Credit on Lending Performance and Speedy Payday Loans

Micro and small businessWe will summarize the empirical testing process over the next three subsections. First, we use the t-test and non-parametric Wilcoxon rank-sum test to verify whether the mean and median are significantly different from the perspectives of information asymmetry and client credit in banks with and without “WOBD” and “HCCL”. As mentioned earlier, we divide borrowers into MSBs and MLBs and discuss each one separately. In the second and third sections, we discuss how the borrowers with their own advantage or situation build relationships with banks with different levels of credit risk and lending performance. To be specific, we regard banks with “WOBD” and “HCCL” as banks with low performance and high credit risk. Our control sample consists of banks without “WOBD” and “HCCL” and with high performance and low credit risk. We use a logit model to investigate whether the borrowers’ information asymmetry and credit records contain statistically significant differences between banks with and without “WOBD” and “HCCL”.

Whether or not the Borrowers are Significantly Different

Comparisons among MSBs

Table 2 displays the findings that banks with “WOBD” and “HCCL” prefer dealing with MSBs that are relatively large in scale. The average capital ranges between NT$0.028 billion and NT$0.033 billion, which is significantly higher than the controlled samples with between NT$0.017 billion and NT$0.018 billion at the 1% level. Compared with factors for the length of time in existence (in months) and the year of establishment, we find that banks with “WOBD” prefer building relationships with MSBs characterized by longer lengths of time in existence. Banks with “HCCL” show no significant differences in this respect.

Compared with the median of existing length, banks with “WOBD” have existed for an average of 158 months, which is significantly longer than for banks without “WOBD”, for which the average is 140 months. The former generally had more clients and a more sustained borrower-lender relationship before the new bank policy to approve the establishment of new banks was introduced. The ratio is significantly higher than the latter at the 1% level. Compared to sole proprietorships, partnerships and limited companies, banks with “WOBD” engage in more lending business with corporations. The ratio is 34%, which is significantly higher than the 30% for the control sample. Banks with “HCCL” prefer relationships with clients that are sole proprietorships, partnerships or limited companies than with corporate types of organizations. The ratio is 29%, which is significantly lower than that for banks without “HCCL” (32%). The banks do not every time approve the credits but Speedy Payday Loans and http://speedy-payday-loans.com are the way out from all the difficult situations occurred. You will know and read more about the service if you follow the link.

As to whether MSBs that belong to conglomerates can decrease or increase the risks posed to the creditor banks with different lending performances presents a severe challenge to the banks’ ability to manage credit. The empirical results show that the ratios of the clients belonging to conglomerates that build relationships with banks with “WOBD and HCCL” are 0.4% and 0.5%, respectively, which are significantly higher than those for the control samples with 0.3% and 0.3% at the 1% level. This indicates that lending to companies belonging to a conglomerate does not ensure that the quality of credit is reliable (for banks with “WOBD”). If banks lend to a subsidiary of a conglomerate, they will be concerned about the efficiency or performance of any related business unit related to that conglomerate, because the whole conglomerate may be affected. organizationsAnother empirical result shows that banks without “WOBD and HCCL” will tend to lend to small foreign companies, for which the ratios are 0.3% and 0.3%, significantly higher than those for the control samples of 0.1% and 0.1%. The implication is that small foreign companies pose less credit risk, and so it is reasonable to promote business lending to them. Lending in the cases of banks with “WOBD and HCCL”, which carry poor credit records are up to 22% and 26%, which were significantly higher than the control samples of 17% and 17%. This explains why these banks have a higher credit risk. When comparing from the aspect of the credit score of the chairperson, the empirical results are quite similar in that cases of lending by banks with “WOBD and HCCL” have an average credit ratings of 509.72 and 473.63, respectively, which is significantly lower than the 541.45 and 548.28 for the control samples.

Comparison among MLBs

From the results of Table 2, we found that the lending behaviors of banks with different lending performances were quite similar, including in terms of asset size, the length of time in existence and the company’s year of establishment. This means that banks with “WOBD and HCCL” prefer building relationships with large-scale companies, whose average total assets are between NT$1,536 million and NT$2,481 million. These averages are significantly larger than those for the control samples where the average total assets were between NT$526 and NT$495 million at the 1% level. From the perspective of the length of time in existence and the company’s year of establishment in the case of the MLBs, banks with “HCCL” were significantly smaller than banks without “HCCL” at the 5% level. Variables related to banks with “WOBD” were significantly larger those for than banks without “WOBD” at the 1% level. From the perspective of the type of organization, banks with “WOBD and HCCL” preferred building relationships with publicly-held or OTC firms (8% and 12%), which were significantly higher than those for the control samples (4% and 4%) at the 1% level. For clients belonging to conglomerates (13% and 19%), the ratios were significantly higher than for the control samples (7% and 7%) at the 1% level.

