• Asia-Pacific Journal of Business
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• v.15 no.2
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• pp.143-157
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• 2024

Purpose - This paper aims to analyze the relationship and correlation between the stock markets of Korea, the US, China, and Japan before and after the outbreak of COVID-19. Design/methodology/approach - This study conducted an empirical analysis using the stock market data from January 2016 to June 2023 for the representative market indices of Korea, the US, China, and Japan. The analysis employed the VAR model, Granger causality test, impulse response function, and variance decomposition. Findings - Analyzing the relationships of these stock markets before and after the outbreak of COVID-19, we obtained the following results. (i) The influence of the U.S. stock market was found to be absolute regardless of the COVID-19 period, and the rise in the U.S. stock market led to rises in other stock markets. (ii) The Chinese stock market had a significant negative impact on the U.S., Korean, and Japanese stock markets before COVID-19, but this influence disappeared after COVID-19. This suggests that the Chinese market exhibited unique characteristics different from the global market after COVID-19. (iii) Analyzing the period excluding the first quarter of 2020, when global stock market volatility was extremely high due to the spread of COVID-19, we found that the results were very similar to the analysis including the first quarter of 2020. Therefore, it is difficult to argue that the increased uncertainty during this period distorted the relationships among the stock markets of these four countries. Research implications or Originality - We anticipate that these findings will offer valuable insights for both individual and institutional investors, aiding them in portfolio diversification and risk mitigation.

• The Korean Journal of Financial Management
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• v.21 no.2
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• pp.125-151
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• 2004

This study tests the price discovery from US Treasury bond markets to Korean bond markets using the daily returns of Korean bond data (CD, 3-year T-note, 5-year T-note, 5-year corporate note) and US treasury bond markets (3-month T-bill, 5-year T-note 10-year T-bond) from July 1, 1998 to December 31, 2003. For further research, we divide full data into two sub-samples on the basis of the start-up of bond valuation system in Korean bond market July 1, 2000, employing uni-variate AR(1)-GARCH(1,1)-M model. The main results are as follows. First the volatility spillover effects from US Treasury bond markets (3-month T-bill, 5-year T-note, 10-year T-bond) to Korean Treasury and Corporate bond markets (CD, 3-year T-note, 5-year T-note, 5-year corporate note) are significantly found at 1% confidence level. Second, the price discovery function from US bond markets to Korean bond markets in the sub-data of the pre-bond valuation system exists much stronger and more persistent than those of the post-bond valuation system. In particular, the role of 10-year T-bond compared with 3-month T-bill and 5-year T-note is outstanding. We imply these findings result from the international capital market integration which is accelerated by the broad opening of Korean capital market after 1997 Korean currency crisis and the development of telecommunication skill. In addition, these results are meaningful for bond investors who are in charge of capital asset pricing valuation, risk management, and international portfolio management.

• The Korean Journal of Financial Management
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• v.7 no.1
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• pp.1-20
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• 1990

In most of studies on market efficiency, the stability of risk measures and the normality of residuals unexplained by the pricing model are presumed. This paper re-examines stock splits, taking the possible violation of two assumptions into accounts. The results does not change the previous studies. But, the size of excess returns during the 2-week period before announcements decreases by 43%. The results also support that betas change around announcements and the serial autocorrelation of residuals is caused by events. Based on the results, the existing excess returns are most likely explained as a compensation to old shareholders for unwanted risk increases in their portfolio, or by uses of incorrect betas in testing models. In addition, the model suggested in the paper provides a measure for the speed of adjustment of the market to the new information arrival and the intensity of information contents.

• Information Systems Review
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• v.25 no.4
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• pp.27-45
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• 2023

The importance of information security has grown alongside the development of information and communication technology. However, companies struggle to select suitable countermeasures within their limited budgets. Sönmez and Kılıç (2021) proposed a model using AHP and mixed integer programming to determine the optimal investment combination for mitigating information security breaches. However, their model had limitations: 1) a lack of objective measurement for countermeasure efficacy against security threats, 2) unrealistic scenarios where risk reduction surpassed pre-investment levels, and 3) cost duplication when using a single countermeasure for multiple threats. This paper enhances the model by objectively quantifying countermeasure efficacy using the beta probability distribution. It also resolves unrealistic scenarios and the issue of duplicating investments for a single countermeasure. An empirical analysis was conducted on domestic SMEs to determine investment budgets and risk levels. The improved model outperformed Sönmez and Kılıç's (2021) optimization model. By employing the proposed effectiveness measurement approach, difficulty to evaluate countermeasures can be quantified. Utilizing the improved optimization model allows for deriving an optimal investment portfolio for each countermeasure within a fixed budget, considering information security costs, quantities, and effectiveness. This aids in securing the information security budget and effectively addressing information security threats.

