• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.72-81
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• 2015

This paper studied the differences of the inventories asset turnover change ratio and several characteristics variable between large and small manufacturing firm group. Large and small firm group were determined based on number of labors and asset size. Several characteristics variable of firms such as assets size, sales growth rate, return on assets, leverage ratio, credit rating and age of firm were used to find out the differences of firm group. As a result, the inventory asset turnover change ratio of large firm was 5.16% and that of the middle and small firm was 9.3%. For the large firm, sales growth rate, ROA and credit rating affect inventory assets turnover change ratio. For the middle and small sized firm, Assets size, sales growth rate and credit rating affect inventory assets turnover change ratio. Using this result, we can say that manufacturing company need to consider their firm size and their characteristics to make their own operation strategy of inventory.

• The Journal of Information Systems
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• v.29 no.1
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• pp.241-265
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• 2020

Purpose Various machine learning techniques are used to implement for predicting corporate credit. However, previous research doesn't utilize time series input features and has a limited prediction timing. Furthermore, in the case of corporate bond credit rating forecast, corporate sample is limited because only large companies are selected for corporate bond credit rating. To address limitations of prior research, this study attempts to implement a predictive model with more sample companies, which can adjust the forecasting point at the present time by using the credit score information and corporate information in time series. Design/methodology/approach To implement this forecasting model, this study uses the sample of 2,191 companies with KIS credit scores for 18 years from 2000 to 2017. For improving the performance of the predictive model, various financial and non-financial features are applied as input variables in a time series through a sliding window technique. In addition, this research also tests various machine learning techniques that were traditionally used to increase the validity of analysis results, and the deep learning technique that is being actively researched of late. Findings RNN-based stateful LSTM model shows good performance in credit rating prediction. By extending the forecasting time point, we find how the performance of the predictive model changes over time and evaluate the feature groups in the short and long terms. In comparison with other studies, the results of 5 classification prediction through label reclassification show good performance relatively. In addition, about 90% accuracy is found in the bad credit forecasts.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Asian Journal of Business Environment
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• v.8 no.1
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• pp.23-29
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• 2018

Purpose - The purpose of this research is to make a comparative assessment of People Credit Funds (PCFs) ranking in Vietnam between the Circular No. 42/2016/TT-NHNN dated December 20, 2016 with the Decision No. 14/2007/QD-NHNN dated 09/4/2007 issued by the Governor of the State Bank. Research design, data, and methodology - This study is mainly based on the Circular No. 42/2016/TT-NHNN dated December 20, 2016 and the Decision No. 14/2007/QD-NHNN dated 09/4/2007 issued by the Governor of the State Bank on PCFs ranking. Results - The study paper has shown positive changes in PCFs ranking in Vietnam in accordance with the Circular No. 42/2016/TT-NHNN, such as increasing Capital Adequacy Ratio (CAR), maintaining CAR, improving assets quality, developing indicators of governance, management and control capability. These changes have implications for the development and efficient performance of PCFs in Vietnam. Conclusions - The classification and evaluation of PCFs will contribute to its healthy development. These finding support PCFs to understand more about rating methodology, significance of rating system and the importance of improving their rating. PCFs in Vietnam desire to develop their business effectively, they need to understand exactly and comply fully with regulations related to their field of operations.

• Asia Marketing Journal
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• v.14 no.1
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• pp.1-24
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• 2012

