• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.35-48
• /
• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.1-17
• /
• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Korea Port Economic Association
• /
• v.37 no.4
• /
• pp.161-173
• /
• 2021

The shipping industry plummeted and was depressed due to the global economic crisis caused by the bankruptcy of Lehman Brothers in the US in 2008. In 2020, the shipping market also suffered from a collapse in the unstable global economic situation due to the COVID-19 pandemic, but unexpectedly, it changed to an upward trend from the end of 2020, and in 2021, it exceeded the market of the boom period of 2008. According to the Clarksons report published in May 2021, the decrease in cargo volume due to the COVID-19 pandemic in 2020 has returned to the pre-corona level by the end of 2020, and the tramper bulk carrier capacity of 103~104% of the Panamax has been in the ports due to congestion. Earnings across the bulker segments have risen to ten-year highs in recent months. In this study, as factors affecting BDI, the capacity and congestion ratio of Cape and Panamax ships on the supply side, iron ore and coal seaborne tonnge on the demand side and Granger causality test, IRF(Impulse Response Function) and FEVD(Forecast Error Variance Decomposition) were performed using VAR model to analyze the impact on BDI by congestion caused by strengthen quarantine at the port due to the COVID-19 pandemic and the loading and discharging operation delay due to the infection of the stevedore, etc and to predict the shipping market after the pandemic. As a result of the Granger causality test of variables and BDI using time series data from January 2016 to July 2021, causality was found in the Fleet and Congestion variables, and as a result of the Impulse Response Function, Congestion variable was found to have significant at both upper and lower limit of the confidence interval. As a result of the Forecast Error Variance Decomposition, Congestion variable showed an explanatory power upto 25% for the change in BDI. If the congestion in ports decreases after With Corona, it is expected that there is down-risk in the shipping market. The COVID-19 pandemic occurred not from economic factors but from an ecological factor by the pandemic is different from the past economic crisis. It is necessary to analyze from a different point of view than the past economic crisis. This study has meaningful to analyze the causality and explanatory power of Congestion factor by pandemic.

• Korean Journal of Construction Engineering and Management
• /
• v.13 no.6
• /
• pp.13-23
• /
• 2012

While Category 1 and Category 2 bridges are intensively inspected and carefully managed, many small bridges that are not in the Category 1 and 2 are often forgotten until they decay in serious condition. Since many of these small bridges located in the populated city, failure of them would cause huge negative impact on the community. This paper focuses on the small size concrete bridges for timely repair and rehabilitation work for the effective operation and management. Test bed is developed to define the key parameters to forecast the long term performance of the bridges, mostly concrete box bridges. Key parameters suggested in this paper are cumulative fatigue due to repetitive heavy traffic loads and the acid attacks for concrete material deterioration. The cumulative fatigue is measured by the use of the mileage concept. For the long term data collection and inspection, stable and easy to use data collection system is installed as a test bed. The contribution of this research work is on the development of the test bed to define the key parameters of bridge deterioration.

• Journal of Digital Convergence
• /
• v.16 no.5
• /
• pp.469-476
• /
• 2018

The purpose of this study was to examine the relations and analyze the mediation effect that exists between the feedback types by professors of university physical education and self-efficacy and sports continuance. The sampling method was used to survey physical education university students from three different universities located in the Greater Seoul Metropolitan Area. 309 samples were ultimately selected as valid samples. Data processing was carried out by using SPSS 18.0 and AMOS 18.0. The fidelity of the whole model was assessed through this process and then the theory was tested. The results were as follows. Firstly, if the perceived feedbacks by the professor were complimentary/encouragement and performance knowledge/positive nonverbal feedbacks it had a positive effect. Negative nonverbal perceived feedback had a negative effect forecast. Secondly, complimentary/encouragement perceived feedbacks by the professor did not have a meaningful impact on sports continuance index. Performance knowledge/positive nonverbal feedback resulted in static effect while negative nonverbal feedback had a negative effect. Lastly, self-efficacy served a meaningful mediation role in the relation between negative nonverbal feedback by the professor and sports continuance.