As to the comparison of the financial variables, we found that clients of banks with “WOBD” had a significantly higher financial leverage ratio (the mean was 246.13%) than the control samples (230.44%) at the 1% level. The median rose to 200.59%. This seems to indicate that banks with “WOBD” accept lending business from MLBs with a higher financial leverage ratio which will lead to higher credit risk. The means of the MLBs of banks with “HCCL” are 0.98% and 5.31% based on the ratios of R&D expenditures and profitability, which are significantly higher than the corresponding 0.58% and 4.08% for the control samples at the 1% level. The cash ratio of 9.89% is significantly lower than the 11.83% for the control samples at the same level. This reveals that when compared to the R&D expenditures and profitability ratios, the cash ratio can better reflect the quality of credit risk. From the perspective of the past credit records of banks with different lending performances, there is no significant difference between the experimental and control samples. The credit score of the chairperson of MLBs of banks with “WOBD and HCCL” (the average scores are 644.32 and 628.96, and the medians are 656 and 639) is significantly lower than for the control samples (the average scores are 657.81 and 659.91, and the medians are 673 and 674) at the 1% level. This seems to reflect the fact that banks with low lending performance ignore the fact that the credit score of the chairperson can serve as a signal of credit risk.

The Logit Model Distinguished by with or without “WOBD” Events

Medium and large businessIn this study, we measure a bank’s lending performance according to whether it encounters the events of “WOBD” and “HCCL.” Therefore, we divide the relationship banks into those with “WOBD” and those with “HCCL.” For purposes of comparison, we also choose the clients of banks without “WOBD” and “HCCL” as the control samples. With respect to the borrowers, “WOBD” results from corporate financial failure and impacts the financing needs directly, while “HCCL” results from retail financing failure, which only impacts financing needs indirectly. However, each circumstance will increase the credit risk of banks. If bad experiences accrue at the same time, the credit risk will increase dramatically. To clarify the different responses from the direct and indirect impacts, we test both events individually, and simply divide the banks into those with or without “WOBD” or “HCCL”. Banks with “WOBD” and “HCCL” are characterized by high credit risk and low lending performance. Banks without “WOBD” and “HCCL” are characterized by low credit risk and high lending performance. To avoid confused perceptions of lending behavior, we exclude the samples of borrowers who maintain relationships with banks with both high and low credit risks. Risk is enormous but not with Speedy Payday Loans service which does not demand documents about business solvency. It is the main advantage.

The empirical findings in Table 3 indicate that enterprises featured by “large size and a long period of time in existence” have good information transparency and tend to build relationships with banks with “WOBD”. The coefficients of the regression model for MSBs are 0.18 and 0.001, and for MLBs are 0.30 and 0.009, respectively. Although “large size and a long period of time in existence” are symbols of good information transparency, they are not an assurance of less operating risk. Therefore banks should deal with loan applications more prudently, especially during times of recession. If banks do not have a sound risk limiting system, nor a facilities ratings system to manage industry concentration risk and they blindly adopt the lending business practices of borrowers with highly transparent information, they may suffer considerable losses in the near future. This phenomenon has become even more severe due to the lending market facing intense competition after the announcement of the policy to approve the establishment of new banks. Banks with high credit risk purposely expand and extend their conditions and criteria of lending to attract new and opaque companies (the coefficient is -0.04). From the perspective of the organization type and industry of the enterprise, compared to private enterprises, foreign companies prefer establishing relationship with low credit risk bank (the coefficient is -1.02). Compared to sole proprietorships, partnerships and limited companies, it seems that corporations prefer building relationships with low credit risk banks (the coefficient is -0.04). Compared to the construction and real estate industry, it seems that firms within the electronic and the non-electronic manufacturing industries and wholesale industries (the coefficients are 0.11, 0.06 and 0.24, respectively) prefer to build relationships with high credit risk bank. Compared to privately-held companies, it seems that publicly-held companies (the coefficient is 0.28, and the odds ratio is 1.32) prefer to build relationships with high credit risk banks. Compared to publicly-held companies, it seems that listed and OTC firms (the coefficient is 0.33, and the odds ratio is 1.4) prefer to establish relationships with high credit risk banks. This implies that such banks do not have a comprehensive understanding of the industrial environment. Although higher public offerings will accompany those companies with more transparency, they do not guarantee better profitability. Hence, it is beneficial to remain alert when dealing with clients in such situations to reduce risk and enhance performance.

