• 반도체디스플레이기술학회지
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• 제22권1호
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• pp.99-103
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• 2023

NFT technology is mostly used to create digital drawings, characters, and items, and to simply buy and sell, but research and development to spread to various contents of NFT are somewhat marginal. Therefore, this study aims to solve the above-described problems. Depending on the exercise performance, it allows users to create and trade custom NFTs. In addition, it supports users to own customized digital works through exercise performance or to make money by trading them. Through it, the aim is to enhance users' positive interest in exercise and provide devices and methods for providing customized NFT creation and trading services that can help them develop exercise habits.

• 산업경영시스템학회지
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• 제36권4호
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• pp.123-129
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• 2013

The aim of this study is to design an intelligent pattern-based real-time trading system (PRTS) using rough set analysis of technical indicators, dynamic time warping (DTW), and genetic algorithm in stock futures market. Rough set is well known as a data-mining tool for extracting trading rules from huge data sets such as real-time data sets, and a technical indicator is used for the construction of the data sets. To measure similarity of patterns, DTW is used over a given period. Through an empirical study, we identify the ideal performances that were profitable in various market conditions.

• 유통과학연구
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• 제15권8호
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• pp.5-14
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• 2017

Purpose - This study empirically analyses the sale changes in large retail stores directly resulting from increased controls on those stores. More specifically, we discuss the economic impacts on Korean regulations that restrict trading hours and mandate statutory store closure 'holidays' twice per month. Research design, data and methodology - we attempt to empirically analyse the economic effects of trading hours regulations through quantitative analysis of the sales revenue data of large retail stores. We introduce the data and methods of empirical analysis used to analyse the economic effects of trading-hour regulations on large retail stores. We use a panel regression to analyse the sales losses of large retail stores caused by the new constraints on business hours. Results - The results of this study show that the sales of large retail stores fell by the average of 3.4% per month during the regulation periods. However, regulations affecting large retail stores have various economic impacts, including variations in sales, changes in consumption patterns, and influences on consumer welfare and national economy. Conclusions - Such changes may also be captured by other metrics: accordingly, further researches are needed to measure the impact of regulations on economic indicators such as employment and GDP.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2002년도 춘계학술대회논문집
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• pp.39-51
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• 2002

Online exchange is a cost-effective approach to trade goods and information among multiple sellers and buyers. Shipping industry includes lots of global entities such as shippers, liners, ship owners and shipping agents. Marine insurance companies and ship repairers and many other groups are also supporting the industry. However, international shipping exchanges are located on few cities in the world. Its our motivation that a shipping market can be online so that market participants do the dealing while sitting where they are with more efficient manner, preferable price and larger pool of candidates of trading partners. This paper presents Korean governmental project of building a cyber shipping exchange. The exchange covers ship sale and purchase, charter, insurance, freight futures, repairs, supplying of ships oil and database service. The workflows of each business were analyzed and designed to fit for online environment. The project includes design of trading mechanism, online documents, data flow, data storage and security. Online match making and trading mechanisms such as auction, reverse auction, bid are used. The whole trading process involves multiple organizations and business processes. So, this Paper focuses on how each organization would play their roles so that users can complete transactions with integrated and transparent view. The online exchange selves also as maritime portal site that links to other sites for cooperation vertically or horizontally, and serves database and information in global perspective. This paper also issues and discusses the justification of an online shipping exchange

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.135-143
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• 2021

Information has an essential role in decision-making for investors who will invest in financial markets, especially regarding the policies on the condition of COVID-19. The purpose of this study is to determine the market reaction to the information published by the government regarding the policy changes to the provisions of Trading Halt on the IDX in an emergency using the event study method. The population in this study was companies listed on the Indonesia Stock Exchange in March 2020; the sample selection technique was purposive sampling. Data analysis used a normality test and one sample T-test. The results of the study found that there were significant abnormal returns on the announcement date, negative abnormal returns around the announcement date, and significant trading volume activity occurring three days after the announcement. The existence of a significant positive abnormal return on the announcement date indicates that the market responds quickly to information published by the government. The practical implication of this research can be taken into consideration for investors in making investment decisions to analyze and determine the right investment options so that investors can minimize the risk of their investment and maximize the profits they want to achieve.

