• Journal of Korean Society of Rural Planning
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• v.24 no.3
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• pp.105-115
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• 2018

This study is conducted to propose urban park utilization and master plan in rural areas. Urban parks designed for the rural areas can be divided into three types: a themed type for rural tourism, a community type for hub regenerations and a waterfront type for using agricultural reservoirs. To use the themes and characteristics of ruralness, these types are required a multi-layered spatial structure. And ecological, cultural and economic networks of local tourism resources have to be integrated by utilizing agricultural reservoirs. Therefore, urban parks in rural areas can be defined as a part of the public benefit project aiming to revitalize the local economy. Also, urban parks are necessary to use attractions and amenities in rural areas. Based on theoretical backgrounds, this study proposed two sustainable master plans as the tourism resource development project for Baelyeonje, Gulye-gun. For ecological and cultural sustainability, this study proposed the environment restoration plan which reinforces the scenic resource of Nogodan in Mt. Jiri by developing the underdevelopment plan with consideration of the local landscape characteristics and resources. For economic sustainability, building the Mt. Jiri tourism complex and economic communities are needed to promote investments for securing mutual economic benefits. To achieve the sustainability, further studies related to the social equity and investment of private capital in rural areas are needed.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.6
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• pp.9-20
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• 2015

The demand of renewable energy is expected to grow in the long run in spite of current stable lower oil prices. Energy consumption for heating in horticulture greenhouse is large and affects the profits of the farms. This study analyzed the availability of biomass in rural area and proposed the strategies for utilizing the biomass for greenhouse heating. Data reveal the annual average fuel consumption in greenhouses is about 78 TOE/ha. Considering biomass resource in rural areas, agricultural residues are not sufficient to meet the biomass demand from greenhouses. Therefore it is recommended to secure further biomass including wild herbaceous biomass and woody biomass from forest. Based on the conditions of biomass gasification equipment investment and fuel prices, maximum allowable price of biomass turned out about 100,000 KRW/t to be competitive to kerosine. Biomass supply chain should be established for facilitating biomass trading between biomass consumers and biomass producers such as farmers who provide crop residues. An online trading system is an example of the system where consumers who utilize biomass make payments to suppliers and get the information about the biomass. Intermediate collection storages are required to store biomass from distributed sources. Operation of biomass heating systems in demonstration greenhouses is necessary to get information to refine and further develop commercial biomass heating systems. Relatively large greenhouses are desirable to have biomass heating systems for economic viability. The location of the greenhouse farms should be selected within the area where enough biomass resources are available for feeding the biomass facility.

• Environmental and Resource Economics Review
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• v.28 no.1
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• pp.39-59
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• 2019

The Korean Demilitarized Zone (DMZ) has the great ecosystem as all the artificial activities in DMZ have been prohibited over half a century. The ecosystem should be conserved even after the reunification of Korea and hence the conservation plan should be established not after the reunification but before it. It requires a considerable budget to conserve DMZ, considering management of ecology resource, recovery, and research. The objective of this paper is to analyze a fund-raising measure for DMZ conservation, using economic incentives mechanism when multiple developers participate in the auction to get the right to develop North Korean regions, have private information about their sunk costs and pay a part of their profits for the fund. First, we analyze the real option model to decide the optimal investment time. Second, we construct the auction for bidders not to misrepresent their private information, based on Bayesian Nash equilibrium.

• Journal of Korean Society of Rural Planning
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• v.25 no.2
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• pp.119-130
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• 2019

In a situation in which rural areas are declining, local governments are seeking to revitalize areas by place marketing. Place marketing, defined by various efforts to promote the image of a place, has been used as an economic tool. As a result, the image has been over-promoted and marketing has been driven in a perfunctory manner, so individual residents' lives and experiences have been ignored. Thus, in addition to the traditional types of cultural place marketing and economic place marketing, this study established a 'project for rural revitalization of Hwayang-eup' so that it could be applied to political place marketing aimed at inducing internal investment and improving the welfare of local residents. To implement this project, the concept was set up as building network organization, sustainable development and symbiotic relationship, and various H/W and S/W plans were developed. First of all, in terms of political place marketing, the Hwaeyang Oulim Center was constructed to strengthen the capacity of local autonomous organizations. In terms of cultural place marketing, we explored cultural resources at the village level and created a small community space. In terms of economic place marketing, the landscape around the main street and the township was reorganized to create a cultural business space for urban and rural exchanges. The reinterpretation of place marketing seen through this project was first, it was more process-oriented than results, second, it was important to induce the community-participating village-making project, and finally, the role of experts was important to expand the community movement.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.3
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• pp.92-102
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• 2018