 liquidity riskFrom the perspective of the borrower’s financial variables, large clients with high profitability ratios, cash ratios and R&D expenditure ratios prefer building relationships with banks with low credit risk (the coefficients are -0.004, -0.009 and -0.025, respectively). Only companies with high financial leverage ratios select or are accepted by banks with high credit risk (the coefficient is 0.0005). However, it is not worthwhile adopting such lending business at the expense of increasing credit risk. Both MSBs and MLBs with poor credit records prefer building relationships with low credit risk banks (the coefficient in large companies is -2.12, which is significant at the 1% level). If the credit score of the borrower’s chairperson is increasing, it will tend to access finance from low credit risk banks. The coefficients of the MSBs and MLBs are -0.0006 and -0.0035, respectively. This perhaps reflects the fact that banks with low credit risk have low liquidity risk. Therefore, they should be concerned about the prospects of such a company and only with caution agree to meet the financing demands of the company. It is critical to only proceed if the bank receives assurances from the chairperson or directors or supervisors.

The Logit Model Distinguished by with or without “HCCL” Events

“HCCL” arises due to the failure of the retail financing business. Although the clients differ from the clients in the case of corporate finance, “HCCL” is still an indicator of credit risk management. When banks experience “HCCL” and maintain high NPL ratios, they still have the capacity to finance clients with information asymmetry and poor credit. This is worth carefully exploring. In this section we focus on investigating how “HCCL” banks with high credit risk affect the borrowers’ funding needs.

The empirical findings in Table 4 show that both MSBs and MLBs that are larger scale prefer building relationships with banks that are categorized as being with “HCCL” (the coefficients are 0.066 and 0.397, respectively, and the odds ratio of the latter reaches as high as 1.487). Companies that have been in existence for a shorter period of time are more opaque, and therefore creditors need to be careful to grasp the overall picture of potential operating risk. Banks with “HCCL” are more willing to deal with such enterprises (the coefficients of MSBs and MLBs are -0.0004 and -0.0011, respectively). Banks with “HCCL” are more eager to deal with MSBs established after the policy to approve the establishment of new banks was introduced (the coefficient is -0.193). There is no doubt that the NPL ratio remains high. Banks with “HCCL” do not wish to deal with corporations among the MSBs (the coefficient compared to the sole proprietorships, partnerships, and limited companies is -0.181). This is similar to banks with “WOBD” as mentioned in the previous section. Only the MSBs belonging to a conglomerate (the coefficient is 0.467, and the odds ratio is 1.595) can ignore the credit risk of its lending banks, for the conglomerate can back up its member businesses internally with funding needs. However, the conglomerates with poor internal controls may deliberately deal with banks with high credit risk, act in collusion and empty the assets of the enterprise on purpose. Compared to the privately-held firms, small foreign enterprises are more likely to refuse to deal with banks with “HCCL” (the coefficient is -0.899). Most of the “HCCL” banks are newly established. It is understood that they actively promote retail finance and intend to expand the market of privately-held companies. For this reason, it is understood that more risk management is needed.

The empirical results also indicate that in comparing listed companies with publicly-held companies and publicly-held companies with privately-held companies, the former prefer building relationships with banks with “HCCL” (the coefficients are 0.391 and 0.563, and the odd ratios are 1.48 and 1.71, respectively). There are no differences in ownership types, regardless of their being stated-owned or privately-owned or even foreign banks. MLBs with high cash and R&D expenditure ratios regularly deal with banks without “HCCL” (the coefficients are -0.007 and -0.031). These findings fully reflect the fact that banks with “WOBD and HCCL” have a shortage of knowledge in terms of analyzing financial statements. MLBs with high financial leverage regularly and positively deal with “HCCL” banks (the coefficient is 0.0003). This implies that those banks that perform poorly in the management of retail finance will perform poorly in the management of corporate finance, too. MLBs with high or low profitability keep maintain the same attitude in dealing with banks with or without “HCCL”. Companies with poor credit records, regardless of whether they are MSBs or MLBs, will tend to build relationships with low credit risk banks without “HCCL”. Compared to MSBs, the intentions of MLBs are 4 times higher (the coefficients are -0.696 and -2.682, respectively, and the odds ratios are about 7 times). When the chairperson’s credit rating improves, the borrower will tend to access finance from low credit risk banks without “HCCL” (the coefficients are -0.002 and -0.006, respectively). This also indicates that if a borrower takes action to improve its credit rating, banks without “HCCL” are sufficiently flexible to accept such loan applications. To sum up, “HCCL” banks are not only short on knowledge as to how to analyze the borrower’s financial statements, but are also inflexible in terms of adjusting their lending policy to accept clients with a poor credit record. This can result in the loss of potential clients with improved credit ratings.

