• International conference on construction engineering and project management
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• 2017.10a
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• pp.321-327
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• 2017

China is experiencing the urbanization at an unprecedented speed and scale in human history. The continuing growth of China's big cities, both in city land and population, has already led to great challenges in China's urban planning and construction activities, such as the continuous increase of construction and demolition (C&D) waste. Therefore, how to characterize cities' construction activities, particularly dynamically quantify the flows of building materials and construction debris, has become a pressing problem to alleviate the current shortage of resources and realize urban sustainable development. Accordingly, this study is designed to employ 4D-GIS (four dimensions-Geographic Information System) and Internet Plus to offer new approach for accurate but dynamic C&D waste management. The present study established a spatio-temporal pattern and material metabolism evolution model to characterize the geo-distribution of C&D waste by combing material flow analysis (MFA) and 4D-GIS. In addition, this study developed a mobile application (APP) for C&D waste trading and information management, which could be more effective for stakeholders to obtain useful information. Moreover, a cloud database was built in the APP to disclose the flows of C&D waste by the monitoring information from vehicles at regional level. To summarize, these findings could provide basic data and management methods for the supply and reverse supply of building materials. Meanwhile, the methodologies are practical to C&D waste management and beyond.

• Economic Analysis
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• v.25 no.3
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• pp.30-69
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• 2019

This paper analyzes the effects of trade on demand for labor of trading firms in Korea. We apply system GMM methodology to estimate the effects of imports and exports on employment of Korean manufacturing firms using firm-level data from the Survey of Business Activities of Statistics Korea between 2006 and 2014. According to our estimated results, for firms with high-productivity, exports have a positive and significant effect on the labor demand, while other firms do not show any such significant effects. Furthermore, our results show that offshoring mitigates the positive effects of exports on employment, since tasks within the firms can be relocated abroad. On the other hand, an increase in imports reduces demand for labor because labor is replaced with low-priced imported inputs. Also, when firms partake in global outsourcing, the negative effects of imports are mitigated as those firms expand their production by enhancing their efficiency in the process of offshoring. Therefore, our results suggest that it is important to consider heterogeneous firm productivity as well as offshoring in analyzing the effect of trade on labor demand of firms.

• Journal of Distribution Research
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• v.16 no.5
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• pp.193-209
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• 2011

The fast expansion of super supermarket(SSM) in Korean retail industries has attracted serious social attentions and some types of regulations to slow down its growth are prepared. However, the regulations are hardly justified because they attempt to establish entry barriers which are not recommendable economic policy. Accordingly, the regulations should be justified at least on the basis of social and political causes. The study interprets the social and political causes as the effects of entry of SSM on trading ares where SSM is located. The study is distinguished from the past studies which focused only on intertype and intratype competition between retailers Another goal of the study is to complement the weakness of past studies and provide additional information to settle the issues. More closely, the study investigates the relationships between the changes in sales of convenience stores, which may be a surrogate measure of the viability of a local economy, and the changes in the composition of business types within 500m radius of a SSM. Further, the study investigates the effects of the establishment of SSM and the retail sales index on the sales of convenience stores. The study analyzed the panel data and adopts Swamy's random coefficient models. The results show that the effects of the establishment of SSM on the sales of convenience stores are not statistically significant. The relationship between the change in the portion of restaurants among the local business and the change in the sales of convenience stores is positive. On the other hand the relationship between the change in the portion of retailers in the composition of local businesses and the change in the sales of convenience stores is negative. In conclusion, even though any negative effects of the establishments of SSM on local economies are expected, as long as other types business especially restaurant businesses fill the space left by retailers, the net effect on the local economy may not be signification or even positive.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of Information Technology Applications and Management
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• v.15 no.4
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• pp.159-188
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• 2008

