• Korean Management Science Review
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• v.32 no.4
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• pp.57-67
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• 2015

It is a familiar Wall Street adage that "It takes volume to make prices move." Numerous researches have found the positive correlation between trading volume and price changes. Recent studies have documented that informed traders have strong influences on stock market prices through their trading with distinctive information power. Ever since 1992 capital market liberalization in Korea, it is said that foreign investors make consistent profits with their superior information and analytical skills. This study aims at whether we can make a profitable trading strategy by using the foreign investors' trading information. We analyse the relation between the KOSPI index returns and the foreign investors trading volume using GARCH models and VAR models. This study suggests the profitable trading strategies based on the documented relation between the foreign investors' trading volume and KOSPI index returns. We simulate the trading system with the real stock market data. The data include the daily KOSPI index returns and foreign investors' trading volume for 2001~2013. We estimate the GARCH and VAR models using 2001~2011 data and simulate the suggested trading system with the remaining out-of-sample data. Empirical results are as follows. First, we found the significant positive relation between the KOSPI index returns and contemporaneous foreign investors' trading volume. Second, we also found the positive relation between the KOSPI index returns and lagged foreign investors' trading volume. But the relation showed no statistical significance. Third, our suggested trading system showed better trading performance than B&H strategy, especially trading system 2. Our results provide good information for uninformed traders in the Korean stock market.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.31-37
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• 2020

The purpose of this research study is to examine the stock market's response to terrorist attacks. The study uses data of terrorist attacks in different parts of the country (Pakistan) from June 1, 2014 to May 31, 2017. The event window procedure applies to a 16-day window in which 5 days before and 10 days after the attack. In addition, several event windows have been built to test the response of the Pakistan Stock Exchange. KSE-100 index is taken as proxy of response. The total terrorist attacks are classified into four categories: attacks on law enforcement agencies, attacks on civilians, attacks on special places and attacks on politicians, government employees and bureaucrats. The standard market model is used to estimate the abnormal return of the Pakistan Stock Exchange, which takes 252 business days each year. Furthermore, BMP test is used to check statistical significance of cumulative abnormal rate of return (CAAR). The results of this study reveal that total number of terrorist attacks and attacks on law enforcement agencies show long-term effects on Pakistan stock exchange. However, attacks on civilians, attacks on special places and attacks on politicians, government employees and bureaucrats have little effect on the Pakistan Stock Exchange.

• East Asian Journal of Business Economics (EAJBE)
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• v.8 no.1
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• pp.57-69
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• 2020

Purpose - This study examines what are the asset market fluctuations in Europe and how each economic variable affects major variables, and explore the dynamics of housing and stock market through Greece. The variables under consideration are balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), M3, real rate of interest (IR_REAL) and household credits (LOAN). We investigate the functional and causal relationships between housing and stock market. Research design, data, and methodology - Vector error correction model (VECM) is used to figure out the dynamic relationships among variables. This study also contains the augmented Dickey-Fuller unit root, cointegration, Granger causality test, and impulse response function and variance decomposition analysis by EViews 11.0. Results - The statistical tests show that all variables under consideration have one unit root and there is a longterm equilibrium relationship among variables for Greece. GDP, IR_REAL, M3, STOCK and LOAN can be considered as causal factors to affect real estate market, while GDP, LOAN, M3, BCA and HOUSING can bring direct effects to stock market in Greece. Conclusions - It can be judged that the policy that affects the lending policy of financial institutions may be more effective than the indirect variable such as monetary interest rate.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.45-55
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• 2015

This study used for KOSPI, KOSDAQ, entertainment culture and digital contents index that is related to cultural contents industry. There was investigated the each stock price index and return trends for a total 597 weeks to July 2015 from March 2004. They looked the content-related stocks about investment worth to comparative analysis the return, volatility, correlation, synchronization phenomena etc. of each stock index. When we saw the growth potential of the cultural contents industry forward, looked forward to the investment possibility of related stocks. Analysis Result cultural content related stocks showed a higher rate after the last 2008 global financial crisis. Recent as high interest in the cultural contents industry, we could see that the investment merit increases slowly. In the future, the cultural content industry is expected to continue to evolve. The increase of investments value in the cultural content related businesses is much expectation.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.387-394
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• 2016

The volatility in the stock market responds differently to information types. That is, the asymmetric volatility exists in the stock market which responds more to unexpected negative returns due to bad news than unexpected positive returns due to good news. This paper examines the asymmetric response of the volatility of KOSPI, large-cap, middle-cap, and small-cap indices returns which is announced in Korea exchange (KRX) by using the MA-GJR model and the MA-EGARCH model. According to empirical analyses, it shows that the asymmetric response of volatility exists in all indices regardless of volatility estimation models and the degree of the asymmetric volatility response of the small-cap index returns is greater than that of the large-cap index returns. Moreover, this results also observed robustly during the period of both before and after the global financial crisis.

