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25
Oct
2015

Results of Credit Ratings and Speedy Payday Loans

 Banking RelationshipsThe empirical analysis procedures of this study are summarized in the following four parts. First, we use the t test and the nonparametric Wilcoxon rank sum methods to verify whether the mean or median of the banks with or without “WOBD” and “HCCL”, banks with four different types of ownership, and companies with high or low credit risk exhibit significant statistical differences in their characteristics. Then, we select publicly-held borrowing companies as empirical and control samples from banks with or without “WOBD” to test the impact of clients’ credit status and relationships on loan spreads under the regression model. We divide our clients with banks with or without WOBD as experimental samples and controlling samples and use the ordinary least squares empirical model to examine the factors impacting the loan spreads. The impacting factors include the financial attributes of banks and borrowing companies. In addition, we take the lending performance, ownership, banking relationship and the credit state (upgrade or downgrade) into consideration. As for understanding whether the loan policy-making process is just, we use the TCRI index to show the credit risk level of borrowing companies. Moreover, in this study we also include the interaction items of different credit systems and lending behavior to further explore the factors impacting the loan spread. In the third section, we apply the same method as in the second section except that the sample bank groups are changed to banks with or without “HCCL”. In the fourth section, we perform the robustness test by extending the empirical period to 2010 and discuss certain important variables in detail.

Descriptive Statistics

As mentioned earlier, we shall determine the creditor banks first, then select the borrowing companies which are publicly-held from related banks as empirical and control samples. Since our samples are from creditor banks and borrowing companies, therefore we introduce the summary statistics of each index individually. With regard to the samples of creditor banks, we discuss this by dividing the banks into those with and without “WOBD”, with and without “HCCL”, as well as four different types of ownership. As to the characteristics of the banks, we use six indexes, namely, those of total assets, the cost-to-revenue ratio, the return on assets, the return on equity, the pretax income ratio and the gross profit ratio. Moreover, we divide the borrowing companies into high and low credit risk companies by the credit ratings of TCRI and discussing the characteristics in terms of total assets, the pretax income ratio, debt ratio, TCRI rating, loan rate and loan spreads. The loans are widespread but Speedy Payday Loans and the website – www.speedy-payday-loans.com becomes more and more popular because they really help people to live from the wage till the wage again.

We use the t test and nonparametric methods to verify whether there exist significant differences among banks with or without “WOBD” and “HCCL” as well as among borrowing companies with high and low credit risk based on the mean and median for each index for the four different types of ownership. Table 2 shows the results of the 126 observations for creditor banks. During the empirical period there are 21 observations for 7 banks with “WOBD” and 105 observations for 35 banks without “WOBD”. Furthermore, there are 21 observations from 7 banks with “HCCL” and 105 observations from 35 banks without “HCCL”, but for M&A reasons, therefore the final number of observations is 104.

Based on a comparison of banks with or without “WOBD”, the mean and median of total assets in banks with “WOBD” are NT$1,646 billion and NT$1,574 billion, respectively, which is significantly higher than NT$1223.1 billion and NT$1102.5 billion for banks without “WOBD” at the 1% level. Ownerships It is worth mentioning that although banks with “WOBD” signal poor performance in the past five years, due to the advantages of their large scale, excellent human resources and adaptability, the operating performance of the subsequent three years are significant higher than banks without “WOBD”. We show the detailed figures for the five indexes as follows: Banks with “WOBD” are obviously better than banks without “WOBD”, the cost revenue ratio ranges from 27.21% to 34.52%, ROA from 0.26% to 0.05%, ROE from 4.82% to -1.74%, the pretax income ratio ranges from 11.00% to 0.38%, and the gross profit ratio from 34.07% to 28.09%. We stated that banks with “HCCL” signal poor performance in retail finance. Panel B in Table 2 shows that banks with “HCCL” are characterized by their smallness of the scale of their assets, poor performance (measured by their cost-to-revenue ratio) and profitability. The mean and median of the total assets of the banks with “HCCL” (NT$957.6 billion and NT$857.3 billion) are significantly lower than for banks without “HCCL” (NT$1452.5 billion and NT$1589.7 billion). Overall, the operating efficiency and profitability of banks with “HCCL” are lower than for banks without “HCCL”. This is displayed by the medians of the following five indexes: the cost-to-revenue ratio ranges from 31.29% to 25.74%, ROA from 0.1% to 0.42%, ROE from 1.68% to 7.79%, the pretax income ratio from 4.28% to 16.53% and the gross profit ratio from 34.48% to 35.63%.