• The Journal of Fisheries Business Administration
• /
• v.51 no.2
• /
• pp.51-69
• /
• 2020

The purpose of this study is to construct an outlook model that is consistent with the "Fisheries Outlook" monthly published by the Fisheries Outlook Center of the Korea Maritime Institute(KMI). In particular, it was designed as a partial equilibrium model limited to abalone items, but a model was constructed with a dynamic ecological equation model(DEEM) system taking into account biological breeding and shipping time. The results of this study are significant in that they can be used as basic data for model development of various items in the future. In this study, due to the limitation of monthly data, the market equilibrium price was calculated by using the recursive model construction method to be calculated directly as an inverse demand. A model was built in the form of a structural equation model that can explain economic causality rather than a conventional time series analysis model. The research results and implications are as follows. As a result of the estimation of the amount of young seashells planting, it was estimated that the coefficient of the amount of young seashells planting from the previous year was estimated to be 0.82 so that there was no significant difference in the amount of young seashells planting this year and last year. It is also meant to be nurtured for a long time after aquaculture license and limited aquaculture area(edge style) and implantation. The economic factor, the coefficient of price from last year was estimated at 0.47. In the case of breeding quantity, it was estimated that the longer the breeding period, the larger the coefficient of breeding quantity in the previous period. It was analyzed that the impact of shipments on the breeding volume increased. In the case of shipments, the coefficient of production price was estimated unelastically. As the period of rearing increased, the estimation coefficient decreased. Such result indicates that the expected price, which is an economic factor variable and that had less influence on the intention to shipments. In addition, the elasticity of the breeding quantity was estimated more unelastically as the breeding period increased. This is also correlated with the relative coefficient size of the expected price. The abalone supply and demand forecast model developed in this study is significant in that it reduces the prediction error than the existing model using the ecological equation modeling system and the economic causal model. However, there are limitations in establishing a system of simultaneous equations that can be linked to production and consumption between industries and items. This is left as a future research project.

• The Research Journal of the Costume Culture
• /
• v.4 no.3
• /
• pp.305-323
• /
• 1996

The purpose of this sty is to forecast the tendency of clothing colors, to provide materials useful for making the color plan for a better costume, and suggest new information in the apparel industry. To achieve these purposes, this study was carried out by suggesting vogue colors of recent apparel, along with the 20 standard colors of Muncell to 350 men and women in their 20s residing in Taegu.. We analysed and compared their preferred colors in suits vidual factor ;age, sex, education ,skin color, and body shape, and additionally classifying their interest in clothing colors, cause for color selection , degree of interest in color of clothes, color satisfaction of apparel market, and color combination of clothing, color satisfaction of apparel market, and color combination of clothing. We compared these elements according to their age, sex, and education. The results of this study is summarized as follows; 1. It was shown that both men and women in their 20s preferred warm colors the first colors mentioned being the most popular ; Pastels, Red, Blue and Black. They disliked dark and sordid colros in the order of Mauve, Khaki and Red. They preferred wearing Pastels and Yellow in the Spring, White, Blue, and Pastels were the favorites for summer, Beige in autumn, and black, Gray, and Beige in winter. The repugnated colors they chose two wear were Black, Red, Mauve, Khaki in spring and summer. Mauve, Red, Yellow were favorites for autumn, and Blue·White, Yellow·Green, and Pastels in winter. 2. The preferred colors for T-shirts were Yellow, Pastels, White in spring. White and Pastels were summer favorites, Beige an Red for autumn, and Black, Red, Beige in Winter. As for suits, the preferred colors in spring were in the order of Pastels, Beige and Yellow. The preferred colors of upper garments in summer were in the order of White and pastels. for lower summer garments Pastels, White and Blue were favorites. In autumn, Beige was shown to be highly preferred, and in winter, the order of preferred colors was Black, Gray and Beige. 3. On of those individual factors showing the greatest difference was sex. 4. Those factors having the greatest impact on the selection of clothing color were the season and their preference color. 5. The in vogue color of men in their 20s was at a higher level than hat of women. The groups that had an upper college level education showed a significant difference in their choices thant hose who were highschool graduates and technical college student and graduates. The consideration of inteterest in color of clothes, holding clothes during new purchasing, body shape and skin color was higher for women than men. In buying and wearing accessaries, women tend to take into consideration the harmony with the garment, and there was shown a significant difference between highschool graduates and the groups above the level of college education.

