• The Journal of Asian Finance, Economics and Business
• /
• v.3 no.3
• /
• pp.79-91
• /
• 2016

This paper is the first of its kind using a non-linear approach based on cross-correlation function (CCF) to investigate the information arrival hypothesis in crude palm oil (CPO) futures market. Based on daily data from 1986 to 2010, our empirical results reveal that: First, the volume of volatility is not a proxy of information flow. Second, dependence causality running from current return to future volume in conditional variance exhibit an asymmetric pattern of time span with different signs of correlation between price and volume series. This finding indicates the presence of noise traders' hypothesis of price-volume interaction in CPO futures market. Both findings suggest that this futures market is weak-form inefficiency. In terms of investors' behavior, they tend to change their expectations on current return based on errors made in previous trade in generating abnormal volume in the subsequent period. As implied, it is advisable for the investors devise their future trading strategies according to time span and changes of return.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1997.10a
• /
• pp.105-108
• /
• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• Management & Information Systems Review
• /
• v.33 no.3
• /
• pp.93-104
• /
• 2014

This study investigates price discovery between BlueNext spot and futures in EU ETS carbon emission markets using vector error correction model, GG and Hasbruck information ratio. Especially EUA is European Union Allowances traded on the Emissions Trading Scheme. This emission asset attracts and increasing attention among operators, investors and brokers on emission markets. In this study, we found BlueNext spot and EUA futures market are cointegrated. Following the preceding studies, we judged that EUA futures market contribute to the price discovery process than BlueNext spot market when this GG and Hasbrouck information ratio for BlueNext market are larger than 0.5. In other words, the futures market of EUA plays a more dominant role in price discovery than the spot market.

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

• International Area Studies Review
• /
• v.14 no.2
• /
• pp.25-40
• /
• 2010

Accurate forecasting of volatility is of considerable interest in financial volatility research, particularly in regard to portfolio allocation, option pricing and risk management because volatility is equal to market risk. So, we attempted to delineate a model with good ability to forecast and identified stylized features of volatility, with a focus on volatility persistence or long memory in the Australian futures market. In this context, we assessed the long-memory property in the volatility of index futures contracts using three conditional volatility models, namely the GARCH, IGARCH and FIGARCH models. We found that the FIGARCH model better captures the long-memory property than do the GARCH and IGARCH models. Additionally, we found that the FIGARCH model provides superior performance in one-day-ahead volatility forecasts. As discussed in this paper, the FIGARCH model should prove a useful technique in forecasting the long-memory volatility in the Australian index futures market.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.127-146
• /
• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

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

• Korean Management Science Review
• /
• v.33 no.4
• /
• pp.1-15
• /
• 2016

Pairs trading is an arbitrage trading strategy using statistical properties of the spreads between two assets. This study analyzes the performance of the statistical pairs trading with the pairs selected from the same category as well as from the different category in the CME and other futures markets. Empirical results show that the pairs trading performance of the same category is poor whereas that of the different category proves profitable. This implies that the spreads between different category pairs can have the mean reversion property if pairs are properly selected using co-integration test, which is contrary to the existing research results on the overseas futures pairs trading.

• The Korean Journal of Financial Management
• /
• v.15 no.1
• /
• pp.73-94
• /
• 1998

This paper contributes to the ongoing controversy over price and volatility spillovers across countries by providing new evidence with the futures data of the S&P 500 and Nikkei 225 index futures contacts from January 3, 1990 to April 16, 1996. Based on the two-stage symmetric and asymmetric GARCH models we document that both the U.S. and the Japanese daytime returns significantly influence the subsequent overnight returns of the other market. We find no signs of volatility spillovers between two international markets with the symmetric model. However, with the asymmetric models, we find that the magnitude of foreign negative shocks are different from the positive ones. The findings generally suggest that the two markets are more sensitive to the bad news originating in the other market. This nature of transmission between two markets would have important implications to the arbitragers who are trying to exploit the short-term dynamics of price and volatility movements across two security markets.

• Journal of the Korean Society of Industry Convergence
• /
• v.8 no.2
• /
• pp.85-95
• /
• 2005

The margin level in the futures market platys an important role in balancing the default probability with the investor's opportunity cost. In this paper, we investigate whether the movement of KOSPI200 futures daily prices can be modeled with the extreme value theory. Based on this investigation, we examine the validity of the margin level set by the extreme value theory. Moreover, we propose an expected profit-maximization model for securities companies. In this model, the extreme value theory is used for cost estimation, and a regression analysis is used for revenue calculation. Computational results are presented to compare the extreme value distribution with the empirical distribution of margin violation in KOSPI200 and to examine the suitability of the expected profit-maximization model.