Table 3 presents a comparison of the four different types of ownership for banks from the perspective of total assets, operating efficiency and profitability. The empirical results indicate that the total assets scale ratings are as follows: the state-owned banks (mean NT$2582.3 billion), private financial holding companies (mean NT$1355.0 billion), private non-financial holding companies (mean NT$439.0 billion) and foreign banks (mean NT$2872 billion). In terms of the cost revenue ratio displaying the operating efficiency, the worst is state-owned banks (median 52.57%) followed by foreign banks (median 38.22%), private non-financial holding companies (median 30.35%) and private financial holding companies, which have the best operating efficiency (median 25.74%). From the perspective of profitability, we find that state-owned and private holding banks are little different, the means of ROA and ROE range from 0.37% to 0.30% and 9.33% to 3.00%, respectively, and are significantly higher than for private non-financial holding banks and foreign banks for which the means range from -0.54% to -0.65% and -10.11% to -14.77%.

Table 4 summarizes the descriptive statistics of the borrowing companies’ characteristics, which include 36,024 observations from 2003 to 2008. Distinguished by their credit risk, companies with TCRI = 7 are regarded as high credit risk companies, with a total of 16,187 observations, while TCRI = 4 are regarded as low credit risk companies, with a total 4,643 observations. From the observations we find that most publicly-held companies are with high credit risk, which is featured by the disadvantages in terms of the small scale of assets, poor profitability, a high debt ratio and loan spreads. Compared to companies with high credit risk, companies with low credit risk display the advantages in the following indexes: total assets ranging from 113.906 billion to 4.879 billion; a pretax income ratio from 10.91% to -1.95%; a debt ratio from 41.40% to 53.41%; a borrowing interest rate from 2.909% to 3.534%, and a borrowing spread from -1.429% to -0.734%.

Factors impacting the loan spreads – banks with or without “WOBD”

This section aims to focus on banks with or without “WOBD”, and from those banks we select the relationship borrowers as samples to explore the factors impacting the loan spreads. performanceBy distinguishing these factors from the aspects of creditor banks and borrowing companies, for the former we emphasize lending performance, ownership type and relationships, and for the latter we focus on credit risk ratings and ratings upgrade or downgrade statuses. The empirical results are shown in Table 5. Model in Table 5 simply verifies the impact of related dimensions on loan spreads. Model includes the interaction items of credit risk of the borrowing companies (measured by a dummy variable) with each dimension verifying whether the level of credit risk impacts the loan spreads. Furthermore, model includes the upgrade and downgrade credit status of borrowing companies to verify how they affect the loan spreads.

The results of model in Table 5 indicate that for borrowing companies with a high credit risk level, the creditor banks will charge higher loan spreads. The loan spreads for banks with WOBD are obviously higher than for banks without WOBD, a finding that is consistent with the empirical results of Coleman et al.. The loan spreads of private financial holding institutions and private non-financial holding banks are significantly lower than those for state-owned banks, which means that after the M&A involving the financial institution, the private financial holding institutions make the most of lowering their interest rates to attract borrowers. The loan spreads of private financial holding institutions are significantly lower than those of the non-financial holding banks which imply that the private financial holding institutions create scale and scope economies by means of expanding the territory and receive more competition advantages in their lending business. The foreign banks seem to be conservative in the Taiwan banking industry for, perhaps out of consideration for the cost of capital, they always required higher loan spreads. The relationship level presents no significant influence on loan spreads which is inconsistent with the finding of Chen and Lai : “the relationship level will affect the loan spreads”. Loans, loans! All people speak about loans but there is some other possibilities to possess money for example via Speedy Payday Loans, this service is available twenty four hours per day.