• Journal of the Korean earth science society
• /
• v.35 no.4
• /
• pp.215-224
• /
• 2014

Data assimilation effect of mobile rawinsonde observation was evaluated using Unified Model (UM) with a Three-Dimensional Variational (3DVAR) data assimilation system during the intensive observation program of 2013 summer season (rainy season: 20 June-7 July 2013, heavy rain period: 8 July-30 July 2013). The analysis was performed by two sets of simulation experiments: (1) ConTroL experiment (CTL) with observation data provided by Korea Meteorological Administration (KMA) and (2) Observing System Experiment (OSE) including both KMA and mobile rawinsonde observation data. In the model verification during the rainy season, there were no distinctive differences for 500 hPa geopotential height, 850 hPa air temperature, and 300 hPa wind speed between CTL and OSE simulation due to data limitation (0000 and 1200 UTC only) at stationary rawinsonde stations. In contrast, precipitation verification using the hourly accumulated precipitation data of Automatic Synoptic Observation System (ASOS) showed that Equivalent Threat Score (ETS) of the OSE was improved by about 2% compared with that of the CTL. For cases having a positive effect of the OSE simulation, ETS of the OSE showed a significantly higher improvement (up to 41%) than that of the CTL. This estimation thus suggests that the use of mobile rawinsonde observation data using UM 3DVAR could be reasonable enough to assess the improvement of prediction accuracy.

• The Korean Journal of Applied Statistics
• /
• v.31 no.1
• /
• pp.77-96
• /
• 2018

A reverse mortgage provides a pension until the death for the insured or last survivor. Long-term risk management is important to estimate the contractual period of a reverse mortgage. It is also necessary to study prediction methods of mortality rates that appropriately reflect the improvement trend of the mortality rate since the extension of the life expectancy, which is the main cause of aging, can have a serious impact on the pension financial soundness. In this study, the Lee-Carter (LC) model reflects the improvement in mortality rates; in addition, multiple life model are also applied to a reverse mortgage. The mortality prediction method by the traditional LC model has shown a dramatic improvement in the mortality rate; therefore, this study suggests mortality projection based on the projection of the skewness for the mortality that has been applied to appropriately reflect the improvement trend of the mortality rate. This paper calculates monthly payments using future mortality rates based on the projection of the skewness of the mortality. As a result, the mortality rates based on this method less reflect the mortality improvement effect than the mortality rates based on a traditional LC model and a larger pension amount is calculated. In conclusion, this method is useful to forecast future mortality trend results in a significant reduction of longevity risk. It can also be used as a risk management method to pay appropriate monthly payments and prevent insufficient payment due to overpayment by the issuing institution and the guarantee institution of the reverse mortgage.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_1
• /
• pp.793-806
• /
• 2020

In this study,for YSU (Yonsei University), MYJ(Mellor-Yamada-Janjic), ACM2 (Asymmetric Convective Model), and BouLac (Bougeault-Lacarrere) PBL schemes, numerical experiments were performed for the case period (June 26-30, 2014). The PBLH calculated by using the backscatter signal produced by the mobile vehicle-mounted lidar system (LIVE) and the PBLH calculated by the prediction of each PBL schemes of WRF were compared and analyzed. In general, the experiments using the non-local schemes showed a higher correlation than the local schemes for lidar observation. The standard deviation of the PBLH difference for daylight hours was small in the order of YSU (≈0.39 km), BouLac (≈0.45 km), ACM2 (≈0.47 km), MYJ (≈0.53 km) PBL schemes. In the RMSE comparison for the case period, the YSU PBL scheme was found to have the highest precision. The meteorological lider mounted on the vehicle is expected to provide guidance for the analysis of the planetary boundary layer in a numerical model under various weather conditions.