• 아태비즈니스연구
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• 제14권4호
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• pp.37-48
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• 2023

Purpose - This study aims to explore the influence of emotional discrepancies among investors on the cryptocurrency market. It focuses on how varying emotions affect market dynamics such as volatility and trading volume in the context of Bitcoin trading. Design/methodology/approach - This study involves analyzing data from Bitcointalk.org, consisting of 57,963 posts and 2,215,776 responses from November 22, 2009, to December 31, 2022. Tools used include the Linguistic Inquiry and Word Count (LIWC) software for classifying emotional content and the Python Pattern library for sentiment analysis. Findings - The results show that heterogeneous emotional feedback, whether positive or negative, significantly influences Bitcoin's intraday volatility, skewness, and trading volume. These findings are more pronounced when the underlying emotion in the feedback is amplified. Research implications or Originality - This study underscores the significance of emotional factors in financial decision-making, especially within the realm of social media. It suggests that investors and market strategists should consider the emotional landscape of online forums when making investment choices or formulating market strategies. The research also paves the way for future studies regarding the behavioral impact of emotions on the cryptocurrency market.

• 자원ㆍ환경경제연구
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• 제22권1호
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• pp.53-76
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• 2013

본 연구는 신재생에너지의 R&D 생산성과 배출권거래제의 연관관계를 OECD의 국가별 특허건수와 R&D 투입액 데이터를 사용하여 분석하였다. 즉 배출권거래제의 실시 전후하여 이러한 R&D 생산성이 실질적으로 향상되었는지를 살펴봄으로써 배출권거래제가 신재생에너지 연구개발 성과를 촉진하였는지를 계량경제학적으로 분석한 것이다. 본 연구에 사용한 분석 방법은 Hausman et al. (1984)가 제시한 Negative Binomial Models을 사용하였다. 분석결과에 의하면 배출권거래제가 신재생에너지의 R&D생산성을 향상시키는 것으로 나타났으며, 이는 99%의 신뢰구간에서 유의한 것으로 나타났다. 또한 부속서 I국가인가의 여부가 신재생에너지의 R&D생산성을 더욱 촉진하는 것으로 나타났다. 본 연구는 단순한 신재생에너지에 대한 연구개발투자의 상호 비교를 통하여 시사점을 도출하기 보다는 실질적인 R&D 생산성을 배출권거래제와 상호 연계하여 분석하였다는 점에서 의의가 있다.

• 정보처리학회논문지B
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• 제11B권2호
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• pp.207-212
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• 2004

본 논문은 주식 매매 시스템을 위한 강화 학습 구조를 제시한다. 매매 시스템에 사용되는 매개변수들은 Q-학습 알고리즘에 의하여 최적화되고, 인공 신경망이 값의 근사치를 구하기 위하여 활용된다 이 구조에서는 서로 유기적으로 협업하는 다중 에이전트를 이용하여 전역적인 추세 예측과 부분적인 매매 전략을 통합하여 개선된 매매 성능을 가능하게 한다. 에이전트들은 서로 통신하여 훈련 에피소드와 학습된 정책을 서로 공유하는데, 이 때 전통적인 Q-학습의 모든 골격을 유지한다. 실험을 통하여, KOSPI 200에서는 제안된 구조에 기반 한 매매 시스템을 통하여 시장 평균 수익률을 상회하며 동시에 상당한 이익을 창출하는 것을 확인하였다. 게다가 위험 관리의 측면에서도 본 시스템은 교사 학습(supervised teaming)에 의하여 훈련된 시스템에 비하여 더 뛰어난 성능을 보여주었다.

• 디지털융복합연구
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• 제19권11호
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• pp.359-364
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• 2021

최근 인공지능의 딥러닝과 머신러닝을 이용한 예측시스템에 관한 연구가 활발히 진행되고 있다. 인공지능의 발전으로 인해 투자관리자의 역할을 인공지능을 대신하고 있으며, 투자관리자보다 높은 수익률로 인해 점차 인공지능으로 거래를 하는 알고리즘 거래가 보편화하고 있다. 알고리즘 매매는 인간의 감정을 배제하고 조건에 따라 기계적으로 매매를 진행하기 때문에 장기적으로 접근했을 때 인간의 매매 수익률보다 높게 나온다. 인공지능의 딥러닝 기법은 과거의 시계열 데이터를 학습하고 미래를 예측하여 인간처럼 학습하게 되고, 변화하는 전략에 대응할 수 있어 활용도가 증가하고 있다. 특히 LSTM기법은 과거의 데이터 일부를 기억하거나 잊어버리는 형태로 최근의 데이터의 비중으로 높여 미래 예측에 사용하고 있다. 최근 facebook에서 개발한 인공지능 알고리즘인 fbprophet은 높은 예측 정확도를 자랑하며 주가나 암호화폐 시세 예측에 사용되고 있다. 따라서 본 연구는 fbprophet을 활용하여 실제 값과 차이를 분석하고 정확한 예측을 위한 조건들을 제시하여 암호화폐 자동매매를 하기 위한 새로운 알고리즘을 제공하여 건전한 투자 문화를 정착시키는 데 이바지하고자 한다.

• 산업경영시스템학회지
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• 제46권1호
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.