The results of Empirical model in Table 5 indicate that companies borrowing from banks with WOBD receive higher loan spreads than companies borrowing from banks without WOBD. However, there is no significant difference in loan spreads between borrowing companies dealing with banks with or without WOBD, which suggests that banks with WOBD did not require higher loan spreads from borrowing companies with high credit risk which somehow reveals that banks are unable to take advantage of the borrowers. Although companies borrowing from private financial holding institutions and private non-financial holding banks receive lower loan spreads than state-owned banks, there is no difference in the case of borrowing companies with high credit risk, which is consistent with the findings of Panetta et al. : “After the M&A of Taiwan financial institutions, the information advantage of the Taiwan financial market disappears and banks post-M&A did not possess the ability to take advantage of borrowers.” As for the borrowers with a high level of credit risk, the banks did not ask for an extra risk premium, which may explain why for post-M&A Taiwan financial institutions, banks have increased their risk tolerance, and therefore did not respond to the interest rate. Empirical model presents the results indicating that the loan spreads of foreign banks are significantly higher than those of state-owned banks when facing borrowing companies with high credit risk, which implies that the pricing strategies of foreign banks can reasonably respond to the credit quality of the borrowing companies and pay more attention to the information presented in the financial statements, which is consistent with the findings of Berger-Udell : banks that are large in size, foreign banks and banks facing a financial crisis tend to engage in transactional lending.

Lending PerformanceThe results of Empirical model in Table 5 indicate that in the case of borrowing companies with upgraded credit ratings, banks adversely increase the loan spreads. With regard to the high-risk borrowing companies, if they can upgrade their credit rating 3 years before financing, the bank will lower the loan spreads, revealing that the lending price only responds to high-risk borrowing companies when their credit quality improves. For common borrowers, banks do not adjust the interest rate for them even if their credit qualities have improved. With respect to the characteristics of the borrowing companies, we find that for borrowing companies that are small in size and have high debt ratios, banks will often grant them higher loan spreads. From the characteristics of the banks, we find that the banks that are large in size and characterized by low efficiency will often give borrowers higher loan spreads.

Factors impacting the loan spreads – banks with or without “HCCL”

The results of model in Table 6 show that borrowing companies with high credit risk often receive higher loan spreads. Banks with HCCL will require higher loan spreads which are consistent with the conclusions of “the companies that borrow from the banks with WOBD will receive higher loan spreads,” which also reveal that banks that are heavily impacted by retail and corporate finance will ask the borrower to pay a higher interest rate. Model also indicates that the private financial holding institutions and private non-financial holding institutions will grant lower loan spreads than state-owned banks. The largest and main banks with closer relationships will grant borrowing companies lower loan spreads, and the largest banks will grant lower loan spreads than the main banks which is consistent with the findings of Berger-Udell that the borrowing companies which have closer relationships with the creditor banks can receive lower loan spreads.

Model includes all interaction items related to the borrowing companies’ credit risk with each dimension, and the results show that companies borrowing from banks with HCCL receive higher loan spreads than companies borrowing from banks without HCCL. However, if we only verify borrowing companies with high credit risk, the adverse results indicate that banks with HCCL will grant lower loan spreads than banks without HCCL. Therefore we refer the heavy losses to banks with HCCL to their ignoring the strict review of the identification, financial position and solvency of the borrowers while issuing the card debt and simultaneously as banks are regarded as having high credit risk. The findings indicate that banks with HCCL failed to learn their lessons from past experiences and still granted lower loan spreads to high credit risk borrowing companies in their corporate finance business. Private financial holding institutions and private non-financial holding institutions grant lower loan spreads than state-owned banks. However, if we focus on a comparison of the borrowing companies with high credit risk, there is no statistically significant difference, which implies that the M&A of financial institutions does not work in terms of increasing the ability to sift borrowers. Through model we find that the largest banks grant lower loan spreads to borrowing companies, especially the borrowing companies with high credit risk, which we still found granted excellent loan spreads to borrowers with closer relationships. price The findings indicate that the fact that the largest and main banks grant lower loan spreads to borrowers with high credit risk are consistent with the prediction of Chen and Lai : when banks build intimate relationships with borrowers, they deeply understand the profitability of the borrowing companies, if they envision the true performance potential of the enterprises, they will be delighted to grant lower loan spreads. Another possible reason is that the loan spreads based on relationship lending do not truly reflect the credit risk of the borrowers.

Finally, the evidence shows that the borrowing companies had raised the credit rating in at least two years during the past three years, and that the creditor banks had adversely increased the loan spreads, which obviously indicates that banks in Taiwan are reluctant to implement the review system even in such a dramatically competitive lending market characterized by significant information asymmetry. If we include the interaction of both high credit risk and the upgrading state in the model, we will find that high-credit risk borrowing companies with an upgraded state can receive lower loan spreads, which is consistent with the results of model in Table 5, and indicates that creditor banks will positively reflect the price of risk while the high credit risk borrowing companies will significantly increase their credit ratings.