The purpose of this study is to investigate if subjective well-being could be improved by visiting urban forests near residential areas. Because visiting an urban forest is not an intense positive experience, this research is focused on frequency of affective experience rather than intensity. The independent variables are number of visits and length of stay. The dependent variables are positive affect, negative affect, and life satisfaction. A polling agency was employed to select 600 respondents by quota sampling, and data was collected by online survey. The results of ANOVA showed that there was no interaction between the number of visits and length of stay. Regardless of the number of visits, the subjective well-being of visitors of urban forests was enhanced: (a) positive affect of respondents who had visited in the past 2 weeks was increased while negative affect was decreased, and (b) life satisfaction for those who had visited at least 1 time per month was enhanced among usual visitors. The stay of length, however, had little effect on the increase or decrease of these three variables. The results of this study support the existing theory that one could reset their genetically determined happiness set point to a higher level by participating in intentional activities such as visiting urban forests that offer ways to achieve long-lasting changes in well-being. This means that it would be a valuable government investment to construct and maintain urban forests for improving citizens' welfare. A few comments were suggested regarding data collection and inclusion of influencing variables to make future subjective well-being studies more reliable.

• Proceedings of the Korean Society of Environment and Ecology Conference
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• 2003.10a
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• pp.105-126
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• 2003

In Korea most of annual rainfall is concentrated in several episodic heavy rains during the season of summer monsoon and typhoon. Because of uneven rainfall distribution many dams have been constructed in order to secure water supply in dry seasons. The Han River system has the most dams among Korean rivers, and the river is a series of dams now. Reservoirs need different strategy of water quality control from river water. Autochthonous organic matter and phosphorus should be the major target to be controlled in lakes. In this Paper some problems are discussed that makes efforts of water quality improvement ineffective in lakes of Korea, even after the substantial investment to wastewater treatment facilities.1) Phosphorus is the key factor controlling eutrophication of lakes and the reduction ofphosphors should be the major target of water treatment. However, water quality management strategy in Korea is still stream-oriented, and focused on BOD removal from sewage. Phosphorus removal efficiency remains as low as 10-30%, because biological treatment is adopted for both secondary treatment and advanced treatment. The standard for TP concentration of the sewage treatment plant effluent is 6 mgP/l in most of regions, and 2 mg/l in enforced region near metropolitan water intake point. TP in the effluents of sewage treatment plants are usually 1-2 mg/1, and most of plants meet the effluent regulation without a further phosphorus removal process. The generous TP standard for effluents discourages further efforts to improve phosphorus removal efficiency of sewage treatment. Considering that TP standard for the effluent is below 0.1 mg/l in some countries, it should be amended to below 0.1 mg/l in Korea, especially in the watershed of large lakes.2) Urban runoff and combined sewer overflow are not treated, even though their total loading into lakes can be comparable to municipal sewage discharges on dry days. Chemical coagulation and rapid settling might be the solution to urban runoff in regard of intermittent operation on only rainy days.3) Aggregated precipitation in Korea that is concentrated on several episodic heavyrains per year causes a large amount of nonpoint source pollution loading into lakes. It makes the treatment of nonpoint source discharge by methods of other countries of even rain pattern, such as retention pond or artificial wetland, impractical in Korea.4) The application rate of fertilizers in Korea is ten times as high as the average ofOECD countries. The total manure discharge from animal farming is thought to be over the capacity of soil treatment in Korea. Even though large portion of manure is composted for organic fertilizer, a lot of nutrients and organic matter emanates from organic compost. The reduction of application rate and discharge rate of phosphorus from agricultural fields should be encouraged by incentives and regulations.5) There is a lot of vegetable fields with high slopes in the upstream region of the HanRiver. Soil erosion is severe due to high slopes, and fertilizer is discharged in the form of adsorbed phosphorus on clay surface. The reduction of soil erosion in the upland area should be the major preventive policy for eutrophication. Uplands of high slope must be recovered to forest, and eroded gullies should be reformed into grass-buffered natural streams which are wider and resistant to bank erosion.

• The Journal of Bigdata
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• v.6 no.1
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• pp.91-113
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• 2021

Despite the influence of real estate in the Korean asset market, it is not easy to predict market trends, and among them, apartments are not easy to predict because they are both residential spaces and contain investment properties. Factors affecting apartment prices vary and regional characteristics should also be considered. This study was conducted to compare the factors and characteristics that affect apartment prices in Seoul as a whole, 3 Gangnam districts, Nowon, Dobong, Gangbuk, Geumcheon, Gwanak and Guro districts and to understand the possibility of price prediction based on this. The analysis used machine learning algorithms such as neural networks, CHAID, linear regression, and random forests. The most important factor affecting the average selling price of all apartments in Seoul was the government's policy element, and easing policies such as easing transaction regulations and easing financial regulations were highly influential. In the case of the three Gangnam districts, the policy influence was low, and in the case of Gangnam-gu District, housing supply was the most important factor. On the other hand, 6 mid-lower-level districts saw government policies act as important variables and were commonly influenced by financial regulatory policies.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.