• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.13-28
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• 2010

Assuming a mixture distribution for credit evaluation studies, we discuss estimating threshold methods to minimize errors that default borrowers are predicted as non defaults or non defaults are regarded as defaults. A method by using statistical hypotheses tests, the most powerful test and generalized likelihood ratio test, for the probability density functions which are defined with the score random variable and the parameter space consisted of only two elements such as the default and non default states is proposed to estimate a threshold. And anther optimal thresholds to maximize classification accuracy measures of the accuracy and the true rate for ROC and CAP curves are estimated as equations related with these probability density functions. Three kinds of optimal thresholds in terms of the hypotheses testing, the accuracy and the true rate are obtained from normal random samples with various means and variances. The sums of the type I and type II errors corresponding to each optimal threshold are obtained and compared. Finally we discuss about their efficiency and derive conclusions.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.755-765
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• 2010

For classification problems on mixture distribution, a threshold based on cost functions is optimal from the viewpoint of a minimum expected cost. Assuming that there is no cost information, we propose cost ratios in the expected cost corresponding to thresholds where the total accuracy and the true rate are maximized to explain the relation of these cost ratios minimizing the expected cost. Other cost ratios are also proposed by comparing the normalized expected costs when classification accuracy is maximized. The values of these cost ratios are located between two cost ratios for the expected costs based on classification accuracies, and converge to that of the minimum expected cost. This work suggests two cost ratios: one is minimized by the expected cost and the normalized expected cost, and the other in the expected cost and the normalized expected cost functions that are maximized classification accuracies. We discuss their compatibility based on the relation of these cost ratios.

• KDI Journal of Economic Policy
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• v.13 no.2
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• pp.141-161
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• 1991

Resale price maintenance has long been employed in book distribution, perhaps longer than for any other product. Another unusual practice in the book trade that has proven to be quite durable in spite of its substantial cost in real resources is the returns policy. Publishers typically grant the right to return unsold books within a stipulated time for full credit against future orders. This paper investigates the functions and effects of resale price maintenance in the book trade, and argues that resale price maintenance and returns are substitute methods of providing the same economic function. Resale price maintenance can be used to compensate booksellers for initially stocking books with uncertain prospects and for providing a conduit through which manufacturers acquire information about consumer demand (market testing services). Permitting the return of unsold books for full credit places a floor under retail prices and transfers a considerable portion of the cost of introducing a new product line back to the publisher. Both reflect publishers' needs to have their books displayed. In the U.S. returns privileges were first proposed in 1913, roughly coincident with the Macy decision outlawing RPM. Publishers slowly granted return privileges, which become nearly universal by 1970. The decline in margins in recent years has been accompanied by an increase in returns as the return policy served to substitute for lost margins on successful titles as a methods of compensating full-line booksellers. In contrast, returns privileges are unusual in countries where price maintenance in books has been practiced. These observations are consistent with our analysis. In Korea, resale price maintenance of books is practiced under an exception to Korean antitrust law. The availability of effective price maintenance is likely to reduce the use of returns programs. Since consumers prefer to obtain books at outlets where they know the books are likely to be stocked rather than taking a chance on stores that carry a more limited line, it also provides a strong incentive for booksellers to expand. But the privilege of resale price maintenance should be confined to books which publishers want to be price maintained. Resale price maintenance and returns system differ in the transactions costs associated with inventory holding, and publishers' judgement on the comparative advantage of the two schemes should be honored. Publishers should also remain free to authorize sales at discount at any time not to impair the ability of booksellers to dispose of product variants that prove unpopular.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.45-52
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• 2023

Recently, as climate change caused by greenhouse gases is intensifying, the international community has committed to reduce greenhouse gas emissions. The purpose of this study is to present the methodology and major considerations for investment judgment. Two actual cases of overseas projects were selected as study subjects. As an analysis method, the major risk factors were defined as a probability distribution, and the NPV was stochastically estimated using the Monte Carlo simulation method. In addition, assuming a policy change, the range of NPV change was analyzed. As a result, the average NPV of project A was lowered by 19%, and the probability of showing a negative NPV was 12.2%. The average value of project B was lowered by 12.5%. Considering the policy change, project A can obtain economic benefits only when it obtains 72.9% or more of the total amount of carbon credits generated, and project B is economically feasible when it acquires 49.5% or more. As a result, the average value of project A is lower than the net present value under basic assumptions, so caution is needed in investment decisions depending on changes in major risk factors. Additionally, considering policy changes, the carbon credit distribution ratio should be differentially applied depending on the project size, and this was presented as a specific figure.

