Determinants of loan securitization in Chinese banking: Cost-benefit-based analysis

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Determinants of loan securitization in Chinese banking: Costbenefit-based analysis ⁎

Jinqing Zhang , Yiwen Yin, Linlin Zhang Institute of Financial Studies, Fudan University, Building 11, No. 220 Handan Road, Yangpu District, Shanghai, China

A R T IC LE I N F O

ABS TRA CT

Keywords: Commercial bank Loan securitization Cost-benefit-based analysis Factor analysis Chinese banking

This study examines the determinants of commercial banks' loan securitization in China. We consider five hypotheses for the securitization determinants—liquidity demand, regulatory arbitrage, performance promotion, risk transfer and cost advantage exploitation, based on the costbenefit analysis framework of banks' incentives. To examine these hypotheses empirically, this study employs the factor analysis to summarize the information from some financial indicators that reveals the banks' determinants for securitization. Moreover, by using the Logistic model and Tobit model to study the Chinese commercial banks' data from 2012 to 2017, we find that the real determinants of Chinese banks' securitization include cost advantage exploitation, performance promotion and capital regulatory arbitrage. Based on these findings, we conclude that China's loan securitization market welcomes large state-owned commercial banks with higher ratings rather than small- and medium-sized banks with poor asset quality. These findings indicate that the regulatory authorities should promote the rating accuracy of credit asset-backed securities in order to increase the transparency of information and should restrict unregulated shadow banking channels, leading banks to extend credit assets and to improve the efficiency of capital with securitization tools.

1. Introduction Since May 20121 the central bank and china banking regulatory commission (CBRC) have highlighted the development of the loan securitization market, as an important part of the market-oriented reform of China's banking industry. Actually, the scale of commercial bank securitization products issued are lower than expected, and the growth rate of the issuances after 2016 have declined as depicted in Fig. 1. One reason for the slow development of the loan securitization market is the lack of incentives for commercial bank's loan securitization2. To govern the loan securitization business rationally and to prosper the loan securitization market, the regulatory authorities need to understand the determinants of asset securitization of commercial banks at the micro level, namely, banks' incentives to make asset securitization decisions. The possible influencing factors of commercial banks engaging in asset securitization business are generally

⁎

Corresponding author. E-mail address: [email protected] (J. Zhang). 1 From 2005 to 2008, China conducted an early small-scale pilot of credit asset securitization, but was suspended due to the impact of the subprime mortgage crisis. After the pilot restarted in 2012, the implementation of the credit asset securitization filing system at the end of 2014 marked the beginning of the normalization of China's credit asset securitization market from the pilot phase. 2 The prices are all based on RMB. The USD prices are subjected to the official exchange rate in the middle of each year. To show the Chinese credit asset-backed securities outstanding in the figure clearly, we truncate the left axis and zoom in on the segment under 0.1. https://doi.org/10.1016/j.pacfin.2018.08.014 Received 26 January 2018; Received in revised form 1 August 2018; Accepted 22 August 2018 0927-538X/ © 2018 Elsevier B.V. All rights reserved.

Please cite this article as: Zhang, J., Pacific-Basin Finance Journal, https://doi.org/10.1016/j.pacfin.2018.08.014

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Fig. 1. United States versus China: Credit asset-backed securities and loans outstanding. Notes: The data sources include WIND database, CBRC, SIFMA and FRS. The data are reported in 10 billions of RMBs. The Chinese credit assetbacked securities and loans outstanding are scaled to the left axis. The U.S. credit asset-backed securities and loans outstanding are scaled to the right axis.

considered to include liquidity demand, performance promotion, capital regulatory arbitrage, and risk transfer (Farruggio and Uhde, 2015). However, the regulatory institutions and supporting mechanisms in China's asset securitization market, such as information disclosure, are still incomplete, which may result in the inability of asset securitization functioning and weak incentives for banks to securitize.3 This study investigates the most important determinants of commercial banks' loan securitization businesses in China under current market conditions. The solution to this problem is a prerequisite to guide the development of the asset securitization market and to supervise the asset securitization business. Therefore, it is also a matter of great concern to the regulatory authorities and the academic community. We follow three steps to solve these problems. First, we theoretically build a cost-benefit analysis framework for commercial banks' securitization. Under this framework, the determinants of commercial bank asset securitization, i.e. liquidity demand, regulatory arbitrage, profit promotion, risk transfer, and cost advantage exploitation, are linked to net income. Second, we select the corresponding origin explanatory variables for each determinant. Due to the high correlation between these variables, we use a factor analysis method to reduce the dimensions. The analysis produces seven determinant factors, which corresponds to five incentives, namely high asset liquidity, low short funding cost, high profitability, low risk/low cost,4 high capital adequacy, high credit risk, and low loan-to-deposit ratio. Third, we empirically examine the influence of the seven factors on the decision-making process regarding asset securitization using a sample of commercial banks in China from 2012 to 2017. The regressions show that high profitability and low cost/low risk enhance bank securitization, while high capital adequacy, and low credit risk hamper bank securitization. As a result, the actual driving factors for the securitization of loans are cost advantage exploitation, capital regulatory arbitrage, and profit promotion. Notably, large-scale state-owned commercial banks with high ratings and stable assets are welcomed by the loan securitization market, while smaller banks owning poor quality loans have less access to the market. The empirical results show that China's commercial banks do have incentives to securitize assets in practice. However, information disclosure and market governance mechanisms such as asset external ratings are incomplete, leading investors to link the credit risk of asset credits to their originators. Therefore, the policy implications of the conclusions are mainly to promote the efficiency of the information disclosure mechanism and to reduce the possible adverse selection of the asset securitization market. As a result, small and medium-sized banks with poor institutional ratings and securitization demand to improve their operating efficiency can be more involved. The main contributions of this study include the following three aspects: First, according to the review of published literature, this current study is the first to use a cost-benefit analysis model to investigate comprehensively the various determinants of asset securitization. Compared with the theoretical studies of bank asset securitization such as Guo and Wu (2014), the analysis of asset securitization decisions in this paper is not a study of a certain securitization determinant, but an analysis of determinant spectrum under a unified framework. Second, compared with the empirical literatures such as Minton et al. (2004), this study uses a factor analysis method to obtain factors corresponding to the determinant hypothesis, which handle the strong multi-collinearity between explanatory variables and clarify the linkages between the determinants. Third, owing to the short development time of China's loan securitization market, there are relatively few empirical research studies available. To the best of our knowledge, only a few studies such as Liu and Xing (2015) have empirically tested the determinants of commercial bank securitization. Compared with these empirical studies, we improve the theoretical and empirical methods and select data on commercial bank securitization from 2012 to 2017 to conduct the analyses. These samples and data more effectively explain the commercial banks' asset securitization and the reason for the lack of incentives after the normalization of the market in 2014. The remainder of this paper is organized as follows: Section 2 presents a review of the literature. Section 3 analyzes the

3

The insufficient marketization of banks also weaken the asset securitization incentives, since the banks have implicit guarantee and have no urgent needs to prevent liquidity shocks. 4 High asset liquidity and low short-funding cost are corresponding to liquidity demand. High capital adequacy and low loan-to-deposit ratio are corresponding to regulatory arbitrage. Because the low-risk and the low-cost banking group have a high degree of coincidence, the low risk/low cost factor corresponds to the two incentives: cost advantage exploitation and the risk transfer. Other factors and incentives are one-to-one correspondence. 2

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determinants of commercial bank loan securitization at the micro level using the cost-benefit framework. Section 4 discusses the sample, data and variables used in the empirical test. We establish explanatory variables by factor analysis. Section 5 constructs an empirical model and analyzes the empirical test results. Section 6 concludes with recommendations for policymakers. 2. Literature review: Why do banks securitize? The existing literature investigating the determinants of commercial banks for asset securitization mainly include two aspects. One is the theoretical studies on commercial bank's asset securitization decision-making mechanism and determinants; the other is the empirical studies on determinants of commercial bank asset securitization. This section provides a brief review of the theoretical and empirical studies. 2.1. Theoretical studies on securitization decision-making mechanism and determinants According to theoretical literatures, there are several hypotheses on why commercial banks securitize. Greenbaum and Thakor (1987) consider securitization as the substitution of debt financing and establish the banks' securitization decision model. DeMarzo (2005) shows that packaging and tranching add value to securitization products and banks securitize loans to earn profits. Guo and Wu (2014) establish a commercial bank asset selection model, in which risk management is a main reason for banks to securitize. To summarize, there are many different reasons for the financial institutions to securitize credit assets. Studies of some certain securitization determinants are not suitable for covering all the determinants in practice; therefore, a unified framework should be designed to include the usual determinants and be compatible with the bank operation characteristics. 2.2. Empirical studies on securitization determinants Empirical literatures have different conclusions on securitization determinants. Minton et al. (2004) suggest that commercial bank securitization determinants includes regulatory arbitrage and performance promotion. Martín-Oliver and Saurina (2007) summarize the determinants as liquidity demands, risk transfer and performance promotion. Bannier and Hänsel (2009) and Cardone-Riportella et al. (2010) add profit earnings as an extra determinant. The Basel Committee on Banking Supervision's (2012) “Report on Asset Securitisation Incentives” summarize two types of securitization determinants as financing based on markets and arbitrage based on regulation. They also list a few specific determinants, such as credit risk transfer, loan asset diversification, liquidity demand, capital regulation arbitrage, performance promotion, costs cutting and non-interest income improvement. Among all the determinants discussed in these studies, liquidity demand, regulatory arbitrage, performance promotion and risk transfer are the most important determinants that are cited most (Bensalah and Fedhila, 2016). The liquidity demand, which means the banks use securitization as an alternative funding source by transforming loans into liquidity assets (Almazan et al., 2015), is proved by Agostino and Mazzuca (2009) and Affinito and Tagliaferri (2010). However, Yao et al. (2012) investigates the effects of bank's credit asset securitization, and find no robust results. The risk transfer refers to banks' incentive to decrease the risk exposure of loans and to increase the asset portfolio quality via securitization. Both DeMarzo and Duffie (1999) and Calem and LaCour-Little (2004) support this determinant. In a study of mortgage securitization, Agarwal et al. (2012) also find that banks sold loans with high prepayment risks before the 2008 subprime mortgage crisis while banks sold loans with high credit risk during the crisis. However, Griffin et al. (2014) as well as Guo and Wu (2014) point out that reputation consideration and regulation demand possibly result in that banks refusal to transfer risk by securitization. The regulation arbitrage reveals banks' incentive to change the asset risk-weight and lower the regulatory capital through securitization. Calem and LaCour-Little (2004) and Ambrose et al. (2005) find evidence from U.S banks that support this determinant, while Martín-Oliver and Saurina (2007) empirically analyze Spanish banks' securitization business to test such determinant and have ambiguous results. Mu and Zhang (2005) theoretically doubt that the determinant exists in China. The performance promotion exhibits banks' incentive to securitize to optimize their loan structure and to invest in profitable assets. Cardone-Riportella et al. (2010) provide some evidence that banks promote their performance by securitization. While Bannier and Hänsel (2009) obtain different empirical results, Farruggio and Uhde (2015) find that European banks had performance promotion incentive before the subprime mortgage crisis but shifted to liquidity demand incentive after the crisis. These contradictory results show that securitization determinants vary across different countries and different time periods. Therefore, an empirical examination of the securitization determinants in China would help us better understand the driving force of Chinese banks' securitization. 3. Theoretical analysis of commercial banks' securitization decisions in China Loan securitization can be regarded as rational decisions for commercial banks in the interest of business profits. Therefore, this study uses the cost-benefit trade-off framework to examine the decision-making mechanism of commercial banks' loan securitization, and systematically analyzes the determinant types of existing loan securitization. 3.1. The cost-benefit decision model The banks' securitization decision is related to whether the net income brought by securitization is positive or negative, so the key 3

