• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.81-90
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• 2018

Modern investment theory has empirically proved that stock returns can be explained by several factors such as market risk, firm size, and book-to-market ratio. Other unknown factors affecting stock returns are also believed to still exist yet to be found. We believe that one of such factors is the operational efficiency of firms in transforming inputs to outputs, considering the fact that operations is a fundamental and primary function of any type of businesses. To support this belief, this study intends to empirically study the relationship between firm efficiency and stock price performance. Firm efficiency is measured using data envelopment analysis (DEA) with inputs and outputs obtained from financial statements. We employ cross-efficiency evaluation to enhance the discrimination power of DEA with a secondary objective function of aggressive formulation. Using the CAPM-based performance regression model, we test the performance of equally weighted portfolios of different sizes selected based upon DEA cross-efficiency scores along with a buy & hold trading strategy. For the empirical test, we collect financial data of domestic firms listed in KOSPI over the period of 2000~2016 from well-known financial databases. As a result, we find that the porfolios with highly efficient firms included outperform the benchmark market portfolio after controlling for the market risk, which indicates that firm efficiency plays a important role in explaining stock returns.

• The Journal of Information Systems
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• v.28 no.3
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• pp.159-202
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• 2019

Purpose The purpose of this study is to explain users' wearable healthcare device adoption using performance expectancy, effort expectancy, the hedonic motivation and price value of UTAUT2, and to identify the causal relationship between intention to use wearable healthcare device and innovation resistance formed by perceived risks. Design/methodology/approach The research model proposed in this study is based on UTAUT2(Extended Unified Theory of Acceptance and Use of Technology) and MIR(Model of Innovation Resistance). In specific, performance expectancy, effort expectancy, hedonic motivation and price value of UTAUT2 and innovation resistance formed by perceived risks of MIR are adopted in our research model. To validate the research model, we carry out the analysis of the survey data using Smart PLS 3.0 to test the hypotheses. Findings According to the empirical analysis results, this study confirms that the performance expectancy, effort expectancy, hedonic motivation, and price value have significant effects on the intention to use wearable healthcare devices. It also finds that perceived risk affects innovation resistance and in turn, innovation resistance affects the intention to use wearable healthcare devices.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.487-493
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• 2009

We consider a multiple defaultable zero coupon bond. Assuming defaults occur according to a marked point process, we explain how to estimate the time-t discounted price of zero coupon bond by simulation. For the special case of a given specific random face value, we show that the real probability measure is the risk neutral probability measure. In this case the time-t discounted conditional price can be obtained by observing a single sample path upto the time t in the real world. Furthermore the time-t discounted price can be estimated by observing real situations or by simulation under the real probability measure.

• Proceedings of the KIEE Conference
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• summer
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• pp.701-703
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• 2004

Although the electricity market structure worldwide may be different in kinds, there generally exists long-term forward market and short-term spot market. Particularly, the bilateral contract in long-term forward market fixes the price between a genco and a customer so that the customer can avoid risks due to price-spike in spot market. The genco also can make an efficient risk-hedge strategy through this bilateral contract. In this paper, we propose a new mechanism for evaluating the optimal bilateral contract price using game theory. This mechanism makes a customer reveal his/her own willingness to purchase electricity so that a fair bilateral contract price can be derived.

• The KIPS Transactions:PartD
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• v.14D no.4 s.114
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• pp.395-406
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• 2007

The development of e-commerce made a great effect on all the aspect of marketing mix. Especially, marketing stimuli of Halfplaza.com, excessive price discounts and auction by order of arrival, spread out gradually in internet shopping mall industry. The study showed that excessive price discounts combined with auction by order of arrival increased consumer's perceived value, but decreased perceived risk. In conclusion, legal protections must be established since consumers tend to be vulnerable to various marketing mix of deceptive e-commerce players.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.6
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• pp.535-545
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• 2013

The goal of stock investment is earning high rate or return with stability. To accomplish this goal, using a portfolio that distributes stocks with high rate of return with less variability and a stock price prediction model with high accuracy is required. In this paper, three methods are suggested to require these conditions. First of all, in portfolio re-balance part, Max-Return and Min-Risk (MRMR) model is suggested to earn the largest rate of return with stability. Secondly, Entering/Leaving Rule (E/L) is suggested to upgrade portfolio when particular stock's rate of return is low. Finally, to use outstanding stock price prediction model, a model based on Semi-Supervised Learning (SSL) which was suggested in last research was applied. The suggested methods were validated and applied on stocks which are listed in KOSPI200 from January 2007 to August 2008.

• Journal of Integrative Natural Science
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• v.14 no.3
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• pp.130-138
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• 2021

The outbreak of COVID-19 has had a huge impact on human life. The World Bank group (WBG) has stated that 2020 is the worst year since World War II for economic growth. An epidemic of an infectious disease such as COVID-19 is classified as a "social disaster" by law. The social disaster caused by COVID-19 puts certain industries, occupations and vulnerable groups at risk of exclusion and isolation. This paper intends to examine the fluctuations in the consumer price index in the cultural, sports and tourism sector before and after the onset of COVID-19. In addition, it predicts the consumer price index by sector until December 2021 and reveals its implications.

• Journal of Korea Port Economic Association
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• v.33 no.1
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• pp.75-87
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• 2017

The purpose of this study is to utilize the system dynamics to carry out a medium and long-term forecasting analysis of the bunker price. In order to secure accurate bunker price forecast, a quantitative analysis was established based on the casual loop diagram between various variables that affects bunker price. Based on various configuration variables such as crude oil price which affects crude oil consumption & production, GDP and exchange rate which influences economic changes and freight rate which is decided by supply and demand in shipping and logistic market were used in accordance with System Dynamics to forecast bunker price and then objectivity was verified through MAPEs. Based on the result of this study, bunker price is expected to rise until 2029 compared to 2016 but it will not be near the surge sighted in 2012. This study holds value in two ways. First, it supports shipping companies to efficiently manage its fleet, offering comprehensive bunker price risk management by presenting structural relationship between various variables affecting bunker price. Second, rational result derived from bunker price forecast by utilizing dynamic casual loop between various variables.

• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.39-50
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• 2022

Purpose - The purpose of this study is to examine how Korea's macroeconomic factors, such as GDP, CPI, Export, Import, Unemployment rate and USD/KRW exchange rate, are affected by the oil price shocks. Design/methodology/approach - This study used monthly and quarterly time-series data of each variable for the period 1983 to 2022, consisting of two sub-periods, to employ Granger causality test and GARCH method in order to identify the role of the oil price movement in macroeconomic factors in Korea. Findings - Korea's currency rate to the US dollar is negatively correlated with the price change of crude oil while the GDP change is positively correlated with the price change of crude oil with strong relationship between Export and Import in particular. The exchange rate and GDP growth are believed to be not correlated with the oil price change for the pre-GFC period. According to the Granger causality test, the price change in crude oil has a causal impact on CPI, Export and Import while other factors are relatively slightly affected. Transmission effect from the oil price to Export is found and there also exists volatility spillover from oil price to economic variables under examination. Comparing two sub-periods, CPI and Export volatility responds negatively to shocks in the oil price for the pre-GFC period while volatility of CPI and Unemployment reacts positively to the oil price shocks for the post-GFC period. Research implications or Originality - The findings of this study could be helpful for both domestic and international investors to build their portfolio for the risk management since rising WTI price can be interpreted as a result of global economic growth and ensuing increase in the worldwide demand of the crude oil. Consequently, the national output is expected to increase and the currency is also expected to be strong in the long run.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.