• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• The Journal of Information Systems
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• v.19 no.2
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• pp.23-36
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• 2010

The focus of this study is to analyze between South Korea and China in terms of diversity in Management Information Systems research. As a result, there is herding phenomenon regarding researches in both of the countries compared to other previous studies. There also seemed to be lack of in-depth study of basic theories and connection to related field of study considering the broad topics of management information, unlike other international journals that pursue diversity in control referencing various methodologies, analysis units and many citations. Therefore, both South Korea and China should stabilize theoretical base of MIS through securing independent field of MIS along with founding the fundamentals by searching in diversified fields, methodologies and analysis units with focused topics. The upshot here is that with respect to diversity in MIS from both countries, to alleviate herding phenomenon, enforcing accuracy of data collection and reducing convenience pursuit should be implemented. Also as criteria to select a topic, different kinds of consulting concepts and taking social issues into consideration that helps tool development and analysis power should be done. Through varied methodologies, tool development and analysis power should be assisted and analysis unit should be shifted to organization unit for Korea and team or individual for China only to augment the accessibility. As a limitation for the paper is that the data used in this analysis is secondary data. In addition, although time period used in both countries were the same, object of analysis had homogeneity in Korean case while Chinese one having heterogeneity from 19 different journals. For the future studies, multicultural comparison or time series analysis and their comparison in deeper approach with regard to object of analysis and methodologies can contribute to further MIS diversity.

• Journal of Navigation and Port Research
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• v.35 no.4
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• pp.343-349
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• 2011

The forecasting of container volume which is the basis of port logistics facilities expansion has a great influence on development of an port. Based on this importance, various previous studies have presented methodology on container volume forecasting. The results of many previous studies pointed out the limitations of future forecasting based on past container volume and emphasized that more various factors should be considered to compensate this. Taking notice of this point, this study forecasted future container volume by using ARIMA model, time series analysis and System Dynamics (SD) method, a dynamic analysis technique and performed the comparative review with the forecast of the Ministry of Land, Transport and Maritime affairs. Recently with rapid changes in economic and social environment, the non-linear change tendency for forecasting container traffic is presented as a new alternative to the country.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1083-1093
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• 2021

It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.

• Journal of Radiation Protection and Research
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• v.44 no.2
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• pp.64-71
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• 2019

Background: It is very difficult to distinguish between a radioactive contamination source and background radiation from natural radionuclides in the marine environment by means of online monitoring system. The objective of this study was to investigate a statistical process for triggering abnormal level of count rate data measured from our on-line seawater radioactivity monitoring. Materials and Methods: Count rate data sets in time series were collected from 9 monitoring posts. All of the count rate data were measured every 15 minutes from the region of interest (ROI) for $^{137}Cs$ ($E_{\gamma}=661.6keV$) on the gamma-ray energy spectrum. The Shewhart ($3{\sigma}$), CUSUM, and Bayesian S-R control chart methods were evaluated and the comparative analysis of determination methods for count rate data was carried out in terms of the false positive incidence rate. All statistical algorithms were developed using R Programming by the authors. Results and Discussion: The $3{\sigma}$, CUSUM, and S-R analyses resulted in the average false positive incidence rate of $0.164{\pm}0.047%$, $0.064{\pm}0.0367%$, and $0.030{\pm}0.018%$, respectively. The S-R method has a lower value than that of the $3{\sigma}$ and CUSUM method, because the Bayesian S-R method use the information to evaluate a posterior distribution, even though the CUSUM control chart accumulate information from recent data points. As the result of comparison between net count rate and gross count rate measured in time series all the year at a monitoring post using the $3{\sigma}$ control charts, the two methods resulted in the false positive incidence rate of 0.142% and 0.219%, respectively. Conclusion: Bayesian S-R and CUSUM control charts are better suited for on-line seawater radioactivity monitoring with an count rate data in time series than $3{\sigma}$ control chart. However, it requires a continuous increasing trend to differentiate between a false positive and actual radioactive contamination. For the determination of count rate, the net count method is better than the gross count method because of relatively a small variation in the data points.

