• 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'.

• Proceedings of the IEEK Conference
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• summer
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• pp.387-390
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• 2004

In this paper, we design and implement practical application service using VoiceXML. And we suggest new solutions of problems can be occurred when implementing a new systems using VoiceXML, based on the fact. Up to now, speech related services were developed using API (Application Program Interface) and programming languages, which methods depend on system architectures. It thus appears that reuse of contents and resource was very difficult. To solve these problems, nowadays, companies develop their applications using VoiceXML. Advantages of using VoiceXML when developing services are as follows. First, we can use web developing technologies and technologies for transmitting web contents. And, we can save labors for low level programming like C language or Assembler language. And we can save labors for managing resources, too. As the result of these advantages, we can reduce developing hours of applications services and we can solve problem of compatibility between systems. But, there's poor grip of actual problems can be occurred when implementing their own services using VoiceXML. To overcome these problems, we implemented interactive stock trading system using VoiceXML and concentrated our effort to find out problems when using VoiceXML. And then, we proposed solutions to these problems and analyzed strong points and weak points of suggested system.

• The Journal of Information Systems
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• v.26 no.3
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• pp.63-90
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• 2017

Purpose This study proposes a novel system trading model using case-based reasoning (CBR) based on absolute similarity threshold. The proposed model is designed to optimize the absolute similarity threshold, feature selection, and instance selection of CBR by using genetic algorithm (GA). With these mechanisms, it enables us to yield higher returns from stock market trading. Design/Methodology/Approach The proposed CBR model uses the absolute similarity threshold varying from 0 to 1, which serves as a criterion for selecting appropriate neighbors in the nearest neighbor (NN) algorithm. Since it determines the nearest neighbors on an absolute basis, it fails to select the appropriate neighbors from time to time. In system trading, it is interpreted as the signal of 'hold'. That is, the system trading model proposed in this study makes trading decisions such as 'buy' or 'sell' only if the model produces a clear signal for stock market prediction. Also, in order to improve the prediction accuracy and the rate of return, the proposed model adopts optimal feature selection and instance selection, which are known to be very effective in enhancing the performance of CBR. To validate the usefulness of the proposed model, we applied it to the index trading of KOSPI200 from 2009 to 2016. Findings Experimental results showed that the proposed model with optimal feature or instance selection could yield higher returns compared to the benchmark as well as the various comparison models (including logistic regression, multiple discriminant analysis, artificial neural network, support vector machine, and traditional CBR). In particular, the proposed model with optimal instance selection showed the best rate of return among all the models. This implies that the application of CBR with the absolute similarity threshold as well as the optimal instance selection may be effective in system trading from the perspective of returns.

• Knowledge Management Research
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• v.12 no.4
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• pp.41-54
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• 2011

Given the prevalence of home trading systems, it has become important to examine user behaviors in a stock investment environment. In this vein, this paper developed an integrated model to deeply understand the key determinants of user's continuance intention to use investment information through constructs prescribed by incorporating trust and perceived risk into expectation-confirmation model. The proposed research model was tested by using survey data collected from 160 users who have experience with stock investment. PLS (partial least squares) was employed for the analysis of the data. The findings of this study showed that the proposed framework provides a statistically significant explanation of the variation in continuance intention to search investment information. The findings revealed that trust and perceived risk are more prevalent predictors of continuance intention to use investment information compared to perceived usefulness. It was also found that user satisfaction serves as the salient antecedents of continuance intention to use investment information. The theoretical and practical implications of the findings were described.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.207-212
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• 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 Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.123-129
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• 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.

• IE interfaces
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• v.16 no.spc
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• pp.123-126
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• 2003

In this paper, we use three cluster algorithms (K-means, Self-Organizing Map, and Fuzzy K-means) to find proper graded stock market brokerage commission rates based on the cumulative transactions on both stock exchange market and HTS (Home Trading System). Stock trading investors for both modes are classified in terms of the total transaction as well as the corresponding mode of investment, respectively. Empirical analysis results indicated that fuzzy K-means cluster analysis is the best fit for the segmentation of customers of both transaction modes in terms of robustness. We then propose the rules for three grouping of customers based on decision tree and apply different brokerage commission to be 0.4%, 0.45%, and 0.5% for exchange market while 0.06%, 0.1%, 0.18% for HTS.

• Management & Information Systems Review
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• v.26
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• pp.293-309
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• 2008

This study examined the market efficiency of korea stock market by comparing variance ratios(VR) of stock groups which is sorted by market capitalization. We compute variance ratios of KOSPI large capitalization, midium capitalization, and small capitalization for 546 trading days from 2006/01/02 to 2008/04/15. For our study, we also use high frequency data that is; intra-day 1 minute data. The characteristics of variance ratios of stock groups by market capitalization as follows: From 1 to 5 minute interval, variance ratios of three stock group increase far from zero(0). The longer time interval, the more variance ratios decrease, but only large capitalization converge on around zero. This means that the market of large capitalization is more efficient compare to other stock groups. The entire sample period can be divided two sub-period because the impact of sub prime crisis arised from U.S.A. influences Korea stock market. Before sub prime crisis, the VRs of mid cap and small cap do not converge on around zero except large cap although the time interval is longer. After sub prime crisis, the VRs of three stock groups decrease when time interval is longer, but only large cap converge on around zero. We conclude that large cap is more efficient than other stock groups in Korea Stock Market.

• Management & Information Systems Review
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• v.33 no.1
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• pp.45-63
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• 2014

A trading system is a computer trading program that automatically submits trades to an exchange. Mechanical a trading system to execute trade is spreading in the stock market. However, a trading system to trade a single asset might occur instability of the profit because payoff of this system is determined a asset movement. Therefore, it is necessary to develop a trading system that is trade two assets such as a pair trading that is to sell overvalued assets and buy the undervalued ones. The aim of this study is to propose a relative value based trading system designed to yield stable and profitable profits regardless of market conditions. In fact, we propose a procedure for building a trading system that is based on the rough set analysis of indicators derived from a price ratio between two assets. KOSPI 200 index futures and S&P 500 index futures are used as a data for evaluation of the proposed trading system. We intend to examine the usefulness of this model through an empirical study.

• Asia-Pacific Journal of Business
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• v.11 no.2
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• pp.15-25
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• 2020

Purpose - The purpose of this study is to examine whether the change of settlement procedures have an impact on the distribution of day of the week effect in the UK and French markets or not. U.K and France changed their systems from fixed settlement date systems to fixed settlement lag systems Design/methodology/approach - This study adopted the data of the specific stock market indices such as FTSE 100 in the U.K market and FRCAC 40 in the French market, This study constructs a test of the differences in mean returns across the days of the week by computing the regression equations for each country index. Findings - First, this study found that the evolving settlement procedures in stock exchanges have an effect on stock return of day of the week. Second, long-run improvements in market efficiency may have diminished the effects of certain anomalies in recent periods. Improvements in market efficiency and evolving settlement systems may cause the disappearance of the weekend effect. Research implications or Originality - The Implication of this study is that recent settlement systems contributed to the disappearance of the weekend effect and explains improvements in market efficiency and diminishments of market anomaly. This study may be the first study which examines whether evolving settlement systems have an effect on the disappearance of the weekend effect in the market or not.