• Knowledge Management Research
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• v.22 no.2
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• pp.247-268
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• 2021

Investors mainly use PER and PBR among financial ratios for valuation and investment decision-making. I conduct an analysis of two basic financial ratios from a statistical perspective. Financial ratios contain key accounting numbers which reflect firm fundamentals and are useful for valuation or risk analysis such as enterprise credit evaluation and default prediction. The distribution of financial data tends to be extremely heavy-tailed, and PER and PBR show exceedingly high level of kurtosis and their extreme cases often contain significant information on financial risk. In this respect, Extreme Value Theory is required to fit its right tail more precisely. I introduce not only GPD but exGPD. GPD is conventionally preferred model in Extreme Value Theory and exGPD is log-transformed distribution of GPD. exGPD has recently proposed as an alternative of GPD(Lee and Kim, 2019). First, I conduct a simulation for comparing performances of the two distributions using the goodness of fit measures and the estimation of 90-99% percentiles. I also conduct an empirical analysis of Information Technology firms in Korea. Finally, exGPD shows better performance especially for PBR, suggesting that exGPD could be an alternative for GPD for the analysis of financial ratios.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.59-90
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• 1991

The purpose of this paper is to determine whether the rate schedule of the Korean income tax system embodies the theoretically desirable distribution of the tax burden by income classes. The paper follows the approach of Young (1990) who has estimated the utility function and calculated the magnitude of sacrifice, i.e., the tax burden. The main point of the study is to estimate the utility function. The estimation results may differ if different data sets are used. Therefore, this paper employs the effective as well as the nominal tax rate schedule. The findings derived in this paper are 1) that the effective rate schedule is more appropriate in estimating the utility function; 2) that the middle class has born the relatively heavier burden over time; 3) that the current income tax credit scheme curtails the tax burden on the middle class while intensifying the tax burden of the lower and upper income classes; 4) that reducing the amount of deduction moves the distribution of tax burdens by income classes closer to the theoretically desirable model; 5) that the rate schedule of the Korean income tax system, particularly in 1991, did not conform to the desired model as did the tax systems of developed countries such as the United States, Italy, Japan, and Germany.

• Journal of Distribution Science
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• v.14 no.12
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• pp.141-151
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• 2016

Purpose - Salesperson perceptions of the fairness and accuracy of a performance evaluation system were examined by managerial and professional employees of large organization. The performance evaluation process is central to many personal decisions such as attitude for job and sales performance. This study investigates the relationship between perceived evaluation fairness, job satisfaction, organizational commitment, and sales performance. The main purpose of this study is to develop and empirically test a comprehensive model of salespersons' perceived evaluation fairness on sales performance. For this purpose, we identified the structural relationship between perceived evaluation fairness, job satisfaction, organizational commitment, and sales performance. Also we investigate the mediating effects on job satisfaction and organizational commitment between perceived evaluation fairness and sales performance. Research design, data, and methodology - To empirically test these relationships, data were collected by in-depth interviews from sales managers and questionnaire surveys from 300 salespersons who work for sales area (credit card company, insurance company). Demographically, the overall sample was 91.6% female, 77.9% 30s and 40s, and 34% college educated, with an average tenure with their present organizations of 4 years. The questionnaire was composed of total 20 items dealing with frequency, quality, and consequences of perceived evaluation fairness, job satisfaction, organizational commitment, and sales performance. To test the research hypotheses, collected data analyzed by confirmatory factor analysis (CFA) and structure equation model (SEM). Results - Through extensive and rigorous literature review process of related literature(Perceived evaluation fairness, Job satisfaction, Organizational commitment, Sales performance), research model and research hypothesis was set up. This study obtains the following research results. First, perceived evaluation fairness has a positive effect on job satisfaction, whereas the effects of perceived evaluation fairness on organizational commitment and sales performance did not show statistically significant result. Second, job satisfaction and organizational commitment have complete mediating roles to the relationship between perceived evaluation fairness and organizational commitment, and relationship between perceived evaluation fairness and sales performance. Conclusions - Based on the results, salespersons' perceived evaluation fairness is one of the key independent variable for making high job satisfaction, organizational commitment, and sales performance. Finally the theoretical, managerial implication and research limitations are mentioned in the discussion.

