• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Environmental and Resource Economics Review
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• v.23 no.4
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• pp.643-665
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• 2014

We study the optimal policy of the contracual arrangement in raising the debt-to-equity ratio for oil, gas and mining project finance deals. We investigate the impact of the optimal contractual relationship between counterparties on the soundness of projects, differing in output price volatility and country risk. Key findings are: first, the existence of EPC sponsors and off-takers generally raises the debt-to-equity ratio. In particular, EPC sponsors and off-taking sponsors jointly mitigate the credit risk caused by counntry risk. Seocond, off-taking and EPC contracts jointly help mitigate the credit risk caused by the country risk, rather than the price volatility. Indeed, the contractual structure raises the debt-to-equity ratio.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.489-498
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• 2023

Cabbage, which is one of the main materials of kimchi, normally has an unstable supply due to cultivation and climate conditions. This unstable supply negatively affects the profitability of kimchi processors in Korea. Thus, kimchi processors found a better method for acquiring a consistent cabbage supply with long-term storage of over 3 months. However, a consensus regarding the best method for the stable and economical acquisition of cabbage remains controversial. This study aimed to analyze the current issue concerning cabbage acquisition by kimchi processors and evaluate the economic feasibility of kimchi storage. Findings obtained through survey and economic analyses using theoretical methodology were as follows: First, A survey conducted on kimchi processors in Korea revealed that even though they recognize the importance of kimchi storage, they struggle to store adequate amounts of cabbage. This is particularly evident with summer cabbage, which experiences the highest supply volatility and thus requires greater attention from kimchi processors in terms of storage. Second, the price analyses using the coefficient of variation show that cabbage in Korea has a high level of price instability, which suggests more storage of cabbage. Third, the evaluation of the economic feasibility of cabbage storage indicated that kimchi processors should consider storing a greater amount of cabbage, particularly during the summer season. This approach can help reduce the overall cost associated with kimchi processing.

• East Asian Economic Review
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• v.27 no.3
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• pp.213-242
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• 2023

This paper investigates whether the price of cryptocurrency is determined by the US dollar index, the price of investment assets such gold and oil, and the implied volatility of the KOSPI. Overall, the returns on cryptocurrencies are best predicted by the trading volume of the cryptocurrency both in-sample and out-of-sample. The estimates of gold and the dollar index are negative in the return prediction, though they are not significant. The dollar index, gold, and the cryptocurrencies seem to share characteristics which hedging instruments have in common. When investors take notice of the imminent market risks, they increase the demand for one of these assets and thereby increase the returns on the asset. The most notable result in the out-of-sample predictability is the predictability of the returns on value-weighted portfolio by gold. The empirical results show that the restricted model fails to encompass the unrestricted model. Therefore, the unrestricted model is significant in improving out-of-sample predictability of the portfolio returns using gold. From the empirical analyses, we can conclude that in-sample predictability cannot guarantee out-of-sample predictability and vice versa. This may shed light on the disparate results between in-sample and out-of-sample predictability in a large body of previous literature.

• Journal of the Korean Solar Energy Society
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• v.37 no.1
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• pp.59-69
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• 2017

In 2012, Korea introduced a Renewable Portfolio Standard (RPS) scheme, replacing the Feed-in Tariff (FIT) scheme as a market support policy of renewable energy in the electricity market. RPS is to allocate obligatory quota of renewable energy sources for electricity suppliers, whereas FIT is to guarantee high prices for electricity from renewable energy sources. This study examines the effect of this policy change on solar photovoltaic market. According to the study, solar PV market grew fast under FIT as well as under RPS. However, under RPS the size of subsidy for solar PV suppliers was shrunk substantially. In addition, market risk increased severly under RPS due to the volatility of price of renewable energy certificate (REC) as well as of the electricity market price. The small and medium suppliers of solar PV were suffered the most severly from these policy effects. Therefore, the policy reform of RPS is needed to alleviate the market risk of small and medium suppliers of solar PV.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.51-59
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• 2018

