• Environmental and Resource Economics Review
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• v.19 no.4
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• pp.805-828
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• 2010

Korea also plans to introduce Renewable Portfolio Standard (RPS) for strengthening market functions after 2012 as United States and several members of EU countries did. Through the introduction of RPS, it requires energy industry to supply new and renewable energy at fixed rate. Therefore, it will contribute to the distribution of new and renewable energy. This paper analyzed the economic effect of the introduction of RPS using CGE. The summary of the paper on the analysis of the economic effect based on endogenous growth theory under imperfect market competition by using CGE is as follows; Since RPS possibly regulates the amount of new and renewable energy, it can achieve the target amount of new and renewable energy without fail. As achieving the target amount accurately, the distribution of more advanced skills can be expected. However, GDP reduction can occur because investment cost increases due to the requirement of new and renewable energy supply. Therefore, in the long run, it is appropriate to introduce RPS because it contributes to the distribution of new and renewable energy and can be utilized as a new growth engine to encourage economic growth.

• 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.

• Journal of Information Technology Services
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• v.20 no.1
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• pp.11-27
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• 2021

The defense IT master plan is the plan for promoting defense informatization and is established by the Ministry of National Defense every five years. In addition, an implementation plan has been established in accordance with the master plan every year to promote the informatization project. However, since there is no systematic methodology to check and analyze the master plan comprehensively, it is difficult to judge whether the goals suggested in the master plan are achieved, and continuous monitoring of the project is limited. This study proposes a concept and framework for defense IT investment management and proposes a methodology for examining and analyzing the progress of the defense IT plan by referring to the U.S. Department of Defense's IT Plan and the performance management structure of the national IT master plan. As a methodology for managing defense IT investment, we propose an IT investment management code system from the perspective of an IT portfolio and verify its applicability through case studies. The results of this study are expected to improve the defense IT performance management system and improve the efficiency and effectiveness of the defense IT project.

• Management & Information Systems Review
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• v.34 no.1
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• pp.65-80
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• 2015

In previous studies concerning turnover, they argue individual stock's turnover must be identical to market portfolio's turnover under one condition where 2 funds separation theorem holds. In this kind of world, all market participants hold and trade the same portfolio and this should be only market portfolio. If one's trading portfolio's shape is different from market portfolio's, this would mean he or she has an advantage over others in information and this kind of information would be private. In accordance with this theory, we develop a metric which measures how far one's trading portfolio from market's and name it as Stock Selection by Investor(SSI). We apply this measurement to the various types of investor groups classified as individual, institutional and foreign who participate in Korea stock market. To test the validity of measure, we regress price ratio on this measurement using SUR method. As a result, individual investor group shows large number in SSI, but the coefficient in regression is not significant and economically meaningless. In case of institutional investor group, the coefficient proves to be significantly negative. We can infer from this fact that their trading is somehow far from informed trading. Stock selection activity by foreign investor groups proves to be informed trading by showing significantly positive coefficient and the magnitude of coefficient is economically meaningful, especially in sell activity.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.6
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• pp.1329-1338
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• 2015

This article aims to develop model for the right policy Tools available from the cause analysis regarding the regional differences of subway modal split in Seoul metropolitan area. This allows two major factors of the most influential subway modal split to be proved and Portfolio Analysis is conducted. The results are as follows. Firstly, the two primary factors affecting subway modal split were shown as subway adjacent area and local line bus. It signifies that expansion of subway adjacent area, establishing the number of the subway stations and increase of local line bus are required in order to improve a diminishing subway modal split. Following that, pattern of the improvement to strengthen better subway connections are classified according to the two areas which are Concentration Area of Improvement in Subway Station Area (CAISSA) and Concentration Area of Improvement in Local Bus (CAILB). Our study revealed that Ganbukgu, Seodaemungu, Geumcheongu, and Gwanakgu were selected as the area of CAILB and Songpagu, and Junggu were selected as the area of CAISSA. As all things are considered, transportation policy makers should be taken into account in the two main factors driven by our study according to types in order to enhance the future subway share proportion.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.3
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• pp.467-477
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• 2009

