• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.277-280
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• 2015

자본주의는 시장 경제를 토대로 하고 있다. 시장 경제는 주식시장이 핵심이며, 주식시장의 위험회피를 위한 파생시장은 결국 자본주의의 가장 근본적인 요소이다. 다양하고 복잡한 파생시장에서 시스템 트레이딩의 중요성은 나날이 커지고 있으며, 감정을 극복하고 전략적인 매매를 하기 위한 최선의 방법이기도하다. 한국의 시스템 트레이딩은 전통적인 TS와 최신기술로 탄생한 Multicharts가 있다. Multicharts는 틱 단위의 신호데이타를 분석하여 실시간 거래를 할 수 있는 뛰어난 시스템이지만 아직 스마트폰 에이전트가 없다. PC에서는 Multicharts의 모든 기능을 수행할 수 있지만 관리자가 어디에서나 상황을 체크하고 제어할 수 있다면 훨씬 효과적인 운용이 가능할 것이다. PC에 기록되는 신호정보와 거래정보를 스마트폰으로 확인하고, 전략 실행을 스마트폰에서 제어하는 것만 가능해도, 보다 여유롭고 효율적인 파생거래를 할 수 있다. 이를 위해 안드로이드 폰과 PC간의 보안 연결을 설정하고 데이터 동기화를 구축하며, 이벤트 처리를 구현했다. 그리고 다수의 샘플 전략을 이용하여 스마트폰 UI를 구성하고 이의 효율성을 테스트하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.652-652
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• 2004

• Journal of Digital Convergence
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• v.12 no.5
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• pp.89-99
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• 2014

Correlation of KOSPI from stock market and Apartment Prices in Seoul HPPCI from real estate market has been found from this research. Furthermore, from the comparison of those indicators' flows, certain precedence was found as well. The purpose of this research is to analyze correlation and precedence among KOSPI, Apartment price in Seoul, HPPCI and CLI. As for predicting KOSPI of stock market and real estate market, it is necessary to find out preceding indices and analyzing their progresses first. For 27 years from the January 1987 to December 2013, KOSPI has been grown by 687%, while CLI showed 443%, Apartment of Seoul showed 391%, HPPCI showed 263% of growth rate in order. As the result of correlation analysis among Apartment of Seoul, CLI, KOSPI and HPPCI, KOSPI and HPPCI showed high correlation coefficient of 0.877, and Apartment of Seoul and CLI showed that of 0.956 which is even higher. Result from the analysis, CLI shows high correlation with stock and real estate market, it is a good option to watch how CLI flows to predict stock and real estate market.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.135-140
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• 2022

In the domestic stock market, the capital market opened in January 1992, and the proportion of foreign capital has steadily increased, accounting for 30% of the domestic market in Overall stock market trend infers that the domestic stock market is more influenced by foreign issues than domestic issues. The trading trend of foreign capital displays a similar flow to exchange rate fluctuations,; thus, preparing an investment strategy by using the Pearson analyzing method the effect of exchange rates of foreign capital trading, fluctuations in exchange rates, and predicting one of the macroeconomic indicators will yield high returns in the stock market. Therefore, this research was conducted to help investment by predicting foreign variables comparing and analyzing exchange rates and foreign capital trading patterns, and predicting appropriate time for buying and selling.

• Proceedings of the Safety Management and Science Conference
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• 2007.04a
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• pp.471-476
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• 2007

본 논문의 목적은 과거의 산업 포트폴리오 수익률이 확률추세로부터 어떻게 전체 주식시장과 두 가지 거시경제 변수인 경기동행지수와 산업생산 등을 예측할 수 있는 지를 알아보는 데에 있다. 이를 위하여 본 연구에서는 연구모형을 설정한 후 세 가지 검정절차를 제시하고 이를 실증적으로 분석하였다. 당월의 전체 주식시장 수익률은 과거의 시차를 지닌 특정 산업부문 포트폴리오 수익률에 대하여 양(+)의 상관관계를 유지하고 있다는 '예측 1'과 전체 주식시장의 수익률은 특정 산업부문의 수익률에 대하여 선행성을 지닐 수 없다는 '예측 2'에 대한 검정 결과는 '예측 1'과 '예측 2'가 지지되고 있음을 파악할 수 있었다. 그리고 산업별 포트폴리오 수익률과 거시경제변수 간의 높은 상관관계를 토대로 하여 전체주식시장 수익률 예측을 가능하게 하는 업종 정보의 점진적 확산 현상이 발생하게 되는가를 검토하기 위하여 각 산업들의 포트폴리오 수익률과 전체 주식시장 수익률이 VAR모형을 토대로 볼 경우 Granger 인과관계를 갖고 있는 지를 분석하였다. 분석결과 21개 업종은 각 산업별 포트폴리오 수익률이 전체 주식시장 수익률을 5% 수준에서 통계적으로 유의한 영향을 주고 있음을 알 수 있었다. 이들 21개의 산업별 포트폴리오 수익률은 경제적으로도 중요한 의미를 지니고 있어 산업제품의 가격 상승과 하락이 경제에 미치는 영향을 파악할 수 있다. 특히 음료 업종에서 전체 주식시장 수익률과 상호간의 인과성을 나타내었으며, 인터넷과 화장품 업종에서는 전체 주식시장 수익률이 이들 업종에 대하여 일방적인 영향을 보이고 있음을 알 수 있었다.>$mgN\;{\cdot}\;L^{-1}$ 및 0.000-0.804 $mgN\;{\cdot}\;L^{-1}$이였다. 규소농도는 0.0-6.2 $mgSi\;{\cdot}\;L^{-1}$의 범위로 3-5월에 매우 낮았으며, 계절적인 변화가 뚜렷히 나타났다. 저질의 입자는 0-125인 silt및 coarse silt로 이루어져 있으며, COD는 51.4-116.9 $mgO_2\;{\cdot}\;gdw^{-1}$로 평균 93.0 $mgO_2\;{\cdot}\;gdw^{-1}$ 이였다. 저질내의 TP및 TN의 농도는 각각 0.04-1.46 $mgP\;{\cdot}\;gdw^{-1}$ 및 0.12-1.03 $mgN\;{\cdot}\;gdw^{-1}$이었다. 표층의 엽록소 a의 정점별 평균값은 정점 1, 2 및 3에서 각각 15.6, 15.2 및 16.0 $mg\;{\cdot}\;m^{-3}$으로 유사하였다. 식물플랑크톤은 총 49종이 출현하였으며, 생물량은 50-23, 350 cells ${\cdot}\;mL^{-1}$로 2001년 9월에 가장 많았다. 이 시기의 우점종은 녹조류인 Schroederia judayi이였으며, 생물량은 20,417 cells ${\cdot}\;mL^{-1}$이였다. 송지호의 수질을 개선하기 위해서는 인위적으로 화학성층을 파괴시켜 심충에 용존산소를 공급시켜야 할 것으로 판단되며, 모래톱으로 인해 막혀져 있는 해수

