• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.661-670
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• 2012

In order to construct a profitable stock trading model, three considerations must be resolved in the model in integrated manner: profit principle, trader's conditions and stock market trends. Generally, a model will be developed through long experiences of stock trading that requires quite amount of expenses and time. This paper analyzes the issues involved in those considerations and proposes a road map for a trading model.

• 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.

• The Journal of Fisheries Business Administration
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• v.32 no.1
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• pp.1-14
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• 2001

The research on the price-volume relation in the market is very important because it examines into regular phenomenon revealed by market participants including producers and middlemen. The purpose of this study is to investigate the relationship between price and trading volume in the oyster producing market. In order to accomplish the purpose of this study, the contents of empirical analysis include the time series properties of price and trading volume, the short-term and long-term relationships between price and trading volume, and the determinants of trading volume. The data used in this study correspond to daily price and trading volume covering the time period from January 1998 to April 2001. The empirical results can be summarized as follows : First, price and trading volume follow random walks and they are integrated of order 1. The first difference is necessary for satisfying the stationary conditions. Second, price and trading volume are cointegrated. This long-run relationship is stronger from trading volume to price. Third, error correction model suggests that feedback effect exists in the long-run and that price tends to lead trading volume by about five days in the short run, that is, to be required period by digging, conveying, and peeling oystershell for selling oyster. Fourth, price and price volatility is a determinant of trading volume. In particular, trading volume is a negative function of price. It is believed that the conclusion drawn from this study would provide a useful standard for the policy makers in charge of reducing the oyster price volatility risk caused by trading volume(selling quantities).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1496-1503
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• 2009

The United Nations Framework Convention on Climate Change (UNFCCC) is an international environmental treaty to stabilize greenhouse gas concentrations in the atmosphere. In order to fulfil the commitments of the countries in an economically efficient way, the UNFCCC adapted the emission trading scheme in the Kyoto Protocol. If the UNFCCC's scheme is enforced in the country, considerable changes in electric power industry are expected due to the imposed greenhouse gas emission reduction. This paper proposes a game theoretic model of the case when generation companies participate in both competitive electricity market and emission market simultaneously. The model is designed such that generation companies select strategically between power quantity and greenhouse gas reduction to maximize their profits in both markets. Demand function and Environmental Welfare of emission trading market is proposed in this model. From the simulation results using the proposed model the impact of the emission trading on generation companies seems very severe in case that the emission prices are significantly high.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.957-965
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• 2012

Korean government has been preparing the introduction of Emission Trading as part of the framework convention on Climate Change as a relief of negative downstream effect over electricity industry. This paper develops a mathematical model amenable to analyzing the economic impact of introduced emission trading system on the national generation expansion planning. The developed model was also employed with a case study to verify its applicability.

• East Asian Economic Review
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• v.16 no.2
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• pp.205-225
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• 2012

I use panel data of sales by the foreign subsidiaries of the U.S. MNCs to examine whether trading blocs create more or less FDI and the impacts on FDI of the extended market size created by forming blocs. By employing a region-fixed effects model, I find that countries forming trading blocs attract more FDI, particularly from non-member countries, but that FDI does not always increase with the market size of the blocs. As the market size increases, FDI increases only for large blocs. However, these findings are sensitive to model specifications. A policy implication is that a country considering forming or joining a trading bloc with a view to attract FDI may want to form a trading bloc with a country or countries with a large market size.

• Journal of applied mathematics & informatics
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• v.2 no.2
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• pp.47-52
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• 1995

in this paper we reduce the trading strategy of discounted security model to trading strategy of original model

• Korea Trade Review
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• v.47 no.1
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• pp.99-114
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• 2022

This paper analyzed the dynamic conditional correlation between the carbon emission trading prices of Korea, China, EU, New Zealand. This paper was analyzed using the daily data of carbon emission trading prices of each country from January 12, 2015 to January 13, 2021 using the DCC-GARCH model. Summarizing the research results, first, the dynamic conditional correlation between carbon emission trading prices in the EU, Korea, and China, excluding New Zealand, was strong, indicating that there was a co-movement phenomenon. Second, it was found that carbon emission trading prices in major countries have a stronger tendency to co-movement due to global shocks. Third, it appears that the dynamic conditional correlation between the carbon emission trading prices of Korea and China is gradually strengthening. This study confirmed that the co-movement between carbon emission trading prices in Korea and other countries gradually intensified as time passed. In particular, it is meaningful in suggesting the implication that the phenomenon of co-movement between carbon emission trading prices in Korea and China is gradually intensifying.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.10
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• pp.616-623
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• 2003

Competition among electric generation companies is a major goal of restructuring in the electricity industry, The trading system in an electricity market has been one of the most important issues in deregulated electricity market. This paper deals with comparisons of the major two types of the trading system: compulsory pool market and bilateral contract market. The two trading systems are compared quantitatively from the viewpoint of consumer's surplus and social welfare, This paper, also, proposes a unified model of Cournot and Bertrand for analyzing the mixed trading system of pool market and bilateral contract market. Nash equilibrium of the unified model is derived by criteria for participating in bilateral contract market. Numerical results from a sample case show that a mixed trading system of pool market and price-competitive bilateral market is beneficial to consumer from the view points of consumer's surplus.