• International Commerce and Information Review
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• v.14 no.3
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• pp.585-610
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• 2012

There have been various attempt to offer commercial service of electronic payment. Actually, however, there has been few electronic payment model that are being used commercial service. Among them, it is estimated that only TSU(Trade Service Utility) of SWIFT will be succeed in providing commercial service possibility. In Korea e-L/C Distribution & Management System was constructed and become the first e-L/C service on a global basis via e-Trade Facilitation 3 Years Project and then started offering the world first e-L/C service. Some scholars have insisted the opinion that SWIFT L/C means e-L/C, but such opinion may be wrong because SWIFT L/C means the L/C advised by SWIFT(Society for Worldwid Interbank Financial Telecommunication) under the condition that a series of procedure from the issuance of L/C to the advice is done by SWIFT system. Because perfect e-L/C should be organically connected by SWIFT network and also, the e-L/C business between overseas banks and their customers should be organically linked with each other. The purpose of this paper is contribute to the commercialization of e-L/C distribution system by studying the current status of infrastructure concerned with domestic and overseas electronic payment and future e-payment infrastructure, seeking to be introduced internationally by doing things.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Journal of Technologic Dentistry
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• v.31 no.4
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• pp.45-52
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• 2009

This study is analyzed to conduct better on-site practices with recognizing importance of the clinical practice of Dept. of dental technology and use it as a basic material in the clinical practice. Target people who are students studying dental technology in D college in Daegu were questioned by survey. Study results below 1. Average score of the survey about satisfaction of the operating method of clinical practice shows 3.26. In detail elements, 'credit assignment(10 credits)' is 3.65 as the highest score, 'execution period(vacation)' is 3.50, 'choice of the clinical practice organization' is 3.25, 'measures after practice' is 2.98 and 'pre-education' is 2.98 as the lowest score. 2. Through the real clinical practice, 'experience of new equipments and technology' is 3.64 as the highest score, 'choice of lecturer' is 3.61, 'guidance way' is 3.49, 'contents properness' is 3.44, 'environment of practice organization' is 3.36, 'evaluation way' is 3.35 and 'practical use of the evaluation material' is 3.18 as the lowest score. 3. The average score of survey about satisfaction after clinical practice of the participated students is 3.46 that is higher than both 'satisfaction about operating method(3.26)' about clinical practice of college and 'satisfaction about organization(3.44)' about environment of dental craft organizations and labs, guidance way of lecturer and evaluation. 4. In the improvement of distribution of the clinical practice evaluation, in the 'practice organization: college' viewpoint, '7:3' is 35.77% as the highest response, '6:4' is 25.20%, '8:2' is 22.76% and '4:6' is 16.26 in regular sequence. 5. In site evaluation reflection of clinical practice, 50% reflection is 32.93% as the highest percentage, 60% reflection is 26.83%, 20% reflection is 20.73% and 80% reflection is 6.10% in regular sequence. In attendance score, it shows percentage of reflecting 50% and 40% is 26.98%, students wanting to reflect 30% is 25.40%, reflecting 10% is 20.63% and no reflecting is 0%. In result of the analyzed data, clinical practice has to be studied more in considering that clinical practice is important point in education of Dept. of Dental Technology and also problems in college and on-site practice need improvements.