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to understand the decision of securitization is to discover the influencing factors of net return from securitization (Cumming, 1987). If the net income is larger than zero, the commercial bank has some incentives for the loan securitization. Otherwise, the bank's loan securitization business will be unprofitable and may even bring losses to the bank, which results in the weak incentive of securitization. Kendall (1996) defines asset securitization as the process that commercial banks package the credit assets into a group of securities, enhance their credit rating, and sell them to third-party investors. Based on the above definition of asset securitization, a cost-benefit decision model can be established for a representative commercial bank. The cost of loan securitization is divided into two parts. First, the apportioned amount of fixed costs that commercial banks use to set up securitization infrastructure is marked as CF. CF will be smaller in larger banks or banks doing more loan securitizations. Second, the variable cost is marked as CV, which is paid each time of securitization, including the cost of credit enhancements (Kendall, 1996) and other variable costs such as agency fees.5 The income from loan securitization comes from changes between the value of assets held by banks before and after securitization. In this study, we consider a generalized process of asset securitization. Assuming that the underlying credit assets are D0, which are converted into asset-backed securities, S0, after securitization. Banks hold S1 shares and sell the other portion of SRto investors to receive cash assets, C0. To maximize profits, commercial banks with loan opportunities will lend some or all of their cash assets after loan securitization. This part of cash assets is marked as CR. New loans created by the process is D1 and the remaining cash assets is C1. The above operating process is depicted in Fig. 2. After the above operations, the net income of the representative commercial bank is

NR = VCB1 + VSB1 + VDB1 − VDB0 − CV − CF B

B

B

(1)

B

where VC1 ,VS1 ,VD1 ,VD0 denote the values of C1,S1,D1,D0 to the bank, respectively. Here, the value that commercial banks obtained from loan securitizations includes the income of the sale of credit asset-backed securities and the value of various assets derived from loan securitizations. If only the former is considered, loan securitization is just considered as a financing behavior. In this way, the model is ignoring the functions of asset securitization, such as profit creation and risk management, and then underestimates the willingness of commercial banks to conduct loan securitizations. Taking the value of each asset into account, the various benefits gained by the issuance of asset-backed securities, such as increased loan income, can be totally included in the net income calculation of loan securitization. 3.2. An analysis of determinants of commercial banks' loan securitization Assuming that the price of C1,S1,D1,S0 in the market is VC1M,VS1M,VD1M,VS0M, since SRand C0 are equivalently exchanged in the market, i.e., VC0M = VSRM, Formula (1) can be equivalently transformed into Formula (2).

NR = (VCB1 + VCMR − VCM0 ) + (VSB1 + VSMR − VSM0 ) + (VDB1 − VCMR ) + (VSM0 − VDB0 − CS ) − (CV − CS + CF )

(2)

Faulkender and Wang (2006) point out that value assessment of the same asset by financial institutions may differ due to the organization's own characteristics and financial status. Formula (2) shows that the net income of bank loan securitization comes from the differences of value assessment of assets between the originator bank and other investors in the market. Specifically, it includes four additions and one reduction. The net income of commercial bank will be greater and the incentives of loan securitization will be stronger when the additions is larger and the reduction is smaller. According to each item, the determinants of loan securitization for commercial banks can be analyzed one by one. 3.2.1. Liquidity demand The first part of net income, VC1B + VCRM − VC0M, which is the first item on the right side of Formula (2), represents the value of liquid assets evaluated by commercial banks minus the market value of liquid assets. This reflects the liquidity demand incentive of loan securitization for a commercial bank. Commercial banks themselves have the need to maintain liquidity. According to the revealed preference theory, the commercial bank will not hold the cash assets via loan securitization when the bank's valuation of cash assets is lower than the market value of these cash assets, i.e. VC1B + VCRM − VC0M ≤ 0. Otherwise, if VC1B + VCRM − VC0M > 0, the liquidity demand of commercial banks promotes loan securitization. When the item is larger, the incentives of loan securitization for the commercial bank is stronger and the return is higher. 3.2.2. Regulatory arbitrage The second part of net income is VS1B + VSRM − VS0M, which represents the difference between the value of asset-backed securities evaluated by the commercial bank and the market value of the securities. This part of the revenue reflects the incentive of regulatory arbitrage for commercial banks. The value of asset-backed securities S0 should be the sum of future cash flow of the fundamental loans discounted by the risk-adjusted rate of return. However, according to Calomiris and Mason (2004), the value of asset-backed securities should include the saved regulatory costs when the securities are held by banks. Regulatory costs consists of two aspects. One is the difference of risk weight of asset-backed securities and risk weight of loans (Farruggio and Uhde, 2015). The second reason is a special phenomenon in China—loans require more deposits than asset-backed securities due to the long-term supervision of loanto-deposit ratios. Therefore, considering the savings in regulatory costs, there is VS1B + VSRM − VS0M > 0. 5

This includes rating fees, underwriting fees, custody fees, etc. 4

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Fig. 2. Flow diagram of bank loan securitization.

3.2.3. Performance promotion The third part of net income is VD1B − VCRM, which is the difference between the value of loans created by commercial banks in the process of loan securitization and the value of equal sum of financing assets, constituting potential incentives for profit growth. Commercial banks have incentives to carry out loan securitization when VD1B − VCRM > 0. Banks optimize their own loan structures via loan securitization and choose more familiar and reliable loan-offering projects, leading to a higher return of new loans than original loans (Hänsel and Krahnen, 2007). 3.2.4. Risk transfer The fourth part of the net income is VS0M − VD0B − CS, which is the difference between the market value of asset-backed securities, minus the basic assets for lending and the costs of credit enhancement invested by commercial banks. This item constitutes a potential incentive of risk transfer. Under the condition of higher information transparency, commercial banks cannot cheat investors in the process of securitization. Therefore, the pricing of S0 is fair andVS0M − VD0B − CS = 0. Otherwise, the originator of loan securitization have more information about credit assets than investors when VS0M − VD0B − CS > 0, and they can transfer part of the risk of credit assets via loan securitization to investors without any associated costs. Two situations might exist in the market when there is a certain degree of information asymmetry between commercial bank and investors. In one case, the bank places reputation in a dominant position and will adopt fair pricing regardless of their own risk level. In the other case, VS0M may be overestimated when risk transfer incentive is dominant for banks. The higher degree of information asymmetry and the greater real risk of loans will cause more overestimation of VS0M (Guo and Wu, 2014). 3.2.5. Cost advantage exploitation The last item in Formula (2) is −(CV − CS + CF), the cost item that should be subtracted from net income, reflecting the cost incentives of loan securitization. According to our knowledge of the current literatures, the securitization incentives of commercial banks usually do not include the costs of securitization. However, it can be shown in the analysis of net income decomposition that costs are indispensable in commercial bank securitization deciding process. Higher securitization costs may hinder commercial banks from conducting securitization businesses. On the contrary, low-cost securitization businesses have become an important competitive advantage for commercial banks. Lower costs will benefit banks conducting loans securitization when the profits are similar to those of rival banks. Here, CS (credit enhancement cost) is excluded from the total cost for CS, which has special influencing factors compared to other costs. The former is influenced by the credit risk of the fundamental assets, while the latter is affected by the bank asset scale, capital structure and other factors. 4. Samples and variables First, this section introduces the sample data sources used to empirically examine the determinants of loan securitization in China's commercial banks. Second, we explain how to select variables for the empirical tests. Finally, we present data characteristics of China Merchants Bank, which is a representative joint-stock bank in China. 4.1. Variable selection and data source This paper conducts an empirical test of the time interval from May 2012, when China restarted its loan securitization business, to Dec. 2017. The number and scale of loan securitization of commercial banks during 2012–2017 are derived from the China Securitization Analytics database, including all credit asset-backed securities issued in the inter-bank market. The characteristic variables of commercial banks are one-year lagged annual data from 2011 to 2016 obtained from the Orbis Bank Focus database. The raw data includes 5 large commercial banks, 12 national joint-stock banks, 96 city commercial banks and 42 rural commercial banks. To make full use of the data, this study uses linear interpolation to fill in missing values. The effective sample data

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Table 1 The valid sample quantity of factor analysis and the asset proportion statistics. Bank type

Term

2011

2012

2013

2014

2015

2016

Large state-owned commercial bank

Sample quantity Ratio of assets Sample quantity Ratio of assets Sample quantity Ratio of assets Sample quantity Ratio of assets Sample quantity Ratio of assets

