• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.439-451
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• 2020

This study focused on researching the factors affecting retail banking efficiency of Vietcombank branches in the Mekong-Delta region. By collecting data from financial statements from 15 branches of VCB in the Mekong-Delta Region between 2015 and 2018, the paper applies DEA estimation to measure the effectiveness of retail banking activities and uses the Tobit regression model to identify factors affecting retail banking efficiency. The results demonstrate that the retail banking efficiency of branches averaged 52.5% during the period. The rating result shows the branches in An Giang, Can Tho, Dong Thap, Kien Giang, Long An, Phu Quoc and Tra Noc rank at the top technical efficiency. In group of medium efficiency, there are branches in Soc Trang, Tien Giang and Vinh Long. In the category of the poor efficiency are the branches in Bac Lieu, Ben Tre, Ca Mau, Chau Doc and Tra Vinh. The results also show that bank scale-related factors, capital adequacy, credit quality, time specific and region impact significantly the retail banking efficiency. The research not, only contributes to enriching the empirical research method but also is significant for the management activities in business developing strategies, improving the operational efficiency of Vietcombank in the region.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.73-81
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• 2022

The focus of the research is to determine the amount of financial information disclosure and the factors that influence it for non-financial enterprises listed on Vietnam's stock exchange. To evaluate the level of financial information disclosure, the study uses a set of disclosure indexes from the world's leading credit rating agency, Standard and Poor's (S&P). It makes some revisions in compliance with regulations for information disclosure on the Vietnam stock market. The study collects data in the form of annual reports for the year 2017-2020 from 350 non-financial firms listed on Vietnam's stock exchange and then uses a multivariate regression model to assess the effects of factors on the amount of financial information disclosure. The findings show that the size of the firm, the size of the board of directors, and foreign ownership all have a positive impact on financial transparency; however, the number of years the company has a negative impact. According to the findings of this study, companies with more total assets, a larger board of directors, and a higher rate of foreign ownership publish more financial information. Still, long-term listed companies on the stock exchange tend to disclose less.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.231-242
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• 2014

On the Pre-Housing-Sale Systems there are many risks that developers might not fulfill the pre-sale obligations. In korea, in order to protect the people who bought houses from these risk, the Housing Sold Guarantee System was introduced and has been operated. Even though this system if there is accident in the pre-sale warranty business, several problems, such as damages caused to the people who bought the houses, occurs. Therefore, research is needed to Housing Sold Guarantee accident factor. But there are few study about it. This study attempted to analyze influencers on the possibility of the accident. We employ 3,026 data which Korea Housing Guarantee Co., Ltd manages and analyze them empirically, using business characteristics, housing market characteristics, and regional characteristics. Especially this study used to the binary logistic regression model. The results of analysis showed that the accident rate of Housing Sold Guarantee had been effected on the business type, house type, project financing guarantee, operator credit rating, housing market, and regional characteristics.

• The Korean Journal of Financial Management
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• v.21 no.2
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• pp.99-123
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• 2004

We focus in this study on incremental financing decisions and estimate a logit model for the probability a firm will choose a private placement over a public bond issue. We hypothesize that information asymmetry, financial risk, agent cost, and proprietary information may affect a firm's choice between public debt and private placements. We find that as the size of firm increases, the probability of choosing a private placement declines significantly. The age of the firm, however, is not a significant factor affecting the firm's choice between public and privately-placed bond. The coefficients on the firm's leverage and non-investment grade dummy are significantly positive, meaning firms with high financial risk and credit risk select private placements. The findings regarding agency-related variables, PER and Tobin's Q, are somewhat complex. We find significant evidence that firms with high PER prefer private placements to public bonds, suggesting that borrowers with options to engage in asset substitution or underinvestment are more likely to choose private placements. The coefficient of Tobin's Q is negative, but not significant, which weakly support the hold-up hypothesis. When we construct an interaction term on the Tobin's Q with a non-investment rating dummy, however, the Tobin's Q interaction term becomes positive and significant. Thus, high Tobin's Q firms with a speculative rating are significantly more likely to choose a private placement, regardless of the potential hold-up problems. The ratio of R&D to sales, proxy for proprietary information, is positively significant. This result can be interpreted as evidence in favor of a role for proprietary information in the debt sourcing decision process for these firms.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.5
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• pp.129-139
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• 2012

Recent changes in landscape architectural field, such as keen attention on central and local government, checks of other related fields, circumstances both inside and outside the construction industry, assume hostile attitude towards qualification system in landscape architecture. By securing the original function of qualification meets the environmental changes and accords to the technical development, practicality and serviceability of qualification as well as credit rating and professional status can be enforced. Framework redesign on landscape architecture National Technical Qualifications(NTQ) is required in order to meet the demand in the industrial fields and to reflect the technical changes. National Competency Standards(NCS) was selected as a precedent study to enhance the practicality and serviceability of NTQ as well as to avoid duplication on qualified requirements. It would provide a model to redesign the framework of landscape architecture NTQ. In this study, questions in NCS and in landscape architecture certification are compared and analyzed to review the suitability of the present landscape architecture certification items. In conclusion, the creation of master landscape architect under the present system, and the subdivision of the technician's license level to planting technician and the facility are recommended. The ability units to be qualified for each level, which would be used for future NTQ standards and university curriculums in relevant fields, are also suggested in this study.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.119-126
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• 2011

High-speed railway raises global interests with the growing concerns on the green development and the green energy. However, since most of the infrastructure investment of Korea was focused on the highway projects for last forty years, the investment on the railway has been limited around 40~50% of that of the highway projects. In addition, due to the world economy crisis and unsatisfactory support of existing policy for the private investment project, the private investment is given a small deal of weight on the social overhead capital investment. Meanwhile, despite the world high-speed railway market is growing rapidly and the Korean contractors have won the international construction contracts over 70 billion USD, past records of railway projects are very rare. Therefore, it is required to develop strategies for encouraging private investment in the domestic market to achieve efficient high-speed railway development and for advancing into foreign high-speed railway market. This study carried out data collection and market analysis for both domestic and foreign market respectively. Through a structured questionnaire survey and expert interviews, contractors' perceptions on the high-speed railway market and needs for the government support are collected. Summary of strategies drawn from this study are as follows: 1) carrying out BTL high-speed railway projects and revising related policies; 2) upwarding incentive level for the private pre-investment projects considering the contractors' credit rating; 3) carrying out Honam-Jeju submarine railway project; 4) establishing a efficient consortium model for foreign market; 5) improving the capacity of the Korea Railway Association that support Korean contractors' foreign advancement; and 6) expand the budget for Global Infra-fund.