• 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.

• Journal of Life Science
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• v.15 no.1 s.68
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• pp.38-44
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• 2005

Infection with hepatitis B virus (HBV) is a major health problem worldwide. Although a tremendous amount has been known about HBV, there have been obstacles in the study of HBV due to the narrow host range of HBV limited to humans and primates. In the present study, we investigated the susceptibility to HBV infection of mouse hepatoma cell line, Hepa-1c1c7. In addition, based on that human hepatocytes infected by HBV increase the expression of the pro-inflammatory cytokine TNF-a, the inducibility of TNF-a expression by HBV in the cells was determined. HBV surface antigen (HBsAg) secretion was measured by the microparticle enzyme immunoassay and steady state mRNA expression was analyzed by quantitative competitive RT-PCR. Transient transfection of Hepa-1c1c7 cells with HBV expression vector resulted in a dose-dependent induction of TNF-a expression. Infection of Hepa-1c1c7 cells with the serum of HBV carrier also increased TNF-a mRNA expression. Both in the transfected and infected cells, HBV mRNA was expressed and significant HBsAg secretion was detected. There was no significant variation in $\beta-actin$ mRNA expression by HBV. These results demonstrate that HBV is infectious to Hepa-lc1c7 in vitro and the viral infection induces TNF-a expression, which suggests that Hepa-lc1c7, a mouse hepatoma cell line, may be a possible model system for analysis of various molecular aspects of HBV infection.