5 100% 12 100% 96 42.86% 41 17.26% 154 90.37%

5 100% 12 100% 96 55.11% 41 14.24% 154 89.78%

5 100% 12 100% 96 86.74% 41 53.21% 154 97.99%

5 100% 12 100% 96 91.21% 41 47.25% 154 97.64%

5 100% 12 100% 96 92.26% 41 40.98% 154 96.26%

5 100% 12 100% 96 89.11% 41 36.50% 154 94.24%

National joint-stock commercial bank City commercial bank Rural commercial bank Sum

Data Source:Orbis Bank Focus,CBRC annual report of 2011–2016 organized by author.

obtained by factor analysis of raw data is summarized in Table 1. Table 1 shows the number of valid samples of various types of banks and the proportion of all banking assets in the country as reported by the China Banking Regulatory Commission6. From this table, it can be seen that the valid samples cover all large-scale commercial banks and the vast majority of joint-stock commercial banks from 2011 to 2016, and cover most of the city commercial banks from 2014 to 2016, but insufficiently covers the rural commercial banks. Overall, the assets of valid sample accounts for more than 90% of the assets of all commercial banks, which indicates that our sample has a strong representativeness. 4.2. The selection of explanatory variables The explanatory variables selected for this study are the determinants of loan securitization business of commercial banks in China. According to the analysis of the cost-benefit decision model, the explanatory variables should reflect the five determinants of loan securitization. Because there are strong correlations between the proxy variables of different determinants, these variables will cause serious multi-collinearity if they enter into the regression equation directly. Therefore, the proxy variables of each determinant cannot be directly substituted into the regression equation. Thus, the first step is choosing proxy variables for each determinant. Then, transfer the proxy variables into factors that correspond to each of the incentives and are independent of each other. 4.2.1. Proxy variables selection and descriptive statistics Based on the connotation of the five determinants of securitization, we select the relevant proxy variables. First, the liquidity demand incentive of a commercial bank depends on the value assessment of liquid assets measured by the level of holdings of liquid assets (Bannier and Hänsel, 2009) and the short-funding cost measured by the proportion of sources of liquidity liabilities (Almazan et al., 2015). The lower level of liquidity holding corresponds to the higher valuation of new liquid assets. We use the liquid assets liability ratio (Liquid_Debt) and liquid assets to total assets ratio (Liquid_Asset) to measure the liquidity asset holding level. The relation between the proportion of liquidity liabilities in total liabilities and the liquidity incentive is complex. Deposits and interbank lending are the main two sources of liquidity liabilities, which a bank will balance according to the financing costs optimization with constraints. The lower deposit ratio and the higher short-funding ratio correspond to the higher liquidity financing costs that commercial banks pay for. This study uses the deposit ratio (Depo_Debt) and other short-term debt ratios (Otstfund_Debt) to measure the structure of liquidity liabilities.7 Second, the regulatory arbitrage incentive of a commercial bank depends on the valuation of asset-backed securities, which is related to the level of regulatory constraints imposed on commercial banks (Van Hoose, 2007). China's banking has mainly supervision requirements on capital adequacy ratio and loan-to-deposit ratio. According to the theoretical analysis, banks with stricter regulatory constraints need more securitization to avoid regulatory violations. Correspondingly, this study follows Calomiris and Mason (2004) in selecting the core capital adequacy ratio (Tier1R) and total capital adequacy ratio (Total_CR) to measure capital regulatory constraints, and selecting the loan-to-deposit ratio (Loan_Depo) to measure loan-size regulatory constraints. To reflect the level of regulatory constraints, this study uses excessive ratio as actual proxy variables. The excessive capital adequacy ratio equals to the capital adequacy ratio minus the corresponding regulatory requirements. The excessive loan-to-deposit ratio equals the absolute value of loan-to-deposit ratio subtracting regulatory requirements.8 Third, the performance promotion incentive of a commercial bank depends on the assessment of the profitability of new bank loans. The stronger profitability of the commercial bank loan business corresponds the higher value of new loans created by loan 6 According to the classification of CBRC, in 2017, there are 5 major commercial banks in China, 12 national joint-stock commercial banks, as well as 134 urban commercial banks and 1114 rural commercial banks. 7 In China, because of non-market low deposit interest rates existing in long-term loans, banks will use deposit financing as much as possible when they could absorb deposits, and only choose higher-cost financing methods when deposit growth is constrained. 8 The authors thank anonymous reviewers for the suggestions about replacing raw ratios with the difference between actual level and regulatory requirements. After 2012, the regulatory requirement for core capital adequacy ratio is 6% and total capital adequacy ratio is 8% in China. Before the end of 2015, the regulatory requirements for loan-to-deposit ratios were no more than 75% in China.

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securitization, since the promotion of turnover rate of loan raise more profit for banks that are more profitable. Thus, banks with higher profitable loan business and higher profitability have stronger incentive for performance promotion. The main income of commercial banks in China comes from the loan business. Therefore, the profitability of a certain commercial bank and the profitability of its new loans are highly correlated. However, we consider both variables to measure the new loan's profitability comprehensively. We use the return of loans (ROL) to measure the profitability of the loan business and use the return on assets (ROA) to measure the profitability of the bank. Fourth, the risk transfer incentive of a commercial bank depends on the difference between the investor's assessment of the underlying asset value and the actual asset value. As information asymmetry exists between investors and commercial banks, the value difference will be positively affected by the bank's total risk and loan risk if the bank can obtain investors' trust. In this case, riskier banks and banks with worse loans are more inclined to sell lower-quality assets to transfer their risk (Guo and Wu, 2014). Under the above mechanism, the increase of the total risk and credit risk will enhance the risk transfer incentives of banks. It should be noted that if investors do not trust banks they can still rely on banks' reputation to evaluate the risk of securitization assets. Therefore, the higher total risk and the higher loan risk correspond to the lower investor's evaluation of the underlying assets in the market. In this case, it will take more cost for banks to transfer risk and to enhance the credit ratings of the securities, weakening the risk transfer incentive of banks. According to the regulation of information disclosure for commercial banks in China, this study uses the bank's credit rating (Rating) to measure the total bank risk9 and uses the non-performing loan ratio (NPLR) and the loan loss provision ratio (LLR) to measure the loan risk. Fifth, the cost advantage exploitation of loan securitization for commercial banks are mainly determined by the cost of loan securitizations (except the credit enhancement costs). The costs of loan securitization include fixed costs and variable costs (except the credit enhancement costs). Fixed cost is related to the scale of the bank. Because of the scope effect, the larger bank scale corresponds to the lower fixed cost per unit of asset-backed securities (Bensalah and Fedhila, 2016). The variable cost is related to the asset structure of commercial banks. The assets that can be securitized by commercial banks in China are mainly loans. The difficulties and costs of mortgage-backed securitization are less than the other loan securitization. Therefore, the higher proportion of mortgage loans to total loans corresponds to the lower variable cost. In this study, we use the logarithm of the book value of total assets (lnAsset) to measure the scale of the assets. Following Almazan et al. (2015), we choose the ratio of mortgage loans to total loans (Mortgage_Loan) to measure the loan structure. The detailed descriptions and calculation methods for the above variables are shown in Table 2. The symbols in parentheses indicate the theoretical hypothesis of the variables' influence direction on the bank loan securitization. From the above analysis of variables, we make the following hypothesis: (1) The decrease of Liquid_Debt, Liquid_Asset and Depo_Debt will strengthen the liquidity demand incentive of loan securitization, while the decrease of Otstfund_Debt will weaken the incentive. (2) The reduction of regulatory constraint indicators (Tier1R, Total_CR and Loan_Depo) will raise the regulatory arbitrage incentive. (3) The increase of profitability indicators (ROA and ROL) will raise the performance promotion incentive. (4) The decrease of Rating and increase of NPLR and LLR will strengthen the risk transfer incentive of banks under asymmetric information. On the contrary, these variations will weaken risk transfer incentives under the condition of high information transparency. (5) The increase of Mortgage_Loan and LnAsset will enhance commercial banks to exploit cost advantage via securitization. Table 3 shows the means and standard deviations of each proxy variable in two subsamples (securitized and not securitized), and shows that whether the mean of the two subsamples is significantly different under t-test. In general, the difference between the subsamples of securitization and non-securitization are in line with the results from the theoretical analysis. However, some variables show unexpected results. The difference of Otstfund_Debt and Loan_Depo between two subsamples are contrary to the theoretical results. The mean value differences in risk indicators (Rating, NPLR, and LLR) are significant and the signs are consistent with the theoretical hypothesis under the reputation mechanism. These results imply that China's bank may not securitize for liquidity demand and risk transfer10.

4.2.2. Correlations of original variables and factor analysis11 This study examines the relevance of the proxy variables in two ways. First, the correlation coefficient between the each pair of variables is calculated. The Pearson correlation coefficient matrix is shown in Table 4. It reports that most of the proxy variables belonging to the same determinant are highly correlated, which is consistent with the theoretical analysis. However, the correlations between a few proxy variables belonging to different determinants are also high, and some are even higher than the correlations of the proxy variables of the same determinant. The above-mentioned phenomenon may be due to the linkage between commercial bank securitization determinants. For example, Rating belonging to the risk transfer incentive has a high correlation with Mortgage_Loan and InAsset, which both belong to the cost advantage exploitation incentive. This is because banks with higher credit ratings lack the need for risk transfer incentives and often have the convenience of conducting their loan securitization business. 9 Referring to He et al. (2012), the credit rating is represented by numbers. The AAA level is denoted as 8; the credit rating is reduced by 1 and the corresponding figure is decreased by 1. For this study's sample, the bank's minimum credit rating is BBB, which is denoted by 1. 10 Due to the low rating coverage of Chinese commercial banks by international rating agencies such as S&P, Moody's, and Fitch, this study uses the ratings given by China's domestic rating agencies. Following He et al. (2012), we set the AAA corresponding value to be 8, the corresponding value of BBB is 1, and the lowest rating in this sample is BBB. 11 Usually researchers use one of the variables with high correlation to enter the regression equation (Liu and Xing, 2015), however, this method will obviously leave out information, and the final choice of variables into the equation is based on statistical significance, which lacks economic significance.