Robustness test – measuring the relationships based on financing ratios for different types of bank ownership

Since dividing the creditor banks into those characterized by high and low credit risk to observe the lending behavior and examine the factors impacting loan spreads may lack generality and validity, we perform a robustness test. First, we do not deliberately select the samples off borrowing companies from the high or low credit risk creditor banks, but on the contrary we randomly select the samples of borrower from listed, OTC and emerging companies for the period 2000-2010, and have a total of 4,381 observations. The total assets of these companies amount to 16.094 billion on average and the average credit rating is higher than level 6 based on TCRI. Then, according to the concept presented by Kuo and Chen, we measure the relationships based on the ratio of the financing amount to the total assets of each sample borrower instead of using the biggest creditor, the main creditor and non-main creditor to distinguish the relationships. By dividing the creditor banks based on the ownership, we find that most (i.e., 2,253) of the sample companies borrow from state-owned banks, followed by private financial holding banks, private non-financial holding banks (i.e., 784), and finally foreign banks (with only 221 observations). Moreover, we find that the sample of borrowers that obtained loans from foreign banks were characterized by the best credit ratings and the largest scale of assets, and therefore the banks granted lower loan spreads. In addition, the sample of borrowers that obtained loans from the private non-financial holding banks was characterized by the worst credit ratings and profitability ratios. In addition, in taking into consideration the existence of big differences among listed, OTC and emerging companies in terms of issuing conditions, we add two dummy variables to denote the organizational types in this section. While focusing on the lending practices and environment in Taiwan, we find that it is easier for borrower with their larger scale of operations to obtain unsecured borrowing and, therefore, in the empirical model in Table 7 we include a dummy variable to denote whether the sampled borrowers provided collateral.

private financial holding banksTo sum up, the differences between Table 7 and Tables 5 and 6 are that in Table 7 we did not use lending performance (with or without WOBD or HCCL) to denote whether the credit risk of creditor banks was high or low. Furthermore, we added two dummy variables to describe the organizational types and a dummy variable to denote whether the sampled borrowers provided collateral. Moreover, we used the financing ratio among the creditor banks to measure the relationships instead of using the greatest creditor bank, main creditor bank and non-main creditor bank. For comparison purposes, the three empirical models in Tables 5, 6 and 7 are similar. Empirical model is regarded as the basic model of loan spreads which include the characteristics of factors impacting borrowing companies and creditor banks, ownership types, relationships, the upgrading and downgrading of credit ratings, organizational types and whether the borrower provides collateral. In addition to the factors impacting borrowing companies and creditor banks in model, models and use the TCRI index to distinguish high from low credit risk, and also include the interaction items of each dimension to further examine the factors impacting the loan spreads.

According to the results of empirical models, and in Table 7, the loan spreads of private financial holding banks and private non-financial holding banks are significantly lower than those of state-owned banks (in model the coefficient values are -0.0078 and -0.0095), and only the foreign banks are an exception. If we compare the same dimensions in Tables 5 and 7, we find that the results of Table 7 are substantially consistent with Tables 5 and 6, which indicates that in the dramatically competitive financial environment characterized by overbanking, banks have developed a low-loan-spreads strategy, which has led to the results of WOBD and HCCL and has even caused the occurrence of problematic banks that adversely affect the financial development of the country. In recent years, about half of the private banks have operated successfully and sustained their normal lending performance. Therefore the results of Tables 5 and 6 suggest that only foreign banks increase their loan spreads while facing borrowers with high credit risk (the coefficient values in Tables 5 are 0.4968 and 0.4970), regardless of whether private banks belong to financial holding companies; when facing borrowers with high credit risk their loan spreads continue to remain unchanged. However, in Table 7, we find that private financial holding banks will increase their loan spreads while facing borrowers with high credit risk (model in Table 7 shows a coefficient of 0.0189). Private non-financial holding banks will significantly increase their loan spreads when facing borrowers with high credit risk (models and in Table 7 reveals coefficients of 0.0216 and 0.01 with statistical significance at the 5% and 10% levels). The foreign banks adversely reduce their loan spreads when facing borrowers with high risk over the past decade (the coefficient is -0.0122).