This study suggests strategies which can enable to creation of new opportunities of competitive advantages while operating a long lasting and consistent business with major trading partners, based on interorganizational information systems (IOISs) specially established and installed for interorganizational transactions. Nowadays, IOISs based mechanism having been widely expanded as a conventional business infrastructure for the interorganizational transactions and/or exchanges, it is customary difficult to obtain any strongly sound advantage over the competitors who have adopted even the simplest deployment of the IOIS mechanisms. In this connection, this study intends to investigate the interorganizational collaborative activities conducted by under the auspicious of IOISs, focused on the prospect of the exploitation of IOISs rather than the implementation of the IOISs. In this study, we, firstly, suggest the concept of Electronic Collaboration which can be defined by the collaborative activities conducted by IOISs, compared to the ones conducted on off-line. In addition, we suggest the Electronic Collaboration as a multi-dimensional concept, constituted by three sub-constructs, the Electronic Information Sharing (EIS), the Electronic Joint Activity (EJA), and the construction of the Electronic Relational Knowledge Store (ERKS). Secondly, we empirically verify the effects of relational and environmental determinants on the Electronic Collaboration. In this study, the relational determinants relate to the variables created in interorganizational relationship like Trust, Influence, Relational Specific Asset-asset invested for the transaction-, and Continuity of the relationship. On the other hand, the environmental determinants relate to the variables surrounding the relationship which are difficult to control. We consider Product Complexity, Technological Uncertainty, and Market Variability as the domain of the environmental determinants. To test our hypotheses, we conducted both paper-based survey and online-based survey. After refining the data with missing responses, a total of 150 data was used for analysis. The results were as follows : Firstly, it is statistically significant that the Electronic Collaboration is composed of EIS, EJA, and ERKS. In particular, the results imply that the firms are able to accumulate relational knowledge base as well as to exchange information or knowledge, and to conduct joint activities through effort to further expand the Electronic Collaboration. Secondly, we have verified the individual effects of the relational and the environmental determinants on the Electronic Collaboration. Product Complexity has been revealed as the most influential variable affecting the Electronic Collaboration. Next, Interorganizational Trust and Technological Uncertainty, in that order, have been seen to have significant effects on the Electronic Collaboration. In other words, when products or services seem to be difficult to standardize, and the core technologies seem to rapidly change, the need for the Electronic Collaboration increase. In addition, the observation dictates that the interorganizational trust turns out to be a critical variable in building a relationship and in seeking further collaboration. The results, further, illustrate that the environmental determinants are relatively more effective than the relational determinants, which is not consistent with a few prior researches relational determinants emphasized. It is because this study doesn't consider the size of the firm. A few researchers have given an emphasis on the relational determinants like trust and influence, especially from the perspective of small firms in interorganizational relationship. However, in our study, where all the sizes of the firms are contained, electronic collaboration is considerably affected by the environmental determinants.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.12 s.159
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• pp.1768-1777
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• 2006

As the fashion industry comes under the influence of globalization throughout all fields of industry, the globalization and the market entry strategies are required for Korean fashion firms. This study attempted to analyze the factors influencing foreign entry mode of Korean fashion business based on Eclectic Theory. Data collection has been carried out from November 25 until December 25, 2005. The questionnaires were sent through e-mail or fax to 622 trading companies. 67 questionnaires were returned for a response rate of 10.7%. Of these returns, 61 usable questionnaires were employed for data analyses. Descriptive analysis, factor analysis, discriminant analysis, and t-test were used for data analysis. First, the most important venture motivation was price competitiveness and many firms were engaged in both production and sales in their target countries, which were mainly in Southeast Asia. Second, the firm's ability and experience were found out as ownership advantage factor, investment stability and market potential as location advantage factor, and contract stability as internalization advantage factor. Third, the result of discriminant analysis showed that location advantage factor was a significant factor in predicting the entry of fashion firms into foreign countries.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.85-98
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• 2016

This study makes an empirical comparison of various realized volatilities (RVs) in terms of overnight returns. In financial asset markets, during overnight or holidays, no or few trading data are available causing a difficulty in computing RVs for a whole span of a day. A review will be made on several RVs reflecting overnight return variations. The comparison is made for forecast accuracies of several RVs for some financial assets: the US S&P500 index, the US NASDAQ index, the KOSPI (Korean Stock Price Index), and the foreign exchange rate of the Korea won relative to the US dollar. The RV of a day is compared with the square of the next day log-return, which is a proxy for the integrated volatility of the day. The comparison is made by investigating the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). Statistical inference of MAE and RMSE is made by applying the model confidence set (MCS) approach and the Diebold-Mariano test. For the three index data, a specific RV emerges as the best one, which addresses overnight return variations by inflating daytime RV.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.16-21
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• 2014

In recent, the maritime traffic has increased with an increase in international trading volumes and the growing popularity of marine leisure activities. As increasing of maritime traffic, marine accidents happened continually and there are possibilities of accidents at sea. According to the analysis of marine accidents, most accidents occurred by human error of seafarers. To reduce the accidents by human error, the various assistance system for assist seafarers have been proposed. It is required to real-time data management method for applying to real-time system, but most proposed assistance system used off-line data for analysis. In this paper, we aim to build a navigation supporting system for providing safety information to deck officer with data of AIS(Automatic Identification System) and ARPA Radar(Automatic Radar Plotting Aids Radar), and proposed a management algorithm for real-time ship information with blackboard system and verified the validity.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.