• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• The Korean Journal of Archival Studies
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• no.16
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• pp.303-339
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• 2007

"The damage incurred from forced mobilization under the Japanese Imperialism" means the life, physical, and property damage suffered by those who were forced to lead a life as soldiers, civilians attached to the military, laborers, and comfort women forcibly mobilized by the Japanese Imperialists during the period between the Manchurian Incident and the Pacific War. Up to the present time, every effort to restore the history on such a compulsory mobilization-borne damage has been made by the damaged parties, bereaved families, civil organizations, and academic circles concerned; as a result, on March 5, 2004, Disclosure act of Forced Mobilization under the Japanese Imperialism[part of it was partially revised on May 17, 2007]was officially established and proclaimed. On the basis of this law, the Truth Commission on Forced Mobilization under the Japanese Imperialism Republic of Korea[Compulsory Mobilization Commission hence after] was launched under the jurisdiction of the Prime Minister on November 10, 2004. Since February 1, 2005, this organ has begun its work with the aim of looking into the real aspects of damage incurred from compulsory mobilization under the Japanese Imperialism, by which making the historical truth open to the world. The major business of this organ is to receive the damage report and investigation of the reported damage[examination of the alleged victims and bereaved families, and decision-making], receipt of the application for the fact-finding & fact finding; fact finding and matters impossible to make judgment; correction of a family register subsequent to the damage judgement; collection & analysis of data concerning compulsory mobilization at home and from abroad and writing up of a report; exhumation of the remains, remains saving, their repatriation, and building project for historical records hall and museum & memorial place, etc. The Truth Commission on Compulsory Mobilization has dug out and collected a variety of records to meet the examination of the damage and fact finding business. As is often the case with other history of damage, the records which had already been made open to the public or have been newly dug out usually have their limits to ascertaining of the diverse historical context involved in compulsory mobilization in their quantity or quality. Of course, there may happen a case where the interested parties' story can fill the vacancy of records or has its foundational value more than its related record itself. The Truth Commission on Compulsory mobilization generated a variety of oral history records through oral interviews with the alleged damage-suffered survivors and puts those data to use for examination business, attempting to make use of those data for public use while managing those on a systematic method. The Truth Commission on compulsory mobilization-possessed oral history archives were generated based on a drastic planning from the beginning of their generation, and induced digital medium-based production of those data while bearing the conveniences of their management and usage in mind from the stage of production. In addition, in order to surpass the limits of the oral history archives produced in the process of the investigating process, this organ conducted several special training sessions for the interviewees and let the interviewees leave their real context in time of their oral testimony in an interview journal. The Truth Commission on compulsory mobilization isn't equipped with an extra records management system for the management of the collected archives. The digital archives are generated through the management system of the real aspects of damage and electronic approval system, and they plays a role in registering and searching the produced, collected, and contributed records. The oral history archives are registered at the digital archive and preserved together with real records. The collected oral history archives are technically classified at the same time of their registration and given a proper number for registration, classification, and keeping. The Truth Commission on compulsory mobilization has continued its publication of oral history archives collection for the positive use of them and is also planning on producing an image-based matters. The oral history archives collected by this organ are produced, managed and used in as positive a way as possible surpassing the limits produced in the process of investigation business and budgetary deficits as well as the absence of records management system, etc. as the form of time-limit structure. The accumulated oral history archives, if a historical records hall and museum should be built as regulated in Disclosure act of forced mobilization, would be more systematically managed and used for the public users.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.4
