• 산업경영시스템학회지
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• 제36권3호
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• pp.34-42
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• 2013

In this study, we develop a method for analyzing business process based on the event-driven process chain (EPC) model. The method consists of five stages such as identifying abnormal events, finding causes for the abnormal events and problems caused by the abnormal events, making cause-and-effect chains, drawing root-cause map, and defining improvement areas. We illustrate how to apply the method with some examples for the domestic registered mail delivery process.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.552-554
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• 2003

This paper is about the method by which Power Control Unit(PCU) of Control Rod Control System(CRCS) logs events in the system and the real-time monitoring display. This method enables the functions like the event logging of Control Rod Drive Mechanism(CRDM)/power Cabinet, the off-line show of the event data logged and the on-line show by communication between the PCU and the monitoring display. Operators in a nuclear power plant must be able to grasp any possible abnormal states correctly. Because our newly designed system has a good ability to log and display the kinds, tine, and the prior and posterior states of urgent or non-urgent events, the operators can judge, maintain and repair the abnormal event more easily.

• The Journal of Asian Finance, Economics and Business
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• 제7권9호
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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.

• The Journal of Asian Finance, Economics and Business
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• 제7권7호
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• pp.131-137
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• 2020

The research investigates the impact of the lockdown period caused by the COVID-19 to the stock market of India. The study examines the extent of the influence of the lockdown on the Indian stock market and whether the market reaction would be the same in pre- and post-lockdown period caused by COVID-19. Market Model Event study methodology is used. A sample of 31 companies listed on Bombay Stock Exchange (BSE) are selected at random for the purpose of the study. The sample period taken for the study is 35 days (24 February-17 April, 2020). An event window of 35 days was taken with 20 days prior to the event and 15 days during the event. The event (t1) being the official announcement of the lockdown. The results indicate that the market reacted positively with significantly positive Average Abnormal Returns during the present lockdown period, and investors anticipated the lockdown and reacted positively, whereas in the pre-lockdown period investors panicked and it was reflected in negative AAR. The study finds evidence of a positive AR around the present lockdown period and confirms that lockdown had a positive impact on the stock market performance of stocks till the situation improves in the Indian context.

• The Journal of Asian Finance, Economics and Business
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• 제7권9호
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• pp.63-74
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• 2020

This study investigates the behavior of foreign investors in the Stock Exchange of Thailand (SET) in the time of coronavirus disease 2019 (COVID-19) as to whether trading is abnormal, what strategy is followed, whether herd behavior is present, and whether the actions destabilize the market. Foreign investors' trading behavior is measured by net buying volume divided by market capitalization, whereas the stock market behavior is measured by logged return on the SET index portfolio. The data are daily from Tuesday, August 28, 2018, to Monday, May 18, 2020. The study extends the conditional-regression model in an event-study framework and extracts the unobserved abnormal trading behavior using the Kalman filtering technique. It then applies vector autoregressions and impulse responses to test for the investors' chosen strategy, herd behavior, and market destabilization. The results show that foreign investors' abnormal trading volume is negative and significant. An analysis of the abnormal trading volume with stock returns reveals that foreign investors are not positive-feedback investors, but rather, they self-herd. Although foreign investors' abnormal trading does not destabilize the market, it induces stock-return volatility of a similar size to normal trade. The methodology is new; the findings are useful for researchers, local authorities, and investors.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.39-47
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• 2021

This research aims to confirm if the COVID-19 pandemic has had an impact on existing sectors, and how that affects the Indonesian Stock Exchange (IDX) market returns. The research method used is an event study employing market models in nine sectors of the Exchange with purposive sampling technique, and supported by Ordinary Least Square (OLS) regression. Based on the calculation of abnormal returns in the period of 30 days before up to 30 days after, the financial property, real estate, and construction sector results show a decreased abnormal return value. The infrastructure, utilities, and transportation sectors also show an abnormal return value that tends to be constant, while the abnormal return value increases in other sectors. Judging from the cumulative value of abnormal returns, the most affected sector is financials, followed by the trade, service, and investment sectors. The consumer goods and mining industry sectors are still optimistic, while other sectors show temporary negative sentiment. Overall, the stocks on the Indonesia Stock Exchange (IDX) were affected by the COVID-19 pandemic with a cumulative negative value of the average abnormal return sample. The results using OLS regression also strengthen the relationships between the COVID-19 pandemic, and negative and significant market returns.

• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.41-51
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• 2022

The research looks into the impact of stock split announcements on stock prices and market efficiency in the Colombo Stock Exchange (CSE). This research uses a sample of 26 stock split announcements that occurred between 2020 and June 2021. According to the Global Industry Classification Standards, the stock split announcements covered in the study pertain to 26 businesses and 9 industries (GICS). To obtain the results, the usual event research methodology is used. The findings demonstrate significant average abnormal returns of 15.01 percent on the day the stock split news is made public and abnormal returns of 4.11 percent and -4.05 percent one day before and after the stock split announcement date, respectively. The study's findings revealed significant positive abnormal returns one day before the disclosure date, indicating information leakage, and significant negative abnormal returns the next day after the announcement date, indicating CSE informational efficiency. Because stock prices adapt so quickly to public information, these findings support the semi-strong form efficient market hypothesis, which states that investors cannot gain an abnormal return by trading in stocks on the day of the stock split announcement.

• 경영과학
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• 제27권3호
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• pp.161-196
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• 2010

This paper empirically investigates the impact of winning a service quality award on the market value in Korea. We estimates the mean "abnormal" change in the stock prices of sample firms when information of winning a service quality award was publicly announced. To access the validity of the research question, this paper employed collected 47 firms data that received the Korean Service Quality Awards so far. Event study methodology was used to analyze the effect of Korean service quality awards. The findings are as follows; The average abnormal returns on the event date are not significant at the 0.05 level which means that the receiving Korean Service Quality Awards has no influence on the firms' market value. On the other hand successive awarded firms have an increasing effect on the market value and it is significant at the 0.05 level. Furthermore, the results show that the factors of firm size such as firm's total assets are critical to vary the firms' abnormal returns. There might be some limitations in this study. The most obvious problem is the limitation of sample size. Although 518 sample cases were found during the period from 2000 to 2008, most of the cases were deleted according to the sample criteria. We are expecting the future research with more data and more precise results. Furthermore, our research consider the only two service award institutions even though there are several different service award authorities in Korea. It is needed to expand the research scope and range to adopt the various service award institutions for the future work.

• Journal of Animal Science and Technology
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• 제63권1호
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• pp.77-90
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• 2021

This study aimed to assess the impact of abnormal climate events on the production of Italian ryegrass (IRG), such as autumn low-temperature, severe winter cold and spring droughts in the central inland, southern inland and southern coastal regions. Seasonal climatic variables, including temperature, precipitation, wind speed, relative humidity, and sunshine duration, were used to set the abnormal climate events using principal component analysis, and the abnormal climate events were distinguished from normal using Euclidean-distance cluster analysis. Furthermore, to estimate the impact caused by abnormal climate events, the dry matter yield (DMY) of IRG between abnormal and normal climate events was compared using a t-test with 5% significance level. As a result, the impact to the DMY of IRG by abnormal climate events in the central inland of Korea was significantly large in order of severe winter cold, spring drought, and autumn low-temperature. In the southern inland regions, severe winter cold was also the most serious abnormal event. These results indicate that the severe cold is critical to IRG in inland regions. Meanwhile, in the southern coastal regions, where severe cold weather is rare, the spring drought was the most serious abnormal climate event. In particular, since 2005, the frequency of spring droughts has tended to increase. In consideration of the trend and frequency of spring drought events, it is likely that drought becomes a NEW NORMAL during spring in Korea. This study was carried out to assess the impact of seasonal abnormal climate events on the DMY of IRG, and it can be helpful to make a guideline for its vulnerability.

• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3620-3630
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• 2022

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.