As mentioned earlier, only the results of Table 6 indicate that closer relationships can reduce the interest burden of the borrowers; the largest and main banks still significantly reduce their loan spreads when facing borrowers with high credit risk. However, after verifying the past ten years of data for the borrowers, model in Table 7 reveals that only state-owned banks with closer relationships will tend to lower their loan spreads (the coefficient is -0.0028); the remaining three types of banks increase their loan spreads adversely (the coefficient values are 0.0014, 0.0017 and 0.0099, respectively). We also find that only non-financial holding companies that maintain closer relationships with borrowers are willing to reduce their loan spreads when facing borrowers with high credit risk. We recall the results of Tables 5 and 6, which indicate that banks will reasonably increase their loan spreads while the borrowers’ credit risk rises even higher. However, Table 5 indicates that when the high credit risk borrowers see their credit ratings upgraded, the banks would like to lower the loan spreads (the coefficient is -0.9321). According to the results of Table 7, only model indicates that the creditor banks will increase their loan spreads (the coefficient is 0.0237), when the credit risk of the borrowers has been rising during the past ten years. If the borrowing companies increase their credit ratings, the creditor banks will obviously reduce their loan spreads (the coefficient is -0.0165), but when facing high credit risk borrowers with upgraded credit ratings, the loan spreads will adversely abnormally increase (the coefficient is 0.0237).

Moreover the results in Table 7 indicate that the loan spreads extended to listed firms and OTC-listed companies are lower than those extended to emerging companies. In general, the larger the scale of banks and firms, the lower the loan spreads that are given and taken. With regard to the debt and profitability ratios, the expected positive or negative directions are confirmed in Tables 5 to 7. The following conditions are unexpected: the cost-to-revenue ratio and loan spreads displayed are significantly negatively associated (the coefficient is -0.0328); the borrowers with collateral adversely receive higher loan spreads (the coefficient values are 0.0103 and 0.0064, respectively).

Table 2. Description Statistics – by lending performance

Sample Mean Median Std. Min. Max.
Panel A with(wi) or without(wo) “WOBD”
wi Total assets (10 billion) 21 164.6*** 157.40*** 50.99 82.29 248.7
Cost-to-revenue ratio (%) 21 27.21*** 25.74*** 3.63 22.80 34.47
ROA (%) 21 0.26*** 0.42*** 0.72 -2.36 0.82
ROE (%) 21 4.82 *** 10.01*** 13.74 -46.08 13.98
Pretax income ratio (%) 21 11.00*** 16.53*** 20.26 -55.59 33.38
Gross profit ratio (%) 21 34.07*** 35.63*** 16.77 -23.17 53.79
w

o

Total assets (10 billion) 105 122.31 110.25 89.74 0.16 359.41
Cost-to-revenue ratio (%) 105 34.52 26.89 23.05 10.32 267.43
ROA (%) 105 0.05 0.17 1.34 -6.67 5.86
ROE (%) 105 -1.74 2.33 19.01 -113.05 14.7
Pretax income ratio (%) 105 0.38 5.75 30.74 -188.84 72.3
Gross profit ratio (%) 105 28.09 35.3 27.41 -160.86 91.21
Panel B with(wi) or without(wo) “HCCL
w

i

Total assets (10 billion) 21 95 76*** 85.73*** 39.63 16.45 158.61
Cost-to-revenue ratio (%) 21 31.36*** 31 29*** 3.91 24.57 62.41
ROA (%) 21 -0.30*** 0.1*** 1.15 -6.67 0.82
ROE (%) 21 -5.74*** 1.68*** 18.87 -70.06 12.8
Pretax income ratio (%) 21 -6.94*** 4.28*** 26.51 -132.63 18.79
Gross profit ratio (%) 21 25.34*** 34.48*** 25.53 -62.74 53.79
w

o

Total assets (10 billion) 104 145.25 158.97 85.28 0.16 359.41
Cost-to-revenue ratio (%) 104 32.22 25.74 21.09 10.32 267.43
ROA (%) 104 0.27 0.42 1.11 -5.55 5.86
ROE (%) 104 2.80 7.79 16.52 -113.05 14.7
Pretax income ratio (%) 104 8.01 16.53 27.15 -188.84 72.3
Gross profit ratio (%) 104 31.95 35.63 23.44 -160.86 91.21

