• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.1
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• pp.71-78
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• 2021

Recently the meaning of the road name address is expended as an information through the revision of the Road Name Address Act. As this revision, the address of things (AoT) become more important indicating the possibility for the expansion to the related business. However, recent study about AoT does not concern how the current priority system works from the first research. In this study, we analyze perception about addressable object between AoT experts and public using AHP analysis. We structured the importance of addressable objects as two categories; urgency and value creation. The necessity in emergency or daily, accessibility and welfare conform the urgency index. Meanwhile, public value creation in public domain or profitability in the business area and economics conform value creation index. We conducted survey for total of 89 of experts and public. The results of this study indicate the relative importance of AoT measured by experts and public. Generally, public tend to concern more about accessibility conforming the urgency index than experts. Moreover, the public WiFi and the sports complex scored the high priority among the remain addressable objects, in respect of the urgency and the value creation. This result could be implemented for the activation of the smart city industry base on the geospatial information including AoT.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.169-177
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• 2007

Competitive international business environment has forced many firms to focus on supply chain management to cope with highly increasing competition. Hence the supplier selection is the most important decision of a company. Because it has a direct effect on cost reduction and quality, profitability and flexibility improvement of a company, so the right supplier selection significantly affect on the organization's efficiency and effectiveness and competitiveness. The primary research objects of this study is to evaluate an importance of supplier selection factors as an index and to present the evaluation model for supplier selection. For this purpose, this study adopts the AHP method to calculate the importance of supplier selection factors. In this study, 16 factors which affect on the supplier selection decision making are classified into three factors-product supply related factor, product related factor, management ability related factor.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.46-54
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• 2020

This study aims to develop and analyze the performance of a selective option straddle strategy based on forecasted volatility to improve the weakness of typical straddle strategy solely based on negative volatility risk premium. The KOSPI 200 option volatility is forecasted by the SVM model combined with the asymmetric volatility spillover effect. The selective straddle strategy enters option position only when the volatility is forecasted downwardly or sideways. The SVM model is trained for 2008-2014 training period and applied for 2015-2018 testing period. The suggested model showed improved performance, that is, its profit becomes higher and risk becomes lower than the benchmark strategies, and consequently typical performance index, Sharpe Ratio, increases. The suggested model gives option traders guidelines as to when they enter option position.

• Journal of Korea Port Economic Association
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• v.32 no.3
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• pp.47-66
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• 2016

Once it has been built, a container terminal is impossible to move to another location. It is hard to rectify wrong decisions in a container terminal. This highlights the importance of decision making for a container terminal. The port management about a container terminal has developed from a cargo interface location between sea and land transport, to the standardization of information and procedures due to globalization among global shipping and terminal operators. This research focuses on the current states of market and management for global container terminal operators by investigating up-to-date data for them. The current market states for global container terminal operators are analyzed by using by Herfindahl-Hirschman Index. The analyses of current management states for global container terminal operators are divided into profitability analysis, activity analysis, and bankruptcy risk analysis. Finally, global container terminal operators are clustered into three groups by the current management states.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.36-46
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• 2019

If stock market is efficient, any well-devised trading rule can't consistently outperform the average stock market returns. This study aims to verify whether the strategy based on bid-ask volume information can beat the stock market. I suggested a day trading strategy using order imbalance indicator and empirically analyzed its profitability with the KOSPI 200 index futures data from 2001 to 2018. Entry rules are as follows: If BSI is over 50%, enter buy order, otherwise enter sell order, assuming that stock price rises after BSI is over 50% and stock price falls after BSI is less than 50%. The empirical results showed that the suggested trading strategy generated very high trading profit, that is, its annual return runs to minimum 71% per annum even after the transaction costs. The profit was generated consistently during 18 years. This study also improved the suggested trading strategy applying the genetic algorithm, which may help the market practitioners who trade the KOSPI 200 index futures.