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Table 2 Original explanatory variable descriptions. Types of determinants

Variable

Variable definition and calculation method

Liquidity demand

Liquid_Debt Liquid_Asset Depo_Debt Otstfund_Debt Tier1r Total_cr Loan_Depo ROA ROL Rating LLR NPLR Mortgage_Loan lnAsset

Liquid assets/Liquid Liabilities Liquid assets/Total assets Deposit ratio = Total deposit/Total liabilities Other short-term liabilities = Other short-term deposits/Total liabilities Excess capital adequacy ratio = (Core capital/Total risk-weighted assets) - 6% Excess total capital adequacy ratio = (Total capital/Total risk-weighted assets) -8% Loan-deposit ratio = 75% - (Total loan/Total deposit) Return on assets = Pretax profit/Total annual total assets Return on loans = Pretax profit/Average annual gross loans Rating of originator Loan loss provision ratio = Loan loss provisions/Total loan Non-performing loan ratio = Non-performing loan/Total loan Mortgage rate = mortgage/total loan ln (Total assets)

Regulatory arbitrage

Profitability promotion Risk transfer

Cost advantage exploitation

Table 3 The statistical characteristics of the original explanatory variables. Variable name(%)

Liq_Debt t-1(−) Liquid_Asset t-1(−) Depo_Debt t-1(−) Otstfund_Debt t-1(+) Tier1r t-1(−) Total_cr t-1(−) Loan_Depo t-1(−) ROA t-1(+) ROL t-1(+) Rating t-1(−/+) LLRt-1(+/−) NPLR t-1(+/−) Mortgage_Loan t-1(+) lnAsset t-1(+) obs.

Securitized sample

Unsecuritized sample

Diff.

Mean

Mean

Mean

20.13 17.82 88.31 6.29 4.15 4.37 21.45 1.05 0.47 7.29 2.87 1.28 9.93 6.76 119

Standard error 0.92 0.83 0.49 0.45 0.12 0.10 0.79 0.02 0.01 0.07 0.06 0.04 0.62 0.15

25.72 22.84 87.38 8.74 5.30 5.12 19.66 1.09 0.50 6.02 3.27 1.32 5.89 4.75 380

Standard error 0.45 0.41 0.35 0.36 0.08 0.07 0.44 0.01 0.01 0.04 0.05 0.03 0.29 0.05

Standard error ⁎⁎⁎

−5.59 −5.02⁎⁎⁎ 0.93 −2.45⁎⁎ −1.16⁎⁎⁎ −0.74⁎⁎⁎ 1.78⁎ −0.05 −0.03 1.27⁎⁎⁎ −0.40⁎⁎⁎ −0.04 4.03⁎⁎⁎ 2.00⁎⁎⁎ –

1.09 0.99 0.82 0.84 0.19 0.17 1.06 0.03 0.02 0.09 0.11 0.07 0.68 0.13

Note: ***, **, *indicate confidence levels of 1%, 5%, 10%, respectively. The symbol in parentheses after the variable indicates the theoretical direction of the variable's influence on the securitization of bank loan assets. Data source:Orbis Bank Focus

Second, this study uses two kinds of correlation tests commonly used in factor analysis to examine whether the variables meet the analyzing requirements.12 The result of the KMO test is 0.577, which is higher than 0.5, indicating that the partial correlation coefficient between the proxy variables is higher on average. Bartlett's test value is 7194.364, rejecting the null hypothesis at the 0.1% confidence level, which assumes there is no correlation between the variables. These tests all show that there is a high degree of multi-collinearity between proxy variables, thus it is necessary to conduct a factor analysis to obtain determinant factors. In this study, seven common factors is extracted using the principal component analysis method, and are rotated by varimax method, which makes the correlation between them zero. We choose seven factors because these factors cover the theoretical determinants and the total explanatory power is larger than 0.9. Table 5 shows the variance explanatory power of the factor and the extracted common factors. From the table, it can be seen that the factor explanatory power diminishes slowly after rotation, and the explanatory power of the first seven factors is more than 5%. This study uses seven common factors as explanatory variables in the regression equation. The seven common factors have been standardized, i.e. independent statistics with a mean of zero and a standard deviation of one. Although the above common factors are all represented by the linear combination of the original explanatory variables, the information contained in each factor is different. Table 6 shows the loading matrix of the rotated factors. Each column of this matrix represents the corresponding coefficient of the proxy variables consisting of each factor. The larger absolute value of the coefficient correspond to the greater contribution weight of the factor in the original proxy variable. The table also shows the unexplained proportion of the common factors to the variance of the proxy variables. The common factors covered most of the information for the initial explanatory variables.

12

To remove the influence of the different variable metric scales, we standardize the proxy variables of all incentives. 8

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Table 4 Pearson correlation matrix. Liquid_Debt Liquid_Asset Depo_Debt Otstfund_Debt Tier1r Total_cr Loan_Depo ROA ROL Rating LLR NPLR Mortgage_Loan Lnasset ROL Rating LLR NPLR Mortgage_Loan Lnasset

1.00 −0.25 0.33 0.24 0.11 −0.24 0.06 −0.10 −0.22 −0.04 −0.23 −0.19 −0.25 ROA 0.87 −0.18 −0.13 −0.30 0.02 −0.09

Liquid_Asset

Depo_Debt

Otstfund_Debt

Tier1r

Total_cr

Loan_Depo

−0.24 0.34 0.22 0.09 −0.25 0.05 −0.11 −0.21 −0.05 −0.24 −0.19 −0.24 ROL

−0.92 −0.03 −0.04 0.05 0.11 0.32 −0.05 0.16 0.15 0.11 0.04 Rating

0.12 0.08 −0.04 −0.03 −0.24 0.02 −0.19 −0.20 −0.12 −0.08 LLR

0.86 0.12 0.29 0.26 −0.30 0.11 0.00 0.04 −0.32 NPLR

0.13 0.32 0.28 −0.20 0.12 −0.02 0.11 −0.19 Mortgage_Loan

0.27 0.61 −0.16 0.08 0.15 0.06 −0.11

−0.21 −0.05 −0.13 0.06 −0.10

−0.17 −0.06 0.35 0.83

0.60 −0.06 −0.21

0.00 −0.08

0.47

Source: Orbis Bank Focus, organized and calculated by author. Note: Values with an absolute value greater than 0.5 are shown in bold. Table 5 Explanatory power of factor variances. Factor

F1 F2 F3 F4 F5 F6 F7

Initial Factor

Factor After Promax Rotation

Characteristic value

Explain variance ratio

Cumulative variance ratio

Characteristic value

Explain variance ratio

Cumulative variance ratio

3.04 2.81 1.79 1.39 1.18 0.69 0.36

0.28 0.26 0.16 0.13 0.11 0.06 0.03

0.28 0.54 0.70 0.83 0.94 1.00 1.04

2.12 1.95 1.92 1.79 1.68 0.95 0.82

0.20 0.18 0.18 0.17 0.16 0.09 0.08

0.20 0.38 0.55 0.72 0.87 0.96 1.04

Source: Orbis Bank Focus. Table 6 Factor loading matrix after varimax. Initial explanatory variable

Liq_Debt Liq_Asset Depo_Debt Otst_Debt Tier1r Total_cr Loan_Depo ROA ROL Rating LLR NPLR Mortgage_Loan lnAsset

F1

F2

F3

F4

F5

F6

F7

High asset liquidity

Low funding liquidity

High profitability

Low cost/ Low bank risk

High capital adequacy

High loan risk

Low loandeposit ratio

0.98 0.98 −0.11 0.19 0.15 0.04 0.20 0.04 −0.06 −0.13 −0.03 −0.19 −0.13 −0.15

−0.14 −0.14 0.96 −0.94 −0.04 −0.04 0.02 0.01 0.21 −0.05 0.15 0.13 0.10 0.04

0.00 −0.01 0.12 −0.05 0.15 0.19 −0.32 0.95 0.88 −0.12 −0.10 −0.23 0.02 −0.03

−0.11 −0.10 0.00 −0.01 −0.17 −0.06 0.09 −0.05 −0.07 0.83 −0.17 −0.07 0.50 0.88

0.08 0.06 −0.01 0.06 0.88 0.88 −0.06 0.16 0.14 −0.14 0.11 0.02 0.16 −0.14

−0.04 −0.05 0.04 −0.08 0.03 0.04 −0.10 −0.10 −0.04 −0.05 0.67 0.68 −0.03 −0.08

0.07 0.08 −0.01 0.00 −0.05 −0.02 0.80 0.03 −0.37 0.08 −0.03 −0.13 −0.09 0.04

Source: Orbis Bank Focus, organized and calculated by author. Note: Values with an absolute value greater than 0.5 are shown in bold.

