• Journal of Technology Innovation
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• v.28 no.2
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• pp.1-36
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• 2020

While the policy intervention of each country for the promotion of renewable energy is strengthened, Korea introduced Feed-in Tariff (FIT) in 2002 to directly support the development of renewable energy. But in 2012, the shift of policy instrument that from FIT to Renewable Portfolio Standard (RPS) is occurred. This is a unique background that is currently found only in Korea, and new answers that focus on the outcomes of the shift of policy instruments are needed in addition to the existing discussion of comparison of FIT and RPS. Therefore, this study analyzed the change of policy efficiency after the shift to RPS using Data Envelopment Analysis(DEA) and Malmquist Index. In the result of analysis, a difference in the improvement of policy efficiency after in shift to RPS is found among each renewable energy source. This result is because renewable energy companies voluntarily entered the market only for energy sources that have secured technology or price competitiveness, and this indicates that the performance of renewable energy after the RPS shift has been concentrated on specific energy sources. As a result of this study, considering that the goal of renewable energy policy is to expand distribution and to drive growth engines, multi-faceted analysis is required in consideration of technology and market in selecting policy instruments.

• Journal of Korean Society of Rural Planning
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• v.24 no.2
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• pp.59-67
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• 2018

As high-rise buildings came in, the landscape of rural areas and natural landscapes often got damaged. Therefore, this study aims to prevent this, grasp the extent of the influence of the surrounding landscape, to grasp the range of height that can be permitted and present the direction of landscape management of agriculture and natural landscape. This study tried to grasp the range of height by using price sensitivity analysis method for two apartment and apartment complex which entered DangJin city and SeoSan city. First, in the case of a two apartment, the range of the height allowable section was from the 6th floor to the 11th floor in close range view, and it was a section from the 7th floor to the 12th floor in medium range view. In the case of the apartment complex, the range of the height allowable range was from the 10th floor to the 17th floor in close range view, the 9th floor to the 16th floor in medium range view. The stress index was found to be positive in a two apartment in close range view, and in the apartment complex case. therefore it was better to set it to a lower in the Range of Acceptable Height(RAH). Second, it showed no difference in the sensitivity of landscape to gender. Thirdly, the results of the landscape sensitivity analysis of major and non-major showed the difference in the medium range view picture of the apartment complex. Majors are lower than the point of minimum height(PMinH) than non-Majors. In the case of major, the stress index was 1.4. it turned out that it was better to make a decision closer to point of minimum height (PMinH). In the case of non-major, the stress index was -1.3. it was also able to accept decision close to the point of maximum height (PMaxH). Since the results of the above research gave changes only in the variable of the height of the landscape, we can not grasp the point of interaction with other variables, and future research is considered necessary.

• Journal of Korean Society of Transportation
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• v.20 no.6
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• pp.45-56
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• 2002

In this research we estimated regional gross fixed capital stock of transport sector, such as road railroad, airport and seaport during 1968-1997 in Korea. We also compared our estimation results with those of Korea and Japan. As basic analytic method, we used the regional allocation method. To estimate regional gross fixed capital stock of transport sector, we used the basic data on national wealth surveys in 1997, regional land price index and regional facilities index in transport sectors. We used the most reasonable data in the process of estimation after reviewing the collected data In order to get the reasonable capital stock by regions. we chose the allocation index which can minimize the difference between the estimated result and the real regional capital stock in the process to allocate the total gross capital into the regions. Compared our results with those of other researches in Korea, estimates in our research project could be said more accurate than those.

• Culinary science and hospitality research
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• v.18 no.4
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• pp.47-58
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• 2012

This study, targeting the students of "K" university in Busan City area, was performed to draw the groups by food-related lifestyle types and to identify the correlation between each group's attributes of selecting places to eat out and obesity index. The purpose of the study was achieved by means of the PASW Statistic 18.0(Predictive Analytics Software) which conducted frequency analysis, factor analysis, reliability analysis, t-test, ${\chi}^2$-test, non-hierarchical cluster analysis and ANOVA. It turned out that the male university students were 175.59 cm tall and weigh 69.53 kg on average. And the female university students showed their average height of 162.81 cm and weight of 53.42 kg. When examined by the body mass index(BMI), male students were composed of 1.7% of underweight, 64.6% of normal weight, 19.7% of overweight and 14.0% of obese. As for the female students, 22.9% were classified as underweight, 62.7% as normal weight, 8.5% as overweight and 5.9% as obese. The food-related lifestyle categories were divided into five factors; health seeking type, safety seeking type, mood seeking type, taste seeking type, and western food seeking type. The four attributes of selecting places to eat out included quality of food and service, price reasonableness, accessibility and atmosphere, and experience to have eaten. With regard to food-related lifestyle, the groups were named by cluster 1 [careless diet group], Cluster 2 [health oriented group], and cluster3 [careless healthcare group]. In terms of the correlation between the clusters by food-related lifestyle and their attributes of selecting places to eat out, Cluster 1 had a high mean value in experience to have eaten, Cluster 2 quality of food and service, Cluster 3 accessibility and atmosphere.

