• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• The Journal of Fisheries Business Administration
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• v.34 no.2
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• pp.75-90
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• 2003

When we think of fundamental problems of the aquaculture industry, there are several strict conditions, and consequently the aquaculture industry is forced to change. Fish aquaculture has a structural supply surplus in production, aggravation of fishing grounds, stagnant low price due to recent recession, and drastic change of distribution circumstances. It is requested for us to initiate discussion on such issue as “how fish aquaculture establishes its status in the coastal fishery\ulcorner, will fish aquaculture grow in the future\ulcorner, and if so “how it will be restructured\ulcorner” The above issues can be observed in the mariculture of yellow tail, sea scallop and eel. But there have not been studied concerning seabream even though the production is over 30% of the total production of fish aquaculture in resent and it occupied an important status in the fish aquaculture. The objectives of this study is to forecast the future movement of sea bream aquaculture. The first goal of the study is to contribute to managerial and economic studies on the aquaculture industry. The second goal is to identify the factors influencing the competition between production areas and to identify the mechanisms involved. This study will examine the competitive power in individual producing area, its behavior, and its compulsory factors based on case study. Producing areas will be categorized according to following parameters : distance to market and availability of transportation, natural environment, the time of formation of producing areas (leaderㆍfollower), major production items, scale of business and producing areas, degree of organization in production and sales. As a factor in shaping the production area of sea bream aquaculture, natural conditions especially the water temperature is very important. Sea bream shows more active feeding and faster growth in areas located where the water temperature does not go below 13∼14$^{\circ}C$ during the winter. Also fish aquaculture is constrained by the transporting distance. Aquacultured yellowtail is a mass-produced and a mass-distributed item. It is sold a unit of cage and transported by ship. On the other hand, sea bream is sold in small amount in markets and transported by truck; so, the transportation cost is higher than yellow tail. Aquacultured sea bream has different product characteristics due to transport distance. We need to study live fish and fresh fish markets separately. Live fish was the original product form of aquacultured sea bream. Transportation of live fish has more constraints than the transportation of fresh fish. Death rate and distance are highly correlated. In addition, loading capacity of live fish is less than fresh fish. In the case of a 10 ton truck, live fish can only be loaded up to 1.5 tons. But, fresh fish which can be placed in a box can be loaded up to 5 to 6 tons. Because of this characteristics, live fish requires closer location to consumption area than fresh fish. In the consumption markets, the size of fresh fish is mainly 0.8 to 2kg.Live fish usually goes through auction, and quality is graded. Main purchaser comes from many small-sized restaurants, so a relatively small farmer and distributer can sell it. Aquacultured sea bream has been transacted as a fresh fish in GMS ,since 1993 when the price plummeted. Economies of scale works in case of fresh fish. The characteristics of fresh fish is as follows : As a large scale demander, General Merchandise Stores are the main purchasers of sea bream and the size of the fish is around 1.3kg. It mainly goes through negotiation. Aquacultured sea bream has been established as a representative food in General Merchandise Stores. GMS require stable and mass supply, consistent size, and low price. And Distribution of fresh fish is undertook by the large scale distributers, which can satisfy requirements of GMS. The market share in Tokyo Central Wholesale Market shows Mie Pref. is dominating in live fish. And Ehime Pref. is dominating in fresh fish. Ehime Pref. showed remarkable growth in 1990s. At present, the dealings of live fish is decreasing. However, the dealings of fresh fish is increasing in Tokyo Central Wholesale Market. The price of live fish is decreasing more than one of fresh fish. Even though Ehime Pref. has an ideal natural environment for sea bream aquaculture, its entry into sea bream aquaculture was late, because it was located at a further distance to consumers than the competing producing areas. However, Ehime Pref. became the number one producing areas through the sales of fresh fish in the 1990s. The production volume is almost 3 times the production volume of Mie Pref. which is the number two production area. More conversion from yellow tail aquaculture to sea bream aquaculture is taking place in Ehime Pref., because Kagosima Pref. has a better natural environment for yellow tail aquaculture. Transportation is worse than Mie Pref., but this region as a far-flung producing area makes up by increasing the business scale. Ehime Pref. increases the market share for fresh fish by creating demand from GMS. Ehime Pref. has developed market strategies such as a quick return at a small profit, a stable and mass supply and standardization in size. Ehime Pref. increases the market power by the capital of a large scale commission agent. Secondly Mie Pref. is close to markets and composed of small scale farmers. Mie Pref. switched to sea bream aquaculture early, because of the price decrease in aquacultured yellou tail and natural environmental problems. Mie Pref. had not changed until 1993 when the price of the sea bream plummeted. Because it had better natural environment and transportation. Mie Pref. has a suitable water temperature range required for sea bream aquaculture. However, the price of live sea bream continued to decline due to excessive production and economic recession. As a consequence, small scale farmers are faced with a market price below the average production cost in 1993. In such kind of situation, the small-sized and inefficient manager in Mie Pref. was obliged to withdraw from sea bream aquaculture. Kumamoto Pref. is located further from market sites and has an unsuitable nature environmental condition required for sea bream aquaculture. Although Kumamoto Pref. is trying to convert to the puffer fish aquaculture which requires different rearing techniques, aquaculture technique for puffer fish is not established yet.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.50-63
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• 2018

