• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• The Korean Journal of Financial Management
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• v.2 no.1
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• pp.21-34
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• 1986

The purpose of this study is to analyse the risk and uncertainty involved in the capital budgeting which is executed in long periods and requires massive capital expenditure. Under risk and uncertainty conditions, the estimates in the capital budgeting are random variables rather than known constants. Two approaches have emerged in performing economic analysis that explicitly incorporate risk and uncertainty conditions in the analysis. One approach is to develop a descriptive model which describes the economic performance of an individual investment alternative. But no recomendation would be forthcoming from the model. Rather, the decision-maker would be furnished descriptive information concerning each alternative; the final choice among the alternatives would required a separate action. The second approach is to develop a normative model which includes an objective function to be maximized or minimized. The output from the model prescribes the course of action to be taken. Owing to the fact that the normative approach considers the fitness of criteria for decision-making its reasonableness looks better. But it is almost imposible that we correctly and easily derive the individuals' utility function. So within we recognize the limits of the descriptive methods, it is more practicle to analyse the investment alternatives by sensitivity analysis.

• Journal of Preventive Medicine and Public Health
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• v.52 no.4
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• pp.234-241
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• 2019

Objectives: To identify simultaneous behavioral changes in alcohol consumption, smoking, and weight using a fixed-effect model and to characterize their associations with disease status. Methods: This study included 7 000 529 individuals who participated in the national biennial health-screening program every 2 years from 2009 to 2016 and were aged 40 or more. We reconstructed the data into an individual-level panel dataset with 4 waves. We used a fixed-effect model for smoking, heavy alcohol drinking, and overweight. The independent variables were sex, age, lifestyle factors, insurance contribution, employment status, and disease status. Results: Becoming a high-risk drinker and losing weight were associated with initiation or resumption of smoking. Initiation or resumption of smoking and weight gain were associated with non-high-risk drinkers becoming high-risk drinkers. Smoking cessation and becoming a high-risk drinker were associated with normal-weight participants becoming overweight. Participants with newly acquired diabetes mellitus, ischemic heart disease, stroke, and cancer tended to stop smoking, discontinue high-risk drinking, and return to a normal weight. Conclusions: These results obtained using a large-scale population-based database documented interactions among lifestyle factors over time.

• Journal of the Korean Society of Costume
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• v.43
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• pp.125-142
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• 1999

This research was aimed to present a model of clothing products evaluation nd to classify the effect of extrinsic cues on clothing products evaluation. In order to accomplish following subjects were established. First it is to find the effect of extrinsic cues -price brand store - on perceived quality perceived risk perceived value and purchase intention of clothing products. Second it is to formulate a model of clothing products evaluation and find the relation among the variables such as extrinsic cues perceived quality perceived risk perceived value and purchase intention. This research was mainly divided into theoretical and empirical part. In the theoretical part previous theories and studies on clothing products cues clothing products evaluation perceived quality perceived risk and perceived value were examined to establish a research model and to present a theoretical frame for clothing products evaluation. In the empirical research a questionnaire was developed and statistical data were collected from during July 1997. The subjects were 862 women in the age of 20-35 living in Seoul and kyungki region. SAS and LISREL were used to analyze the collected data. frequency percentage factor analysis ANOVA duncan test correlation analysis regression analysis and LISREL were applied. The results of this research are as follows: First perceived quality consists of performance quality external quality and utility quality in a form of multi dimensional structural. Perceived risk is structured by social/resultant risk financial/fashionable risk and performance/management risk. Second this research proved that extrinsic cues are influenced by each individual variable and extrinsic cues interact with each other through the variable. The perceived quality is influenced most by price Among the perceived risk social/resultant risk by brand financial/fashionable risk by price and performance/management risk by store. respectively. Perceived value is inflenced by price and brand. Third in evaluating process consumer use extrinsic cues to first formulate perceived quality and perceived risk of clothing products and then formulate perceived value ot decide on purchase intention.

