• Journal of Food Hygiene and Safety
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• v.34 no.5
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• pp.489-494
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• 2019

The appropriateness of introducing blockchain technology into food safety management systems was evaluated by conducting a survey of experts on the effectiveness and constraint evaluation indicators, and a portfolio analysis was conducted to set the priorities of blockchain application. The food safety management activities considered in this study were issuing documents on food import/export, food hygiene rating scheme, civil complaint management in the food sector, food- related certification, risk information management, and food traceability systems. The sectors that can be expected to be effective in the introduction of blockchain technology were food- related certification, food hygiene rating scheme, risk information management, and issuing documents on food import/export. In the case of food traceability systems and civil complaint management, the introduction of blockchain technology was not recommended due to ineffectiveness. From the evaluation of the constraints (e.g., technical limits, cost, legal amendment, personal information disclosure, timeliness, and ease of connection) to be overcome when introducing blockchain into food safety management, it was found that there are more than average constraints in all six areas. In particular, the food traceability system was evaluated to have the most constraints. Issuing documents on food import/export is very effective with the introduction of blockchain technology, but due to high cost and legal restrictions, it is necessary to improve the institutional system in order to introduce blockchain. Among the evaluation sectors, food- related certification, food hygiene rating scheme, and risk information management on foods were suitable for preferentially adopting blockchain technology since these areas might experience greatly improved reliability and transparency through the introduction of blockchain, with relatively low constraints.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.89-94
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• 2023

Recently, credit risk in the Chinese corporate bond market has increased significantly, and there is a possibility that banks that have invested in corporate bonds may become insolvent. The purpose of this study is to empirically analyze the effect of Chinese commercial banks' investment in securities on financial performance. The analysis results are as follows. First, it is estimated that as the share of securities investment by Chinese commercial banks increases, the bank's profitability decreases. It was found that investment in securities did not have a positive impact on profitability due to the increase in credit risk in the corporate bond market and the increase in marginal companies. Second, it is estimated that as the proportion of securities investment by Chinese commercial banks increases, the bank's soundness deteriorates. As credit risk in China's capital market is increasing, continuous management of non-performing assets is required. Chinese commercial banks need portfolio management through securities investment in addition to loan assets to improve profitability. However, volatility should be managed by adjusting the scale of securities management to an appropriate level.

• The Korean Journal of Financial Management
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• v.24 no.4
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• pp.75-106
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• 2007

In this study, it is analyzed whether oil price plays a major role in the pricing return on Koran stock market and examined why the covariance risk between oil and return on stock is different in each industry. Firstly, this study explores whether the expected rate of return on stock is pricing due to global oil price factors as a function of risk premium by using a two-factor APT. Also, it is examined whether spill-over effects of oil price volatility affect the beta risk to oil price. Considering the asymmetry of oil price volatility, we use the GJR model. As a result, it shows that oil price is an independent pricing factor and oil price volatility transmits to stock return in only electricity and electrical equipment. Secondly, the two step-analyzing process is introduced to find why the covariance between oil price factor and stock return is different in each industry. The first step is to study whether beta risk exists in each industry by using two proxy variables like size and liquidity as control variables. The second step is to grasp the systematic relationship between the difference of liquidity and size and beta to oil price factor by using the panel-data model which can be analyzed efficiently using the cross-sectional data formed with time series. Through the analysis, we can argue that oil price factor is an independent pricing factor in only electricity and electrical equipment having the greatest market capitalization, and know that beta risk to oil price factor is a proxy of size in the other industries. According to the result of panel-data model, it is argued that the beta to oil price factor augments when market capitalization increases and this fact supports the first assertion. In conclusion, the expected rate of return of electricity and electrical equipment works as a function of risk premium to market portfolio and oil price, and the reason to make beta risk power differentiated in each industry attributes to the size.

• Journal of the Economic Geographical Society of Korea
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• v.14 no.2
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• pp.157-175
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• 2011

This paper aims to analyze the characteristics of healthcare REITs' performance and operation, and to examine their use for activating the silver industries in Korea. The results of this study are as follows. First, according to the results of analyzing healthcare REITs' performance, healthcare REITs outperformed average REITs and extended investment proportion compared to total REITs after 2007. This outperforming came from not only rapid growth of silver industries, but also REITs strategy using the structure of sale-and-leaseback and triple-net lease. Second, healthcare REITs use selection and concentration strategy in terms of asset sector, but use portfolio diversification strategy in terms of investment region. Third, according to the analysis results, healthcare REITs are to be useful for activating the silver industries in Korea. In this context, major implications are presented to use healthcare REITs effectively.

• 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.

• 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.

• 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.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.