• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.513-523
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• 2000

The X-12-ARIMA program was utilized on the analysis of the time series trend on 76 Korean industrial activities data in order to ensure that the trading day effect adjustment as well as the seasonal effect adjustment is needed to extract the fundamental trend-cycle factors from various economic time series data. The trading day effect is strongly correlated with the activity of production and shipping but not with the activity of inventory. Furthermore, the industrial activities were classified with respect to the sensitivity on the tranding day effect.

• Journal of the Korean Society of Costume
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• v.50 no.8
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• pp.165-175
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• 2000

The Purposes of this study are to analyze fashion trading area's conditions of Busan area to establish fashion marketing strategies for the conditions of location in choosing the new retailers and to propose the most efficient, optimum fashion trading area(FTA) under the management mind of low cost and high efficiency according to the changes of 21C management paradigm. The subjects of investigation for this study were 1083 women visited FTA in Busan. The data were analyzed by using MANOVA, ANOVA, frequency and trend analysis, and the Cronabach $\alpha$ and Turkey HSD were also applied. The results of this study were summarized as follows. 1 The characteristics of consumer spatial behavior according to fashion trading area show significant difference in starling position, movement means, movement time, visit purpose and visit frequency. 2 The buying behavior of fashion items according to fashion trading area shows difference in fashion trading area shows difference in fashion trading area, store and buying behavior.

• 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 Journal of Society for e-Business Studies
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• v.20 no.2
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• pp.1-14
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• 2015

In this paper, we investigate the relationship between Internet search trends and stock market. Under the assumption that investors may use Internet search engine to obtain information for companies of their interests before taking actual investment actions, the relationship between the changes on Internet search volume and the fluctuation of trading volume as well as stock price of a company is analyzed with actual market data. A search trend investment strategy that reflects the changes on Internet search volume is applied to large enterprises' group and to small and medium enterprises' (SMEs) group, and the correlation between profit rate and trading volume is analyzed for each company group. Our search trend investment strategy has outperformed average stock market returns in both KOSPI and KOSDAQ markets during the seven-year study period (2007~2013). It is also shown that search trend investment strategy is more effective to SMEs than to large enterprises. The relationship between changes on Internet search volume and stock trading volume is stronger at SMEs than at large enterprises.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.3
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• pp.75-92
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• 2002

This paper is concerned with proposing a new market strategy for the segmented markets of the Internet stock trading. Many companies are providing various services for customers. However, the internet stock trading market is glowing rapidly absorbing a wide variety of customers showing different tastes and demographic information, so that it is necessary for us to investigate specific strategy for the segmented markets. General strategy so far in the Internet stock trading market has been to lower transaction fee according to the market trend. As the advent of rapidly enlarging market, however, more specific strategies need to be suggested for the segmented markets. In this respect, this paper applied a self-organizing map (SOM) to 83 questionnaire data collected from the Internet stock trading market in Korea, and obtained meaningful results.

• Korean Management Science Review
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• v.30 no.1
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• pp.43-58
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• 2013

Although analysis of charts, which used in stock trading by distinguishing standardized patterns in the movements of stock prices, is simple and easy to use, there can be problems stemming from specific patterns being distinguished as a result of the subjective perspectives of analysts. In accordance with such problems, through the method of template pattern matching, 4 trend reversal patterns were designed and the fitness of the patterns were quantitatively measured. In cases when a stock is purchased when the template pattern fitness value is within a certain range and held for at least 20-days, the average return ratio was analyzed to be higher-with the difference being statistically significant-than the average return ratio attained from trading a stock according to the same method per the Efficient Market Hypothesis. From the results of stock trades of 2 domestic corporations to which the values of the 4 patterns had been applied based on the 4 strategies, it was possible to ascertain differences in the strategy- and pattern-dependent return ratios. Through this study, along with presenting the exceptions for the Efficient Market Hypothesis in stock trading, the fitness level of quantitative chart patterns was measured and the theoretical basis for application of such fitness level was proposed.

• Korean System Dynamics Review
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• v.10 no.2
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• pp.53-79
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• 2009

The main purpose of this study is to find what steps are needed for a system for CO2 emissions trading to be formulated as government policy, using System Thinking approach. First, this paper analyzed Korean newspapers to consider the social issue regarding CO2 emissions trading. There were more articles related to international issues than domestic ones before 2008. This trend, however, became reversed from January 2008, which means that consideration of CO2 emissions trading has been discussed as a domestic social issue from 2008. Second, it analyzed speeches by former president Roh Moo-Hyun and current president Lee Myung-bak. In particularly, President Lee Myung-bak declared "Low Carbon and Green Growth" as a new growth engine and a Korea's vision of the future national development. Third, it examined which government agencies, including departments and committees, are pursuing policies regarding climate change, global warming, and CO2 emissions trading. Most policy has originated in the Ministry of Environment, although policy alternatives have been proposed in other agencies including the Ministry of Knowledge Economy. The study concludes that the political consideration has played a major role in the policy agenda-setting process of the CO2 emissions trading in Korea.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.210-218
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• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.3_4 s.141
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• pp.511-522
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• 2005

The purpose of this study is to analyze the organization of trading area and fashion trend in Gumi based on the observation of fashion brand stores which had been undertaken on a regular basis from July 2001 through July 2004. Gumi has over 1,000 stores located in main trading streets, about $25\%$ of which was fashion retail stores. In July 2004, $64.6\%$ of them was selling branded products, and the number of unisex casual wear stores was the greatest, followed by women's casual wear stores, sportswear stores, and children's wear stores. On the main streets of Gumi, casual attires as well as casual wear stores can be easily observed because the population of eumi is young compared to that of other regions. Among casual wear brands, especially sensory, or trendy casual wear brands such like BNX, A6, Coax, Koolhaas, EXR, and Smex came into Gumi area in large numbers since fall of 2002. From the observation data, dynamic and systemic effects of economic state, population, seasonal elements, product characteristics, etc. on the organizations of trading area were identified.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.785-794
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• 2002

Automatic stock trading systems should be able to solve various kinds of optimization problems such as market trend prediction, stock selection, and trading strategies, in a unified framework. But most of the previous trading systems based on supervised learning have a limit in the ultimate performance, because they are not mainly concerned in the integration of those subproblems. This paper proposes a stock trading system, called R-Trader, based on reinforcement teaming, regarding the process of stock price changes as Markov decision process (MDP). Reinforcement learning is suitable for Joint optimization of predictions and trading strategies. R-Trader adopts two popular reinforcement learning algorithms, temporal-difference (TD) and Q, for selecting stocks and optimizing other trading parameters respectively. Technical analysis is also adopted to devise the input features of the system and value functions are approximated by feedforward neural networks. Experimental results on the Korea stock market show that the proposed system outperforms the market average and also a simple trading system trained by supervised learning both in profit and risk management.