9

Unexplained ratio

0.00 0.00 0.05 0.05 0.15 0.19 0.20 0.06 0.02 0.25 0.48 0.41 0.68 0.18

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Factors in Table 6 are sorted in descending order of the explanatory power of original variables. The economic meaning of each factor is mainly related to variables with a large factor loading. Loadings whose absolute value are greater than 0.5 are underlined in Table 6. It can be seen that factors 1 and factor 2 correspond to liquidity demand incentive. Factor 1 is called high asset liquidity factor, which has large positive contribution weights in the original proxy variables such as Liquid_Debt and Liquid_Asset, reflecting the amount of liquid assets held by banks. Factor 2 is called low short-term funding cost factor, which has large loadings of Depo_Debt and Otstfund_Debt, reflecting the bank's liquidity funding cost. Factor 3 is called the high profitability factor, which mainly affects ROL and ROA, corresponds to the performance promotion incentive. Factor 4 is a special for its large loadings of Mortgage_Loan, lnAsset, and Rating. Mortgage_Loan and lnAsset are proxy variables of cost advantage exploitation incentive, and Rating is a proxy variable of risk transfer incentive. Because of the strong correlation, they are difficult to separate and jointly constitute factor 4. Thus, factor 4 is called a low cost/low risk factor. Factor 5 is called the high capital adequacy factor, which mainly affects Tier1R and Total_CR, corresponding to the capital regulatory arbitrage incentive. Factor 6 is called high loan risk factor, which has large loadings of NPLR and LLR, corresponding to the risk transfer incentive. Factor 7 is called low loan-to-deposit ratio factor, which has large loadings of Loan_Depo and corresponds to the loan regulatory arbitrage incentive. The factor analysis results show that the statistical factors and theoretical determinants can be exactly matched. An increase in the high asset liquidity factor (factor 1) and a reduction in the low short-term funding cost factor (factor 2) will lead to an increase in liquidity demand incentive. A decrease in the high capital adequacy factor (factor 5) and low loan-to-deposit ratio factor (factor 7) will lead to an increase of the regulatory arbitrage incentive. An increase in the high profitability factor (factor 3) will lead to an increase in the performance promotion incentive. An increase in the high loan risk factor (factor 6) and a decrease in the low cost/low risk factor (factor 4) will lead to an increase in the risk transfer incentive. An increase in the low cost/low risk factor (factor 4) leads to an increase in the incentive for cost advantage exploitation. 4.3. The explained variables and control variables This study uses the commercial banks' loan securitization decisions during 2012–2017 as explained variables, i.e. the dummy variable indicating whether the banks securitized (Issec), the number of securitization issuance (Secnum), and the total amount (Secamt) of loan securitizations issued by the banks the current year. To control for the impact of market demand for asset-backed securities on banks' securitization decisions, this study selects macroenvironment variables as control variables, including the M2 growth rate (M2 g), 5-year Treasury yield (y_tr), and the credit spread of 5-year AAA corporate bonds (y_aaa). This study also controls time dummy variables to exclude the impact caused by economic environment changes of different years, and bank class dummy variables to exclude the impact caused by characteristics of different types of banks. We use bank class dummy variables to mark four commercial bank classes, i.e., the large-scale national banks, the joint-stock banks, the city banks and the rural banks. To remove the endogenous effects, we use the one-year lagged explanatory variables to regress. 4.4. A case study on the China Merchants Bank's securitization business Since the restart of the pilot project for credit asset securitization, China Merchants Bank (CMB) has launched its securitization business. As of the end of 2017, it has had the largest number of securitization issuance and the largest funding amount via securitization among joint-stock banks.13 This case study summarizes the development of CMB's securitization business from 2012 to 2017, and shows the CMB's securitization determinants based on an analysis of its variables. CMB has been interested in loan securitization since long time. We analyze the 2008–2017 securities business data of CMB from China Securitization Analytics.14 Before the financial crisis in 2008, the bank has begun a pilot of the securitization business (i.e. Zhaoyuan 2008-1). On March 21, 2014, after the securitization business's stopping-period, the bank restarted the securitization business (i.e. Zhaoyuan 2014-1 and Zhaoyuan 2014-2) in response to the CBRC's encouragement, as the one of the earliest players in the joint-stock banks. Since then, the bank has strengthened its securitization business.15 The number and amount of the bank's assetbacked securities issuance during 2014–2017 are depicted in Fig. 3. As can be seen in Fig. 3, CMB has strong incentives in the asset securitization business compared to other joint-stock banks. Based on the analysis about the securitization determinants in this paper, we select relevant financial indicators, and calculate the value of the explained variables and original explanatory variables of CMB's. The results are shown in Table 7. Based on the analysis of the explained variables in Table 7, the years that CMB issues asset-backed securities are not significantly more than the average level of joint-stock banks, but the issuance number and issuance amount are significantly higher in statistics. This shows that CMB does conduct more asset securitization business than an average joint-stock bank. The values of CMB's original explanatory variables shows that CMB has similar liquidity and risk as other joint-stock banks but has significantly different characteristics in other aspects. First, the capital adequacy ratio and loan-to-deposit ratio of CMB are higher than average, implying that 13

Data source: WIND. Website of China Securitization Analytics: www.cn-abs.com 15 On June 17, 2015, CMB launched the first product (i.e. Zhao Yuan 2015–2) under the filing system of asset-backed securities issuance, and invited overseas RQFII funds to participate in domestic ABS investments for the first time. In 2016, CMB issued the first domestic non-performing asset-backed securities (i.e. He Cui 2016–1) and bank notes asset-backed securities (i.e. Ju Yuan 2016–1). 14

10

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800 700 600 500 400 300 200 100 0

10 8 6 4 2 0

2014

2015

2016

2017

issue ammout (100 mio.)

numbers of issues

12

year

CMB number of issues

average number of issues

CMB issue amount

average issue amount

Fig. 3. CMB's number of securitization and security issuance amounts from 2014 to 2017. Data Source: China Securitization Analytics.

Table 7 CMB's variables description in 2014–2017. Types of variables

Explained variable

Securitization

Original explanatory variables

Liquidity demand

Regulatory arbitrage

Profitability promotion Risk transfer

Cost advantage exploitation

Names

Average values (CMB)

Average values (joint-stock banks)

Diff.

Issec Secnum Secamt Liquid_Debt t-1(−) Liquid_Asset t-1(−) Depo_Debt t-1(−) Otstfund_Debt t-1(+) Tier1r t-1(−) Total_cr t-1(−) Loan_Depo t-1(−) ROA t-1(+) ROL t-1(+) Rating t-1(−/+) LLRt-1(+/−) NPLR t-1(+/−) Mortgage_Loan t-1(+) lnAsset (+)

0.67 3.83 223.50 15.72 14.38 92.86 4.96 3.77 4.06 11.93 1.28 0.70 8.00 2.60 1.11 17.11 54.82

0.52 1.14 48.21 22.80 20.64 87.49 8.59 2.87 3.30 21.00 0.98 0.43 7.85 2.41 1.06 9.78 43.70

0.15 2.70⁎⁎⁎ 175.29⁎⁎⁎ −7.08 −6.26 5.37 −3.63 0.90⁎⁎⁎ 0.76⁎⁎ −9.07⁎⁎ 0.29⁎⁎⁎ 0.27⁎⁎⁎ 0.15 0.19 0.05 7.34⁎⁎⁎ 11.12⁎⁎⁎

Data source: Wind database, calculated by author. Note: ***, **, * indicate confidence levels of 1%, 5%, 10%, respectively. The symbol in parentheses after the variable indicates the theoretical direction of the variable's influence on the securitization of bank loan assets.

the capital arbitrage incentive is relatively weak while the loan arbitrage incentive is relatively strong. Second, CMB's asset profitability and loan profitability both are higher than average, indicating that the performance promotion incentive is relatively strong. Third, CMB has a high proportion of mortgage loans, a large asset size, and consequently a strong incentive for cost advantage exploitation. Further, this study also compares the difference of each determinant factor value between the China Merchants Bank and the average national joint-stock bank for the period 2014–2017, as shown in Table 8. As can be seen from Table 8, compared to an average joint-stock bank, CMB's high profitability factor, low-cost/low-risk factor, and low loan-to-deposit ratio factor are of significant difference and consistent with the corresponding determinant hypothesis. The high capital adequacy ratio factor is significantly different but does not match the determinant hypothesis. Compared to the analysis results of original explanatory variables, the above results are similar but clearer. This study uses the seven determinant factors as the Table 8 CMB's determinants factors description in 2014–2017. Determinants factor

Average values (CMB)

Average values (joint-stock banks)

Diff.

High asset liquidity(−) Low funding liquidity(−) High profitability(+) Low cost/Low bank risk(+/−) High capital adequacy(−) High loan risk(+/−) Low loan-deposit ratio(−)

−0.33 0.44 0.74 1.81 −0.33 −0.24 d

0.14 0.04 −0.16 1.30 −0.78 −0.31 −0.12

−0.47 0.40 0.90⁎⁎⁎ 0.51⁎⁎⁎ 0.45⁎⁎⁎ 0.07 −0.70⁎⁎

Data source: Wind database, calculated by author. Note: ***, **, * indicate confidence levels of 1%, 5%, 10%, respectively. The symbol in parentheses after the variable indicates the theoretical direction of the variable's influence on the securitization of bank loan assets. 11

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explanatory variables in multiple regression equations. 5. Multiple regression model and empirical results 5.1. Regression model The regression model is constructed following Farruggio and Uhde (2015). Since Issec is a dummy variable, we use the Logistic regression model, which includes the seven factors as the explanatory variables. The coefficient of the Logistic model shows the influences of factor variation on the bank securitization probability. The Logistic model is constructed as follows: 7

P (seci, t = 1 | Fi, t − 1) = α +

∑ βj Fi,jt−1 + Controli,t−1 + εi,t

(3)

j=1

The probability distribution function on the left is the Logistic function. Because Secnum and Secamt are both based on censored data, which is above or equal to zero, we use the Tobit regression model to test whether the common factor can further explain the number and amount of credit asset securitizations issued by commercial banks. The Tobit model is constructed as follows. 7

yi∗, t = α +

∑ βj Fi,jt−1 + Controli,t−1 + εi,t j=1

yi, t =

∗ ⎧ yi, t

yi∗, t > 0

⎨ ⎩0

others

(4)

The explained variable yi, t is Secnum or Secamt. 5.2. Empirical results 5.2.1. The impact of determinant factors on banks' securitization decision We first examine whether each determinant factor significantly influences the annual securitization decision of commercial banks, as shown in Table 9. In columns (1) and (2), neglecting the panel structure of the sample, we show results of pooled regression. First, the coefficient of high profitability factor is positive, which is in line with the hypothesis of profit promotion incentive, but its significance is low, which cannot support this incentive. Second, the low cost/low risk factor coefficient is significantly positive, indicating that commercial banks will engage more in asset securitization business with lower securitization costs, and that the cost advantage exploitation incentive will exist. Considering that the coefficient of high loan risk factor is negative and insignificant, the result does not support the risk transfer hypothesis. This is in contrast to some existing literatures (Calem and Lacour-Little, 2004; Affinito and Tagliaferri, 2010). The reason might be that high-risk banks have little access to the securitization market. Owing to the facts of low information transparency, investors do not have enough accurate information about the quality of securitized assets and bank's reputation functions as a signal of its originating securities (Farruggio and Uhde, 2015). Third, the coefficient of high capital adequacy factor is significantly negative, which suggests that increasing capital adequacy ratio has a negative impact on the probability of securitization, providing evidence for the capital arbitrage hypothesis. Fourth, macro control variables have a significant impact on securitization decisions. Increasing money supply and increasing credit spreads both will reduce bank securitization operations. Results from the Hausman test shows that a random-effect regression model is suitable. The random-effect regression results shown in Columns (3) and (4) of Table 9 are consistent with the pooled regression. The results of the pooled regression and random-effect regression show that commercial banks with stronger capital arbitrage incentive and lower securitization costs are more willing to initiate asset securitizations. Furthermore, to eliminate the heterogeneity of different commercial banks that do not change over time, we use a fixed effect regression model and the result is shown in Column (5) of Table 9. Since the sample data of some commercial banks during the time interval used have little variation in time dimension, the number of samples available for the fixed effects model (384 observations) is greatly reduced compared to the random effects model (696 observations). These coefficients has some difference with the aforementioned results. The coefficients of the low cost/low risk factor and the coefficient of high capital adequacy factor remain unchanged, while the significance disappears; the symbol of the high loan risk factor coefficient remains unchanged, while the significance has a large increase. This result actually shows that the cost advantage exploitation incentive and capital arbitrage incentive are stable in some banks and do not change over time and that increasing loan risk will reduce the implementation of the asset securitization business for certain banks. We summarize the above analysis as follows. First, the determinants of regulatory arbitrage, performance promotion, liquidity demand, risk transfer, and cost advantage exploitation all have an impact on the securitization decisions of commercial banks theoretically. However, not all factors are effective in practice. According to our regression results, only regulatory arbitrage and cost advantage exploitation are supported. Second, the influence of determinants are in the same direction on the cross-section and time dimension, but the strength is different. If we consider which individual commercial bank will engage in the securitization business, capital management arbitrage and cost advantage exploitation are main determinants. The more restricted capital supervision and the lower cost of securitization correspond to the higher possibility for the commercial bank to securitize. If we consider when a 12

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Table 9 Logistic regression results of Banks' securitization decisions.

