• KDI Journal of Economic Policy
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• v.32 no.3
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• pp.71-99
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• 2010

The concept of CoVaR introduced by Adrian and Brunnermeier (2009) is a useful tool to measure the risk spillover effect. It can capture the risk contribution of each institution to overall systemic risk. While Adrian and Brunnermeier rely on the quantile regression method in the estimation of CoVaR, we propose a new estimation method using parametric distribution functions such as bivariate normal and $S_U$-normal distribution functions. Based on our estimates of CoVaR for Korean banking industry, we investigate the practical usefulness of CoVaR for a systemic risk measure, and compare the estimation performance of each model. Empirical results show that bank makes a positive contribution to system risk. We also find that quantile regression and normal distribution models tend to considerably underestimate the CoVaR (in absolute value) compared to $S_U$-normal distribution model, and this underestimation becomes serious when the crisis in a financial system is assumed.

• Korean Journal of Construction Engineering and Management
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• v.5 no.2 s.18
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• pp.115-122
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• 2004

This study identifies various risk factors associated with activities of early construction stage, then establishes the Risk Breakdown Structure(RBS) by classifying the risks into the three groups; Common risks, risks for Earth works, and risks for Foundation works. The Common risks are identified and classified by considering various aspects of the early construction stage such as financial, political, constructional aspects, etc. The risks for Earth works and Foundation works are identified in detail by surveying technical specifications, relevant claim cases and interviewing with experts. These risks are classified based on the Wok Breakdown Structure(WBS) of the early construction stage. The WBS presented in this study classifies the works of early construction stage into four categories; excavation, sheeting works, foundation works, footing works. This study suggests a risk analysis method using fuzzy theory for construction projects. Construction risks are generally evaluated as vague linguistic value by subjective decision making. Fuzzy theory is a proper method to quantify vague conditions of construction activities. Therefore, this study utilizes fuzzy theory to analyse construction risks. The weight of risks is estimated by reflecting the interrelationship among risk factors from absolute weights obtained by fuzzy measure into the relative weights by Analytical Hierarchy Process(AHP). The interrelationship is estimated by Sugeno-fuzzy measure.

• Korean Journal of Construction Engineering and Management
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• v.14 no.3
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• pp.22-32
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• 2013

Generally, asset management procedure consists of exact information collection, decision of service level, analysis of aspiration level, analysis of financial condition and available budget, preparation of asset management plan, and value of modified asset. In this study, for the risk-based asset management, condition assessment and performance measuring, assessment of failure modes and risks, evaluation/selection of treatment options, and implementation of optimum solution are additionally included. For this, bridge inventory and performance measure considering risks are classified and method of quantitative/qualitative performance measure is suggested. Also, evaluation method of risk analysis for bridge asset management is suggested and basic research is carried out for applicable method of risk-based asset management. Using suggested risk procedure and method of risk-based bridge service level evaluation, it is possible to perform resonable asset management. Moreover, it is concluded that the proposed applicable method of risk-based asset management will provide a solution to contribute the development of systematical asset management for optimal decision making and prototype asset management system.

• The Korean Journal of Financial Management
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• v.27 no.2
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• pp.35-62
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• 2010

This paper examines the existence of the fund performance persistence and the smart money effect in Korean stock market and tests the flow-induced price pressure (FIPP) hypothesis, that is, fund flows affect individual stock returns and mutual fund performance. This paper also tests whether the FIPP effect can cause the performance persistence using the monthly returns and stock holdings data of 2,702 Korean mutual funds from January 2002 to June 2008. The empirical results indicate that the performance persistence exists significantly for a long time but the smart money effect does not. The hedge portfolio constructed by buying funds with the highest past 12 months performance and selling funds with the lowest past 12 months performance earns 0.11%~1.05% monthly abnormal returns, on average, in 3 years from portfolio formation month, but the hedge portfolio constructed by buying funds with the highest past net fund inflows and selling funds with the lowest past net fund inflows cannot earn positive monthly abnormal returns and the size of negative abnormal returns of the portfolio increase as time goes on. We find the evidence that the FIPP hypothesis is significantly supported. We first estimate the FIPP measure for each individual stock using the trading volume resulting from past fund flows and then construct the hedge portfolio by buying stocks with the highest FIPP measure and selling stocks with the lowest FIPP measure. That portfolio earns significantly positive abnormal return, 1.01% at only portfolio formation month and cannot earn significant abnormal returns after formation month. But, the FIPP effect cannot cause the performance persistence because, within the same FIPP measure group, funds with higher past performance still earn higher monthly abnormal returns than those with lower past performance by 0.08%~0.77%, on average, in 2 years. These results imply that the main cause of the performance persistence in Korean stock market is the difference of fund managers' ability rather than the FIPP effect.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.6
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• pp.857-872
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• 2014

