• Journal of the Semiconductor & Display Technology
• /
• v.22 no.1
• /
• pp.99-103
• /
• 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.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.36 no.4
• /
• pp.123-129
• /
• 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.

• Journal of Distribution Science
• /
• v.15 no.8
• /
• pp.5-14
• /
• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2002.03a
• /
• pp.39-51
• /
• 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
• /
• v.8 no.4
• /
• pp.135-143
• /
• 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.

• Asia-Pacific Journal of Business
• /
• v.14 no.4
• /
• pp.37-48
• /
• 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.

• Environmental and Resource Economics Review
• /
• v.22 no.1
• /
• pp.53-76
• /
• 2013

This paper analyzed on the relationship between R&D productivity of renewable energy and the Emissions Trading Scheme using OECD's country-specific patents and R & D input data. We empirically tested whether this R & D productivity has been substantially improved before and after the implementation of the emissions trading scheme and whether emission trading scheme has been promoted technology progress of renewable energy. Analytical methods used in this study, Negative Binomial Models which was proposed by Hausman et al. (1984). According to the results of this analysis, the R & D productivity of renewable energy was improved by emissions trading scheme, which was statistically significant at the 99% confidence interval [CI]. The R&D productivity of renewable energy was higher in Annex I countries. This research is significant in that R&D productivity was analyzed in associated with the emission trading scheme rather than it was analyzed by simply comparing R&D productivity.

• The KIPS Transactions:PartB
• /
• v.11B no.2
• /
• pp.207-212
• /
• 2004

This paper presents a reinforcement learning framework for stock trading systems. Trading system parameters are optimized by Q-learning algorithm and neural networks are adopted for value approximation. In this framework, cooperative multiple agents are used to efficiently integrate global trend prediction and local trading strategy for obtaining better trading performance. Agents Communicate With Others Sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on KOSPI 200 show that a trading system based on the proposed framework outperforms the market average and makes appreciable profits. Furthermore, in view of risk management, the system is superior to a system trained by supervised learning.

• Journal of Digital Convergence
• /
• v.19 no.11
• /
• pp.359-364
• /
• 2021

Recently, research on predictive systems using deep learning and machine learning of artificial intelligence is being actively conducted. Due to the development of artificial intelligence, the role of the investment manager is being replaced by artificial intelligence, and due to the higher rate of return than the investment manager, algorithmic trading using artificial intelligence is becoming more common. Algorithmic trading excludes human emotions and trades mechanically according to conditions, so it comes out higher than human trading yields when approached in the long term. The deep learning technique of artificial intelligence learns past time series data and predicts the future, so it learns like a human and can respond to changing strategies. In particular, the LSTM technique is used to predict the future by increasing the weight of recent data by remembering or forgetting part of past data. fbprophet, an artificial intelligence algorithm recently developed by Facebook, boasts high prediction accuracy and is used to predict stock prices and cryptocurrency prices. Therefore, this study intends to establish a sound investment culture by providing a new algorithm for automatic cryptocurrency trading by analyzing the actual value and difference using fbprophet and presenting conditions for accurate prediction.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.1
• /
• pp.105-111
• /
• 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.