Nowadays, customer satisfaction has been one of company's major objectives, and the index to measure and communicate customer satisfaction has been generally accepted among business practices. The major issues of CSI(customer satisfaction index) are three questions, as follows: (a)what level of customer satisfaction is tolerable, (b)whether customer satisfaction and company performance has positive causality, and (c)what to do to improve customer satisfaction. Among these, the second issue is recently attracting academic research in several perspectives. On this study, the second issue will be addressed. Many researchers including Anderson have regarded customer satisfaction as core competencies, such as brand equity, customer equity. They want to verify following causality "customer satisfaction → market performance(market share, sales growth rate) → financial performance(operating margin, profitability) → corporate value performance(stock price, credit ratings)" based on the process model of marketing performance. On the other hand, Insoo Jeon and Aeju Jeong(2009) verified sequential causality based on the process model by the domestic data. According to the rejection of several hypotheses, they suggested the balance model of marketing performance as an alternative. The objective of this study, based on the existing process model, is to examine the causal relationship between customer satisfaction and corporate value performance. Anderson and Mansi(2009) proved the relationship between ACSI(American Customer Satisfaction Index) and credit ratings using 2,574 samples from 1994 to 2004 on the assumption that credit rating could be an indicator of a corporate value performance. The similar study(Sangwoon Yoon, 2010) was processed in Korean data, but it didn't confirm the relationship between KCSI(Korean CSI) and credit ratings, unlike the results of Anderson and Mansi(2009). The summary of these studies is in the Table 1. Two studies analyzing the relationship between customer satisfaction and credit ratings weren't consistent results. So, in this study we are to test the conflicting results of the relationship between customer satisfaction and credit ratings based on the research model considering Korean credit ratings. To prove the hypothesis, we suggest the research model as follows. Two important features of this model are the inclusion of important variables in the existing Korean credit rating system and government support. To control their influences on credit ratings, we included three important variables of Korean credit rating system and government support, in case of financial institutions including banks. ROA, ER, TA, these three variables are chosen among various kinds of financial indicators since they are the most frequent variables in many previous studies. The results of the research model are relatively favorable : R², F-value and p-value is .631, 233.15 and .000 respectively. Thus, the explanatory power of the research model as a whole is good and the model is statistically significant. The research model has good explanatory power, the regression coefficients of the KCSI is .096 as positive(+) and t-value and p-value is 2.220 and .0135 respectively. As a results, we can say the hypothesis is supported. Meanwhile, all other explanatory variables including ROA, ER, log(TA), GS_DV are identified as significant and each variables has a positive(+) relationship with CRS. In particular, the t-value of log(TA) is 23.557 and log(TA) as an explanatory variables of the corporate credit ratings shows very high level of statistical significance. Considering interrelationship between financial indicators such as ROA, ER which include total asset in their formula, we can expect multicollinearity problem. But indicators like VIF and tolerance limits that shows whether multicollinearity exists or not, say that there is no statistically significant multicollinearity in all the explanatory variables. KCSI, the main subject of this study, is a statistically significant level even though the standardized regression coefficients and t-value of KCSI is .055 and 2.220 respectively and a relatively low level among explanatory variables. Considering that we chose other explanatory variables based on the level of explanatory power out of many indicators in the previous studies, KCSI is validated as one of the most significant explanatory variables for credit rating score. And this result can provide new insights on the determinants of credit ratings. However, KCSI has relatively lower impact than main financial indicators like log(TA), ER. Therefore, KCSI is one of the determinants of credit ratings, but don't have an exceedingly significant influence. In addition, this study found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size, and on service companies than manufacturers. The findings of this study is consistent with Anderson and Mansi(2009), but different from Sangwoon Yoon(2010). Although research model of this study is a bit different from Anderson and Mansi(2009), we can conclude that customer satisfaction has a significant influence on company's credit ratings either Korea or the United State. In addition, this paper found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size and on service companies than manufacturers. Until now there are a few of researches about the relationship between customer satisfaction and various business performance, some of which were supported, some weren't. The contribution of this study is that credit rating is applied as a corporate value performance in addition to stock price. It is somewhat important, because credit ratings determine the cost of debt. But so far it doesn't get attention of marketing researches. Based on this study, we can say that customer satisfaction is partially related to all indicators of corporate business performances. Practical meanings for customer satisfaction department are that it needs to actively invest in the customer satisfaction, because active investment also contributes to higher credit ratings and other business performances. A suggestion for credit evaluators is that they need to design new credit rating model which reflect qualitative customer satisfaction as well as existing variables like ROA, ER, TA.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.615-631
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• 2008

Current credit evaluation models based on only financial data except non-financial data are used continuous data and produce credit scores for the ranking. In this work, some problems of the credit evaluation models based on transformed continuous financial data are discussed and we propose improved credit evaluation models based on categorized financial data. After analyzing and comparing goodness-of-fit tests of two models, the availability of the credit evaluation models for categorized financial data is explained.

• Korea Trade Review
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• v.48 no.3
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• pp.89-106
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• 2023

This study is to investigate the problem of credit rating by Korea Trade Insurance Corporation(KSURE) which evaluates overseas buyers in a state of capital impairment as G-grade regardless of the cause of capital impairment. This study classifies capital impairment into two types: deficit-type capital impairment due to accumulated operating losses and surplus-type capital impairment due to shareholder return policies such as dividends and treasury stock buybacks. It is proposed to improve the credit evaluation method on companies with surplus capital impairment from a formal review to a substantive review. This study is expected to improve credit rating of KSURE on overseas buyers for better support of trade credit insurance for exporters.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.157-167
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• 2022

This study was conducted to confirm the relationship between the proactive accounts receivable management of SMEs on credit sales decision making and business performance, and to derive effective accounts receivable management plan and systematic credit sales decision making plan. Based on 455 copies of data collected through a survey targeting SMEs, it was confirmed through factor analysis, reliability analysis, confirmatory factor analysis, and model fit verification, and the research hypothesis was verified with a structural equation model. As a result of the verification, credit rating had a positive effect on financial performance, sales performance and credit sales decision, while credit control had a positive effect on financial performance, while negative effect on sales performance and credit sales decision. In the mediating effect hypothesis test, credit sales decision had a positive effect between credit rating and business performance and a negative effect between credit control and business performance. The study suggests that if small and medium-sized enterprises improve their business performance through effective accounts receivable management, they can create a synergistic effect in enhancing the business performance of companies if they simultaneously improve their proactive accounts receivable management and credit sales decision ability. Future research is required to study the impact of factors such as segmentation of research subjects and credit transaction motives and accounts receivables management.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1207-1216
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• 2015

This study aims to compare the model power in developing corporate credit rating models and to suggest a good way to build models based on the characteristic of data. Among many measurement methods, AR is used to measure the model power under various conditions. SAS/MACRO is in use for similar repetitions to reduce time to build models under several combination of conditions. A corporate credit rating model is composed of two sub-models; a credit scoring model and a default prediction model. We verify that the latter performs better than the former under various conditions. From the result of size comparisons, models of large size corporate are more powerful and more meaningful in financial viewpoint than those of small size corporate. As a corporate size gets smaller, the gap between sub-models becomes huge and the effect of outliers becomes serious.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1065-1075
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• 2008

For the model validation of credit rating models, Kolmogorov-Smirnov(K-S) statistic has been widely used as a testing method of discriminatory power from the probabilities of default for default and non-default. For the credit rating works, K-S statistics are to test two identical distribution functions which are partitioned from a distribution. In this paper under the assumption that the distribution is known, modified K-S statistic which is formulated by using known distributions is proposed and compared K-S statistic.