High asset liquidity Low funding liquidity High profitability Low cost/Low bank risk High capital adequacy High loan risk Low loan-deposit ratio

(1)

(2)

(3)

(4)

(5)

(6)

Pool

Pool

RE

RE

FE

FE

−0.162 (−1.01) −0.0555 (−0.33) 0.0498 (0.30) 1.439⁎⁎⁎ (6.09) −0.633⁎⁎⁎ (−2.74) −0.340 (−1.51) −0.102 (−0.65)

−0.152 (−0.80) −0.0449 (−0.22) 0.0406 (0.20) 1.619⁎⁎⁎ (5.19) −0.688⁎⁎ (−2.57) −0.432 (−1.56) −0.120 (−0.63)

Yes No

0.262 (0.69) 0.146 (0.39) 0.400 (0.90) 0.0356 (0.03) −0.624 (−1.56) −1.475⁎⁎ (−2.29) −0.502 (−1.26) −5.145 (−0.02) 1.022 (0.01) 20.81 (0.02) −26.06 (−0.01) Yes No

−71.193

−71.193

279.39⁎⁎⁎ 0.4097 696

279.39⁎⁎⁎ 0.4097 696

Yes Yes −4.831⁎⁎⁎ (−3.80) −198.336 76.88⁎⁎⁎ 5.83⁎⁎

−0.152 (−0.80) −0.0449 (−0.22) 0.0406 (0.20) 1.619⁎⁎⁎ (5.19) −0.688⁎⁎ (−2.57) −0.432 (−1.56) −0.120 (−0.63) −10.52⁎ (−1.72) 3.731⁎ (1.74) 6.921⁎⁎⁎ (4.74) 3.583 (0.47) Yes Yes −50.60 (−1.89) −198.336 76.88⁎⁎⁎ 5.83⁎⁎

0.262 (0.69) 0.146 (0.39) 0.400 (0.90) 0.0356 (0.03) −0.624 (−1.56) −1.475⁎⁎ (−2.29) −0.502 (−1.26)

Yes Yes −4.242⁎⁎⁎ (−4.08) −201.252

−0.162 (−1.01) −0.0555 (−0.33) 0.0498 (0.30) 1.439⁎⁎⁎ (6.09) −0.633⁎⁎⁎ (−2.74) −0.340 (−1.51) −0.102 (−0.65) −9.323 (−1.62) 3.317⁎ (1.65) 6.239⁎⁎⁎ (4.67) 3.048 (0.42) Yes Yes −45.04 (−1.80) −201.252

145.28⁎⁎⁎

145.28⁎⁎⁎

696

696

384

384

y_depo m2 y_tr y_aaaspread Year Dummy Class Dummy _cons Log Likelihood Wald test LR test Pseudo R2 N

Notes: For brevity, insignificant specific factors are omitted. T-values are in parenthesis. *, **, *** indicate statistical significance at the 10%, 5%, 1% level.

commercial bank conducts its securitization business, the risk exposure of the bank is the main determinant. The lower bank risk correspond to the more securitization.

5.2.2. The impact of factors on the amount and frequency of banks' securitization We use the Tobit regression method to study the influence of determinant factors on the securitization amount of commercial banks. Table 10 and Table 11 show the results of the Tobit regression on the amount of issuances of asset-backed securities and the number of issuance (i.e. frequency). Column (7) and column (8) of Table 10 show that the impact of low cost/low risk factor and high loan risk factor on the annual securitization amount is significantly negative, which is consistent with the impact on annual securitization decisions. The impact of the high capital adequacy factor on the annual securitization amount is still negative but not significant; the impact of the high profitability factor on the annual securitization amount is positive and significant. The above differences between the securitization decision-making regression and the securitization scale regression is possibly due to the incentives of capital arbitrage and performance promotion have various strengths in the different-sized banks. In larger banks, the performance promotion incentives are strong, while in small-sized banks, capital arbitrage incentives are strong. Table 12 corroborates this inference. The results of random-effects regression shown in Columns (9) and (10) are basically the same as the results above. Table 11 examines how the determinants influence the annual issue frequency of asset-backed securities. The regression coefficients of the factors in this table are highly consistent with the outcomes of securitization decisions and issuance amounts. Combining results in Tables 10 and 11, we can see the impact strength of each factor. Suppose that the high profitability factor increases by one standard deviation, a commercial bank will originate about 0.3 periods16 less on average, equivalent to RMB 1.6 billion less of asset-backed securities. Suppose that the low cost/low risk factor increases by a standard deviation on average, a commercial bank will issue about 1.5 periods more of asset securitizations, equivalent to about 8.4 billion more of asset-backed securities. If the high loan risk factor increases by one standard deviation, the commercial bank will issue an average of 0.5 periods less of asset securitizations, equivalent to 3.1 billion more of securities. These results indicate that the low cost/low risk factor has a greater impact on 16

The periods means number of securitizations. 13

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Table 10 Panel Tobit regression results of securitization amounts.

High asset liquidity Low funding liquidity High profitability Low cost/Low bank risk High capital adequacy High loan risk Low loan-deposit ratio

(7)

(8)

(9)

(10)

Pool

Pool

RE

RE

3.238 (0.36) 0.833 (0.09) 12.92 (1.35) 87.61⁎⁎⁎ (6.33) −5.959 (−0.48) −26.91⁎ (−1.93) −13.00 (−1.45)

4.428 (0.47) 1.575 (0.16) 12.01 (1.21) 86.41⁎⁎⁎ (5.99) −5.538 (−0.44) −27.29⁎ (−1.91) −12.76 (−1.38)

Yes Yes −303.2⁎⁎⁎ (−5.03) −937.785

9.557 (1.05) 11.67 (1.32) 20.23⁎⁎ (2.13) 82.34⁎⁎⁎ (5.93) −9.264 (−0.73) −35.55⁎⁎ (−2.41) −10.51 (−1.15) 8.667 (0.03) −46.88 (−0.40) 51.33 (1.02) −172.8 (−0.41) No Yes 564.8 (0.39) −964.373

Yes Yes −300.7⁎⁎⁎ (−4.89) −937.427 202.93⁎⁎⁎

10.41 (1.10) 12.32 (1.36) 20.06⁎⁎ (2.08) 81.51⁎⁎⁎ (5.69) −9.048 (−0.71) −35.97⁎⁎ (−2.39) −10.54 (−1.14) 11.64 (0.03) −48.29 (−0.41) 51.01 (1.02) −177.4 (−0.42) No Yes 585.4 (0.40) −964.275 192.49⁎⁎⁎

329.40⁎⁎⁎ 0.1494 696

276.23⁎⁎⁎ 0.1253 696

696

696

y_depo m2 y_tr y_aaaspread Year Dummy Class Dummy _cons Log Likelihood Wald test LR test Pseudo R2 N

Notes: For brevity, insignificant specific factors are omitted. T-values are in parenthesis. *, **, *** indicate statistical significance at the 10%, 5%, 1% level.

the scale (amount and frequency) decisions of commercial bank securitization. The results in Tables 10 and 11 show that the summary on determinants of commercial bank securitization is robust. First, the main determinants of asset securitization of China's commercial banks include performance promotion, capital regulatory arbitrage and cost advantage exploitation. Second, the empirical result is against the existence of risk transfer incentive and shows that lower risk banks securitize more. Third, empirical results cannot prove the liquidity demand incentive. 5.3. Robust tests 5.3.1. The influence of year on banks' securitization decision We examine whether the determinants for commercial bank securitization change before and after the CBRC announced the adoption of the filing system for the securitization of credit assets in November 2014. The filing policy simplifies the approval procedures for the loan securitization business and reduces the cost of asset securitization. Its purpose is to encourage commercial banks to participate in the asset securitization business. Dividing the time period by the end of 2014, we can observe the difference in determinants of the asset securitizations before and after the implementation of the filing system. Table 12 shows the regression results of sub-period regressions with securitization decisions and scale (amount and frequency) as explained variables. Columns (15) to (17) are the regression results for the 2012–2014 sub-sample. The low cost/low risk factor has a significantly positive coefficient, the high capital adequacy factor has a significantly negative coefficient, and the high loan risk factor has a significantly negative coefficient. This is consistent with the results of the full sample regression in Table 9. Columns (18) to (20) report the results from 2015 to 2017. The coefficient of low cost/low risk factor is still significantly negative, but coefficients of the high capital adequacy factor and high loan risk factor are not significant. The above analysis seems to suggest that the commercial banks' incentive to securitize have weakened since 2015. However, it should be noted that the constant items of the 2015–2017 subsample regression are significantly higher than the constant items of the 2012–2014 subsample, indicating that the average commercial bank asset securitization engagements, amounts, and frequencies have improved significantly after 2015. The above differences indicate that commercial banks have indeed carried out more asset securitization businesses since 2015. The reason have no relevance with the incentive mechanism under cost-benefit considerations, but with changes in the supervision authority's restriction to securitization. To verify this conjecture, we use the dummy variables 14

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Table 11 Tobit regression on number of securitizations each year.