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• pp.24-36
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• 2014

The result of the research conducted for the purpose of investigating the semantic value and the layout of the Cheongryu Gugok of Dorimsa Valley, which exhibits a high level of completeness and scenic preservation value among the three gugoks distributed in the area around Mt. Dongak of Gogseong is as follows.4) The area around Cheongryu Gugok shows a case where the gugok culture, which has been enjoyed as a model of the Neo-Confucianism culture and bedrock scenery, such as waterfall, riverside, pond, and flatland, following the beautiful valley, has been actually substituted, and is an outstanding scenery site as stated in a local map of Gokseong-hyeon in 1872 as "Samnam Jeil Amban Gyeryu Cheongryu-dong(三南第一巖盤溪流 淸流洞: Cheongryu-dong, the best rock mooring in the Samnam area)." Cheongryu Gugok, which is differentiated through the seasonal scenery and epigrams established on both land route and waterway, was probably established by the lead of Sun-tae Jeong(丁舜泰, ?~1916) and Byeong-sun Cho(曺秉順, 1876~1921) before 1916 during the Japanese colonization period. However, based on the fact that a number of Janggugiso of ancient sages, such as political activists, Buddhist leaders, and Neo-Confucian scholars, have been established, it is presumed to have been utilized as a hermit site and scenery site visited by masters from long ago. Cheongryu Gugok, which is formed on the rock floor of the bed rock of Dorimsa Valley, is formed in a total length of 1.2km and average gok(曲) length of 149m on a mountain type stream, which appears to be shorter compared to other gugoks in Korea. The rock writings of the three gugoks in Mt. Dongak, such as Cheongryu Gugok, which was the only one verified in the Jeonnam area, total 165 in number, which is determined to be the assembly place for the highest number of rock writings in the nation. In particular, a result of analyzing the rock writings in Cheongryu Gugok totaling 112 places showed 49pieces(43.8%) with the meaning of 'moral training' in epigram, 21pieces (18.8%) of human life, 16pieces(14.2%) of seasonal scenery, and 12pieces(10.6%) of Janggugiso such as Jangguchur, and the ratio occupied by poem verses appeared to be six cases(3.6%). Sweyeonmun(鎖烟門), which was the first gok of land route, and Jesiinganbyeolyucheon(除是人間別有天) which was the ninth gok of the waterway, corresponds to the Hongdanyeonse(虹斷烟鎖) of the first gok and Jesiinganbyeolyucheon of the ninth gok established in Jaecheon, Chungbuk by Se-hwa Park(朴世和, 1834~1910), which is inferred to be the name of Gugok having the same origin. In addition, the Daeeunbyeong(大隱屛) of the sixth gok. of land route corresponds to the Chu Hsi's Wuyi-Gugok of the seventh gok, which is acknowledged as the basis for Gugok Wollim, and the rock writings and stonework of 'Amseojae(巖棲齋)' and 'Pogyeongjae(抱經齋)' between the seventh gok and eighth gok is a trace comparable with Wuyi Jeongsa(武夷精舍) placed below Wuyi Gugok Eunbyeon-bong, which is understood to be the activity base of Cheongryu-dong of the Giho Sarim(畿湖士林). The rock writings in the Mt. Dongak area, including famous sayings by masters such as Sunsaeuhje(鮮史御帝, Emperor Gojong), Bogahyowoo(保家孝友, Emperor Gojong), Manchunmungywol(萬川明月, King Joengjo), Biryeobudong(非禮不動, Chongzhen Emperor of the Ming Dynasty)', Samusa(思無邪, Euijong of the Ming Dynasty), Baksechungpwoong(百世淸風, Chu Hsi), and Chungryususuk-Dongakpungkyung(淸流水石 動樂風景, Heungseon Daewongun) can be said to be a repository of semantic symbolic cultural scenery, instead of only expressing Confucian aesthetics. In addition, Cheongryu Gugok is noticeable with its feature as a cluster of cultural scenery of the three religions of Confucian-Buddhism-Taoism, where the Confucianism value system, Buddhist concept, and Taoist concept co-exists for mind training and cultivation. Cheongryu Gugok has a semantic feature and spatial character as a basis for history and cultural struggle for the Anti-Japan spirit that has been conceived during the process of establishing and utilizing the spirit of the learning, loyalty for the Emperor and expulsion of barbarians, and inspiration of Anti-Japan force, by inheriting the sense of Dotong(道統) of Neo-Confucianism by the Confucian scholar class at the end of the Joseon era that is represented by Ik-hyun Choi(崔益鉉, 1833~1906), Woo Jeon(田愚, 1841~1922), Woo-man Gi(奇宇萬, 1846~1916), Byung-sun Song(宋秉璿, 1836~1905), and Hyeon Hwang(黃玹, 1855~1910).