Renewable Portfolio Standard (RPS) is a regulatory policy that requires the generation companies to increase the proportion of renewable energy sources such as wind, solar, LFG, fuel cell, and small hydro. Recently, Korean government decided to increase the portion of renewable energy to 3% to total electricity generation by 2012 from the current level of 0.13%. To achieve this goal, an innovative plan for market competitiveness would be required in addition to the present Feed-In-Tariff (FIT). That is Korean government has taken it into consideration to introduce a Renewable Portfolio Standard (RPS) as an alternative to FIT. This paper reviews the impact of RPS on the long-term fuel mix in 2020. The studies have been carried out with the GATE-PRO (Generation And Transmission Expansion PROgram) program, a mixed-integer non-linear program developed by Hongik university and Korea Energy Economics Institute. Detailed studies on long-term fuel mix in Korea have been carried out with four RPS scenarios of 3%, 5%, 10% and 20%. The important findings and comments on the results are given to provide an insight on future regulatory policies.

• Asia-Pacific Journal of Business
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• v.5 no.2
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• pp.37-50
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• 2014

Banking institutions have been facing variety of difficulties but the major cause of serious banking problems relates to lax credit standards for borrowers and counterparties, poor portfolio risk management, or a lack of attention to changes in economic or other circumstances that can lead to deterioration in the credit standing of a bank's counterparties. Although credit risk is an important factor that financial institutions should cope with, but the determinants of measuring credit risk have been studied less. This paper attempts to explore the determinants of credit risk measurement and to identify the factors that contribute to credit risk measurement practices in Indian banks and to compare credit risk measurement practices followed by Indian public and private sector banks, the empirical study has been conducted and views of employees of various banks have been tested using statistical tools. This study explored the phenomenon from different perspectives and revealed that single-name credit risk measurement and portfolio credit risk measurement are the key components that contribute to credit risk measurement in Indian banks. From the descriptive and analytical results, it can be concluded that Indian banks efficiently measure credit risk. The results also indicate that there is a significant difference between the Indian public and private sector banks in single-name credit risk measurement while, these banks do not significantly differ in portfolio credit risk measurement aspect.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1135-1143
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• 2016

A private company's 1,000 MW coal-fired power plant will be the first coal-fired power plant that was included in the 5th 'Basic Plan on Electricity Demand and Supply' (2010). Now it is facing the task to abide by the RPS(Renewable Portfolio Standard) policy after commercial operation. If they fail to supply the necessary REC (Renewable Energy Certificate) mandated by the RPS policy, they are subject to be fined by the government and forced to modify the cost function to reflect the burden. Eventually the company's coal-fired power plant will be forced to reduce generation to maximize profit because the amount of electricity generated by the power plant and the REC obligation is positively correlated. This paper analyzed the change of cost function of private coal-fired power plant according to the introduction of RPS policy from the viewpoint of private company, and finally proposed the optimal generation to maximize the profit of private coal-fired power plant under the current RPS policy.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.23-30
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• 2019

As deep learning with the network-based algorithms evolve, artificial intelligence is rapidly growing around the world. Among them, finance is expected to be the field where artificial intelligence is most used, and many studies have been done recently. The existing financial strategy using deep-run is vulnerable to volatility because it focuses on stock price forecasts for a single stock. Therefore, this study proposes to construct ETF products constructed through portfolio methods by calculating the stocks constituting funds by using deep learning. We analyze the performance of the proposed model in the KOSPI 100 index. Experimental results showed that the proposed model showed improved results in terms of returns or volatility.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.139-143
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• 2023

The product portfolio problem(PPP) is an optimization problem that determines the production quantity of a particular product to obtain the maximum profit among the n products. Linear programming(LP) is known as the only way to solve this optimization problem. The linear programming method is a problem that optimizes n linear functions and uses LINGO or Excel solver. This paper proposes a simple algorithm that uses CPR, a product cost-profit ratio, to sort in CPR descending order and then determines the maximum allowed production quantity by hand as the actual production quantity. As a result of applying the proposed algorithm to six experimental data, it was shown that more accurate results can be obtained compared to the linear programming method.