• Information Systems Review
• /
• v.20 no.4
• /
• pp.79-96
• /
• 2018

This study started by focusing on the internalization of the technology appraisal model into the credit rating model to increase the discriminative power of the credit rating model not only for SMEs but also for all companies, reflecting the items related to the financial stability of the enterprises among the technology appraisal items. Therefore, it is aimed to verify whether the technology appraisal model can be applied to identify high-stability SMEs in advance. We classified companies into industries (manufacturing vs. non-manufacturing) and the age of company (initial vs. non-initial), and defined as a high-stability company that has achieved an average debt ratio less than 1/2 of the group for three years. The C5.0 was applied to verify the discriminant power of the model. As a result of the analysis, there is a difference in importance according to the type of industry and the age of company at the sub-item level, but in the mid-item level the R&D capability was a key variable for discriminating high-stability SMEs. In the early stage of establishment, the funding capacity (diversification of funding methods, capital structure and capital cost which taking into account profitability) is an important variable in financial stability. However, we concluded that technology development infrastructure, which enables continuous performance as the age of company increase, becomes an important variable affecting financial stability. The classification accuracy of the model according to the age of company and industry is 71~91%, and it is confirmed that it is possible to identify high-stability SMEs by using technology appraisal items.

• Journal of Korean Society for Geospatial Information Science
• /
• v.8 no.1 s.15
• /
• pp.51-63
• /
• 2000

Dynamic traffic assignment(DTA) has been a topic of substantial research during the past decade. While DTA is gradually maturing, many aspects of DTA still need improvement, especially regarding its formulation and solution algerian Recently, with its promise for In(Intelligent Transportation System) and GIS(Geographic Information System) applications, DTA have received increasing attention. This potential also implies higher requirement for DTA modeling, especially regarding its solution efficiency for real-time implementation. But DTA have many mathematical difficulties in searching process due to the complexity of spatial and temporal variables. Although many solution algorithms have been studied, conventional methods cannot iud the solution in case that objective function or constraints is not convex. In this paper, the genetic algorithm to find the solution of DTA is applied and the Merchant-Nemhauser model is used as DTA model because it has a nonconvex constraint set. To handle the nonconvex constraint set the GENOCOP III system which is a kind of the genetic algorithm is used in this study. Results for the sample network have been compared with the results of conventional method.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.3
• /
• pp.17-32
• /
• 2023

In recent years, the need for social ventures that aim to grow while solving social problems through the efficiency and effectiveness of commercial organizations in the market has increased, while there is a limit to how much the government and the public can do to solve social problems. Against this background, the number of social venture startups is increasing in the domestic startup ecosystem, and interest in impact investors, which are investors in social ventures, is also increasing. Therefore, this research utilized judgment analysis technology to objectively analyze the validity and weight of judgment information based on the cognitive process and decision-making environment in the investment decision-making of impact investors. We proceeded with the research by constructing three classifications; first, investment priorities at the initial investment stage for financial benefit and return on investment as an investor, second, the political skills of the entrepreneurs (teams) for the social impact and ripple power, and social venture coexistence and solidarity, third, the social mission of a social venture that meets the purpose of an impact investment fund. As a result of this research, first of all, the investment decision-making priorities of impact investors are the expertise of the entrepreneur (team), the potential rate of return when the entrepreneur (team) succeeds, and the social mission of the entrepreneur (team). Second, impact investors do not have a uniform understanding of the investment decision-making factors, and the factors that determine investment decisions are different, and there are differences in the degree of the weighting. Third, among the various investment decision-making factors of impact investment, "entrepreneur's (team's) networking ability", "entrepreneur's (team's) social insight", "entrepreneur's (team's) interpersonal influence" was relatively lower than the other four factors. The practical contribution through this research is to help social ventures understand the investment determinant factors of impact investors in the process of financing, and impact investors can be expected to improve the quality of investment decision-making by referring to the judgment cases and analysis of impact investors. The academic contribution is that it empirically investigated the investment priorities and weighting differences of impact investors.