Despite the advantages of energy arbitrage using energy storage systems (ESSs), the high cost of ESSs has not attracted storage owners for the arbitrage. However, as the costs of ESS have decreased and the price volatility of the electricity market has increased, many studies have been conducted on energy arbitrage using ESSs. In this study, the existing two-period model is modified in consideration of the ESS cost and risk-free contracts. Optimal investment strategies that maximize the sum of external effects caused by price changes and arbitrage profits are formulated by market participants. The optimal amounts of ESS investment for three types of investors in three different market structures are determined with game theory, and strategies in the form of the mixed-complementarity problem are solved by using the PATH solver of GAMS. Results show that when all market participants can participate in investment simultaneously, only customers invest in ESSs, which means that customers can obtain market power by operating their ESSs. Attracting other types of ESS investors, such as merchant storage owners and producers, to mitigate market power can be achieved by increasing risk-free contracts.

• Environmental and Resource Economics Review
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• v.15 no.2
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• pp.319-353
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• 2006

This paper shows how weather derivatives can be used to hedge against the price risk and volume risk of purchasing relatively large amounts of electricity. Our specific approach to designing new contracts for electricity is to focus on the return over a summer season rather than on the daily levels of demand and price. It is shown that correct market signals can be preserved in a contract and the associated financial risk can be offset by weather options. The advantage of combining a forward contract with a weather derivative is that the high prices on hot days or when the temperature is high reflect the underlying high cost of producing power when the load is high and that the combined contract with a weather derivative substantially reduces the volatility of the return.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.575_576
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• 2009

최근 전력산업의 구조적인 측면에서는 수직적 형태의 분리 및 경쟁 도입, 그리고 민영화를 통한 효율 증진 등 전력산업 개편이 전세계적으로 이루어지고 있다. 전력산업의 개편 과정에서 전력공급자(ESP)는 불완전한 시장으로 인한 재정적인 위험에 직면한다. ESP가 재정적인 위험에서 근본적으로 벗어나기 위해서는 합리적인 전기 소매 가격의 책정이 필요하다. 본 논문에서는 현재 적용되고 있는 고정 소매 가격제에 대한 문제점을 제시하고 이를 극복하기 위해서 전기 공급 도매 가격의 변동성을 예측함으로써 헤징을 통한 새로운 요금제의 도입의 필요성을 제안하는 것이 본 논문의 목적이다. 본 논문에서 소개될 새로운 요금제인 Critical Peak Pricing(CPP)에서 전기 공급 도매 가격의 변동성의 예측은 CPP 요금을 적용하는데 중요한 역할을 담당하는 지표로 활용된다. CPP 요금을 적용함으로써 ESP의 재정적인 위험을 최소화하고 수요 탄력성이 반영되어 전기 소비자들과의 관계 향상 또한 유도될 수 있다.[4],[6]

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.165-187
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• 2005

This paper tries to investigate the relationships among stock return volatility, time-varying risk premium and Korea Discount. Using Korean Composite Stock Price Index (KOSPI) return from January 4, 1980 to August 31, 2005, this study finds possible links between time-varying risk premium and Korea Discount. First of all, this study classifies Korean stock returns during the sample period by three regime-switching volatility period that is to say, low-volatile period medium-volatile period and highly-volatile period by estimating Markov-Switching ARCH model. During the highly volatile period of Korean stock return (09/01/1997-05/31/2001), the estimated time-varying unit risk premium from the jump-diffusion GARCH model was 0.3625, where as during the low volatile period (01/04/1980-l1/30/1985), the time-varying unit risk premium was estimated 0.0284 from the jump diffusion GARCH model, which was about thirteen times less than that. This study seems to find the evidence that highly volatile Korean stock market may induce large time-varying risk premium from the investors and this may lead to Korea discount.