• 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.

• Journal of Korean Home Economics Education Association
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• v.23 no.3
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• pp.139-160
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• 2011

The purpose of this study is twofold: (i) to develop a teaching and learning plan and practical performance assessment tools for the improvement of teenager-parent communication and relationships as well as explore their effects on the communication in the everyday family life; and (ii) to find the underlying problems of teenager- parent communication through conversation analysis and to provide a improved dialogue model. We provided the experimental group with a performance task of communication training between teenagers and their parents in the real family situation while the control group practiced communication skills in a learning situation. However for both classes, before and after performance tasks were equally provided. The experimental group exhibited a longer conversation time with their parents, better communication skills, and higher degrees of relational satisfaction than the control group. Conversation analysis revealed that the experimental group reduced the use of blocking techniques in the teenager-parent conversations more than the control group, and all so raised the frequency of functional communications more than the control group. In both areas of communication in the experimental group was significantly improved, Most notably, a problem-solving case through no-lose conflict resolution methods was effective, succeeding by 70% in the e experimental group and 43.3% in the control group. Parents use blocking techniques like admonition, lecturing, blaming. sarcastic remarking, ordering and so forth, while teenagers use dispute, avoidance, blaming, and teasing in this order. The communication problems during the conversation process, teenagers' evasive and rebellious way of speaking instigates adverse communication responses from parents, so their conversation tends to unfold as ambiguous evasion opposed to: inquiring or evasion by short answers vs. ordering-preaching, or disputing vs. criticizing-making sarcastic, disputing vs. disputing-teaching, and criticizing vs. criticizing.

• Journal of Technologic Dentistry
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• v.28 no.1
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• pp.177-198
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• 2006

This study purposed to offer basic data for safety education of the dental laboratory technology after the investigation of how much the students in the dept. of dental laboratory technology are aware of the danger of each instrument, equipment or laboratory procedure that they use during laboratory and how much they demand safety education for this. The objects for this study were 423 students who were in the dept. of dental laboratory technology. In this regard, four colleges which have the dept. of dental laboratory technology were randomly selected to do a questionnaire survey. SPSS 12.0 was used to analyze the collected data. The results were as follows: As for satisfaction with their major, the respondents answered Satisfied (59.1%), Average (35.5%) and Dissatisfied (5.4%). In terms of the production process of a partial denture, they considered casting, polishing the casting body, polishing denture and burn out were most dangerous in order. As for the production process of a full denture, what they regarded as the most dangerous in order was polishing denture, deflasking and wax wash. Regarding the laboratory procedures of porcelain material, casting, trimming casting body, polishing porcelain material and burn out were the most dangerous procedures that they perceived. With regard to materials for use, alcohol, polishing, metal and wire were the most dangerous ones they thought. As for the handling characteristics of each material, small towns showed a higher demand for safety of the handling characteristics of alcohol. In terms of school year and sex, juniors and girls had higher scores in the demand for safety of the handling characteristics of acid. Regarding the handling characteristics of each equipment and instrument, all of small towns, juniors and girls showed the highest demand for safety of the handling characteristics of alcohol lamps. With regard to scores in the demand for safety of other characteristics, all of small towns, juniors and girls had the highest demand for safety of emergency treatment. Concerning the demand for safety education by the completion of safety education, in terms of each material, highest was the demand for safety of acid from the group which completed safety education. In regard to equipments and instruments, when it came to the demand for safety of the handling characteristics of casting machine, the educated group's demand for safety of acid was higher. Regarding other characteristics, the group which was not educated gained higher scores in the demand for safety of emergency treatment. 11. In all areas(materials, machines and others), small towns, girls and juniors showed higher scores in the demand for safety. Based on the above results, it was found that when students conduct the laboratory of dental technology, they would think that many materials, instruments or equipments for use are very dangerous. However, safety education was not fully given to them. Regarding the scores in the damned for safety education, the highest was 4.16 and the lowest was 3.43, which suggests that the scores were generally very high. In this regard, it is necessary to continue delivering a systematic safety education of materials, equipments or instruments used during the laboratory of dental technology. Therefore, through the analysis of each material, instruments or facility used in every laboratory and each process, safety accident types and accident risk factors should be investigated to develop educational materials for this. Moreover, it is required to open safety education as a single course of study or insert safety contents of all materials and machines into the class of dental laboratory instrument or dental materials for the purpose of a systematic and thorough safety education to prevent a safety accident during laboratory.

