• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.1-13
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• 2014

This study aims to show the risk reduction effects of round-the-clock trading environment. We analyse the trading results of the currency futures contracts in CME Globex which are open 23 hours a day. These include Euro FX, Japanese Yen, Australian Dollar, and British Pound from January 2005 to August 2013. We generate new price series using only daytime prices during about 7-hour period. This hypothetical "G" data series may have greater gap risk than the original "R" data series. Empirical results show the trading risk reduction effects, that is R data series have higher profits and lower risks than G data series.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.94-103
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• 2021

This study aims to propose technical trading rules for Bitcoin futures and empirically analyze investment performance. Investment strategies include standard trading rules such as VMA, TRB, FR, MACD, RSI, BB, using Bitcoin futures daily data from December 18, 2017 to March 31, 2021. The trend-following rules showed higher investment performance than the comparative strategy B&H. Compared to KOSPI200 index futures, Bitcoin futures investment performance was higher. In particular, the investment performance has increased significantly in Sortino Ratio, which reflects downside risk. This study can find academic significance in that it is the first attempt to systematically analyze the investment performance of standard technical trading rules of Bitcoin futures. In future research, it is necessary to improve investment performance through the use of deep learning models or machine learning models to predict the price of Bitcoin futures.

• Journal of Navigation and Port Research
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• v.40 no.5
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• pp.337-343
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• 2016

The core decisions of bulk shipping businesses can be summarized as the timing and the choice of period for which carrying capacity is traded. In particular, frequent decisions to trade freight either with repeated spot transactions or with a one-off long-term deal critically impact business performance. Even though a variety of freight trading strategies can be employed to facilitate the decisions, chartering practitioners have not been active in utilizing these strategies, and academic research has rarely proposed applicable solutions. The specific properties of freight as a tradable commodity are not properly reflected in existing studies, and limitations have been reported in their application to the real world. This research focused on the establishment of applicable freight trading strategies by taking into account two properties of freight: time perishability and term-dependant pricing. In addition to traditional trading strategies, artificial neural networks were applied for the first time to the test of freight trading strategies. The performances of the trading strategies were measured and compared to produce a remarkable outperformance of the ANN. This research is expected to make a significant contribution to chartering practices by enhancing the quality of chartering decisions and eventually enabling the effective management of freight rate risk. In addition to methodological expansion, the result will propose a way to approach the controversial issue of freight market efficiency.

• 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.

• Journal of Distribution Science
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• v.21 no.3
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• pp.93-100
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• 2023

Purpose: The purpose of this paper is to confirm whether stocks belonging to the distribution industry in Korea have reversals, following large daily stock price changes accompanied by large trading volumes. Research design, data, and methodology: We examined whether there were reversals after the event date when large-scale stock price changes appeared for the entire sample of distribution-related companies listed on the Korea Composite Stock Price Index from January 2004 to July 2022. In addition, we reviewed whether the reversals differed depending on abnormal trading volume on the event date. Using multiple regression analysis, we tested whether high trading volume had a significant effect on the cumulative rate of return after the event date. Results: Reversals were confirmed after the stock price shock in the Korean distribution industry and the return after the event date varied depending on the size of the trading volume on the event day. In addition, even after considering both company-specific and event-specific factors, the trading volume on the event day was found to have significant explanatory power on the cumulative rate of return after the event date. Conclusions: Reversals identified in this paper can be used as a useful tool for establishing a trading strategy.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.510-513
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• 2008

As number of cyber traders are growing, the uses of technical analyzing indicators in trading increase as well. Parabolic SAR, which indicates changes of trend in the market, is one of the most used indicators by cyber traders. Especially when a market shows a specific trend, it is very useful. However, this indicator often gives late signals and shows less trustful ones in a stable market. This paper proposes a method that give more conservative signals by a composition of Parabolic SAR and Moving Average. The experiment will compare the earning rates of using only Parabolic SAR strategy and of using a composition strategy of Parabolic SAR and Moving Average.

• Fashion & Textile Research Journal
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• v.14 no.6
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• pp.928-940
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• 2012

The present study aimed to propose the competitive strategy to fashion brand franchise stores by analyzing articles regarding success stores as published in fashion magazines. A total of 91 articles were selected from three fashion magazines and content analysis was applied to extract major factors. Four types of trading areas and eight product types were compared by the major factors. As results, six major factors composing competitive strategy were analyzed such as personal selling, management of sales forces, promotion, customer relationship management, management of store space, and relationship with headquarter. Comparing competitive factors by the types of trading area, management of sales forces and personal selling were crucial for central district and for outlets/interchange district. On the other hand, personal selling and customer relationship management were important for local district while management of store space and personal selling were critical for tenants of the large discount store/shopping mall area. Comparing by product types, personal selling was the most important factor for all product types except young casualwear whereas the second important one was management of sales forces for adult casualwear, womenbbbs wear, and others. For menbbbs wear, sales promotion was the second important one whereas management of store space was the second crucial one for underwear and childrenbbbs clothing. Based on the present study result, it is proposed that competitive strategy of individual fashion brand franchise store should be differently developed because the characteristics of trading area and product type are different and in turn, customers benefit and competition might be different.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.155-161
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• 2016

In current algorithm trading system, general users need to program their algorithms using programing language and APIs provided from financial companies. Therefore, such environment keeps general personal investors away from using algorithm trading. Therefore, this paper focuses on developing user-friendly algorithm trading system which enables general investors to make their own trading algorithms without knowledge on program language and APIs. In the system, investors input their investment criteria through user interface and this automatically creates their own trading algorithms. The proposed system is composed with two parts: server intercommunicating with financial company server to send and to receive financial informations for trading, and client including user convenience-based user interface representing secondary indexes and strategies, and a part generating algorithm. The proposed system performance is proven through simulated-investment in which user sets up his investment strategy, algorithm is generated, and trading is performed based on the algorithm

• Journal of Information Technology Applications and Management
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• v.21 no.4
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• pp.97-125
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• 2014

This study investigates the relationship between stock index and its associated nearby futures markets based on the cost-of-carry model. The purpose of this study is to explore the existence of mispriced futures contracts, and to test whether traders can earn trading profits in real financial market using the information about the mispriced futures contracts. This study suggests the concordance correlation coefficient to investigate the existence of mispriced futures contracts. The concordance correlation coefficient gives a desirable result for trading profits that results from a comparative analysis among profits from trading at the time to indicate trading opportunities determined by the degree of the difference between the observed market price and the theoretical price of a futures contract. In addition, this study also explains that the concordance correlation coefficient developed from the mean square error (MSE) has a statistically theoretical meaning. In conclusion, this study shows that the concordance correlation coefficient is appropriate for analyzing the relationship between the observed stock index futures market price and the theoretical stock index futures price derived from the cost-of-carry model.