• The Journal of the Korea Contents Association
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• v.15 no.8
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• pp.460-472
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• 2015

The aim of this study is to analyze the Weekend and January effect in the Ghana Stock Exchange (GSE) using daily closing prices of GSE-All Share Index (ASI) and Composite Index (CI) between the period of January 4th, 2005 and December 31st, 2013. The dataset covers the period of 2005 to 2010 (6 years) for the ASI and 2011 to 2013 (3 years) for the CI. The following results are obtained based on a parametric regression using dummy variables. First, no weekly effect or anomaly is documented for both GSE-ASI and GSE-CI. Second, market abnormalities are captured for both GSE-ASI and GSE-CI over their respective entire periods. However, no consistent April effect is found for ASI when the period was segregated into two periods of three years. The April effect is uncovered for the GSE-ASI at 5% significant level while the January effect is found for the GSE-CI at 1% significant level.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.287-295
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• 2007

This paper proposes a novel data editing techniques with genetic algorithm (GA) in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in compelax problem solving. Nonetheless, compared to other machine teaming techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However. designing a good matching and retrieval mechanism for CBR system is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for data editing in CBR.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.155-174
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• 2021

This study noted the emergence of the Resell investment within the fashion market, among emerging investment techniques. Worldwide, the market size is growing rapidly, and currently, there is a craze taking place throughout Korea. Therefore, we would like to use shoe data from StockX, the representative site of Resell, to present basic guidelines to consumers and to break down barriers to entry into the Resell market. Moreover, it showed the current status of the Resell craze, which was based on information from various media outlets, and then presented the current status and research model of the Resell market through prior research. Raw data was collected and analyzed using the XGBoost algorithm and the Prophet model. Analysis showed that the factors that affect the Resell market were identified, and the shoes suitable for the Resell market were also identified. Furthermore, historical data on shoes allowed us to predict future prices, thereby predicting future profitability. Through this study, the market will allow unfamiliar consumers to actively participate in the market with the given information. It also provides a variety of vital information regarding Resell investments, thus. forming a fundamental guideline for the market and further contributing to addressing entry barriers.

• Korean Journal of Construction Engineering and Management
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• v.9 no.2
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• pp.146-158
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• 2008

This paper examines the relationship between the real estate policies of Korean government and the stock market of Korea. It is the purpose of this paper whether the government policies are effective or not when the Korean government release new real estate policies outlining higher taxes and more housing supply as part of its plan to suppress speculation. This paper studies the properties of daily stock returns of the construction sector in Korea securities market when the government announcements of the real estate policies are released. On the demand side, multiple home owners and those purchasing property for speculative purposes are expected to be hit the hardest If the government policies are effective. The empirical results of this paper show that most of the cumulative abnormal returns(CARs) are statistically significant from the year 2002 to the year 2006 except the year 2004.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.203-213
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• 2016

We investigate volatility spillover aspects of realized volatilities (RVs) for the log returns of the Korea Composite Stock Price Index (KOSPI) and the Hang Seng Index (HSI) from 2009-2013. For all RVs, significant long memories and asymmetries are identified. For a model selection, we consider three commonly used time series models as well as three models that incorporate long memory and asymmetry. Taking into account of goodness-of-fit and forecasting ability, Leverage heteroskedastic autoregressive realized volatility (LHAR) model is selected for the given data. The LHAR model finds significant decompositions of the spillover effect from the HSI to the KOSPI into moderate negative daily spillover, positive weekly spillover and positive monthly spillover, and from the KOSPI to the HSI into substantial negative weekly spillover and positive monthly spillover. An interesting result from the analysis is that the daily volatility spillover from the HSI to the KOSPI is significant versus the insignificant daily volatility spillover of the KOSPI to HSI. The daily volatility in Hong Kong affects next day volatility in Korea but the daily volatility in Korea does not affect next day volatility in Hong Kong.