• Journal of Korea Trade
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• v.24 no.1
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• pp.131-145
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• 2020

Purpose - The recent growth of South Korean products in the international market is the benchmark for both developed as well as developing countries. According to the development index, the role of international trade is indeed crucial for the development of the national economy. However, the visualization of the international trade profile of the country is the prerequisite of governmental policy decision-makers and guidance for forecasting of foreign trade. Design/methodology - We have utilized data visualization techniques in order to visualize the import & export product space and trade partners of South Korea. Economic Complexity Index (ECI) and Revealed Comparative Advantage (RCA) were used to identify the Korean international trade diversification, whereas the time series approach is used to forecast the economy and foreign trade variables. Findings - Our results show that Chine, U.S, Vietnam, Hong Kong, and Japan are the leading trade partners of Korea. Overall, the ECI of South Korea is growing significantly as compared to China, Hong Kong, and other developed countries of the world. The expected values of total import and export volume of South Korea are approximately US$535.21 and US$ 781.23B, with the balance of trade US$ 254.02B in 2025. It was also observed from our analysis that imports & exports are equally substantial to the GDP of Korea and have a significant correlation with GDP, GDP per capita, and ECI. Originality/value - To maintain the growth rate of international trade and efficient competitor for the trade partners, we have visualized the South Korea trade profile, which provides the information of significant export and import products as well as main trade partners and forecasting.

• The Journal of Information Systems
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• v.29 no.3
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

• Environmental and Resource Economics Review
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• v.26 no.3
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• pp.287-317
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• 2017

Asbestos has been actively used for various places. Since it was designated as the first grade carcinogen in the 1970s, strict regulations on using asbestos has been implemented globally. Considering long-term latent periods between asbestos exposure and environmental diseases, we analyze the time lag between asbestos consumption and the incidence of mesothelioma in Korea and estimate the long-run relationship. In addition, we conduct a comparative analysis on the effectiveness of asbestos regulations in the United Kingdom and the United States, which have accumulated long-term time-series observations. The latent period analysis indicates that the consumption of asbestos and the incidence of the disease are highly correlated in all three countries, being long-term lags of more than 30 years. Also, we find a long-run equilibrium relationship between asbestos consumption and the incidence of mesothelioma in the presence of long-term lags between the variables in all three countries. Furthermore, using a distributed lag model, asbestos consumption has statistically significant positive effects on mesothelioma with a long-term lag.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.2
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• pp.57-78
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• 2017

SNS is a useful tool to quickly deliver information in an emergency given their speed and expandability. Especially, SNS in the event of a disaster or an accident can offer on-site, accurate and detailed updates about essential information such as the safety of victims and the development of the situation, served as a valuable complement to the conventional media. This study aims to perform a comparative analysis on how social media are currently used by emergency management authorities in South Korea and other countries. Based on the results, this study proposed more effective ways to exploit SNS and improve efficiency of disaster management. To accomplish the goals, this study collected tweet information from various sources including the FEMA of the U. S., the FDMA and the Central Disaster Council of Japan, and the MPSS of Korea. The collected tweet information was analyzed by feedback, time series, and information types. The feedback analysis aims to quantify the number of monthly user feedback in order to assess user satisfaction about the tweet information. The time series analysis identifies the number of tweet information, feedback index and keywords by country for certain duration, examining why certain messages showed high feedback indices and what kind of contents should be offered by the authorities. Finally, the analysis of information type reviews the type of information contained in the tweet information that drew users' attention to identify the information type in which the authorities should deliver information to users. Based on these analyses, this study proposed improvement methods to use Tweeter in MPSS.

• Korean Journal of Social Welfare
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• v.57 no.4
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• pp.91-118
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• 2005

This paper examines the impact of the institutional complementarity between welfare regimes and production regimes on income inequality. Using comparative welfare data by various sources for 14 OECD nations from 1980 to 1997, this paper attempts to answer two questions. First, is there a institutional complementary in regulatory process between distribution and production? Second, if it is correct, what kind of causal structure do we predict? Panel Corrected Standard Errors(PCSE) model, a data analysis method in pooled cross-sectional time-series, is employed to examine the interaction effects between the two variables: coordination in the sphere of distribution; coordination in the sphere of production The evidence suggest that there are powerful interaction effects between distributive coordination and production coordination and that the institutional complementary has effects on income inequality. First, the income inequality effect of coordination in the sphere of distribution becomes less positive(more negative) as coordination in the sphere of production increases. Second, the income inequality effect of coordination in the sphere of production becomes less positive(more negative) as coordination in the sphere of distribution increases.