• Journal of Korean Society of Transportation
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• v.35 no.3
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• pp.169-182
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• 2017

This research reviews regulations on logistics/transport industry and attempts to quantify the effects of regulation mitigation on GDP per capita. South Korea's transport industry has been gradually expanding, however, the industrial structure is still short rooted. In 2014, average number of hours worked is 5th highest and wage margin 12th smallest out of 18 industries. Furthermore, the regulations for this industry appear to be stricter than those of other industries. OECD's logistics/transport industry regulatory index for South Korea has been decreasing for the last 40 years but still exceeds those of EU, Japan, US, and other countries. This paper provides supporting reasons for regulatory reforms by analyzing the ripple effects on real GDP. Factors such as the ratio of trade among GDP, the enrollment rate to primary school, energy usage per capita, and population are controlled in the fixed-effect model. Estimation results showed that 1 unit decrease in transport/logistics regulatory index is correlated with 8.1% increase of the real GDP per capita, that is, 10% of deregulation is expected to yield 2.16% increase in GDP per capita. Thus, it is expected that mitigating regulations on market entries, price determination, ownership structures of network industry, vertical integrations can improve the economy of South Korea.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Korea Trade Review
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• v.41 no.4
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• pp.61-90
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• 2016

This study aims to identify the sources of productivity change in export manufacturing firms. After estimating the Malmquist productivity index, a panel regression was used to calculate the source of productivity change. Upon conducting a literature review of this field, six variables were selected as explanatory variables. The results of an analysis of 355 export manufacturing firms operating from 2009 through 2015 are as follows: First, both innovation activity and total assets had a positive impact on productivity change. However, employment cost intensity, equity ratio, and current ratio had a negative impact on productivity change in export manufacturing firms. Second, innovation activity and intangible assets had a positive impact on productivity change, but employment cost intensity, selling expense intensity, and equity ratio had a negative impact on productivity change in large export manufacturing firms. Third, innovation activity had a positive impact on productivity change, but employment cost intensity and equity ratio had a negative impact on productivity change in small and medium export manufacturing firms. Fourth, intangible assets had a positive impact on productivity change, but employment cost intensity, selling expense intensity, and current ratio had a negative impact on productivity change in export manufacturing firms listed on the Korea Composite Stock Price Index. Fifth, innovation activity and total assets had a positive impact on productivity change, but employment cost intensity and equity ratio had a negative impact on productivity change in manufacturing firms listed on the Korean Securities Dealers Automated Quotations. The managerial implications of this study are also discussed.

• Korea Trade Review
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• v.43 no.3
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• pp.125-145
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• 2018

The Baltic Shipping Exchange is reporting the Baltic Dry Index (BDI) which represents the average charter rate for bulk carriers transporting major cargoes such as iron ore, coal, grain, and so on. And the current BDI index is reflected in the proportion of capesize 40%, panamax 30% and spramax 30%. Like mentioned above, the capesize plays a major role among the various sizes of bulk carriers and this study is to analyze the influence of the factors influencing on charter rate of capesize carriers which transport iron ore and coal as the major cargoes. For this purpose, this study verified causality between variables using Vector Error Correction Model (VECM) and tried to derive a long-run equilibrium model between the dependent variable and independent variables. Regression analysis showed that every six independent variable has a significant effect on the capesize charter rate, even at the 1% level of significance. Charter rate decreases by 0.08% when capesize total fleet increases by 1%, charter rate increases by 0.04% when bunker oil price increases by 1%, and charter rate decreases by 0.01% when Yen/Dollar rate increases by 1%. And charter rate increases by 0.02% when global GDP increases by one unit (1%). In addition, the increase in cargo volume of iron ore and coal which are major transportation items of capesize carriers has also been shown to increase charter rates. Charter rate increases by 0.11% in case of 1% increase in iron ore cargo volume, and 0.09% in case of 1% increase in coal cargo volume. Although there have been some studies to analyze the influence of factors affecting the charterage of bulk carriers in the past, there have been few studies on the analysis of specific size vessels. At present moment when ship size is getting bigger, this study carried out research on capesize vessels, which are biggest among bulk carriers, and whose utilization is continuously increasing. This study is also expected to contribute to the establishment of trade policies for specific cargoes such as iron ore and coal.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.883-893
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• 2023

While South Korea's dependence on imported grains is very high, droughts impacts from exporting countries have been overlooked. Using the Evaporative Stress Index (ESI), this study globally analyzed frequency, extent, and long-term trends of agricultural droughts and their relation to natural oscillations and global crop prices. Results showed that global-scale correlations were found between ESI and soil moisture anomalies, and they were particularly strong in crop cultivation areas. The high correlations in crop cultivation areas imply a strong land-atmosphere coupling, which can lead to relatively large yield losses with a minor soil moisture deficits. ESI showed a clear decreasing trend in crop cultivation areas from 1991 to 2022, and this trend may continue due to global warming. The sharp increases in the grain prices in 2012 and 2022 were likely related to increased drought areas in major grain-exporting countries, and they seemed to elevate South Korea's producer price index. This study suggests the need for drought risk management for grain-exporting countries to reduce socioeconomic impacts in South Korea.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.