This study investigates the effect of service ubiquity perceptions on consumers' responses to virtual stores such as benefit and risk perceptions, shopping value perceptions, and service usage intention. Data were collected via a self-administered online survey from nationwide consumer panels of an online marketing research firm. Questionnaire items were adopted from previous literature and developed by authors via pretesting to measure variables. The results revealed that virtual store service ubiquity affects consumer benefit perceptions as well as risk perceptions. All benefit perceptions (including time effectiveness, user control, and compatibility) had significant mediating effects between service ubiquity and hedonic/utilitarian shopping service value perceptions. The mediating effect of financial risk was significant only in the relationship between service ubiquity and utilitarian value perception. The findings offer retailers and marketers information in regards to consumers' perception of a virtual store usage, which can enhance service and product strategy.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.275-282
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• 2020

The objectives of this study are to understand the meaning of cloud accounting, to investigate whether it is favorable for performance of the organization and what are the challenges if a country like Bangladesh wants to implement it. Primary data have been collected from 300 respondents selected from the field of accounting, such as accountants, accounting graduates of different universities, teachers and bankers. To measure the reliability and validity of the sample size and data, KMO and Bartlett's test have been adopted and the results proved to be reliable and valid for the study. Regression analysis has been done to find out the positive impact of cloud accounting on organizational performance and negative impact of cloud accounting on existing accounting system of the organization. The results of regression analysis supported our alternative hypotheses that cloud accounting can improve organizational performance, but it has also some negative impacts. Descriptive statistics have been used to find out the probable challenges that may be faced by organizations that want to implement it. This is a pioneering study because there is little research on this topic, thus it is expected to develop awareness about cloud accounting in field of accounting in Bangladesh.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.485-494
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• 2020

The study aims to identify and measure factors affecting the salaries of employees in manufacturing enterprises in Hanoi, the important area of Vietnam's economy. We conducted a questionnaire consisting of 31 observation variables with a 5-point Likert scale. Independent variables were measured from 1 "without effect" to 5 "strongly". Based on the literature review and results of interviews, a total of 350 questionnaires were sent to participants; 300 of them met the standards and were subject to be analyzed. The results of Exploratory Factor Analysis (EFA) and Multiple Regression Analysis (MRA) identify six main determinants influencing the salaries of employees in manufacturing enterprises in Hanoi, including Paying views of business leaders (PV), Financial ability of the enterprise (FA), Capacity of workers (CW), Capacity of the contingent of employees engaged in salary work (CC), Role of grassroots trade unions (TU), and State policies and laws on labor - salaries (STL). Based on the findings, some recommendations have been proposed to help the firm leaders design appropriate personnel policies for creating better job satisfactions for employees in the future. On this basis, the authors propose a number of recommendations to improve the salaries of employees in manufacturing enterprises in Hanoi.

• Journal of Information Technology Applications and Management
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• v.27 no.1
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• pp.1-13
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• 2020

This study aims to measure the efficiency and productivity change of 30 domestic construction companies from 2010 to 2018 using data envelopment analysis(DEA) and Malmquist productivity index (MI). In particular, we used the number of employees, capital stock, and non-current assets as input variables, and sales and net income as ouput variables for the analysis. The dataset used for the analysis of efficiency and productivity changes is the employee profile and financial statements for the companies from 2010 to 2018. We found that the MI of the 30 companies is greater than one since 2013. This is because many years of TEC (Technical Efficiency Change) is greater than 1, which means that the productivity index increases as the TEC increases. In addition, the MI value was less than 1, which lowered the productivity of construction firms in 2018. The results of the study may help decision makers to find effective future management plans by analyzing the internal and external factors.

• Journal of Korean Academy of Nursing
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• v.32 no.3
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• pp.395-405
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• 2002

The purpose of this study was to develop the instrument to measure family functioning for Korean family with a chronic ill child, and to test the validity and reliability of the instrument. Method: The items of instrument were consisted based on researchers' previous study of concept analysis of the Korean family functioning. Twenty six item scale was developed with six domains. In order to test reliability and validity of the scale, data were collected from the 231 families, who have a child with a chronic illness. Data was collected between August and September in 2001 in a General Hospital in Seoul, Korea. Result: The results were as follows: As a result of the item analysis, 24 items were selected from the total of 26 items, excluding items with low correlation with total scale. Six factors were evolved by factor analysis. Six factors explained 61.4% of the total variance. The first factor 'Affective bonding' explained 15.4%, 2nd factor 'External relationship' 11.8%, 3rd factor 'Family norm' 10.5%, 4th factor 'Role and responsibilities' 8.3%, 5th factor ' Communication' 7.9%, and the 6th factor 'Financial resource' explained 7.3%. Cronbach's $\alpha$ coefficient of this scale was .87 and Guttman spilt- half coefficient was .84. Conclusion: The study support the reliability and validity of the scale. There were distinct differences in dimensions of family functioning scales developed in the U. S.