• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.1
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• pp.19-32
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• 2013

As cyber stock trading grows rapidly, stock trading using Home Trading System have been brisk recently. Home Trading System is a heavy-weight in the stock market, and the system has shown 75% and 40% market shares for KOSPI and KOSDAQ, respectively. However, since Home Trading System focuses on the convenience and the availability, it has some security problems. In this paper, we found that the authentication information in memory remains during the stock trading and we proposed its countermeasure through two-channel authentication using a mobile device such as a cell phone.

• Korean Management Science Review
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• v.31 no.1
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• pp.49-61
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• 2014

We investigate the hedging effectiveness of incorporating single-stock futures into the corresponding stocks. Investing in only stocks frequently causes too much risk when market volatility suddenly rises. We found that single-stock futures help reduce the variance and risk levels of the corresponding stocks invested. We use daily prices of Korean stocks and their corresponding futures for the time period from December 2009 to August 2013 to test the hedging effect. We also use system trading technique that uses automatic trading program which also has several simulation functions. Moving average strategy, Stochastic's strategy, Larry William's %R strategy have been considered for hedging strategy of the futures. Hedging effectiveness of each strategy was analyzed by percent reduction in the variance between the hedged and the unhedged variance. The results clearly showed that examined hedging strategies reduce price volatility risk compared to unhedged portfolio.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.785-794
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• 2002

Automatic stock trading systems should be able to solve various kinds of optimization problems such as market trend prediction, stock selection, and trading strategies, in a unified framework. But most of the previous trading systems based on supervised learning have a limit in the ultimate performance, because they are not mainly concerned in the integration of those subproblems. This paper proposes a stock trading system, called R-Trader, based on reinforcement teaming, regarding the process of stock price changes as Markov decision process (MDP). Reinforcement learning is suitable for Joint optimization of predictions and trading strategies. R-Trader adopts two popular reinforcement learning algorithms, temporal-difference (TD) and Q, for selecting stocks and optimizing other trading parameters respectively. Technical analysis is also adopted to devise the input features of the system and value functions are approximated by feedforward neural networks. Experimental results on the Korea stock market show that the proposed system outperforms the market average and also a simple trading system trained by supervised learning both in profit and risk management.

• Journal of Information Technology Applications and Management
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• v.17 no.3
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• pp.151-162
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• 2010

Recent developments in financial market liberalization and information technology are accelerating the interdependence of national stock markets. This study explores the information spillover effect of the US stock market on the overnight and daytime returns of the Korean stock market. We develop a profitable intra-day trading strategy based on the information spillover effect. Our study provides several important conclusions. First, an information spillover effect still exists from the overnight US stock market to the current Korean stock market. Second, Korean investors overreact to both good and bad news overnight from the US. Therefore, there are significant price reversals in the KOSPI 200 index futures prices from market open to market close. Third, the overreaction effect is different between weekdays and weekends. Finally, the suggested intra-day trading system based on the documented overreaction hypothesis is profitable.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.203-210
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• 2023

In this paper, we develope a stock trading system that automatically buy and sell stocks in Kiwoom Securities' HTS system. The system is made by using Kiwoom Open API+ with the Python programming language. A trading strategy is based on an enhanced system query method called a Condition-Search. The Condition-Search script is edited in Kiwoom Hero 4 HTS and the script is stored in the Kiwoom server. The Condition-Search script has the advantage of being easy to change the trading strategy because it can be modified and changed as needed. In the HTS system, up to ten Condition-Search scripts are supported, so it is possible to apply various trading methods. But there are some restrictions on transactions and Condition-Search in Kiwoom Open API+. To avoid one problem that has transaction number and frequency are restricted, a method of adjusting the time interval between transactions is applied and the other problem that do not support a threading technique is solved by an IPC(Inter-Process Communication) with multiple login IDs.

• Korean Management Science Review
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• v.32 no.4
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• pp.57-67
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• 2015

It is a familiar Wall Street adage that "It takes volume to make prices move." Numerous researches have found the positive correlation between trading volume and price changes. Recent studies have documented that informed traders have strong influences on stock market prices through their trading with distinctive information power. Ever since 1992 capital market liberalization in Korea, it is said that foreign investors make consistent profits with their superior information and analytical skills. This study aims at whether we can make a profitable trading strategy by using the foreign investors' trading information. We analyse the relation between the KOSPI index returns and the foreign investors trading volume using GARCH models and VAR models. This study suggests the profitable trading strategies based on the documented relation between the foreign investors' trading volume and KOSPI index returns. We simulate the trading system with the real stock market data. The data include the daily KOSPI index returns and foreign investors' trading volume for 2001~2013. We estimate the GARCH and VAR models using 2001~2011 data and simulate the suggested trading system with the remaining out-of-sample data. Empirical results are as follows. First, we found the significant positive relation between the KOSPI index returns and contemporaneous foreign investors' trading volume. Second, we also found the positive relation between the KOSPI index returns and lagged foreign investors' trading volume. But the relation showed no statistical significance. Third, our suggested trading system showed better trading performance than B&H strategy, especially trading system 2. Our results provide good information for uninformed traders in the Korean stock market.

• 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 KIPS Transactions:PartB
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• v.14B no.2
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• pp.127-134
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• 2007

There are many researches about the intelligent stock trading systems with the help of the advance of the artificial intelligence such as machine learning techniques, Though the establishment of the reasonable trading policy plays an important role in the performance of the trading systems most researches focused on the improvement of the predictability. Also some previous works, which treated the trading policy, treated the simplified versions dependent on the predictors in less systematic ways. In this paper, we propose the integrated multiple simulation' as a method of optimizing trading performance of stock trading systems. The propose method is adopted in the NXShell a development environment for neural network based stock trading systems. Under the proposed integrated multiple simulation', we simulate the multiple tradings for all combinations of the neural network's outputs and the trading policy parameters, evaluate the learning performance according to the various metrics and establish the optimal policy for a given prediction module based on the resulting performance. In the experiment, we present the trading policy comparison results using the stock value data from the KOSPI and KOSDAQ.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2658-2663
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• 2014

To minimize low latency and improve the processing speed of the stock trading system, various technologies have been introduced. However, expensive network equipment has limitation for improving speed of trading system. Also, it is true that there is not much advantage by introducing those kind of systems. In this paper, we propose a low-Latency SPT(Safe Proper Time) scheme for overcoming the stock trading system in a cloud. The proposed method minimizes the CPI in order to reduce the CPU overhead that is based on the understanding of the kernel. and this approach satisfies the data timeliness.

• 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.