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.31-36
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• 2021

As the global economy stagnated due to the Corona 19 virus from Wuhan, China, most countries, including the US Federal Reserve System, introduced policies to boost the economy by increasing the amount of money. Most of the stock investors tend to invest only by listening to the recommendations of famous YouTubers or acquaintances without analyzing the financial statements of the company, so there is a high possibility of the loss of stock investments. Therefore, in this research, I have used artificial intelligence deep learning techniques developed under the existing automatic trading conditions to analyze and predict macro-indicators that affect stock prices, giving weights on individual stock price predictions through correlations that affect stock prices. In addition, since stock prices react sensitively to real-time stock market news, a more accurate stock price prediction is made by reflecting the weight to the stock price predicted by artificial intelligence through stock market news text mining, providing stock investors with the basis for deciding to make a proper stock investment.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.169-176
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• 2009

Rule discovery is an operation that discovers patterns frequently occurring in a given database. Rule discovery makes it possible to find useful rules from a stock database, thereby recommending buying or selling times to stock investors. In this paper, we discuss storage structures for efficient processing of queries in a system that recommends stock investments. First, we propose five storage structures for efficient recommending of stock investments. Next, we discuss their characteristics, advantages, and disadvantages. Then, we verify their performances by extensive experiments with real-life stock data. The results show that the histogram-based structure improves the query performance of the previous one up to about 170 times.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.472-482
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• 2012

There are many investors in the stock market, and more and more people get interested in the stock investment. In order to avoid risks and make profit in the stock investment, we have to determine several aspects using various information. That is, we have to select profitable stocks and determine appropriate buying/selling prices and holding period. This paper proposes a data mining tool for the investors' decision support. The data mining tool makes stock investors apply machine learning techniques and generate stock price prediction model. Also it helps determine buying/selling prices and holding period. It supports individual investor's own decision making using past data. Using the proposed tool, users can manage stock data, generate their own stock price prediction models, and establish trading policy via investment simulation. Users can select technical indicators which they think affect future stock price. Then they can generate stock price prediction models using the indicators and test the models. They also perform investment simulation using proper models to find appropriate trading policy consisting of buying/selling prices and holding period. Using the proposed data mining tool, stock investors can expect more profit with the help of stock price prediction model and trading policy validated on past data, instead of with an emotional decision.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.87-93
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• 2023

Determining the timing of buying and selling in stock investment is one of the most important factors to increase the return on stock investment. Buying low and selling high makes a profit, but buying high and selling low makes a loss. The price is determined by the quantity of buying and selling, which determines the price of a stock, and buying and selling is also related to corporate performance and economic indicators. The fear and greed index provided by CNN uses seven factors, and by assigning weights to each element, the weighted average defined as greed and fear is calculated on a scale between 0 and 100 and published every day. When the index is close to 0, the stock market sentiment is fearful, and when the index is close to 100, it is greedy. Therefore, we analyze the trading criteria that generate the maximum return when buying and selling the US S&P 500 index according to CNN fear and greed index, suggesting the optimal buying and selling timing to suggest a way to increase the return on stock investment.

• Korean Journal of Construction Engineering and Management
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• v.11 no.5
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• pp.41-52
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• 2010

Even though REITs (Real Estate Investment Trusts) are listed on the stock market, REITs have characteristics that allow them to invest in real estate and financing for real estate development. Therefore REITs is related with stock market and construction business and real estate business. Using time-series analysis, this study analyzed REITs in relation to construction businesses, real estate businesses, and the stock market, and derived influence factor of REITs. We used the VAR (vector auto-regression) and the VECM (vector error correction model) for the time-series analysis. This study classified three steps in the analysis. First, we performed the time-series analysis between REITs and construction KOSPI(The Korea composite stock price index) and the result showed that construction KOSPI influenced REITs. Second, we analyzed the relationship between REITs and construction commencement area of the coincident construction composite index, office index and housing price index in real estate business indexes. REITs and the housing price index influence each other, although there is no causal relationship between them. Third, we analyzed the relationship between REITs and the construction permit area of the leading construction composite index. The construction permit area is influenced by REITs, although there is no causal relationship between these two indexes, REITs influenced the stock market and housing price indexes and the construction permit area of the leading composite index in construction businesses, but exerted a relatively small influence in construction starts coincident with the composite office indexes in this study.