• Journal of Korean Academy of Nursing
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• v.3 no.1
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• pp.13-40
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• 1972

Much has teed changed in the field of hospital administration in the It wake of the rapid development of sciences, techniques ana systematic hospital management. However, we still have a long way to go in organization, in the quality of hospital employees and hospital equipment and facilities, and in financial support in order to achieve proper hospital management. The above factors greatly effect the ability of hospitals to fulfill their obligation in patient care and nursing services. The purpose of this study is to determine the optimal methods of standardization and quality nursing so as to improve present nursing services through investigations and analyses of various problems concerning nursing administration. This study has been undertaken during the six month period from October 1971 to March 1972. The 41 comprehensive hospitals have been selected iron amongst the 139 in the whole country. These have been categorized according-to the specific purposes of their establishment, such as 7 university hospitals, 18 national or public hospitals, 12 religious hospitals and 4 enterprise ones. The following conclusions have been acquired thus far from information obtained through interviews with nursing directors who are in charge of the nursing administration in each hospital, and further investigations concerning the purposes of establishment, the organization, personnel arrangements, working conditions, practices of service, and budgets of the nursing service department. 1. The nursing administration along with its activities in this country has been uncritical1y adopted from that of the developed countries. It is necessary for us to re-establish a new medical and nursing system which is adequate for our social environments through continuous study and research. 2. The survey shows that the 7 university hospitals were chiefly concerned with education, medical care and research; the 18 national or public hospitals with medical care, public health and charity work; the 2 religious hospitals with medical care, charity and missionary works; and the 4 enterprise hospitals with public health, medical care and charity works. In general, the main purposes of the hospitals were those of charity organizations in the pursuit of medical care, education and public benefits. 3. The survey shows that in general hospital facilities rate 64 per cent and medical care 60 per-cent against a 100 per cent optimum basis in accordance with the medical treatment law and approved criteria for training hospitals. In these respects, university hospitals have achieved the highest standards, followed by religious ones, enterprise ones, and national or public ones in that order. 4. The ages of nursing directors range from 30 to 50. The level of education achieved by most of the directors is that of graduation from a nursing technical high school and a three year nursing junior college; a very few have graduated from college or have taken graduate courses. 5. As for the career tenure of nurses in the hospitals: one-third of the nurses, or 38 per cent, have worked less than one year; those in the category of one year to two represent 24 pet cent. This means that a total of 62 per cent of the career nurses have been practicing their profession for less than two years. Career nurses with over 5 years experience number only 16 per cent: therefore the efficiency of nursing services has been rated very low. 6. As for the standard of education of the nurses: 62 per cent of them have taken a three year course of nursing in junior colleges, and 22 per cent in nursing technical high schools. College graduate nurses come up to only 15 per cent; and those with graduate course only 0.4 per cent. This indicates that most of the nurses are front nursing technical high schools and three year nursing junior colleges. Accordingly, it is advisable that nursing services be divided according to their functions, such as professional, technical nurses and nurse's aides. 7. The survey also shows that the purpose of nursing service administration in the hospitals has been regulated in writing in 74 per cent of the hospitals and not regulated in writing in 26 per cent of the hospitals. The general purposes of nursing are as follows: patient care, assistance in medical care and education. The main purpose of these nursing services is to establish proper operational and personnel management which focus on in-service education. 8. The nursing service departments belong to the medical departments in almost 60 per cent of the hospitals. Even though the nursing service department is formally separated, about 24 per cent of the hospitals regard it as a functional unit in the medical department. Only 5 per cent of the hospitals keep the department as a separate one. To the contrary, approximately 12 per cent of the hospitals have not established a nursing service department at all but surbodinate it to the other department. In this respect, it is required that a new hospital organization be made to acknowledge the independent function of the nursing department. In 76 per cent of the hospitals they have advisory committees under the nursing department, such as a dormitory self·regulating committee, an in-service education committee and a nursing procedure and policy committee. 9. Personnel arrangement and working conditions of nurses 1) The ratio of nurses to patients is as follows: In university hospitals, 1 to 2.9 for hospitalized patients and 1 to 4.0 for out-patients; in religious hospitals, 1 to 2.3 for hospitalized patients and 1 to 5.4 for out-patients. Grouped together this indicates that one nurse covers 2.2 hospitalized patients and 4.3 out-patients on a daily basis. The current medical treatment law stipulates that one nurse should care for 2.5 hospitalized patients or 30.0 out-patients. Therefore the statistics indicate that nursing services are being peformed with an insufficient number of nurses to cover out-patients. The current law concerns the minimum number of nurses and disregards the required number of nurses for operation rooms, recovery rooms, delivery rooms, new-born baby rooms, central supply rooms and emergency rooms. Accordingly, tile medical treatment law has been requested to be amended. 