• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.297-307
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• 2019

Despite the fact that the implementation of 5% rule is widely recognized to enhance the transparency of capital market and fairness of corporate governance market, a few evidences present information effect of 5% rule. Using 7,088 non-financial firm-year observations listed on the Korea Stock Exchange from 2006 to 2012, we analyze the relation between trading volume and 5% rule disclosure. The results show that the daily and abnormal trading volume is increased when 5% rule disclosure is released. Moreover, the trading volume is significantly increased during cooling period. Specifically, trading volume is significantly greater when one day before cooling period or the expiration day of cooling period. We also find the information effect of firms with stable ownership structure before 5% rule disclosure is relatively smaller than the firms with unstable ownership structure with unstable ownership structure. These results imply that capital market participants use the information from 5% rule disclosure and reflect in their real economic decision.

• The Journal of Information Systems
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• v.12 no.2
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• pp.1-20
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• 2003

This paper deals with the application of the genetic algorithm to the technical trading rule of the stock market. MACD(Moving Average Convergence & Divergence) and the Stochastic techniques are widely used technical trading rules in the financial markets. But, it is necessary to determine the parameters of these trading rules in order to use the trading rules. We use the genetic algorithm to obtain the appropriate values of the parameters. We use the daily KOSPI data of eight years during January 1995 and October 2002 as the experimental data. We divide the total experimental period into learning period and testing period. The genetic algorithm determines the values of parameters for the trading rules during the teaming period and we test the performance of the algorithm during the testing period with the determined parameters. Also, we compare the return of the genetic algorithm with the returns of buy-hold strategy and risk-free asset. From the experiment, we can see that the genetic algorithm outperforms the other strategies. Thus, we can conclude that genetic algorithm can be used successfully to the technical trading rule.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.337-346
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• 1999

In this paper, we propose the rough set approach to extract trading rules able to discriminate between bullish and bearish markets in stock market. The rough set approach is very valuable to extract trading rules. First, it does not make any assumption about the distribution of the data. Second, it not only handles noise well, but also eliminates irrelevant factors. In addition, the rough set approach appropriate for detecting stock market timing because this approach does not generate the signal for trade when the pattern of market is uncertain. The experimental results are encouraging and prove the usefulness of the rough set approach for stock market analysis with respect to profitability.

• Management & Information Systems Review
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• v.31 no.3
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• pp.21-46
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• 2012

This paper attempts to offer an effective strategy of hedge fund based on trade probability control in the futures market. By using various technical indicators, we create an association rule and transforms it into a trading rule to be used as an investment strategy. Association rules are made by the combination of various technical indicators and the range of individual indicator value. Adjustments of trade probabilities are performed by depending on the rule combinations and it can be utilized to establish an effective investment strategy onto the risk management. In order to demonstrate the superiority of the investment strategy proposed, we analyzed a profitability using the futures index based on KOSPI200. Experiments results show that our proposed strategy could effectively manage and response the dynamics investment risks.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.881-893
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• 2012

This paper proposes to develop system trading strategy using rough set, decision tree in futures market. While there is a great deal of literature about the analysis of data mining, there is relatively little work on developing trading strategies in futures markets. There are three objectives in this paper. The first objective is to analysis performance of decision tree in rule-based system trading. The second objective is to find proper profitable trading interval. The last objective is to find optimized training period of trading rule training. The results of this study show that proposed model is useful trading strategy in foreign exchange market and can be desirable solution which gives lots of investors an important investment information.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.281-292
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• 2014

As the importance of algorithm trading is getting stronger, researches for artificial intelligence (AI) based trading strategy is also being more important. However, there are not enough studies about using more than two AI methodologies in one trading system. The main aim of this study is development of algorithm trading strategy based on the rough set theory that is one of rule-based AI methodologies. Especially, this study used genetic algorithm for optimizing profit of rough set based strategy rule. The most important contribution of this study is proposing efficient convergence of two different AI methodology in algorithm trading system. Target of purposed trading system is KOPSI200 futures market. In empirical study, we prove that purposed trading system earns significant profit from 2009 to 2012. Moreover, our system is evaluated higher shape ratio than buy-and-hold strategy.

