[0, 1] Bank State-owned Log total assets Leverage EBIT Tobin’s Q R-squared Industry FE 0. 010*** (0. 001) -0. 004** (0. 014) 0. 001 (0. 485) 0. 002 (0. 826) -0. 060*** (0. 009) 0. 001 (0. 794) 0. 013** (0. 013) -0. 006** (0. 036) 0. 003 (0. 258) -0. 006 (0. 572) -0. 044 (0. 149) -0. 000 (0. 974) 0. 011** (0. 033) -0. 005 (0. 193) 0. 002 (0. 511) -0. 016 (0. 302) -0. 016 (0. 641) 0. 002 (0. 608) 0. 019** (0. 046) -0. 018*** (0. 001) 0. 003 (0. 276) -0. 038** (0. 047) 0. 051 (0. 314) 0. 005 (0. 185) 0. 006** (0. 029) -0. 001 (0. 627) -0. 001 (0. 622) -0. 001 (0. 844) -0. 051*** (0. 002) -0. 002 (0. 441) 0. 008*** (0. 001) -0. 003* (0. 087) 0. 001 (0. 476) 0. 004 (0. 458) -0. 009 (0. 702) -0. 001 (0. 329) 0. 036 yes 0. 038 yes 0. 019 yes 0. 059 yes 0. 044 yes 0. 058 yes

Bank types Local bank Big 4 bank CAR[-1, 1] -0. 011** (0. 027) Big 4 bank Bank (0. 818) 0. 002 (0. 233) 0. 008*** (0. 003) -0. 004** (0. 012) 0. 001 (0. 476) 0. 002 (0. 828) -0. 060** -0. 059*** (0. 012) (0. 009) 0. 001 (0. 781) 0. 036 yes 0. 011*** (0. 000) State-owned -0. 004*** (0. 009) Log total asset 0. 001 (0. 506) Leverage EBIT Tobin’s Q 0. 002 0. 001 (0. 717) R-squared Industry FE 0. 040 yes

The influence Bank CARs on Firm’s CAR (sub-sample) (1) Local bank ˟ Bank CAR Local bank 1. 532** (0. 046) -0. 042*** (0. 007) (2) Big 4 bank State-owned Log total asset Leverage EBIT Tobin’s Q 0. 018 (0. 679) -0. 003 (0. 495) 0. 001 (0. 509) -0. 013 (0. 219) -0. 116** (0. 034) 0. 001 (0. 822) -0. 016 (0. 669) -0. 003 (0. 505) 0. 002 (0. 317) -0. 015 (0. 163) -0. 112** (0. 033) 0. 001 (0. 832) -0. 001 (0. 803) -0. 053* (0. 085) -0. 008** (0. 033) 602 0. 065 602 0. 071 Bank total assets Bank liquidity ratio Bank equity ratio Observations R-squared (4) 2. 012*** (0. 002) -0. 045*** (0. 008) Big 4 bank ˟ Bank CAR (3) 0. 495** (0. 033) 0. 005 (0. 274) -0. 448* (0. 063) -0. 003 (0. 571) 0. 001 (0. 549) -0. 013 (0. 232) -0. 109** (0. 042) 0. 001 (0. 846) 602 0. 505** (0. 026) 0. 000 (0. 994) -0. 505** (0. 023) -0. 003 (0. 585) 0. 002 (0. 337) -0. 014 (0. 187) -0. 105** (0. 044) 0. 001 (0. 860) 0. 001 (0. 864) -0. 052 (0. 268) -0. 009** (0. 043) 602 0. 065 0. 071

Bank Interbank Liquidity Position A bank’s interbank liquidity position: interbank assets over interbank liability of the bank in the second quarter of 2013 (1) (2) (3) (4) CAR[-1, 1] BIG 4 * Bank Interbank Position BIG 4 Observations R-squared Firm level controls Bank balance sheet controls Bank FE Industry FE Cluster (5) (6) CAR[-2, 2] 0. 012* 0. 011* 0. 008 0. 029*** 0. 026*** 0. 021*** (0. 080) -0. 005 (0. 521) -0. 004 (0. 378) 443 0. 064 yes (0. 088) -0. 004 (0. 596) -0. 004 (0. 327) 443 0. 066 yes (0. 127) 0. 013 (0. 234) -0. 001 (0. 825) 443 0. 077 yes (0. 000) -0. 007 (0. 563) -0. 017* (0. 075) 443 0. 065 yes (0. 001) -0. 004 (0. 751) -0. 014 (0. 118) 443 0. 065 yes (0. 003) -0. 031* (0. 090) -0. 010 (0. 376) 443 0. 073 yes yes no yes Industry yes yes Industry

Conclusion and implications ¯ Firms on average have negative CARs during interbank liquidity crunch ¯ Firms with lending relationships with banks outperform others in terms of firm CARs. Bank-firm relationship is valuable ¯ Lending relationships with local banks are associated with lower firm CARs. ¯ A positive relation between firms’ CARs and banks’ CARs, as well as banks’ interbank liquidity position

31