• Journal of Construction Engineering and Project Management
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• v.6 no.3
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• pp.1-7
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• 2016

The purpose of this paper is to develop a model for selecting the best contractor in the Gaza Strip using the Artificial Neural Network (ANN). The contractor's selection methods and criteria were identified using a field survey. Fifty four engineers were asked to fill a questionnaire that covers factors related to the selection criteria of contractors practiced in Gaza Strip. The results shows that the dominant part of respondents (91%) confirmed that the current awarding method "the lowest bid price" is considered one of the major problems of the construction sector, "award the bid to the highest weight after combination of the technical and financial scores" represented 50% of the respondents. The criteria weights were determined based on Relative Importance Index (RII. Ninety-one tenders(13 projects) were used to train and test the ANN model after re-evaluating the contractors depend on the weights of factors to select the best contractor who achieves the highest score. Neurosolution software was used to train the models. The results of the trained models indicated that neural network reasonably succeeded in selection the best contractor with 95.96% accuracy. The performed sensitivity analysis showed that the profitability and capital of company are the most influential parameters in selection contractors. This model gives chance to the owner to be more accurate in selecting the most appropriate contractor.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.349-362
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• 2021

This study aims to examine the effect of environmental performance, independent board of commissioners, and firm size on environmental disclosure measured by the Indonesian environmental index. The population in this study is manufacturing and coal mining companies that follow "PROPER" and are listed on the Indonesia Stock Exchange (IDX) from 2017 to 2019. This research was conducted by reviewing annual reports to collect information on environmental disclosures. The sampling used in this study was purposive sampling technique and obtained a sample of 117. Also, the data analysis technique used was multiple linear regression analysis with statistical hypothesis testing. The results showed that environmental performance and firm size had a positive effect on financial performance. Meanwhile, the independent board of commissioners does not affect financial performance. Furthermore, environmental performance, firm size, and financial performance have a positive effect on environmental disclosure. While the independent board of commissioners does not affect environmental disclosure. The findings of this research suggest that environmental performance has a significant positive effect on financial performance. The hypothesis is accepted, meaning that companies that are sensitive to environmental problems and run eco-efficiency operations will strengthen the company's profitability.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.158-167
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• 2021

Fishery industry is an important part of agricultural industry in coastal countries. The purpose of this study is to use the theory of industrial competitiveness for reference, use Analytic Hierarchy Process (AHP) and Delphi method to establish the evaluation index system, and analyze the fishery industry competitiveness of 11 coastal provinces in China. This study data came from China Fishery Statistical Yearbook 2020. The results show that Shandong Province is the most competitive province in fishery industry among the coastal provinces. The inter-provincial differences are great. Not only the resource endowment is the factor that affects the fishery industry competitiveness, but also the long-term profitability is too. The proportion of recreational fishery in the fishery economic output value has become one of the main indicators to measure the competitiveness of the fishery industry. The findings of this study suggest that all regions should adopt measures in accordance with local conditions, promote the integrated development of the fishery industry, enhance the added value of fishery products and enhance the competitiveness of fishery industry. The disadvantage of this study is that the fishery industry competitiveness of China's coastal provinces is only compared and analyzed. The future direction is to carry out a comparative study on the international competitiveness of fishery with other east Asian countries.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.159-168
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• 2013

This study analyze the business efficiency of securities company based on the 2011 performance of 29 securities firms which engage in domestic investment brokerage by applying a combination model of BSC and DEA. And we evaluate business state focused on efficiency which is based on logical system of BSC as business innovation method. The analysis of result is that companies with high customer efficiency index appeared that business efficiency composite index tended to be higher and we identified that customer perspective have an important factor to calculate business efficiency composite index of korea security company. In addition, based on the results of the efficiency analysis we analyze correlation analysis between traditional financial ratio and business efficiency composite index. We confirmed that company of high business efficiency level in terms of BSC have a good record in terms of profitability. BSC-DEA combination model expect to be utilized in security industry sector as well as other industrial sectors as good business indicator to determine the business efficiency and to be used a model can be evaluated the integrated firm valuation of tangible and intangible assets.