• Journal of Distribution Science
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• v.16 no.11
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• pp.21-27
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• 2018

Purpose - This study investigates the financial ratio of savings banks and the effect of the ratio having influence upon bankruptcy by quantitative empirical analysis of forecast model to give material of better management and objective evidence of management strategy and way of advancement and risk control. Research design, data, and methodology - The author added two growth indexes, three fluidity indexes, five profitability indexes, and four activity indexes CAMEL rating to not only the balance sheets but also the income statement of thirty savings banks that suspended business from 2011 to 2015 and collected fourteen financial ratio indexes. IBMSPSS VER. 21.0 was used. Results - Variables having influence upon bankruptcy forecast models included total asset increase ratio and operating income increase ratio of growth index and sales to account receivable ratio, and tangible equity ratio and liquidity ratio of liquidity ratio. The study selected total asset operating ratio, and earning and expenditure ratio from profitability index, and receivable turnover ratio of activity index. Conclusions - Financial supervising system should be improved and financial consumers should be protected to develop saving bank and to control risk, and information on financial companies should be strengthened.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.307-318
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• 2015

In this study, the efficiency of two-dimensional inundation analysis using road network was demonstrated in order to reduce the simulation time of numerical model in urban area. For this objective, three simulation conditions were set up: Case 1 considered only inundation within road zone, while Case 2 and 3 considered inundation within road and building zone together. Accordingly, Case 1 used grids generated based on road network, while Case 2 and 3 used uniform and non-uniform grids for whole study area, respectively. Three simulation conditions were applied to Samsung drainage where flood damage occurred due to storm event on Sep. 21, 2010. The efficiency of suggested method in this study was verified by comparison the accuracy and simulation time of Case 1 and those of Case 2 and 3. The results presented that the simulation time was fast in the order of Case 1, 2 and 3, and the fit of inundation area between each case was more than 85% within road zone. Additionally, inundation area of building zone estimated from inundation rating index gave a similar agreement under each case. As a result, it is helpful for study on real-time inundation forecast warning to use a proposed method based on road network and inundation rating index for building zone.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.158-167
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• 2017

This paper presents the technology rating methodology that is applicable to defense R&D technology. First, a technology profitability index was developed using multiple regression analysis to forecast the revenue from technology transfer. Secondly, the technology evaluation index was derived using hierarchical analysis with expert opinion. Finally, the weighted average of the technology profitability index and technology evaluation index were calculated to derive the technology rating. This study is significant in that it is first attempt to evaluate defense R&D technology by rating. If the defense R&D technology rating methodology is applied in practice, it can contribute to efficient R&D budget allocation. In addition, it will help in the vitalization of technology transfer in the defense R&D sector.

• 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.

• Korean Journal of Hydrosciences
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• v.4
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• pp.11-19
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• 1993

The peak flood discharge at a downstream station and the flood travel time between a pair of dams due to a specific flood release from the upper reservoir are computed using a hydraulic river channel routing method. The study covered the whole large reservoir system in the Han River, Korea. The computed flood discharges and the travel times between dams were correlated with the duration and the magnitude of flood release rate at the upstream reservoir, and hence a multiple regression model is proposed for each river reach between a pair of dams. The peak flood discharge at a downstream location can be converted to the peak flood stage by a rating curve. Hence, the proposed regression model could be used to forecast the peak flood stage at a downstream location and the flood travel time between dams using the information on the flood travel time, release rate and duration from the upper dam.

• Journal of Korea Water Resources Association
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• v.45 no.11
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• pp.1107-1119
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• 2012

The objectives of this study are to develop the flow nomograph for real-time flood forecasting and to assess its applicability in restored Cheonggye stream. The Cheonggye stream basin has the high impermeability and short concentration time and complicated hydrological characteristics. Therefore, the flood prediction method using runoff model is ineffective due to the limit of forecast. Flow nomograph which is able to forecast flood only with rainfall information. To set the forecast criteria of flow nomograph at selected flood forecast points and calculated criterion flood water level for each point, and in order to reflect various flood events set up simulated rainfall scenario and calculated rainfall intensity and rainfall duration time for each condition of rainfall. Besides, using a rating curve, determined scope of flood discharge following criterion flood water level and using SWMM model calculated flood discharge for each forecasting point. Using rainfall information following rainfall scenario calculated above and flood discharge following criterion flood water level developed flow nomograph and evaluated it by applying it to real flood event. As a result of performing this study, the applicability of flow nomograph to the basin of Cheonggye stream appeared to be high. In the future, it is reckoned to have high applicability as a method of prediction of flood of urban stream basin like Cheonggye stream.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Proceedings of the Safety Management and Science Conference
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• 2002.11a
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• pp.321-339
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• 2002

The result of variance decomposition through yield of Treasury of 30 year maturity of USA, S&P 500 index, stock price of KEPCO has 76.12% of impulse of KEPCO stock price at short-term horizon, but they have 51.40% at long-term horizon. After one year, they occupy 13.65%, and 33.25%. So their effects are increased. By the way, S&P 500 index and yield of Treasury of 30 year maturity of USA have relatively more effect for forecast of stock price oi KEPCO at short-term & long-term. The yield of Treasury of 30 year maturity of USA more than S&P 500 index have more effect for stock price of KEPCO. It is why. That foreign investors through fall of stock price of USA invest for emerging market is less than movement for emerging market of hedge funds through effect of fall of yield of Treasury of 30 year maturity of USA, according to relative effects for stock price of Korea companies. The result of variance decomposition through won/dollar foreign exchange rate, yield of corporate bond of 3 year maturity, Korea Stock Price index(KOSPI), stock price of KEPCO has 81.33% of impulse of KEPCO stock price at short-term horizon, but they have 41.73% at long-term horizon. After one year, they occupy 23.57% and 34.70%. So their effects are increased. By the way, KOSPI and won/dollar foreign exchange rate have relatively more effect for forecast of stock price of KEPCO at short-term & long-term. The won/dollar foreign exchange rate more than KOSPI have more effect for stock price of KEPCO. It is why. The recovery of economic condition through improvement of company revenue causes of rising of KOSPI. But, if persistence of low interest rate continues, fall of won/dollar foreign exchange rate will be more aggravated. And it will give positive effect for stock price of KEPCO. This gives more positive effect at two main reason. Firstly, through fall of won/dollar foreign exchange rate and rising of credit rating of Korea will be followed. Therefore, foreign investors will invest more funds to Korea. Secondly, inflow of foreign investment funds through profit of won/dollar foreign exchange rate and stock investment will be occurred. If appreciation of won against dollar is forecasted, foreign investors will buy won. Through this won, investors will do investment. Won/dollar foreign exchange rate is affected through external factors of yen/dollar foreign exchange rate, etc. Therefore, the exclusion of instable factors for foreign investors through rising of credit rating of Korea is necessary things.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Water for future
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• v.25 no.3
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• pp.115-124
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• 1992

The peak flood discharge at a downstream station and the flood travel time between a pair of dams due to a specific flood release from the upper reservoir are computed using a hydraulic river channel routing method. The study covered the whole reservoir system in the Han River. The computed peak flood discharges and the travel times between dams were correlated with the duration and the magnitude of flood release rate at the upstream reservoir, and hence a multiple regression model is proposed for each river reach between a pair of dams. The peak flood discharge at a downstream location can be converted to the peak flood stage by rating curve. Hence, the proposed regression model could be used to forecast the peak flood stage at a downstream location and the flood travel time between dams using the information on the flood release rate and duration from the upper dam.