• Journal of Distribution Science
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• v.15 no.11
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• pp.73-82
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• 2017

Purpose - This paper aims to investigate a profit maximizing incentive of foreign traders in distributing the KOSPI 200 Futures. Such an incentive may induce unsophisticated retail traders to suffer loss from speculative trading. Since Korean government increased the entry barriers of the market to protect unsophisticated traders, the market size has been decreasing while the proportion of the contract held by foreign traders has been increasing. These on going changes make the market imperfectly competitive, where a profit maximization incentives of foreign traders are expected to grow. In this paper, we attempt to find any evidence of such behavior, thereby providing implications regarding market policy and market efficiency. Research design, data, and methodology - According to Kyle(1985), an informed trader exploits his/her monopoly power optimally in a dynamic context so that he/she makes positive profit, where he/she could conceal his/her trading utilizing noise trading as camouflage. We apply the KOSPI 200 Futures market to the Kyle's model: foreign traders who take into account the effect of his/her trading to maximize expected profits as an informed trader, retail investors as noise traders, and financial institutions as market makers. To find any evidence of monopolistic behavior, we test the variants of trading volume and price data of the KOSPI 200 Futures over the period of 2009 and 2017. Results - First, we find that the price of the KOSPI 200 Futures are more volatile than the price of underlying asset. Second, we find that monopolistic foreign trader's trading order flows are consistent with exploiting his/her monopoly power to maximize profit. Finally, we find that retail investors' trading order flows are inversely consistent with maximizing profit, that is, uninformed retail investors suffer loss continuously in speculative trading against informed traders. Conclusions - Our results show that the quantity of strategic order flows may have a large effect on the price, therefore, resulting the market inefficiency. The results also imply that, in implementing regulations, the depth of the market must be considered to maintain market liquidity, and suggesting interesting research topics regarding the market structure.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.381-384
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• 2008

This paper presents an operational technique to maximize profit of a cogeneration power plant. To minimize errors in a loss and gain analysis of a cogeneration power plant, the energy sale profit in the cost-based-pool electric power trade market, the heat sale profit, and the supplementary fund profit for electric power industry are taken into consideration. The objective is to optimize the heat-electric power ratio to maximize profit of a cogeneration power plant. Furthermore, the constrained bidding technique to optimize heat-electric power ratiocan be obtained. Profits from of a cogeneration power plant are composed of three categories, such as the energy sale profit in the cost-based-pool electric power trade market, the heat sale profit, and the supplementary fund profit for electric power industry. Profits of a cogeneration power plant are varied enormously by the operation modes. The profits are mainly determined by the amount of constrained heat generation in each trading time. And the three profit categories arecoupled tightly via the heat-electric power ratio. The result of this case study can be used as a reference to a cogeneration power plant under the power trading system considered in this case.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.59-71
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• 2014

System trading is becoming more popular among Korean traders recently. System traders use automatic order systems based on the system generated buy and sell signals. These signals are generated from the predetermined entry and exit rules that were coded by system traders. Most researches on system trading have focused on designing profitable entry and exit rules using technical indicators. However, market conditions, strategy characteristics, and money management also have influences on the profitability of the system trading. Unexpected price deviations from the predetermined trading rules can incur large losses to system traders. Therefore, most professional traders use strategy portfolios rather than only one strategy. Building a good strategy portfolio is important because trading performance depends on strategy portfolios. Despite of the importance of designing strategy portfolio, rule of thumb methods have been used to select trading strategies. In this study, we propose a SVM-based strategy portfolio management system. SVM were introduced by Vapnik and is known to be effective for data mining area. It can build good portfolios within a very short period of time. Since SVM minimizes structural risks, it is best suitable for the futures trading market in which prices do not move exactly the same as the past. Our system trading strategies include moving-average cross system, MACD cross system, trend-following system, buy dips and sell rallies system, DMI system, Keltner channel system, Bollinger Bands system, and Fibonacci system. These strategies are well known and frequently being used by many professional traders. We program these strategies for generating automated system signals for entry and exit. We propose SVM-based strategies selection system and portfolio construction and order routing system. Strategies selection system is a portfolio training system. It generates training data and makes SVM model using optimal portfolio. We make $m{\times}n$ data matrix by dividing KOSPI 200 index futures data with a same period. Optimal strategy portfolio is derived from analyzing each strategy performance. SVM model is generated based on this data and optimal strategy portfolio. We use 80% of the data for training and the remaining 20% is used for testing the strategy. For training, we select two strategies which show the highest profit in the next day. Selection method 1 selects two strategies and method 2 selects maximum two strategies which show profit more than 0.1 point. We use one-against-all method which has fast processing time. We analyse the daily data of KOSPI 200 index futures contracts from January 1990 to November 2011. Price change rates for 50 days are used as SVM input data. The training period is from January 1990 to March 2007 and the test period is from March 2007 to November 2011. We suggest three benchmark strategies portfolio. BM1 holds two contracts of KOSPI 200 index futures for testing period. BM2 is constructed as two strategies which show the largest cumulative profit during 30 days before testing starts. BM3 has two strategies which show best profits during testing period. Trading cost include brokerage commission cost and slippage cost. The proposed strategy portfolio management system shows profit more than double of the benchmark portfolios. BM1 shows 103.44 point profit, BM2 shows 488.61 point profit, and BM3 shows 502.41 point profit after deducting trading cost. The best benchmark is the portfolio of the two best profit strategies during the test period. The proposed system 1 shows 706.22 point profit and proposed system 2 shows 768.95 point profit after deducting trading cost. The equity curves for the entire period show stable pattern. With higher profit, this suggests a good trading direction for system traders. We can make more stable and more profitable portfolios if we add money management module to the system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.151-157
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• 2010