• Management & Information Systems Review
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• v.29 no.2
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• pp.1-25
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• 2010

This study attempted to empirically test the determinants of stock returns in Korean stock market applying multi-factor model proposed by Haugen and Baker(1996). Regression models were developed using 16 variables related to liquidity, risk, historical price, price level, and profitability as independent variables and 690 stock monthly returns as dependent variable. For the statistical analysis, the data were collected from the Kis Value database and the tests of forecasting power in this study minimized various possible bias discussed in the literature as possible. The statistical results indicated that: 1) Liquidity, one-month excess return, three-month excess return, PER, ROE, and volatility of total return affect stock returns simultaneously. 2) Liquidity, one-month excess return, three-month excess return, six-month excess return, PSR, PBR, ROE, and EPS have an antecedent influence on stock returns. Meanwhile, realized returns of decile portfolios increase in proportion to predicted returns. This results supported previous study by Haugen and Baker(1996) and indicated that firm-characteristic model can better predict stock returns than CAPM. 3) The firm-characteristic model has better predictive power than Fama-French three-factor model, which indicates that a portfolio constructed based on this model can achieve excess return. This study found that expected return factor models are accurate, which is consistent with other countries' results. There exists a surprising degree of commonality in the factors that are most important in determining the expected returns among different stocks.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.2 no.2
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• pp.49-68
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• 2007

This paper develop self pre-checkup lists for the validity of business model as web business starters can utilize to open business. In particular, self pre-checkup lists invented by Dr. Bruce Merrifield, is reapplied and modified in appropriate to internet shopping mall business. This paper complete many literature reviews to identify appropriate factors of evaluation such as about the characters of internet business, business validity testing theory for internet business model, pros and cons of e-business and startup ventures, factor analysis of technology valuation, and pros and cons for internet shopping mall. This paper define six different factors; scale of sales, the growth rate of market, competitiveness, risk portfolio, industry upside down, and social conditions, as the factors of evaluating the business attractiveness. Meanwhile, it define characters of CEO, content's power, mutual inclusion, commerce, fulfillment, marketing power as the factors of business appropriateness. This paper also conducts several case studies; company I, D, G of applying the former model. This paper sort out internet business model in imaginations by utilizing self pre-checkup lists of business evaluation. Also, the outcomes of evaluation is expected to provide meaningful future business implications.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.189-212
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• 2005

This study examines the interactions between KTB spot and futures markets using the daily prices from March 4, 2002 to January 31, 2005. We use Granger causality test, impulse Response Analysis and Variance Decomposition through vector autoregressive analysis (VAR). However, considering the long-term relationships between the level variables of KTB spot and futures, we introduced Vector Error Correction Model. The main results are as follows. According to the results of Granger-causality test and impulse response analysis, we find that the yields of KTB forward have a great influence on the change of KTB spot but not vice versa. In terms of volatility analysis, there is no inter-dependence between KTB forward and spot markets. In the variance decomposition analysis we find that the short-term KTB forward has much more impact on the KTB spot market than the long-term KTB forward does. We think these results are meaningful for bond investors who are in charge of capital asset pricing valuation, risk management and international portfolio management.

• Korean Journal of Construction Engineering and Management
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• v.20 no.1
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• pp.73-85
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• 2019

Since the beginning of a new century, many Korean construction and engineering companies are facing a very dynamic and fast changing business environment which includes severe competition, higher risk, economic depression, declining revenues and profits, etc. In order to cope with these challenges, they need to secure special capabilities to actively adapt to the paradigm changes. One of those capabilities could be project management capability which allows us to manage organizational resources dynamically and integratively based on project portfolio management concept. The objective of this study is to investigate how the dynamic capability of a project-based organization to control the resource affects the firm performance and the competitive advantages. Data was collected from the construction and engineering companies in South Korea by using survey questionnaire, and analyzed for empirical tests by using statistical methods such as structural equation modelling and path analysis. The results showed that the organizational resources, if they had the VRIN characteristics, would have positive impacts on creating the dynamic capabilities for project organization. In turn, the dynamic capabilities of a project organization would have impacts on improving business performance and creating competitive advantages. Also, it was found that the organizational resources may have direct impact on business performance and competitive advantages. The academic contribution of this study is that it attempts to integrate resource based view and the dynamic capability theory about creating competitive advantages for project based organization. This study also provided practical implications to the companies in construction industry by showing how to use organizational resources strategically to create competitive advantages.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.22-28
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• 2023

In this study, we conducted research on the automated trading of U.S. stocks through a volatility strategy using the Fear and Greed index. Volatility in the stock market is a common phenomenon that can lead to fluctuations in stock prices. Investors can capitalize on this volatility by implementing a strategy based on it, involving the buying and selling of stocks based on their expected level of volatility. The goal of this thesis is to investigate the effectiveness of the volatility strategy in generating profits in the stock market.This study employs a quantitative research methodology using secondary data from the stock market. The dataset comprises daily stock prices and daily volatility measures for the S&P 500 index stocks. Over a five-year period spanning from 2016 to 2020, the stocks were listed on the New York Stock Exchange (NYSE). The strategy involves purchasing stocks from the low volatility group and selling stocks from the high volatility group. The results indicate that the volatility strategy yields positive returns, with an average annual return of 9.2%, compared to the benchmark return of 7.5% for the sample period. Furthermore, the findings demonstrate that the strategy outperforms the benchmark return in four out of the five years within the sample period. Particularly noteworthy is the strategy's performance during periods of high market volatility, such as the COVID-19 pandemic in 2020, where it generated a return of 14.6%, as opposed to the benchmark return of 5.5%.