Table 3. Descriptive Statistics – by ownership of banks

Sample Mean Median Std. Min. Max.
State-owned banks
Total assets (10 billion) 8 258.23 242.15 57.56 184.84 359.41
Cost-to-revenue ratio (%) 8 41.13 52.57 17.01 22.80 64.09
ROA (%) 8 0.37 0.41 0.05 0.31 0.42
ROE (%) 8 9.33 9.73 1.70 7.18 11.30
Pretax income ratio (%) 8 14.84 16.52 2.29 11.71 16.53
Gross profit ratio (%) 8 32.32 31.54 2.08 30.19 35.15
Private financial holding companies
Total assets (10 billion) 42 135.50 148.74 48.20 22.67 205.05
Cost-to-revenue ratio (%) 42 27.66 25.74 17.87 10.32 267.43
ROA (%) 42 0.30 0.49 1.09 -5.17 5.86
ROE (%) 42 3.00 8.13 15.96 -108.02 13.98
Pretax income ratio (%) 42 8.77 16.55 24.25 -121.92 72.30
Gross profit ratio (%) 42 34.14 37.60 20.36 -87.34 91.21
Private non-financial holding companies
Total assets (10 billion) 34 43.90 30.97 39.61 0.16 116.21
Cost-to-revenue ratio (%) 34 36.15 30.35 21.26 15.36 122.44
ROA (%) 34 -0.54 0.01 1.38 -6.67 1.77
ROE (%) 34 10.11 0.24 21.56 -113.05 14.70
Pretax income ratio (%) 34 -14.70 0.72 37.03 -188.84 35.90
Gross profit ratio (%) 34 15.85 24.76 35.84 -160.86 56.49
Foreign-owned banks
Total assets (10 billion) 17 28.72 28.44 20.26 0.76 63.64
Cost-to-revenue ratio (%) 17 46.07 38.22 15.54 29.95 94.82
ROA (%) 17 -0.65 -0.69 0.61 -1.26 0.00
ROE (%) 17 -14.77 -15.55 13.80 -28.53 0.06
Pretax income ratio (%) 17 -18.81 -19.07 14.53 -33.32 -3.95
Gross profit ratio (%) 17 18.50 17.72 17.21 1.34 36.76

Table 4. Descriptive Statistics – by credit risk of borrowing companies

Sample Mean Median Std. Min. Max.
Total sample
Total assets (10 billion) 36,024 214.26 36.92 1,107.47 1.47 15,545.37
Pretax income ratio (%) 36,024 3.74 3.73 13.81 -99.53 98.79
Debt ratio 36,024 48.60 48.41 13.82 3.91 97.4
TCRI credit rating 36,024 6.27 6 1.53 1 9
Borrowing interest rate (%) 21,613 3.235 2.890 1.292 0.020 10.050
Borrowing spread (%) 21,613 -1.050 -1.401 1.382 -5.563 6.127
Sample with TCRI^7
Total assets (10 billion) 16,187 48 79*** 22.49*** 111.28 1.47 2180.60
Pretax income ratio (%) 16,187 1 95*** 0.85*** 14.18 -99.53 98.79
Debt ratio 16,187 53.41*** 53.80*** 13.91 3.91 97.40
Borrowing interest rate (%) 9,082 3.534*** 3.257*** 1.267 0.020 10.050
Borrowing spread (%) 9,082 -0.734*** -1.010*** 1.345 -5.028 6.127
Sample with TCRI^4
Total assets (10 billion) 4,643 1,139.06 266.93 2,842.43 10.28 15,545.37
Pretax income ratio (%) 4,643 10.91 8.06 13.32 -28.06 98.20
Debt ratio 4,643 41.40 40.06 11.05 8.44 70.67
Borrowing interest rate (%) 2,950 2.909 2.540 1.335 0.020 9.100
Borrowing spread (%) 2,950 -1.429 -1.770 1.422 -5.563 5.466

The empirical analysis procedures of this study are summarized in the following four parts. First, we use the t test and the nonparametric Wilcoxon rank sum methods to verify whether the mean or median of the banks with or without “WOBD” and “HCCL”, banks with four different types of ownership, and companies with high or low credit risk exhibit significant statistical differences in their characteristics. Then, we select publicly-held borrowing companies as empirical and control samples from banks with or without “WOBD” to test the impact of clients’ credit status and relationships on loan spreads under the regression model. We divide our clients with banks with or without WOBD as experimental samples and controlling samples and use the ordinary least

About The Author

Kevin J. Brandon

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