Table 2. Comparison of MSBs and MLBs from Different Perspectives

Dimensions Impact Variables Borrowers: MSBs Borrowers: MLBs
“WOBD” Events “HCCL” Events “WOBD” Events “HCCL” Events
Y N Y N Y N Y N
Samples 57,948 75,619 35,369 98,198 10,023 5,691 5,343 10,371
Info Info_01 or Info_02 M 0.28*** 0.17 0.33*** 0.18 5.26 24.81*** 4.95 4.95
m 0.05*** 0.05 0.05*** 0.05 1.33 2.37*** 1.4 1.4
Info_03 M 172.99*** 156.57 2

5

167.98 204.88 217.52** 222.95 222.95
m 8

5

140 134*** 154 189 199*** 209 209
Info_04 M 0.46*** 0.4 0.37*** 0.44 0.56 0.6*** 0.63 0.63
m 0*** 0 0*** 0 1 1*** 1 1
Fin Fin_01 M 230.44 236.36** 242.55 242.55
m 179.07 194.23 192.10 192.10
Fin 02 M 0.69 0.98*** 0.58 0.58
m 0 0*** 0 0
Fin_03 M 4.59 5.31*** 4.08 4.08
m 2.84 3.24*** 2.68 2.68
Fin_04 M 13.46 9 89*** 11.83 11.83
m 6.97 5.39*** 6.16 6.16
Credit Credit_01 M 0.22*** 0.17 0.26*** 0.17 0.004 0.004 0.003 0.003
m 0*** 0 0*** 0 0 0 0 0
Credit_02 M 509.72*** 541.45 473.63*** 548.28 657.81 628.96*** 659.91 659.91
m 624*** 640 580*** 647 673 639*** 674 674

Table 3. Using Logit Model to Distinguish Credit Risk by “WOBD” Event

Dimensions Status_01 MSBs Odd Ratio MLBs Odd Ratio
intercept 0.283*** 2.2194***
Info Info_01 0.1801*** 1.197
Info_02 0.2966*** 1.345
Info_03 0.00105*** 1.001 0.0009*** 1.001
Info_04 -0.0417** 0.959 0.1412** 1.152
Com Com_01 0.2787** 1.321
Com_02 0.3326*** 1.395
Com_03 -0.0429*** 0.958
Com_04 -0.1043 0.901 -0.0962 0.908
Com_05 -1.0232*** 0.359 -0.9261 0.396
Com_06 0.0492 1.05 8.5285 >999.999
Com_07 0.1149*** 1.122 0.0115 1.012
Com_08 0.0553*** 1.057 0.0737 1.077
Com_09 0.2391*** 1.27 0.4329*** 1.542
Fin Fin_01 0.00054*** 1.001
Fin_02 -0.0247*** 0.976
Fin_03 -0.00422*** 0.996
Fin_04 -0.00934*** 0.991
Credit Credit_01 -0.00755 0.992 -2.1188*** 0.12
Credit_02 -0.00056*** 0.999 -0.00348*** 0.997

Table 4. Using Logit Model to Distinguish Credit Risk by “HCCL” Events

Dimensions Status_02 MSBs Odds

Ratio

MLBs Odds

Ratio

intercept 0.7085*** 3.0584***
Info Info 01 0.0657*** 1.068
Info 02 0.3966 *** 1.487
Info 03 -0.00042*** 1 -0.00114*** 0.999
Info 04 -0.1927 *** 0.825 0.0523 1.054
Com Com 01 0.3908 *** 1.478
Com 02 0.5363 *** 1.71
Com 03 -0.181 *** 0.834
Com 04 0.4668 *** 1.595 0.082 1.086
Com 05 -0.8986*** 0.407 0.0633 1.065
Com 06 0.5598 1.75 9.6137 >999.999
Com 07 0.1518*** 1.164 0.0798 1.083
Com 08 -0.0451** 0.956 -0.0616 0.94
Com 09 0.00679 1.007 0.1427*** 1.153
Fin FIN_01 0.00029 *** 1
FIN 02 -0.0307 *** 0.97
FIN 03 -0.00116 0.999
FIN 04 -0.00706 *** 0.993
Credit Credit 01 -0.6958*** 0.499 -2.6842 *** 0.068
Credit_02 -0.00223*** 0.998 -0.00595 *** 0.994