• Journal of Korea Spatial Information System Society
• /
• v.5 no.1 s.9
• /
• pp.49-63
• /
• 2003

Recently, two international standard organizations, ISO and OGC, have done the work of standardization for GIS. Current standardization work for providing interoperability among GIS DB focuses on the design of open interfaces. But, this work has not considered procedures and methods for designing GIS DB. Eventually, GIS DB has its own model. When we share the data by open interface among heterogeneous GIS DB, differences between models result in the loss of information. Our aim in this paper is to revise the design guidelines for geographic information databases in order to make consistent spatial data models, logical structures, and semantic structure of populated geographical databases. In details, we propose standard guidelines which convert ISO abstract schema into relation model, object-relation model, object-centered model, and geometry-centered model. Furthermore, we provide sample models for applying these guidelines in commercial GIS S/Ws. Building GIS DB based on design guidelines proposed in the paper has the following advantages: the interoperability among databases, the standardization of schema definitions, and the catalogue of GIS databases through.

• International Area Studies Review
• /
• v.22 no.4
• /
• pp.55-75
• /
• 2018

In China, different from the private enterprises or the locally-administered state enterprises, central state-owned enterprises generally spread over cornerstone industry which is greatly influenced by the public policy, which results in the objective existence of government influence in their productive activities. As the strategic resource, listed companies controlled by central state-owned enterprises, mostly distributed in the lifeblood and security of key industries. Therefore, listed companies controlled by central state-owned enterprises' governance efficiency play an important role in optimal allocation of state-owned assets, improve capital operation, improve the return on capital, and maintain state-owned assets safety. As the immune systems of national governance, the government audit strengthen the supervision of listed companies controlled by central state-owned enterprises in case of the loss of state-owned assets and significant risk events occur, to ensure that the value of state-owned assets. As an important component of national governance, government audit produced in entrusted with the economic responsibility of public relationship. Government audit can play an important role in maintaining financial security and corruption, and also improve listed company's accounting stability and transparency. While government audit can improve governance efficiency and maintain state-owned assets safety, present literature is scarce. Under the corporate governance theory and the economical responsibility theory, the thesis select data from 2010-2017 to verify the relationship between government audit and listed companies controlled by central state-owned enterprises' corporate performance. Results show that listed companies controlled by central state-owned enterprises are more likely to be audited by government of poor performance. Results also show that the government audit will have a promoting effect on listed companies controlled by central state-owned enterprises, and through to the improvement of the governance efficiency will enhance its companies' value. The results show that China's government audit has appealing role in accomplishing central state-owned enterprises to realize the business objectives and in promoting the governance efficiency.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.135-149
• /
• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Korean Journal of Poultry Science
• /
• v.42 no.3
• /
• pp.223-230
• /
• 2015

Eggs are an important source of protein for the human diet. Consumers want fresher, more delicious and more sanitary eggs. In Korea, the Shell Egg Grading system (EGS) was employed in 2001. The portion of graded shell eggs has increased every year, but graded shell eggs account for only 6% of all eggs. The EGS should satisfy producers, distributors and consumers. However, the EGS does not have an official function because of many problems. Consumers cannot select various graded shell eggs in the market, and producers do not receive enough profit even though they produce top-quality graded shell eggs. There are few studies on the EGS, Therefore, this study was performed to improve the EGS. We surveyed the EGS, GP Centers and farmers. Large companies (farmers) are more satisfied than small companies with the EGS. There was a high tendency for the companies (farmers) that are not involved with the EGS to think that graded and ungraded shell eggs are similar, in contrast to the companies (farmers) connected to the EGS. We should have to change the grading system of grade shell eggs, establish of the cold chain system, change of the law for the school meals, minimize payment for the grading shell eggs for developing EGS. Based on this study, the egg industry can benefit through the improvement of the EGS.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.77-110
• /
• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Management & Information Systems Review
• /
• v.34 no.3
• /
• pp.17-39
• /
• 2015