High asset liquidity Low funding liquidity High profitability Low cost/Low bank risk High capital adequacy High loan risk Low loan-deposit ratio

(11)

(12)

(13)

(14)

Pool

Pool

RE

RE

0.00306 (0.02) −0.0764 (−0.47) 0.230 (1.38) 1.544⁎⁎⁎ (6.38) −0.282 (−1.28) −0.452⁎ (−1.90) −0.151 (−0.98)

0.0379 (0.23) −0.0435 (−0.25) 0.198 (1.13) 1.502⁎⁎⁎ (5.75) −0.242 (−1.07) −0.469⁎ (−1.88) −0.143 (−0.88)

Yes Yes −5.251⁎⁎⁎ (−5.02) −399.998

0.122 (0.75) 0.151 (0.98) 0.377⁎⁎ (2.23) 1.473⁎⁎⁎ (5.90) −0.359 (−1.54) −0.622⁎⁎ (−2.39) −0.103 (−0.64) −0.704 (−0.12) −0.510 (−0.24) 0.852 (0.95) −2.211 (−0.29) No Yes 6.922 (0.26) −430.764

Yes Yes −5.142⁎⁎⁎ (−4.74) −398.497 181.92⁎⁎⁎

0.158 (0.93) 0.188 (1.15) 0.373⁎⁎ (2.11) 1.434⁎⁎⁎ (5.38) −0.333 (−1.39) −0.647⁎⁎ (−2.38) −0.106 (−0.63) −0.652 (−0.11) −0.544 (−0.26) 0.839 (0.96) −2.306 (−0.31) No Yes 7.488 (0.29) −430.086 167.33⁎⁎⁎

332.47⁎⁎⁎ 0.2936 696

270.94⁎⁎⁎ 0.2392 696

696

696

y_depo m2 y_tr y_aaaspread Year Dummy Class Dummy _cons Log Likelihood Wald test LR test Pseudo R2 N

Notes: For brevity, insignificant specific factors are omitted. T-values are in parenthesis. *, **, *** indicate statistical significance at the 10%, 5%, 1% level.

that represent 2014 and its interaction terms with each factor to perform another regression. The results are shown in Table 13. In Table 13, Columns (21) to (24) report the regression results for securitization decisions (with the dummy D and without the dummy D), total securitization amounts, and the total number of securitization issuance, respectively. In each regression, the interaction of dummy variable D and a determinant factor was added to examine changes in the securitization determinants of commercial banks before and after the implementation of the securitization filing system. D is 0 before 2014 (including 2014), and 1 after 2014. The regression coefficient of each factor is consistent with the coefficient obtained by regression in Table 8. Most of the coefficients of the interaction terms are not significant. The interaction of low cost/low risk factor and D is the only significant coefficient, and the sign is the same with the coefficient of low cost/low risk factor itself. Meanwhile, the coefficient of D is significantly positive, indicating that the securitization business of commercial banks increased significantly in 2015 and later; however, the increased part of business cannot be explained by the determinant factor under the cost-benefit framework. The above results corroborate the results of the analysis in Table 12 that the incentive mechanism under the cost-benefit framework does not change before and after the end of 2014. The implementation of the filing system policy has released signals of encouragement to the commercial banks' securitization business, but it has not fundamentally increased the return-based securitization incentives. Thus, the influence may diminish over time. 5.3.2. The influence of commercial bank type on the securitization decision Finally, we examine the differences between the incentive mechanisms of different types of commercial banks. We merge largescale commercial banks and joint-stock banks into one category, which is called the nationwide commercial bank group, and merge city commercial banks and rural commercial banks into another category, which is called the regional commercial bank group. The analysis of columns (25), (26) and (27) in Table 14 reveals the factors influence of nationwide commercial banks. First, the empirical results of nationwide commercial banks show that the low cost/low risk factors has a positive impact on securitization decisions, and the high capital adequacy factor has a negative impact on securitization decisions. The two factors have insignificant coefficients. The impact of high profitability factor on securitization decisions is significantly positive. Therefore, the main securitization determinant of nationwide commercial banks is the performance promotion, and possible determinants include capital regulatory arbitrage and cost advantage exploitation. Second, the impact of high loan risk factor on the decision to securitize is significantly negative, which indicates that the risk transfer incentive does not exist. Analyzing the regression results from columns 15

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Table 12 Logistic regression on Banks' securitization decisions in sub-periods. 2012–2014

High asset liquidity Low funding liquidity High profitability Low cost/Low bank risk High capital adequacy High loan risk Low loan-deposit ratio Year Dummy Class Dummy _cons Log Likelihood LR test Pseudo R2 N

2015–2017

(15)

(16)

(17)

(18)

(19)

(20)

Issec

Secamt

Secnum

Issec

Secamt

Secnum

−0.201 (−0.67) 0.299 (0.94) 0.265 (0.67) 1.929⁎⁎⁎ (4.12) −1.506⁎⁎⁎ (−2.74) −1.114⁎⁎ (−2.02) 0.471 (1.31) No Yes −4.690⁎⁎⁎ (−3.83) −71.704 60.99⁎⁎⁎ 0.2984 348

−6.392 (−0.51) 11.74 (0.92) 19.89 (1.32) 91.03⁎⁎⁎ (4.08) −62.05⁎⁎⁎ (−2.65) −47.32⁎⁎ (−2.04) 11.91 (0.82) No Yes −229.0⁎⁎⁎ (−3.68) −226.461 67.74⁎⁎⁎ 0.1301 348

−0.0936 (−0.36) 0.249 (0.94) 0.359 (1.13) 1.855⁎⁎⁎ (3.90) −1.264⁎⁎ (−2.58) −1.081⁎⁎ (−2.17) 0.238 (0.78) No Yes −4.701⁎⁎⁎ (−3.50) −110.050 63.83⁎⁎⁎ 0.2248 348

−0.0266 (−0.15) −0.0404 (−0.22) 0.162 (0.96) 1.095⁎⁎⁎ (4.30) −0.699⁎⁎ (−2.52) −0.342 (−1.32) −0.0951 (−0.55) No Yes −0.924⁎⁎⁎ (−2.66) −149.322 87.98⁎⁎⁎ 0.2276 333

1.480 (0.14) −3.239 (−0.28) 12.98 (1.19) 81.92⁎⁎⁎ (4.99) −5.169 (−0.33) −22.48 (−1.39) −12.61 (−1.15) No Yes 28.60 (0.52) −727.250 174.07⁎⁎⁎ 0.1069 348

0.0285 (0.16) −0.136 (−0.69) 0.284 (1.55) 1.391⁎⁎⁎ (5.00) −0.286 (−1.06) −0.386 (−1.42) −0.149 (−0.82) No Yes 0.684 (0.73) −308.247 172.44⁎⁎⁎ 0.2186 348

Notes: For brevity, insignificant specific factors are omitted. T-values are in parenthesis. *, **, *** indicate statistical significance at the 10%, 5%, 1% level.

(28) to (30), we show the factor influence of local commercial banks. The impact of high profitability factor on securitization decisions is positive but not significant. The low cost/low risk factor has a significant positive impact. The high capital adequacy factor has a significant negative impact on securitization decisions. Therefore, the main determinant of securitization of local commercial banks is driven by cost advantage exploitation and capital regulatory arbitrage. The performance promotion incentive exists in the regional bank sample. Also, consistent with the results of the nationwide bank sample, the high loan risk factor of local commercial banks has a significant negative impact on securitization decisions, which indicates that the risk transfer incentive is not working. In analyzing the above subsample regressions, we have the following conclusions. From a time-line perspective, there was no significant change in the securitization causes of commercial banks around the implementation of the filing system at the end of 2014. From a cross-section perspective, the nationwide commercial banks and the regional commercial banks are the same on securitization determinants, but are different in their strength. In sum, the empirical results of the subsamples support the empirical results in the full sample, which indicates that the empirical tests in this study are robust. 6. Conclusion In this study, we investigate the determinants of China's commercial bank loan securitization business at the micro-level. We establish a model of the net return on loan securitization under the framework of the newly constructed cost-benefit analysis and empirically examine the theoretical determinant hypothesis using a sample of China's commercial banks during 2012–2017. The findings from this study are discussed as following. First, we find that the net return from the securitization of commercial bank loans can be decomposed into five independent components, which denote the liquidity demand incentive, the regulatory arbitrage incentive, the performance promotion incentive, the risk transfer incentive, and the incentive for cost advantage exploitation. Each component reflects one of the determinants for commercial banks to engage in securitization of loans. When the total sum of the net return is greater than zero, the commercial bank will securitize its loans. The frequency and amount of securitization is supposed to increase with the banks' net return. Second, to eliminate multi-collinearity between proxy variables of securitization determinants, we use a factor analysis method to translate the proxy variables corresponding to the determinants into seven factors. Based on the regression of factors on the securitization business variables, this study provides evidence that banks' securitization businesses increase with the high profitability factor and low cost/low risk factor and that securitization businesses decrease with high capital adequacy factor and high loan risk factor. These results mean that the real determinants of Chinese banks' securitization include performance promotion, cost advantage exploitation, and capital regulatory arbitrage, while the determinants not include risk transfer. The comparative results between the 2012–2014 and 2015–2017 subsample regressions show that the implementation of the filing system at the end of 2014 enabled more commercial banks to carry out asset securitization businesses in China, while it not increased the incentives consistent with the cost16

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Table 13 Filing system's effect on the Banks' securitization behaviors.