• Journal of the Korean Society of Food Science and Nutrition
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• v.38 no.6
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• pp.709-720
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• 2009

This study was conducted to identify daily caffeine intakes in beverages for elementary school children and to evaluate its effectiveness after nutrition education. The caffeine contents of 140 commercial beverages were analysed by high performance liquid chromatography-ultraviolet detector (HPLC-UV) and information about their consumption were obtained by surveying 267 children. Researchers gave nutrition education to the children, who were 6 to 11 years old and attended 9 classes of 3 elementary schools, by lecture, Powerpoint file and moving picture. Their preference and intake amount on beverages were investigated by questionnaire before and after nutrition education. The order on caffeine contents was coffee ($33.8{\pm}2.4{\sim}49.1{\pm}5.6\;mg/100\;mL$)> coffee milk ($10.6{\pm}3.3\;mg/100\;mL$)> cola ($6.0{\pm}2.4\;mg/100\;mL$)> green black oolong tea drink ($6.0{\pm}2.4\;mg/100\;mL$)> chocolate milk and chocolate drink ($1.6{\pm}0.7{\sim}1.7\;mg/100\;mL$)> black ice tea mix ($1.3{\pm}1.7\;mg/100\;mL$). The order on children's preference was carbonated drink and fruit and vegetable drink (27%)> sports drink (26%)> processed cocoa mix (7%)> milk (6%)> vitamin & functional drink (3%)> green tea drink (2%)> black tea drink and coffee (1%). The average daily caffeine intakes except tea drink was $5.9{\pm}11.2$ mg/person/day ($0.17{\pm}0.32$ mg/kg bw/day), ranged from $0.0{\sim}80.5$ mg/person/day for children. The sources of caffeine were coffee 57% (3.4 mg/person/day), coffee milk 20% (1.2 mg/person/day), carbonated drink 15% (0.9 mg/person/day), chocolate milk and chocolate drink 6% (0.4 mg/person/day), and vitamin & functional drink 2% (0.1 mg/person/day). After nutrition education, the preference of carbonated drink, coffee, vitamin drinks & functional drink was decreased significantly (p<0.05, p<0.05, p<0.01) and the intakes of carbonated drink, chocolate milk & chocolate drink, and vitamin & functional drink were also decreased significantly (p<0.01, p<0.05, p<0.01). This study has shown that nutrition education influences the preference and the intake behavior of caffeinated beverages.

• Journal of Korean Home Economics Education Association
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• v.26 no.4
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• pp.85-114
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• 2014

The main purpose of this study was to develop teaching and learning plans and a smart textbook for food and nutrition education in Home Economics focusing on 'healthy diet and meal plans' unit in "Technology home Economics" textbooks for 7th graders to evaluate the effectiveness of the instruction conducted with the smart textbook. The content of the study to achieve the purpose is as follows: First, design a smart education-based teaching and learning curriculum for food and nutrition education in Home Economics, focusing on 'healthy diet and meal plans' unit. Second, develop a smart textbook for food and nutrition education based on the teaching and learning curriculum, using a smart content authoring tool. Third, evaluate the effectiveness of the instruction after applying the curriculum in real classroom situations. The results of this study were as follows: First, teaching and learning plans and materials were developed for two units, 'issues regarding teenagers' diet' and 'implementation of a healthy and balanced diet', under 'teenagers' life'. The first unit, 'issues regarding teenagers' diet', dealt with topics such as teenagers' dietary behaviors, nutrition, and health. Learning objectives for this unit were to help students identify and evaluate their own dietary behaviors. The second unit, 'implementation of a healthy and balanced diet', encouraged students to diagnose problems with their diet and plan nutrient rich meals. The objectives for this unit were to help students implement a healthy and balanced diet by providing them with nutrition and dietary guidelines for Koreans, sample meal plans, and guidelines for developing healthy eating habits for teenagers. In order to develop a teaching and learning plans to achieve these objectives, teaching and learning materials including inquiry tasks, materials for group activities, multimedia, applications and various pop-up learning materials were developed. Second, a smart textbook using DocZoom, which was a smart content authoring tool was developed. The textbook dealt with issues regarding teenagers' diet and implementation of a healthy and balanced diet. Multimedia material used in the textbook come from the Ministry of Food and Drug Safety's food and nutrition education web sites and other sources. To develop student-oriented material, relevant video clips were added to the smart textbook to motivate students and enhance their interest in the course. Third, the outcome of this study indicated that the instruction using teaching and learning plans and learning materials with the smart textbook was effective for enhancing students' interest in Home Economics classes (t-value=-3.99, p<.001), creating enthusiasm for learning(t-value = -2.61, p<.05), encouraging self-directed and independent learning(t-value = -4.77, p<.001), and improving students' interest in food and nutrition courses(t-value = -3.83, p<.001). The students' evaluation of the instruction were as follows: the instruction using teaching and learning plans and learning materials with smart textbooks, instead of paper textbooks, helped them save time looking for learning materials; students evaluated that it was easier for them to see and understand video clips and charts. In addition, most students answered that instruction with smart textbooks were more fun and convenient, and they agreed that the courses enhanced their learning experience.

• 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.