2) The ratio of doctors to nurses: In university hospitals, the ratio is 1 to 1.1; in national of public hospitals, 1 to 0.8; in religious hospitals 1 to 0.5; and in private hospitals 1 to 0.7. The average ratio is 1 to 0.8; generally the ideal ratio is 3 to 1. Since the number of doctors working in hospitals has been recently increasing, the nursing services have consequently teen overloaded, sacrificing the services to the patients. 3) The ratio of nurses to clerical staff is 1 to 0.4. However, the ideal ratio is 5 to 1, that is, 1 to 0.2. This means that clerical personnel far outnumber the nursing staff. 4) The ratio of nurses to nurse's-aides; The average 2.5 to 1 indicates that most of the nursing service are delegated to nurse's-aides owing to the shortage of registered nurses. This is the main cause of the deterioration in the quality of nursing services. It is a real problem in the guest for better nursing services that certain hospitals employ a disproportionate number of nurse's-aides in order to meet financial requirements. 5) As for the working conditions, most of hospitals employ a three-shift day with 8 hours of duty each. However, certain hospitals still use two shifts a day. 6) As for the working environment, most of the hospitals lack welfare and hygienic facilities. 7) The salary basis is the highest in the private university hospitals, with enterprise hospitals next and religious hospitals and national or public ones lowest. 8) Method of employment is made through paper screening, and further that the appointment of nurses is conditional upon the favorable opinion of the nursing directors. 9) The unemployment ratio for one year in 1971 averaged 29 per cent. The reasons for unemployment indicate that the highest is because of marriage up to 40 per cent, and next is because of overseas employment. This high unemployment ratio further causes the deterioration of efficiency in nursing services and supplementary activities. The hospital authorities concerned should take this matter into a jeep consideration in order to reduce unemployment. 10) The importance of in-service education is well recognized and established. 1% has been noted that on the-job nurses. training has been most active, with nursing directors taking charge of the orientation programs of newly employed nurses. However, it is most necessary that a comprehensive study be made of instructors, contents and methods of education with a separate section for in-service education. 10. Nursing services'activities 1) Division of services and job descriptions are urgently required. 81 per rent of the hospitals keep written regulations of services in accordance with nursing service manuals. 19 per cent of the hospitals do not keep written regulations. Most of hospitals delegate to the nursing directors or certain supervisors the power of stipulating service regulations. In 21 per cent of the total hospitals they have policy committees, standardization committees and advisory committees to proceed with the stipulation of regulations. 2) Approximately 81 per cent of the hospitals have service channels in which directors, supervisors, head nurses and staff nurses perform their appropriate services according to the service plans and make up the service reports. In approximately 19 per cent of the hospitals the staff perform their nursing services without utilizing the above channels. 3) In the performance of nursing services, a ward manual is considered the most important one to be utilized in about 32 percent of hospitals. 25 per cent of hospitals indicate they use a kardex; 17 per cent use ward-rounding, and others take advantage of work sheets or coordination with other departments through conferences. 4) In about 78 per cent of hospitals they have records which indicate the status of personnel, and in 22 per cent they have not. 5) It has been advised that morale among nurses may be increased, ensuring more efficient services, by their being able to exchange opinions and views with each other. 6) The satisfactory performance of nursing services rely on the following factors to the degree indicated: approximately 32 per cent to the systematic nursing activities and services; 27 per cent to the head nurses ability for nursing diagnosis; 22 per cent to an effective supervisory system; 16 per cent to the hospital facilities and proper supply, and 3 per cent to effective in·service education. This means that nurses, supervisors, head nurses and directors play the most important roles in the performance of nursing services. 11. About 87 per cent of the hospitals do not have separate budgets for their nursing departments, and only 13 per cent of the hospitals have separate budgets. It is recommended that the planning and execution of the nursing administration be delegated to the pertinent administrators in order to bring about improved proved performances and activities in nursing services.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of Technologic Dentistry
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• v.17 no.1
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• pp.41-57
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• 1995

