• Journal of Energy Engineering
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• v.14 no.2 s.42
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• pp.82-97
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• 2005

RPS (Renewable Portfolio Standards) is a policy tool to disseminate renewable energies through market mechanism. RPS promotes renewable power generation by obligating electricity market participants to deliver the required amount of electricity from renewable energies. To promote and encourage renewable energies, Korean government is considering to introduce RPS to domestic market in the near future. The purpose of this paper is to analyze the definition and market mechanism of RPS and to review key considerations in its design. In conclusion, we recommend some prerequisite in its introduction to Korea.

• Journal of Korean Society for Quality Management
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• v.41 no.4
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• pp.637-648
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• 2013

Purpose: The objective of this study is to provide a methodology that can facilitate efficient development of a product portfolio by utilizing Korean Standards(KS) networks. Methods: A case study on a steel manufacturing company is provided. Social network analysis h as been conducted on KS network and KS certification information of the company. Core test standards of a company have been identified. The core standards, then, used to construct a product-standard network of a corresponding industry. Results: As a result of analyzing product-standard networks, a product portfolio of a company has been developed. It has been shown that the candidate product portfolio is a cost-effective alternative in terms of standard maintenance cost. Conclusion: By using social network analysis, standards information can be used to support new product development process.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.135-150
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• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.

• IE interfaces
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• v.23 no.4
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• pp.300-310
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• 2010

This paper is about an optimal investment portfolio strategy. Financial data of stocks, bonds, and savings from January 2. 2001 through October 30. 2009 were utilized in order to suggest the optimal portfolio strategies. Fundamental analysis and technical analysis were used in stocks-related strategy, whereas passive investment strategy and active investment strategy were used in bond-related strategy. The score is assigned to each stock index according to the suggested strategies and set trading rules are based on the scores. The simulation has been executed about each 29,400-portfolios and we figured out with the simulation result that 26.75% of 7,864 portfolios are more profitable than average stock market profit (22.6%, Annualized). The outcome of this research is summarized in two parts. First, it's the rebalancing strategy of portfolio. The result shows that value-oriented investment(long-term investment) strategy yields much higher than short-term investment strategies of stocks or active investment of bonds. Second, it's about the rebalancing cycle forming the portfolios. The result shows that the rate of return for the portfolio is the best when rebalancing cycle is 12 or 18 months.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.5
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• pp.275-284
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• 2004

This paper analyzes the environmental impact and economic effect of introducing the Renewable Portfolio Standard (RPS) into Korean electricity market using the DECADES (Database and Methodologies for Comparative Assessment of Different Energy Source for Electricity Generation) model, a comparative assessment tool developed by IAEA. A bottom up approach is adopted for the evaluation of air pollutant emission and its impact of several RPS scenarios. The environmental damage costs of RPS scenarios are evaluated based on the Extern-E results and Thailand externality study carried by EGA T. The results of this study can be applied in determining or analyzing the national electricity policy and energy policy.

• East Asian Economic Review
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• v.20 no.3
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• pp.391-423
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• 2016

By examining the relationship between private credit growth and the possibility of credit risk while focusing on international capital in 21 countries over the period 2000:1Q-2015:2Q, this paper shows that the impact of private credit growth on credit risk is apparent under the high ratio of capital inflows, and its impact on credit risk in the seven Asian countries is even stronger. And the possibility of credit risk caused by private credit is mainly coming from portfolio inflows rather than direct inflows. Finally, portfolio inflows strengthen the positive relationship between credit excess and credit risk in Asian countries, and this trend is seen more in these after the global financial crisis. Taken together, the stronger positive relationship between credit excess and credit risk can be strengthen under the massive portfolio inflows in particular in the seven Asian countries such as Hong Kong, India, Indonesia, Korea, Malaysia, Singapore, and Thailand.

• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.63-76
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• 2016

Multiperiod portfolio selection problem attracts more and more attentions because it is in accordance with the practical investment decision-making problem. However, the existing literature on this field is almost undertaken by regarding security returns as random variables in the framework of probability theory. Different from these works, we assume that security returns are uncertain variables which may be given by the experts, and take absolute deviation as a risk measure in the framework of uncertainty theory. In this paper, a new multiperiod mean absolute deviation uncertain portfolio selection models is presented by taking transaction costs, borrowing constraints and threshold constraints into account, which an optimal investment policy can be generated to help investors not only achieve an optimal return, but also have a good risk control. Threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Based on uncertain theories, the model is converted to a dynamic optimization problem. Because of the transaction costs, the model is a dynamic optimization problem with path dependence. To solve the new model in general cases, the forward dynamic programming method is presented. In addition, a numerical example is also presented to illustrate the modeling idea and the effectiveness of the designed algorithm.

• 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.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.467-476
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• 2022

The Bank of Korea raised the benchmark interest rate by a quarter percentage point to 1.75 percent per year, and analysts predict that South Korea's policy rate will reach 2.00 percent by the end of calendar year 2022. Furthermore, because market volatility has been significantly increased by a variety of factors, including rising rates, inflation, and market volatility, many investors have struggled to meet their financial objectives or deliver returns. Banks and financial institutions are attempting to provide Robo-Advisors to manage client portfolios without human intervention in this situation. In this regard, determining the best hyper-parameter combination is becoming increasingly important. This study compares some activation functions of the Deep Deterministic Policy Gradient(DDPG) and Twin-delayed Deep Deterministic Policy Gradient (TD3) Algorithms to choose a sequence of actions that maximizes long-term reward. The DDPG and TD3 outperformed its benchmark index, according to the results. One reason for this is that we need to understand the action probabilities in order to choose an action and receive a reward, which we then compare to the state value to determine an advantage. As interest in machine learning has grown and research into deep reinforcement learning has become more active, finding an optimal hyper-parameter combination for DDPG and TD3 has become increasingly important.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.1
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• pp.17-28
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• 2019

In the financial investment management strategy, the distributed investment selecting and combining various financial assets is called portfolio management theory. In recent years, the blockchain based financial assets, such as cryptocurrencies, have been traded on several well-known exchanges, and an efficient portfolio management approach is required in order for investors to steadily raise their return on investment in cryptocurrencies. On the other hand, deep learning has shown remarkable results in various fields, and research on application of deep reinforcement learning algorithm to portfolio management has begun. In this paper, we propose an efficient financial portfolio investment management method based on Asynchronous Advantage Actor-Critic (A3C), which is a representative asynchronous reinforcement learning algorithm. In addition, since the conventional cross-entropy function can not be applied to portfolio management, we propose a proper method where the existing cross-entropy is modified to fit the portfolio investment method. Finally, we compare the proposed A3C model with the existing reinforcement learning based cryptography portfolio investment algorithm, and prove that the performance of the proposed A3C model is better than the existing one.