• 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.

• The Journal of Information Systems
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• v.14 no.2
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• pp.155-172
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• 2005

Securities companies which faced with severe competition should not only attract new customers but also retain their on-line customers. This study examines the factors affecting loyalty of on-line stock trading customers. The research model based on the previous studies was established and the research hypotheses were generated. The test results based on the data gathered from 87 users of on-line stock trading services show that user satisfaction, learning cost, transaction fees, and reputation influence customer loyalty. User satisfaction, learning cost and reputation are positively related to customer loyalty, whereas transaction fee is negatively related to customer loyalty. The results also support that information quality and system quality are positively related to user satisfaction. The hypothesis that transaction fee is related to user satisfaction is not supported. There is no significant information to say that security risk is related to user satisfaction. It is considered that the study results may help managers to increase customer retention.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.225-231
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• 2010

This paper proposes the strategy of ladder trades in a symbol pool and simulates a performance of it on the Multicharts' system trading tool. The ladder trading strategy is based on a multi-entry strategy which is efficient for several symbols. A symbol pool is composed of verified stocks comprising KOSPI200 and 17 symbols are selected for an examination which equity is over 10 billion. From July to December in 2009, optimum parameters are searched and the results are delta is 4% and ladder is 5. When those parameters are adopted, the profit is 1,000,000 won as a single trade and the number of trade is 188. The next study is to modify a ladder shape for increasing probability and to find the difference of optimum parameters according to trading months.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.05a
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• pp.43-48
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• 2001

Price of Used Vessel is Key-point of Ship trading but it is very troublesome part. Because it is different from the Seller and the Buyer's caculated price, It is appropriated by The Sellers and The Buyers' mutual agreement. In case of the Buyers. it is difficult in calculating price of used ship without entrusting the ship broker, beca- use evaluation method of secondhanded vessel has not formulated and the ship broker has experientially computed Remain Value of used vessel. For this work, structure of ship trading market, trading flow, the factor of price composition and Affecting factor of price is reviewed, and it examined relationship with price. On these base, a computer software integrated database system and object-oriented technique is developed. The developed system is expected helpful to evaluation of remain value of the used vessel.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.210-218
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• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.363-369
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• 2024

In this paper, we develope a LOB(Limit Order Book) analyzing tool for an automated trading system, which features real-time and offline analysis of LOB data in conjunction with execution data. The 10-tier LOB data analyzer developed in this paper, which contains ask/bid prices and the execution data, receivs transaction requests in real-time from the Kiwoom Open API+ server. In the OnReceiveTrData event, the transaction data from the server is received and processed. The real-time data, triggered by the transaction, is received and processed in the OnReceiveRealData event. These two types of data are stored in a database and replayed in the same way as if it were a real-time situation in simulation mode. The LOB data are selectively read and analyzed in a necessary time points. The tool provides various features such as bar chart analysis and pattern analysis of the total shares on the bid side and ask side, which are used to develop a tool to accurately determine the timing of stock trading.

• Journal of Intelligence and Information Systems
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• v.16 no.1
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• pp.71-92
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• 2010

As the use of trading systems increases recently, many researchers are interested in developing intelligent trading systems using artificial intelligence techniques. However, most prior studies on trading systems have common limitations. First, they just adopted several technical indicators based on stock indices as independent variables although there are a variety of variables that can be used as independent variables for predicting the market. In addition, most of them focus on developing a model that predicts the direction of the stock market indices rather than one that can generate trading signals for maximizing returns. Thus, in this study, we propose a novel intelligent trading system that mitigates these limitations. It is designed to use both the technical indicators and the other non-price variables on the market. Also, it adopts 'two-threshold mechanism' so that it can transform the outcome of the stock market prediction model based on support vector machines to the trading decision signals like buy, sell or hold. To validate the usefulness of the proposed system, we applied it to the real world data-the KOSPI200 index from May 2004 to December 2009. As a result, we found that the proposed system outperformed other comparative models from the perspective of 'rate of return'.

• The Journal of Fisheries Business Administration
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• v.44 no.1
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• pp.25-37
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• 2013

The permit system is the most typical regulation system in Korean fishery. Even now, it has caused resource management failure, it has also invited economic inefficiency and inequity. Accordingly, the Korean government has recently introduced the fishing boats market, it would be improve entry barrier for new capital and labor in fishing industry. This study aims to survey and to analyze the status of coastal fishing boat market using the executive documents on permit system in Gyongnam province. And also, this study put focus on improve fishing permit trade take advantage of the fishing boat trading. The survey results indicated that the number of fishing boats trading is estimated about 5,000 per year, however, fishing boat market is very narrow. An analysis on fishing permit trading showed that policy support is essential to reduce transaction cost. Therefore, it is necessary to explore corresponding policy tasks, this will be reduce transaction costs and sunk cost.

• The Journal of Information Systems
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• v.20 no.1
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• pp.107-126
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• 2011

Model-based decision support system (DSS) has acted as a crucial role in strengthening the business competitiveness by providing a way of modeling and solving real-world decision problems in a quantitative and scientific manner. It is even more important for trading OTC derivatives, which requires extensive financial-engineering expertise while actively reacting to the continuously changing financial market. This paper proposes a flexible model-based DSS architecture that can support user-friendly interface for executing and analyzing the models and can adapt to the changes of financial market seamlessly. For user-friendliness, we implement the user-interfaces (UIs) using Microsoft Excel, a very widely used spreadsheet program for its great generality and extensibility. Users can utilize the analysis results of DSS or reprocess them for their special needs through the UIs in the form of familiar spreadsheets easily. For adaptiveness to the markets, the proposed architecture is constructed based on the object-oriented concepts, which enables such changes as release of a new financial product can be updated into the system without any delay at the lowest cost. We investigate the practical benefits and limitations of the proposed architecture by a case study on the construction of Model-based Trading Support System (MTSS), performed by a commercial bank in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.34-49
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• 2015

Market means of spectrum trading have been utilized as a vital method of spectrum sharing and access in future cognitive radio system. In this paper, we consider the spectrum trading with multiple primary carrier providers (PCP) leasing the spectrum to multiple secondary carrier providers (SCP) for a short period of time. Several factors including the price of the resource, duration of leasing, and the spectrum quality guides the proposed model. We formulate three trading policies based on the game theory for dynamic spectrum access in a LTE based cognitive radio system (CRS). In the first, we consider utility function based resource sharing (UFRS) without any knowledge of past transaction. In the second policy, each SCP deals with PCP using a non-cooperative resource sharing (NCRS) method which employs optimal strategy based on reinforcement learning. In variation of second policy, third policy adopts a Nash bargaining while incorporating a recommendation entity in resource sharing (RERS). The simulation results suggest overall increase in throughput while maintaining higher spectrum efficiency and fairness.