This study was conducted to collect and analyze previous information in order to manage efficience, improve experience effect and promote employment rate. The questionnaire interview with 27 chief of dental Laboratory refered clinical experience in technology department about clinical experience in 14 Jumior colleges were also investigated. The results were summarried as follows : The portion of age of 35-39 among chief of dental Laboratory was 40.7% which was the highest, that of male was 96.3%, that of junior college graduate was 97.5%, that of 10years experience was 92.6% and that of ceramic technician was 85.2%, 63.0% dental laboratory for clinical experience was a bore space of 30pyong. Aspect of dental laboratory management, manufacturing all part of prosthetic restoration was 29.6%, othodontic appliance and ceramic restoration was 7.4%, 3.8%, each. The percentage of 40.7 was having connection with 30-3a dental clinics and referring case per day was 10-19 cases(40.7%), manufacturing time of referred prosthetic restoration was 3-4 days(77.8%), places preparing seminar room for education was 29.6%, above a place of 40pyong was 11.1% 30-34 pyong and 35-39 pyong was 7.4% each. During training of 2 years education course student, 18.5% was rack of thorough occupational career. While 44.4% will want the more salary among 3years education course student, 74.1% will expect the more dental techmicians would engaged in their field, 51.9% will hope improve of their theory and practice, 29.6% be expected better skill and 14.8% be expected better theory. Attitude of clinical experience places was distributed by 59.3% of offering only experience chance, 25.9% of wasting time and 29.0% of annoying. The big emphasis of climical experience was thorough occupational career(44.4%). The clinical experience places of our college were selected after direct visiting, so their condition of management was not that bad but most of dental laboratory were poor in management state and working environment. Therefore it is difficult to choose appropriate places and dental Laboratory are also limited manpower and time as suppliers. So that it recommended to induce flexible management of experience period by interval and rotation of experience places among college and to applicate intern-system for employment ant industry-college cooperation aspect.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.169-196
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• 2017

The fourth industrial revolution represents a revolutionary change in the business environment and its ecosystem, which is a fusion of Information Technology (IT) and other industries. In line with these recent changes, the Ministry of Employment and Labor of South Korea announced 'the Fourth Industrial Revolution Leader Training Program,' which includes five key support areas such as (1) smart manufacturing, (2) Internet of Things (IoT), (3) big data including Artificial Intelligence (AI), (4) information security, and (5) bio innovation. Based on this program, we can get a glimpse of the South Korean government's efforts and willingness to emit leading human resource with advanced IT knowledge in various fusion technology-related and newly emerging industries. On the other hand, in order to nurture excellent IT manpower in preparation for the fourth industrial revolution, the role of educational institutions capable of providing high quality IT education services is most of importance. However, these days, most IT educational institutions have had difficulties in providing customized IT education services that meet the needs of consumers (i.e., learners), without breaking away from the traditional framework of providing supplier-oriented education services. From previous studies, it has been found that the provision of customized education services centered on learners leads to high satisfaction of learners, and that higher satisfaction increases not only task performance and the possibility of business application but also learners' recommendation intention. However, since research has not yet been conducted in a comprehensive way that consider both antecedent and consequent factors of the learner's satisfaction, more empirical research on this is highly desirable. With the advent of the fourth industrial revolution, a rising interest in various convergence technologies utilizing information technology (IT) has brought with the growing realization of the important role played by IT-related education services. However, research on the role of IT education service quality in the context of IT education is relatively scarce in spite of the fact that research on general education service quality and satisfaction has been actively conducted in various contexts. In this study, therefore, the five dimensions of IT education service quality (i.e., tangibles, reliability, responsiveness, assurance, and empathy) are derived from the context of IT education, based on the SERVPERF model and related previous studies. In addition, the effects of these detailed IT education service quality factors on learners' educational satisfaction and their work application/recommendation intentions are examined. Furthermore, the moderating roles of learner position (i.e., practitioner group vs. manager group) and participation motivation (i.e., voluntary participation vs. involuntary participation) in relationships between IT education service quality factors and learners' educational satisfaction, work application intention, and recommendation intention are also investigated. In an analysis using the structural equation model (SEM) technique based on a questionnaire given to 203 participants of IT education programs in an 'M' IT educational institution in Seoul, South Korea, tangibles, reliability, and assurance were found to have a significant effect on educational satisfaction. This educational satisfaction was found to have a significant effect on both work application intention and recommendation intention. Moreover, it was discovered that learner position and participation motivation have a partial moderating impact on the relationship between IT education service quality factors and educational satisfaction. This study holds academic implications in that it is one of the first studies to apply the SERVPERF model (rather than the SERVQUAL model, which has been widely adopted by prior studies) is to demonstrate the influence of IT education service quality on learners' educational satisfaction, work application intention, and recommendation intention in an IT education environment. The results of this study are expected to provide practical guidance for IT education service providers who wish to enhance learners' educational satisfaction and service management efficiency.