• Journal of architectural history
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• v.21 no.2
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• pp.37-52
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• 2012

This research has set up the fire fighting general index for Fire fighting of Crowded Wooden Building Cultural Asset which is composed of traditional wooden building instinct or complex. The results of this study are as follows. First, Fire fighting general index for crowded wooden building cultural asset, it is necessary to set fire fighting priority by considering fire risk and cultural asset characteristic and establish the system to cope with fire disaster in the most effective way by arranging facilities with restricted resource. Second, Fire risk is the index to draw fire and spread risk of cultural asset by applying index calculation processes such as fire load, burning velocity and ignition material spread characteristic to various aspects such as individual building and complex and combining their results. Cultural asset importance index consists of individual building evaluation, publicity security degree, area importance evaluation and historical landscape degree evaluation. Third, for each index combination process, weight of each index is drawn on the basis of AHP analysis result that is performed to the specialists of related fields. The formula to apply and combine it is prepared to apply the model to include meaning of each index and comparative importance degree.

• Korean Journal of Social Welfare
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• v.59 no.4
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• pp.245-272
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• 2007

The purpose of this study was to create and validate Scale of Resilience to Burnout and Scale of Burnout Risk for social workers in medical settings. For the purpose, instrument development model, a kind of mixed methods research was used. In the first phase, six dimensions of resilience(: professional competency, accomplishment and worthwhileness, firm belief and value about their profession, good teamwork, support by their agency, and individual resources) and six dimensions of risk to burnout(: dissatisfaction with organizational condition, interpersonal stress among team members, organizational conflict, work related stress, deficiency of professionalism, and individual stress) were suggested thorough the preceding papers with qualitative approaches. The second phase involved analysis of a survey of 185 participants that appeared to validate the dimensions of the measures. The construct validity and reliability of each measure were tested. And it was founded that there were its own factors in each concept, although resilience to burnout related negatively to burnout risk. The results of this study suggest mixed methods research is useful to develop measures reflecting voices in the social work field.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.323-334
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• 2015

he current VaR model based on the J.P. Morgan's RiskMetrics structurally can not reflect the future economic situation. In this study, we propose a One-factor model resulting from the Wiener stochastic process decomposed into a systematic risk factor and an idiosyncratic risk factor. Therefore, we are able to perform a preemptive risk management by means of reflecting the predicted common risk factors in the model. Stocks in the portfolio are satisfied with the independence to each other because the common factors are fixed by the predicted value. Therefore, we can easily determine the investment in each stock to minimize the variance of the portfolio. In addition, the portfolio VaR is decomposed into the sum of the individual VaR. So we can effectively implement the constitution of the portfolio to meet the target maximum losses.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.373-383
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• 2006

The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

• Asia pacific journal of information systems
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• v.22 no.1
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• pp.29-52
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• 2012