• Asia-Pacific Journal of Business
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• v.11 no.2
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• pp.159-172
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• 2020

Purpose - The purpose of this study is to investigate economic variables which have impact on the prices and returns of preferred stocks and to provide investors, underwriters, and policy makers with information regarding correlations and causal relations between them. Design/methodology/approach - This study collected 98 monthly data from Korea Exchange and Bank of Korea. The Granger causal relation analysis, unit-root test and the multiple regression analysis were hired in order to analyze the data. Findings - First, our study derives the economic variables affecting the prices and returns of preferred stocks and their implications, while previous studies focused mainly on the differential characteristics and related economic factors between common and preferred stocks. Empirical results show that the significant variables influencing the prices and returns of preffered stocks are consumer sentiment index, consumer price index, industrial production index, KOSPI volatility index, and exchange rate between Korean won and US dollar. Second, consumer sentiment index, consumer price index, and industrial production index have significant casual relations with the returns of preferred stocks, providing market participants with important information regarding investment in preferred stocks. Research implications or Originality - This study is different from previous studies in that preferred stocks themselves are investigated rather than the gap between common stocks and preferred stocks. In addition, we derive the major macro variables affecting the prices and returns of preferred stocks and find some useful causal relations between the macro variables and returns of preferred stocks. These findings give important implications to market participants, including stock investors, underwriters, and policy makers.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.1-6
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• 2019

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.3
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• pp.1-21
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• 2016

The Markowitz portfolio selection model uses estimators to deduce input parameters. However, the estimation errors of input parameters negatively influence the performance of portfolios. Therefore, this model cannot be reliably applied to real-world investments. To overcome this problem, we suggest an algorithm that can exclude stocks with large estimation error from the portfolio by applying a tracking signal to the Markowitz portfolio selection model. By calculating the tracking signal of each stock, we can monitor whether unexpected departures occur on the outcomes of the forecasts on rate of returns. Thereafter, unreliable stocks are removed. By using this approach, portfolios can comprise relatively reliable stocks that have comparatively small estimation errors. To evaluate the performance of the proposed approach, a 10-year investment experiment was conducted using historical stock returns data from 6 different stock markets around the world. Performance was assessed and compared by the Markowitz portfolio selection model with additional constraints and other benchmarks such as minimum variance portfolio and the index of each stock market. Results showed that a portfolio using the proposed approach exhibited a better Sharpe ratio and rate of return than other benchmarks.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.277-283
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• 2005

In general, stock prices do not follow normal distributions and mean trend indexes, volatility indexes, and volume indicators relating to these non-normal stock price are widely used as buy-sell strategies. These general buy-sell strategies are rather intuitive than statistical reasoning. The non-normality problem can be solved by normalizing process and statistical buy-sell strategy can be obtained by using mean trend and volatility indexes together with normalized stock prices. In this paper, buy-sell strategy based on mean trend and volatility index with normalized stock prices are proposed and applied to KOSPI200 data to see the feasibility of the proposed buy-sell strategy.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1097-1107
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• 2020

Assessment of soil carbon stock is essential for climate change mitigation and soil fertility. The digital soil mapping (DSM) is well known as a general technique to estimate the soil carbon stocks and upgrade previous soil maps. The aim of this study is to calculate the soil carbon stock in the top soil layer (0 to 30 cm) in Jeolla Province of South Korea using the DSM technique. To predict spatial carbon stock, we used Cubist, which a data-mining algorithm model base on tree regression. Soil samples (130 in total) were collected from three depths (0 to 10 cm, 10 to 20 cm, 20 to 30 cm) considering spatial distribution in Jeolla Province. These data were randomly divided into two sets for model calibration (70%) and validation (30%). The results showed that clay content, topographic wetness index (TWI), and digital elevation model (DEM) were the most important environmental covariate predictors of soil carbon stock. The predicted average soil carbon density was 3.88 kg·m-2. The R2 value representing the model's performance was 0.6, which was relatively high compared to a previous study. The total soil carbon stocks at a depth of 0 to 30 cm in Jeolla Province were estimated to be about 81 megatons.