The rapid urbanization had led to a distortion of natural hydrological cycle system. The change in hydrological cycle structure is causing streamflow depletion, changing the existing use tendency of water resources. To manage such phenomena, a streamflow depletion impact assessment technology to forecast depletion is required. For performing such technology, it is indispensable to build GIS-based spatial data as fundamental data, but there is a shortage of related research. Therefore, this study was conducted to use the use of GIS-based time series spatial data for streamflow depletion assessment. For this study, GIS data over decades of changes on a national scale were constructed, targeting 6 streamflow depletion impact factors (weather, soil depth, forest density, road network, groundwater usage and landuse) and the data were used as the basic data for the operation of continuous hydrologic model. Focusing on these impact factors, the causes for streamflow depletion were analyzed depending on time series. Then, using distributed continuous hydrologic model based DrySAT, annual runoff of each streamflow depletion impact factor was measured and depletion assessment was conducted. As a result, the default value of annual runoff was measured at 977.9mm under the given weather condition without considering other factors. When considering the decrease in soil depth, the increase in forest density, road development, and groundwater usage, along with the change in land use and development, and annual runoff were measured at 1,003.5mm, 942.1mm, 961.9mm, 915.5mm, and 1003.7mm, respectively. The results showed that the major causes of the streaflow depletion were lowered soil depth to decrease the infiltration volume and surface runoff thereby decreasing streamflow; the increased forest density to decrease surface runoff; the increased road network to decrease the sub-surface flow; the increased groundwater use from undiscriminated development to decrease the baseflow; increased impervious areas to increase surface runoff. Also, each standard watershed depending on the grade of depletion was indicated, based on the definition of streamflow depletion and the range of grade. Considering the weather, the decrease in soil depth, the increase in forest density, road development, and groundwater usage, and the change in land use and development, the grade of depletion were 2.1, 2.2, 2.5, 2.3, 2.8, 2.2, respectively. Among the five streamflow depletion impact factors except rainfall condition, the change in groundwater usage showed the biggest influence on depletion, followed by the change in forest density, road construction, land use, and soil depth. In conclusion, it is anticipated that a national streamflow depletion assessment system to be develop in the future would provide customized depletion management and prevention plans based on the system assessment results regarding future data changes of the six streamflow depletion impact factors and the prospect of depletion progress.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Korea journal of population studies
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• v.9 no.1
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• pp.41-53
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• 1986

The purpose of this paper is two-fold. One is to investigate the determinants of the demand supply of labor, and another is to project long-term demand and supply of labor. The paper consists of three parts. In the first part, theoretical models and important hypotheses are discussed: for the case of a labor supply model, issues regarding discouraged worker model, permanent wage hypothesis, and relative wage hypothesis are examined and for the case of a demand model, issues regarding estimating an employment demand equation within the framework of an inverted short-run produc- tion function are inspected. Particularly, a theoretical justification for introducing a demographic cohort variable in a labor supply equation is also investigated. In the second part, empirical results of the estimated supply and demand equations are analyzed. Supply equations are specified differently between primary and secondary labor force. That is, for the case of primary labor force groups including males aged 25 and over, attempts are made to explain the variations in participation behavior within the framework of a neo-classical economics oriented permanent wage hypothesis. On the other hand, for the case of females and young male labor force, variations in participation rates are explained in terms of a relative wage hypothesis. In other words, the participation behavior of primary labor force is related to short-rum business fluctuations, while that of secondary labor force is associated with intermediate swings of business cycles and demographic changes in the age structure of population. Some major findings arc summarized as follows. (1) For the case of males aged 14~19 and 2O~24 groups and females aged 14∼19, the effect of schhool enrollment rate is dominant and thus it plays a key role in explaining the recent declining trend of participation rates of these groups. (2) Except for females aged 20∼24, a demographic cohort variable, which captures the impact of changes in the age structure on participation behavior, turns out to show positive and significant coefficients for secondary labor force groups. (3) A cyclical variable produce significant coefficients for prime-age males and females reflecting that as compared to other groups the labor supply behavior of these groups is more closely related to short-run cyclical variations (4) The wage variable, which represents a labor-leisure trade-off turns out to yield significant coefficients only for older age groups (6O and over) for both males and females. This result reveals that unlike the experiences of other higer-income nations, the participation decision of the labor force of our nation is not highly sensitive with respect to wage changes. (5)The estimated result of the employment demand equation displays that given that the level of GNP remains constant the ability of the economy to absord labor force has been declining;that is, the elasticity of GNP with respect to labor absorption decreasre over time. In the third part, the results of long-term projections (for the period of 1986 and 1995) for age-sex specific participation rates are discussed. The participation rate of total males is anticipated to increase slightly, which is contrary to the recent trend of declining participation rates of this group. For the groups aged 25 and below, the participation rates are forecast to decline although the magnitude of decrease is likely to shrink. On the other hand, the participation rate of prime- age males (25 to 59 years old) is predicted to increase slightly during 1985 and 1990. For the case of females, except for 20∼24 and 25∼34 age groups, the participation rates are projected to decrease: the participation rates of 25∼34 age group is likely to remain at its current level, while the participation rate of 20∼24 age group is expected to increase considerably in the future (specifi- cally, from 55% in 1985 to 61% in 1990 and to 69% in 1995). In conclusion, while the number of an excess supply of labor will increase in absolute magnitude, its size as a ratio of total labor force is not likely to increase. However, the age composition of labor force is predicted to change; that is, the proportion of prime-age male and female labor force is projected to increase.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Knowledge Management Research
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• v.21 no.3
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• pp.45-65
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• 2020