This study examined the structural changes and volatility in the global stock markets using a Markov Regime Switching ARCH model developed by the Hamilton and Susmel (1994). Firstly, the US, Italy and Ireland showed that variance in the high volatility regime was more than five times that in the low volatility, while Korea, Russia, India, and Greece exhibited that variance in the high volatility regime was increased more than eight times that in the low. On average, a jump from regime 1 to regime 2 implied roughly three times increased in risk, while the risk during regime 3 was up to almost thirteen times than during regime 1 over the study period. And Korea, the US, India, Italy showed ARCH(1) and ARCH(2) effects, leverage and asymmetric effects. Secondly, 278 days were estimated in the persistence of low volatility regime, indicating that the mean transition probability between volatilities exhibited the highest long-term persistence in Korea. Thirdly, the coefficients appeared to be unstable structural changes and volatility for the stock markets in Chow tests during the Asian, Global and European financial crisis. In addition, 1-Step prediction error tests showed that stock markets were unstable during the Asian crisis of 1997-1998 except for Russia, and the Global crisis of 2007-2008 except for Korea and the European crisis of 2010-2011 except for Korea, the US, Russia and India. N-Step tests exhibited that most of stock markets were unstable during the Asian and Global crisis. There was little change in the Asian crisis in CUSUM tests, while stock markets were stable until the late 2000s except for some countries. Also there were stable and unstable stock markets mixed across countries in CUSUMSQ test during the crises. Fourthly, I confirmed a close relevance of the volatility between Korea and other countries in the stock markets through the likelihood ratio tests. Accordingly, I have identified the episode or events that generated the high volatility in the stock markets for the financial crisis, and for all seven stock markets the significant switch between the volatility regimes implied a considerable change in the market risk. It appeared that the high stock market volatility was related with business recession at the beginning in 1990s. By closely examining the history of political and economical events in the global countries, I found that the results of Lamoureux and Lastrapes (1990) were consistent with those of this paper, indicating there were the structural changes and volatility during the crises and specificly every high volatility regime in SWARCH-L(3,2) student t-model was accompanied by some important policy changes or financial crises in countries or other critical events in the international economy. The sophisticated nonlinear models are needed to further analysis.

• Journal of Bio-Environment Control
• /
• v.30 no.4
• /
• pp.328-334
• /
• 2021

This study was conducted to analyze the effect of fog cooling during daytime and heatpump cooling at night in greenhouses in summer. During daytime, the average temp. and RH of the control greenhouse which had shading screen were 32.1℃ and 59.4%. and the average temp. and RH of the test greenhouse which had fog cooling were 30.0℃ and 74.3%. At this time, the average outside temp. and RH were 31.4℃ and 57.7%. So, the temp. of the control was 0.7℃ higher than outside temp., but the temp. of the test was 1.4℃ lower than outside and 2.1℃ lower than control. The average RH was 74.3% in the test and 59.4% in control. The average temp. and RH of the control greenhouse which had natural ventilation at night were 25.2℃ and 85.1%, and the average temp. and RH of the test greenhouse which had heat pump cooling were 23.4℃, 82.4%. The average outside temp. and RH at night were 24.4℃ and 88.2%. The temp. of the control was 0.8℃ higher than outside temp., but the temp. of the test was 1.0℃ lower than outside and 1.8℃ lower than control. The average RH was 82.4% in test and 85.1% in control greenhouse. There was no significant difference between the plants growth eight weeks after planting. But after the cooling treatment, the values of stem diameter, plant height, chlorophyll in test were higher than control. The total yield was 81.3kg in test, 73.8kg in control, so yield of test was 10.2% higher than control. As a result of economic analysis, 142,166 won in profits occurred in control greenhouse, but 28,727 won in losses occurred in test greenhouse, indicating that cooling treatment was less economical.