Consumers differ in the way they make a purchase. An audio mania would willingly make a bold, yet serious, decision to buy a top-of-the-line home theater system, while he is not interested in replacing his two-decade-old shabby car. On the contrary, an automobile enthusiast wouldn't mind spending forty thousand dollars to buy a new Jaguar convertible, yet cares little about his junky component system. It is product involvement that helps us explain such differences among individuals in the purchase style. Product involvement refers to the extent to which a product is perceived to be important to a consumer (Zaichkowsky, 2001). Product involvement is an important factor that strongly influences consumer's purchase decision-making process, and thus has been of prime interest to consumer behavior researchers. Furthermore, researchers found that involvement is closely related to perceived risk (Dholakia, 2001). While abundant research exists addressing how product involvement relates to overall perceived risk, little attention has been paid to the relationship between involvement and different types of perceived risk in an electronic commerce setting. Given that perceived risk can be a substantial barrier to the online purchase (Jarvenpaa, 2000), research addressing such an issue will offer useful implications on what specific types of perceived risk an online firm should focus on mitigating if it is to increase sales to a fullest potential. Meanwhile, past research has focused on such consumer responses as information search and dissemination as a consequence of involvement, neglecting other behavioral responses like online merchant selection. For one example, will a consumer seriously considering the purchase of a pricey Guzzi bag perceive a great degree of risk associated with online buying and therefore choose to buy it from a digital storefront rather than from an online marketplace to mitigate risk? Will a consumer require greater trust on the part of the online merchant when the perceived risk of online buying is rather high? We intend to find answers to these research questions through an empirical study. This paper explores the impact of enduring product involvement and perceived risks on required trust level, and further on online merchant choice. For the purpose of the research, five types or components of perceived risk are taken into consideration, including financial, performance, delivery, psychological, and social risks. A research model has been built around the constructs under consideration, and 12 hypotheses have been developed based on the research model to examine the relationships between enduring involvement and five components of perceived risk, between five components of perceived risk and required trust level, between enduring involvement and required trust level, and finally between required trust level and preference toward an e-tailer. To attain our research objectives, we conducted an empirical analysis consisting of two phases of data collection: a pilot test and main survey. The pilot test was conducted using 25 college students to ensure that the questionnaire items are clear and straightforward. Then the main survey was conducted using 295 college students at a major university for nine days between December 13, 2010 and December 21, 2010. The measures employed to test the model included eight constructs: (1) enduring involvement, (2) financial risk, (3) performance risk, (4) delivery risk, (5) psychological risk, (6) social risk, (7) required trust level, (8) preference toward an e-tailer. The statistical package, SPSS 17.0, was used to test the internal consistency among the items within the individual measures. Based on the Cronbach's ${\alpha}$ coefficients of the individual measure, the reliability of all the variables is supported. Meanwhile, the Amos 18.0 package was employed to perform a confirmatory factor analysis designed to assess the unidimensionality of the measures. The goodness of fit for the measurement model was satisfied. Unidimensionality was tested using convergent, discriminant, and nomological validity. The statistical evidences proved that the three types of validity were all satisfied. Now the structured equation modeling technique was used to analyze the individual paths along the relationships among the research constructs. The results indicated that enduring involvement has significant positive relationships with all the five components of perceived risk, while only performance risk is significantly related to trust level required by consumers for purchase. It can be inferred from the findings that product performance problems are mostly likely to occur when a merchant behaves in an opportunistic manner. Positive relationships were also found between involvement and required trust level and between required trust level and online merchant choice. Enduring involvement is concerned with the pleasure a consumer derives from a product class and/or with the desire for knowledge for the product class, and thus is likely to motivate the consumer to look for ways of mitigating perceived risk by requiring a higher level of trust on the part of the online merchant. Likewise, a consumer requiring a high level of trust on the merchant will choose a digital storefront rather than an e-marketplace, since a digital storefront is believed to be trustworthier than an e-marketplace, as it fulfills orders by itself rather than acting as an intermediary. The findings of the present research provide both academic and practical implications. The first academic implication is that enduring product involvement is a strong motivator of consumer responses, especially the selection of a merchant, in the context of electronic shopping. Secondly, academicians are advised to pay attention to the finding that an individual component or type of perceived risk can be used as an important research construct, since it would allow one to pinpoint the specific types of risk that are influenced by antecedents or that influence consequents. Meanwhile, our research provides implications useful for online merchants (both online storefronts and e-marketplaces). Merchants may develop strategies to attract consumers by managing perceived performance risk involved in purchase decisions, since it was found to have significant positive relationship with the level of trust required by a consumer on the part of the merchant. One way to manage performance risk would be to thoroughly examine the product before shipping to ensure that it has no deficiencies or flaws. Secondly, digital storefronts are advised to focus on symbolic goods (e.g., cars, cell phones, fashion outfits, and handbags) in which consumers are relatively more involved than others, whereas e- marketplaces should put their emphasis on non-symbolic goods (e.g., drinks, books, MP3 players, and bike accessories).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.10 s.89
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• pp.969-976
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• 2004

The wide and growing use of cellular phones has raised the question about the possible health risks associated with radio-frequency electromagnetic fields. It would be helpful for phone users to blow the exposure levels during cellular phone use. But it is very difficult to recognize the amount of exposure, because measuring accurate level of RF is a difficult matter. In this study, we developed a model to estimate the exposure level and the individual risk of exposure by utilizing the available informations that we can get. We used such parameters as usage time a day, total using period, distance between cellular phone and head, slope of cellular phone, hands-free usage, antenna pulled out or not SAR(Specific Absorption Rate) of cellular phone, and flip or folder type. We proposed a model presenting individual risk of RF exposure from level 0 to 10 by using a neural network.