It is desirable to invest in wine that increases its value, but wine investment itself is unfamiliar in Korea. Also, the process itself is unreasonable, and information is often forged, because pricing in the wine market is done by a small number of people. With the right solution, however, the wine market can be a desirable investment destination in that the longer one invests, the higher one can expect. Also, it is expected that the domestic wine consumption market will expand through the steady increase in domestic wine imports. This study presents the consortium block chain framework for revitalizing the wine market and enhancing transparency as the "right solution" of the nation's wine investment market. Blockchain governance can compensate for the shortcomings of the wine market because it guarantees desirable decision-making rights and accountability. Because the data stored in the block chain can be checked by consumers, it reduces the likelihood of counterfeit wine appearing and complements the process of unreasonably priced. In addition, digitization of assets resolves low cash liquidity and saves money and time throughout the supply chain through smart contracts, lowering entry barriers to wine investment. In particular, if the governance of the block chain is composed of 'chateau-distributor-investor' through consortium blockchains, it can create a desirable wine market. The production process is stored in the block chain to secure production costs, set a reasonable launch price, and efficiently operate the distribution system by storing the distribution process in the block chain, and forecast the amount of orders for futures trading. Finally, investors make rational decisions by viewing all of these data. The study presented a new perspective on alternative investment in that ownership can be treated like a share. We also look forward to the simplification of food import procedures and the formation of trust within the wine industry by presenting a framework for wine-owned sales. In future studies, we would like to expand the framework to study the areas to be applied.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.4
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• pp.425-437
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• 2007

Flow distribution of water and sedimentation rate were investigated to understand the hydrodynamics and settling characteristics of particulate materials in a constructed wetland for treatment of non-point sources pollutants, the Sihwa constructed wetland, Korea. The Sihwa constructed wetland is divided into three sub-wetlands(the Banwol, the Donghwa and the Samhwa wetlands) to treat the polluted water from three streams, the Banwol stream, the Donghwa stream and the Samhwa stream. From the results of water flow experiment using dye(Rhodamine 50WT Red), it was found that the water flow in the wetland was prevailing at the waterway and open water. Dye was spread slowly in the closed water area planted by plants. The mean hydraulic retention time(HRT) at the upper area of high wetland and lower wetland of Banwol, was found to be 34.1 hr at the upper area and 74.6 hr at the lower area respectively, totaling approximately 108.7 hr(4.5 days). The sedimentation rate was higher at lower area(sites of B, C and D) of the wetland than upper area(site of A which is settling zone). Based on the forecast for 20 years as to the amount of sediment that can be deposited in the open water in the future, the sediment depth of each area would be like this: A: 6.3 cm, B: 8.3 cm, C: 7.0 cm, D: 9.5 cm. The contents of organic materials in the sediment deposited within the sediment trap were found to be higher overly in the first investigation period which had much rainfall, and B, C and D areas were found to have an increased COD accumulation than A area. Also, nitrogen and phosphorus were found to increase in the down-stream of the wetland. The results of this study suggest that a sustainable research and management for the characteristics of water flow pattern and sedimentation changeable as time passes is needs to maintain or improve the efficiency of water treatment in the constructed wetland.