• Journal of Information Technology Applications and Management
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• v.9 no.3
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• pp.13-30
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• 2002

ebXML (Electronic Business using extensible Markup language), sponsored by UN/CEFACT and OASIS, is a modular suite of specifications that enables enterprises of any size and in any geographical location to conduct business over the Internet. Using ebXML, companies now have a standard method to exchange business messages, conduct trading relationships, communicate data In common terms and define and resistor business processes. It is the mission of ebXML that provides an open XML-based infrastructure enabling the global use of electronic business information in an interoperable, secure and consistent manner by all parties for creating a single e1ectronic electronic market. This paper briefly overviews the concept of core component, context, assembly rule and context rule. Then, It designs by standard specifications of ebXML core component commonly used in an industry and among industries, and assembles business messages by using XML schema. Therefore, it suggests the mechanism which effectively exchanges business messages among the trading partners. This paper designs core component by using only three business messages of retail industry : orders, dispatch report, sales report.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.257-264
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• 2003

In the context of a dynamic trading environment, the ultimate goal of the financial forecasting system is to optimize a specific trading objective. This paper proposes a two-phase (extraction and filtering) stock trading system that aims at maximizing the rates of returns. Extraction of stocks is performed by searching specific time-series patterns described by a combination of values of technical indicators. In the filtering phase, several rules are applied to the extracted sets of stocks to select stocks to be actually traded. The filtering rules are automatically induced from past data. From a large database of daily stock prices, the values of technical indicators are calculated. They are used to make the extraction patterns, and the distributions of the discretization intervals of the values are calculated for both positive and negative data sets. We assumed that the values in the intervals of distinctive distribution may contribute to the prediction of future trend of stocks, so the rules for filtering stocks are automatically induced from the data in those intervals. We show the rates of returns when using our trading system outperform the market average. These results mean rule induction method using distributional differences is useful.

• International Commerce and Information Review
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• v.10 no.2
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• pp.305-323
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• 2008

Climate change is one of the broadest and the most complex issues of international environmental cooperation. Concern about climate change has been steadily increasing and has become a worldwide issue. According to IPCC(Intergovernmental Panel for Climate Change)'s recently report, global warming has accelerated vest serious problems. GHG(Green House Gas) emissions trading schemes, including the Kyoto mechanism that spread to solving the problems. Based on the evaluation on GHG emissions trading schemes, we also find some policy implications on the future development of emissions trading the conventional air pollutants in Korea which start to 2007. The regulatory authority needs to make clear how to allocate allowances to new entrants and also to keep the balance between the opportunity costs of reduction between potential shutdown facilities and new entrants. Under the current rule that does not allow shutdown credits, an equivalent level of allowances needs to be allocated to new entrants free of charge. We believe our policy recommendations may be useful not only for Korea but also for a the other countries, since they are facing a similar policy environment as Korea, particularly in the case of climate change.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.377-396
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• 2023

As the cryptocurrency market continues to grow, it has developed into a new financial market. The need for investment strategy research on the cryptocurrency market is also emerging. This study aims to conduct an empirical analysis on an investment methodology of cryptocurrency that combines short-term trading strategy and deep learning. Daily price data of the Ethereum was collected through the API of Upbit, the Korean cryptocurrency exchange. The investment performance of the experimental model was analyzed by finding the optimal parameters based on past data. The experimental model is a volatility breakout strategy(VBS), a Long Short Term Memory(LSTM) model, moving average cross strategy and a combined model. VBS is a short-term trading strategy that buys when volatility rises significantly on a daily basis and sells at the closing price of the day. LSTM is suitable for time series data among deep learning models, and the predicted closing price obtained through the prediction model was applied to the simple trading rule. The moving average cross strategy determines whether to buy or sell when the moving average crosses. The combined model is a trading rule made by using derived variables of the VBS and LSTM model using AND/OR for the buy conditions. The result shows that combined model is better investment performance than the single model. This study has academic significance in that it goes beyond simple deep learning-based cryptocurrency price prediction and improves investment performance by combining deep learning and short-term trading strategies, and has practical significance in that it shows the applicability in actual investment.