This paper proposes the Pyramid strategy which is based on the straddle sell. The Pyamid strategy has multi-entry features with starting date and delta parameters. And It is hedged against a loss by mutual trades and dynamic ripples. This paper analyzes the profit and MDD(maximum draw down) of the Pyramid strategy on system trading. The portfolio tool is used for the experiment which is one of the Multicharts' package. The Multicharts is a good trading system of recent years. For the experiment, three call options and three put options are used at october in 2009. Two parameters are used which are the starting date from first October to twentieth October in 2009 and delta from eight percent to fifty percent. As a result, the profit of composite option is about 3 million won. If the strategy starts before the beginning of option month, investors feel uncomfortable because of a large MDD. If a delta belows 20%, it shows high profit and the ratio of profit and MDD builds up a low value. However a low delta makes frequent trades and results in a loss unless increasing entry levels which mean more amount of investment. This work provides a safer trade system than native option trades. It is important how much levels of multi-entry are acceptable. And an amount of investment with appropriate levels of multi-entry is a subject of a future study.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.3
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• pp.211-219
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• 2010

This paper designs the dynamic symbol automatic trading system in Korean option market. This system is based on Multichart program which is convenient and efficient system trading tool. But the Multichart has an important restriction which has only one constant symbol per chart. This restriction causes very useful strategies impossible. The proposed design uses global variables, signal chart selection and position order exchange. So an automatic trading system with dynamic symbol works on Multichart program. To verify the proposed system, BS(Buythensell)-SB(Sellthenbuy) strategies are tested which uses the change of open-interest of stock index futures within a day. These strategies buy both call and put option in ATM at start candle and liquidate all at 12 o'clock and then sell both call and put option in ATM at 12 o'clock and also liquidate all at 14:40. From 23 March 2009 to 31 May 2010, 301-trading days, is adopted for experiment. As a result, the average daily profit rate of this simple strategies riches 1.09%. This profit rate is up to eight times of commision price which is 0.15 % per option trade. If the method which raises the profitable rate of wining trade or lower commission than 0.15% is found, these strategies make fascinated lossless trading system which is based on the proposed dynamic symbol automatic trading system.

• Plant Journal
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• v.5 no.1
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• pp.40-44
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• 2009

This study presents an operational technique to maximize the profit of a cogeneration power plant under cost-based pool power market. In benefit side energy sale profit, heat sale profit, and supplementary fund profit for electric power industry are included and the changeable cost was considered in cost side. The profit of a cogeneration power plant is varied enormously by the operation conditions, and constraint conditions. The result of this case study can be used as a reference to a cogeneration power plant under the same power trading system.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.489-497
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• 2000

After IMF situation, the money market environment is changing rapidly. Therefore, many companies including financial institutions and many individual investors are concerned about forecasting the money market, and they make an effort to insure the various profit and hedge methods using derivatives like option, futures and swap. In this research, we developed a prototype of forecasting system for KOSPI 200 option, especially call option, trading using artificial neural networks(ANN), To avoid the overfitting problem and the problem involved int the choice of ANN structure and parameters, we employed the ANN ensemble approach. We conducted two types of simulation. One is conducted with the hold signals taken into account, and the other is conducted without hold signals. Even though our models show low accuracy for the sample set extracted from the data collected in the early stage of IMF situation, they perform better in terms of profit and stability than the model that uses only the theoretical price.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.281-292
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• 2014

As the importance of algorithm trading is getting stronger, researches for artificial intelligence (AI) based trading strategy is also being more important. However, there are not enough studies about using more than two AI methodologies in one trading system. The main aim of this study is development of algorithm trading strategy based on the rough set theory that is one of rule-based AI methodologies. Especially, this study used genetic algorithm for optimizing profit of rough set based strategy rule. The most important contribution of this study is proposing efficient convergence of two different AI methodology in algorithm trading system. Target of purposed trading system is KOPSI200 futures market. In empirical study, we prove that purposed trading system earns significant profit from 2009 to 2012. Moreover, our system is evaluated higher shape ratio than buy-and-hold strategy.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.36-46
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• 2019

If stock market is efficient, any well-devised trading rule can't consistently outperform the average stock market returns. This study aims to verify whether the strategy based on bid-ask volume information can beat the stock market. I suggested a day trading strategy using order imbalance indicator and empirically analyzed its profitability with the KOSPI 200 index futures data from 2001 to 2018. Entry rules are as follows: If BSI is over 50%, enter buy order, otherwise enter sell order, assuming that stock price rises after BSI is over 50% and stock price falls after BSI is less than 50%. The empirical results showed that the suggested trading strategy generated very high trading profit, that is, its annual return runs to minimum 71% per annum even after the transaction costs. The profit was generated consistently during 18 years. This study also improved the suggested trading strategy applying the genetic algorithm, which may help the market practitioners who trade the KOSPI 200 index futures.

• 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.