• Korean System Dynamics Review
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• v.14 no.3
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• pp.135-163
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• 2013

This study aims to analyze the feedback loops and policy simulation of price fluctuation of Korean Cattle. The Korean Cattle market shows the 'Cycle of Beef' since 1970. In general, the market for agricultural commodities exhibit repeated cycles of prices and production. Why Beef products market in Korea shows the fluctuation of cattle and beef price repeatedly for forty years? To find an answer, this paper explores the feedback structure of the dynamics of the beef market by the systems thinking and build a stock-flow diagram model for the simulation of future behavior of the market sector of the Cattle. The dynamic simulation model was developed to identify and analyze the cyclical behavior among many variables, which is the number of cattle (calves, cow, etc.), the price of cattle, the demand for beef, the desirable number of cattle, slaughter, etc. The results of this study demonstrate that dominant feedback loops between the number of cattle and livestock prices. The demand for Beef and slaughter with time delay, also the results of the simulation to explain the persistence of future price fluctuations and actions meat market until 2025.

• Convergence Security Journal
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• v.15 no.3_2
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• pp.83-93
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• 2015

In this research, we used KOSPI, KOSDAQ and a stock price of Information Security related Company - S1, Ahnlab, Suprema, Raonscure and Igloosecurity. From August 2010 to July 2014, that is during 208 weeks(4 years), we had grasped index and stock price trend. Also we had attempted various Empirical analysis - Basic statistics of Security related Stock, Analysis of variance, Correlation analysis and Weekly Rate of Rise trends. The first purpose of this research is to see correlation between Security related Company and KOSPI, KOSDAQ. The second purpose of this research is to analyze whether stock items have investment value or not while watching features of flow of stock price per item. We expect possibility and merit of investment when we suppose Security industry's high potential to grow. It seems that Security related Company deserves to be invested. We expect investment for Security related Company that has high possibility of growing will create high yields compared to Market yields.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.67-75
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• 2023

Stock price prediction is an important topic extensively discussed in the financial market, but it is considered a challenging subject due to numerous factors that can influence it. In this research, performance was compared and analyzed by applying time series prediction models (LSTM, GRU) and non-time series prediction models (RF, SVR, KNN, LGBM) that do not take into account the temporal dependence of data into stock price prediction. In addition, various data such as stock price data, technical indicators, financial statements indicators, buy sell indicators, short selling, and foreign indicators were combined to find optimal predictors and analyze major factors affecting stock price prediction by industry. Through the hyperparameter optimization process, the process of improving the prediction performance for each algorithm was also conducted to analyze the factors affecting the performance. As a result of feature selection and hyperparameter optimization, it was found that the forecast accuracy of the time series prediction algorithm GRU and LSTM+GRU was the highest.

• Journal of Industrial Convergence
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• v.20 no.2
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• pp.11-19
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• 2022

This study examines the role of economic uncertainty in stock price determination in the domestic stock market. To this end, we analyzed the relationship between economic uncertainty indices at home and abroad (USA, China) and stock returns for non-financial companies in Korea from January 2000 to 2017. For the analysis model, the 3-factor model of Fama and French (1992) and the 5-factor model including momentum and liquidity were used. As a result of the analysis, a portfolio with a high beta of economic uncertainty showed higher stock returns than a portfolio with a low beta. This was the same as the US analysis result. Also, the analysis results using the US uncertainty index were more significant than the regression analysis results using the Korean economic uncertainty index.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.703-710
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• 2020

The stock market serves as a representation of economic well-being in a country. Along with the myriad of economic predictors, specific knowledge possession may lead to different macro consequences of stock performance and market value. This study empirically investigates the capacity of possessing excellent intellectual capital to increase the performance and values of listed banks in Indonesia. The selection of banks as the primary data represents such sectors' capability to attract, employ, or exploit the excellent internal capacity under the discussion of resource-based view theory. At best to the authors' knowledge, this topic's findings are still elusive and debatable upon considering the direct and indirect relationships between the proposed exogenous and endogenous variables. Eighteen listed banks form the panel data throughout 2011-2016. This study employs a path analysis and Sobel test to obtain the results of the proposed hypothesis. The results report some positive relationships of the intellectual capital to firms' performances and values, directly and indirectly, with a substantial effect on the second model compared to the first model. This study highlighted knowledge's capacity as a vital basis to gauge the banks' performance and valuation. However, a better formulation of intellectual capital is required to capture a better measurement.