High asset liquidity Low funding liquidity High profitability Low cost/Low bank risk High capital adequacy High loan risk Low loan-deposit ratio High asset liquidity×D Low funding liquidity×D High profitability×D Low cost/Low bank risk×D High capital adequacy×D High loan risk×D Low loan-deposit ratio×D D Year Dummy Class Dummy _cons Log Likelihood LR test Pseudo R2 N

(21)

(22)

(23)

(24)

Issec

Issec

Secamt

Secnum

−0.174 (−0.62) 0.237 (0.87) 0.130 (0.37) 0.953⁎⁎⁎ (3.01) −0.967⁎⁎ (−2.33) −0.772⁎ (−1.65) 0.523⁎ (1.66) 0.102 (0.31) −0.190 (−0.58) 0.0497 (0.13) 0.474 (1.50) 0.204 (0.45) 0.424 (0.82) −0.697⁎⁎ (−1.97) 2.111⁎⁎⁎ (4.65) No Yes −2.148⁎⁎ (−2.60) −227.014 227.87⁎⁎⁎ 0.3342 696

0.0907 (0.29) 0.0584 (0.17) 0.235 (0.53) 1.440⁎⁎⁎ (3.54) −0.524 (−1.22) −0.921⁎ (−1.74) 0.0678 (0.19) −0.344 (−0.94) −0.140 (−0.35) −0.188 (−0.39) 0.0587 (0.14) −0.213 (−0.46) 0.713 (1.25) −0.225 (−0.58)

−7.831 (−0.51) 12.63 (0.90) 14.87 (0.83) 55.01⁎⁎⁎ (3.22) −40.45⁎ (−1.86) −38.78 (−1.53) 15.59 (0.94) 6.848 (0.37) −10.18 (−0.57) −1.481 (−0.07) 48.13⁎⁎⁎ (3.12) 30.30 (1.30) 15.39 (0.53) −33.16⁎ (−1.72) 109.3⁎⁎⁎ (4.54) No Yes −147.3⁎⁎⁎ (−3.33) −961.361 282.25⁎⁎⁎ 0.1280 696

−0.137 (−0.51) 0.228 (0.94) 0.232 (0.74) 0.965⁎⁎⁎ (3.20) −0.681⁎ (−1.81) −0.748⁎ (−1.67) 0.289 (0.99) 0.122 (0.38) −0.270 (−0.88) 0.0613 (0.17) 0.836⁎⁎⁎ (3.07) 0.254 (0.62) 0.332 (0.66) −0.524 (−1.56) 2.128⁎⁎⁎ (5.08) No Yes −2.575⁎⁎ (−3.27) −425.1492 282.17⁎⁎⁎ 0.2492 696

Yes Yes −4.415⁎⁎⁎ (−3.46) −199.687 282.52⁎⁎⁎ 0.4143 696

Notes: For brevity, insignificant specific factors are omitted. T-values are in parenthesis. *, **, *** indicate statistical significance at the 10%, 5%, 1% level.

benefit framework. The results of comparison between the nationwide banks and regional banks subsample regressions show that the two types of commercial banks have different determinants of securitization. In the subsample of nationwide commercial banks, securitization businesses increases with high the profitability factor, while in the subsample of regional commercial banks, securitization businesses increases with the low cost/low risk factor and high capital adequacy factor. The findings from this study have strong policy implications. First, banks with a larger low cost/low risk factor and larger high loan risk factor engaged more in the loan securitization businesses. Regardless of whether the approval system of securitization issuance changed to the filing system at the end of 2014, the above proposition is correct. This finding indicates that banks have difficulty in transferring credit risk at unreasonably high prices in the current market environment; in the meantime, the number of participants of asset securitization is also limited. It is difficult for banks with high-risk exposure to conduct asset securitization businesses for the purpose of manage credit risk. Since information on fundamental assets are not transparent to investors, investors judging the risk of asset-backed securities can only rely on the originating bank's reputation. Consequently, investors do not trust high-risk banks. Policies aimed at strengthening information disclosure in asset securitization businesses and increasing information transparency will reduce possible adverse selection problems in the asset securitization market and allow high-risk banks to participate in asset securitization businesses. In China, a key problem related to asymmetric information is that the rating agencies have low public credibility. Thus, it is necessary to improve rating efficiency and the credibility of rating agencies. Second, banks with larger high profitability factors and smaller capital adequacy factors are more active in the asset securitization market. This indicates that the asset securitization businesses can help banks improve their operating efficiency and capital efficiency. Prior to the introduction of the new regulations on asset management in 2018, commercial banks in China mainly used shadow banking businesses such as wealth management products (WMPs) to expand credit, neglecting a great system risk due to lack of supervision and transparency. The overall findings from this study indicate that other shadow banking channels should be restricted 17

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Table 14 Regression on Banks' securitization decisions in sub-classes of banks. National commercial banks

High asset liquidity Low funding liquidity High profitability Low cost/Low bank risk High capital adequacy High loan risk Low loan-deposit ratio y_depo m2 y_tr y_aaaspread Year Dummy Class Dummy _cons Log Likelihood LR test Pseudo R2 N

Regional commercial banks

(25)

(26)

(27)

(28)

(29)

(30)

Issec

Secamt

Secnum

Issec

Secamt

Secnum

0.269 (0.48) 0.488 (0.79) 1.669⁎ (1.86) 1.499 (1.09) −1.911⁎⁎ (−2.06) −2.488⁎⁎ (−2.19) 1.259⁎⁎ (1.99) 6.295 (0.38) −4.022 (−0.69) −0.927 (−0.39) −12.64 (−0.60) No Yes 57.17 (0.78) −35.030 68.15⁎⁎⁎ 0.4931 102

40.06 (1.44) 41.77 (1.15) 110.6⁎⁎ (2.45) 89.53 (1.27) 38.98 (0.85) −137.4⁎⁎⁎ (−2.68) 40.95 (1.41) 351.9 (0.51) −237.8 (−0.99) −9.704 (−0.10) −616.7 (−0.72) No Yes 2954.6 (0.99) −394.539 92.31⁎⁎⁎ 0.1047 102

0.593 (1.46) 0.610 (1.19) 1.918⁎⁎⁎ (2.90) 1.223 (1.19) 0.309 (0.46) −1.924⁎⁎ (−2.57) 0.833⁎ (1.96) 4.391 (0.43) −3.333 (−0.94) −0.430 (−0.29) −8.639 (−0.68) N Yes 44.30 (1.01) −139.220 98.91⁎⁎⁎ 0.2621 102

−0.103 (−0.62) 0.0123 (0.08) 0.134 (0.83) 1.347⁎⁎⁎ (5.73) −0.701⁎⁎⁎ (−2.95) −0.399⁎ (−1.69) −0.180 (−1.09) −2.293 (−0.33) 0.491 (0.20) 0.707 (0.75) 0.849 (0.10) No Yes −6.574 (−0.22) −184.017 78.59⁎⁎⁎ 0.1760 594

−0.760 (−0.20) −0.474 (−0.12) 4.999 (1.24) 40.02⁎⁎⁎ (6.35) −19.81⁎⁎⁎ (−3.34) −12.00⁎⁎ (−1.99) −4.761 (−1.17) −101.3 (−0.64) 27.43 (0.50) 26.48 (1.16) 75.19 (0.38) No Yes −365.1 (−0.54) −509.463 93.81⁎⁎⁎ 0.0843 594

−0.0303 (−0.22) −0.0818 (−0.61) 0.197 (1.39) 1.244⁎⁎⁎ (5.50) −0.660⁎⁎⁎ (−3.14) −0.432⁎⁎ (−2.02) −0.146 (−1.02) −2.109 (−0.37) 0.429 (0.22) 0.695 (0.86) 0.679 (0.10) No Yes −5.803 (−0.24) −263.841 87.51⁎⁎⁎ 0.1422 594

Notes: For brevity, insignificant specific factors are omitted. T-values are in parenthesis. *, **, *** indicate statistical significance at the 10%, 5%, 1% level.

and commercial banks should be encouraged to conduct the asset securitization business to activating stock credit assets and improve the efficiency of capital financing. This provides evidence supporting China's series of supervision institution reforms such as the new regulations put in place on asset management in 2018. Acknowledgement The authors acknowledge the support from the National Nature Science Funds of China (No. 71471043, No. 71771056). References Affinito, M., Tagliaferri, E., 2010. Why do (or did?) Banks Securitize their loans? Evidence from Italy. J. Financ. Stab. 6 (4), 189–203. Agarwal, S., Chang, Y., Yavas, A., 2012. Adverse selection in Mortgage Securitization. J. Financ. Econ. 105 (3), 640–661. Agostino, M., Mazzuca, M., 2009. Why do Banks Securitize? Evidence from Italy. BANCARIA 9, 18–38. Almazan, A., Martín-Oliver, A., Saurina, J., 2015. Securitization and Banks' Capital Structure. Rev. Corporate Finan. Stud. 4 (2), 206–238. Ambrose, B., Lacour-Little, M., Sanders, A., 2005. Does regulatory capital arbitrage, reputation, or asymmetric information drive securitization? J. Financ. Serv. Res. 28, 113–134. Bannier, C.E., Hänsel, D.N., 2008. Determinants of European Banks' Engagement in Loan Securitization. Deutsche Bundesbank (SSRN Working Paper). Basel Committee on Banking Supervision, 2012, “Report on Asset Securitisation Incentives,” Working Paper. Bensalah, N., Fedhila, H., 2016. What explains the Recourse of U.S. Commercial Banks to Securitization? Rev. Account. Finan. 15 (3), 317–329. Calem, P.S., Lacour-Little, M., 2004. Risk-based capital requirements for Mortgage loans. J. Bank. Financ. 28 (3), 647–673. Calomiris, C.W., Mason, J.R., 2004. Credit card securitization and regulatory arbitrage. J. Financ. Serv. Res. 26 (1), 5–27. Cardone-Riportella, C., Samaniego-Medina, R., Trujillo-Ponce, A., 2010. What drives bank securitisation? The Spanish experience. J. Bank. Financ. 34 (11), 2639–2651. Cumming, C., 1987. The economics of securitization. Quart. Rev. 11–23. Demarzo, P.M., 2005. The Pooling and Tranching of Securities: a Model of Informed Intermediation. Rev. Financ. Stud. 18 (1), 1–35. Demarzo, P., Duffie, D., 1999. A liquidity-based model of security design. Econometrica 67 (1), 65–99. Farruggio, C., Uhde, A., 2015. Determinants of loan securitization in European Banking. J. Bank. Financ. 56, 12–27. Faulkender, M., Wang, R., 2006. Corporate financial policy and the value of cash. J. Financ. 61 (4), 1957–1990. Greenbaum, S.I., Thakor, A.V., 1987. Bank funding modes: Securitization versus deposits. J. Bank. Financ. 11 (3), 379–401. Griffin, J., Lowery, R., Saretto, A., 2014. Complex securities and underwriter reputation: do Reputable underwriters produce Better Securities? Rev. Financ. Stud. 27

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