• 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.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.51-59
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• 2020

The objective of this study is to enrich the literature by investigating the impact of introduction of index future trading on spot market returns and trading volume in Vietnam. Data used in this study mainly consist of daily VN30-Index and market trading volume series during the period from February 6^th, 2012 to December 31^st, 2019. Using OLS, GARCH(1,1) and EGARCH(1,1) models, the empirical findings consistently confirm that the introduction of index future trading has no impact on the spot market returns. In addition, the results of the EGARCH(1,1) model indicate that the leverage effect on the spot market volatility is existence in HOSE. Specifically, bad news has a greater effect on the market volatility than good news of the same size. Moreover, our empirical findings reveal that the introduction of index future contracts has the positive impact on the underlying market trading volume. Specifically, the trading volume of the post-index futures introduction increases by 7.5 percent compared with the pre-index futures introduction. Finally, the results obtained from the Granger causality test for the relationship between the spot market returns and the future trading activity confirm that only uni-directional causality running from the market returns to the future trading activity exists in HOSE.

• Management & Information Systems Review
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• v.34 no.1
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• pp.65-80
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• 2015

In previous studies concerning turnover, they argue individual stock's turnover must be identical to market portfolio's turnover under one condition where 2 funds separation theorem holds. In this kind of world, all market participants hold and trade the same portfolio and this should be only market portfolio. If one's trading portfolio's shape is different from market portfolio's, this would mean he or she has an advantage over others in information and this kind of information would be private. In accordance with this theory, we develop a metric which measures how far one's trading portfolio from market's and name it as Stock Selection by Investor(SSI). We apply this measurement to the various types of investor groups classified as individual, institutional and foreign who participate in Korea stock market. To test the validity of measure, we regress price ratio on this measurement using SUR method. As a result, individual investor group shows large number in SSI, but the coefficient in regression is not significant and economically meaningless. In case of institutional investor group, the coefficient proves to be significantly negative. We can infer from this fact that their trading is somehow far from informed trading. Stock selection activity by foreign investor groups proves to be informed trading by showing significantly positive coefficient and the magnitude of coefficient is economically meaningful, especially in sell activity.

• Korean Management Science Review
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• v.30 no.1
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• pp.73-89
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• 2013

This paper develops an investment algorithm based on Markowitz's Portfolio Selection Theory, using historical stock return data, and empirically evaluates the performance of the proposed algorithm in the U.S. and the Hong Kong stock markets. The proposed investment algorithm is empirically tested with the 30 constituents of Dow Jones Industrial Average in the U.S. stock market, and the 30 constituents of Hang Seng Index in the Hong Kong stock market. During the 6-year investment period, starting on the first trading day of 2006 and ending on the last trading day of 2011, growth rates of 12.63% and 23.25% were observed for Dow Jones Industrial Average and Hang Seng Index, respectively, while the proposed investment algorithm achieved substantially higher cumulative returns of 35.7% in the U.S. stock market, and 150.62% in the Hong Kong stock market. When compared in terms of Sharpe ratio, Dow Jones Industrial Average and Hang Seng Index achieved 0.075 and 0.155 each, while the proposed investment algorithm showed superior performance, achieving 0.363 and 1.074 in the U.S. and Hong Kong stock markets, respectively. Further, performance in the U.S. stock market is shown to be less sensitive to an investor's risk preference, while aggressive performance goals are shown to achieve relatively higher performance in the Hong Kong stock market. In conclusion, this paper empirically demonstrates that an investment based on a mathematical model using objective historical stock return data for constructing optimal portfolios achieves outstanding performance, in terms of both cumulative returns and Sharpe ratios.

• 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 Information Technology Applications and Management
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• v.26 no.3
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• pp.29-41
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• 2019

As users' interests of cryptocurrency has been increased, investment volume of it also increases. In the cryptocurrency market, it cannot always be distributed homogenous information to all investors, similar to the stock market because it reflects the characteristics of a market microstructure. Cryptocurrency traders, thus, like stock investors, can experience the information asymmetry in the market and cannot but help to depend on private information. The purpose of this study is to estimate the trading intensity of informed traders and uninformed traders among cryptocurrency investors around the world based on PIN (Probability of Informed Trading). We have an aim to compare the difference of information asymmetry according to the ten types of cryptocurrency. The results of this study are expected to prevent the continuous increase of suspicious transactions related to cryptocurrency and contribute to the development of a sound cryptocurrency market.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.977-985
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• 2021

The main aim of the study is to provide empirical evidence about the association between stock market exchange data and weighted price index. This research utilized monthly reported data from the Amman stock exchange market (ASE) and the Central Bank of Jordan (CBJ). The weighted price index was employed as the dependent variable and the independent variables were weighted price index (WPI), turnover ratio (TOR), number of trading days (NTD), price-earnings ratio (PER), and dividends yield ratio (DY). The time period of the study was from January 2015 to October 2020. The study's methodology follows a quantitative approach using the multiple regression method to test the hypotheses of the study. The final results of the study provided conclusive evidence that the market-weighted price index is strongly and positively correlated to three predetermined variables, namely; turnover ratio, price-earnings ratio, and dividend yield but no evidence was obtained for the effect of the number of trading days. The finding of the current study proved that the market price index is not only influenced by macro factors, but also by other variables assumed to not beneficial for the judgment of price index movements.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.1
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• pp.121-127
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• 2004

This paper is to give some hints to solve the problems of the Korean The third Market suffering from the extreme shortage of the liquidity. To solve that problem, this paper mainly compare the liquidity indices of the Third Market with that of the Japanese third market, that is Mothers. The main liqudity indices of the Mothers shows better than that of the Korean Third Market redardless of the small numbers of the listed Firms. The main differences in the liquidity levels between two markets is to caused by the trading system. The Korean Third Market has been adapting the one-to-one trading system which most stock markets of the world gave up that system owing to the inefficiency. This paper shows the proper trading system for the Third Market is competitve trading system partialy combined with the market maker system beacause of the small firm characterristics.

• Korean Management Science Review
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• v.27 no.3
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• pp.33-41
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• 2010

A lot of researches find negative volatility risk premium in options market. We can make a trading profit by exploiting the negative volatility premium. This study proposes negative volatility risk premium hypotheses in the KOSPI 200 stock price index options market and empirically test the proposed hypotheses with intra-day short straddle strategy. This strategy sells both at-the-money call option and at-the-money put option at market open and exits the position at market close. Using MySQL 5.1, we create our database with 1 minute option price data of the KOSPI 200 index options from 2004 to 2009. Empirical results show that negative volatility risk premium exists in the KOSPI 200 stock price index options market. Furthermore, intra-day short straddle strategy consistently produces annual profits except one year.

• Journal of Intelligence and Information Systems
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• v.2 no.2
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• pp.43-54
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• 1996

Financial markets are operating 24 hours a day throughout the world and interrelated in increasingly complex ways. Telecommunications and computer networks tie together markets in the from of electronic entities. Financial practitioners are inundated with an ever larger stream of data, produced by the rise of sophisticated database technologies, on the rising number of market instruments. As conventional analytic techniques reach their limit in recognizing data patterns, financial firms and institutions find neural network techniques to solve this complex task. Neural networks have found an important niche in financial a, pp.ications. We a, pp.y neural networks to Standard and Poor's (S&P) 500 stock index futures trading to predict the futures marker behavior. The results through experiments with a commercial neural, network software do su, pp.rt future use of neural networks in S&P 500 stock index futures trading.