• The Journal of Asian Finance, Economics and Business
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• 제8권12호
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• pp.1-7
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• 2021

The study examines the influence of COVID-19 on the stock market returns of Saudi Arabia. The data was analyzed through event study methodology using daily price data of Tadawul All Share Index (TASI). The study examines the behavior pattern of the Saudi Arabian stock market in different phases during the event period by selecting six-event windows with a range of 10 days. The results report a negative Abnormal Return (AR) of -0.003 on the event date, while the abnormal returns reversed the next day to 0.005 positively. The result of Cumulative Abnormal Return (CAR) is negative and significant at the 1 percent level in all the six-event windows starting from the event date to day 59 after the event for the TASI index. Even though the influence of the COVID-19 pandemic decreased after 30 days of the event date, it increased during the last ten days of the event window. The stock market volatility of Saudi Arabia increased during the post-event period compared to the pre-event period with a negative mean return of -0.326 and a greater standard deviation. In a conclusion, the study found a significant influence of the COVID-19 pandemic on the stock market returns of TASI.

• 산업융합연구
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• 제1권1호
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• pp.127-142
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• 2003

This Study examines the implications for event studies using the daily stock data. The output present the event study results. The event period is defined from 30 days before through 30 days after the event date, and is broken into four "windows" for abnormal return cumulation: the pre-event period, days -30 through -2; dajys -1 and 0, a period commonly investigated for the immediate impact of the event; and the post-event period, days +1 through +30. It shows how firm's information offerings affect the price process and consequent issues. The Patell Z test is an examples of a standardized abnormal return approach, which estimate a separate standard error for each security-event and assumes cross-sectional independence. The generalized sign test adjusts for the fraction of positive abnormal returns in the estimation period instead of assuming 0.5.

• 정보처리학회논문지D
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• 제14D권5호
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• pp.483-490
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• 2007

최근에 데이터 스트림을 분석하여 잠재되어 있는 지식을 발견하기 위한 마이닝 방법에 대한 연구가 진행되고 있다. 그러나 대부분의 지지도 기반의 마이닝 방법들은 일정 주기 동안에 미리 정의된 지지도 이상의 발생 빈도를 갖는 이벤트만을 고려함으로써 발생 빈도에 비하여 중요도가 높은 이벤트를 간과하는 문제점을 가지고 있다. 본 논문에서는 이상 이벤트에 대한 연관 규칙을 탐사할 수 있는 SM-AF 방법을 제안한다. SM-AF 방법은 이상 이벤트가 감지된 윈도우만 고려하여 연관 정보를 탐사함으로써 자주 발생하지 않더라도 중요도가 높은 이벤트에 대한 연관 정보를 탐사할 수 있다. 또한 SM-AF 방법은 이상 이벤트에 대한 의미 있는 희소 항목 집합과 주기적인 이벤트 집합도 탐사한다. 그리고 다양한 실험을 통하여 SM-AF 방법이 기존의 연관 규칙 방법들에 비하여 우수함을 확인하였다.

• 정보과학회 논문지
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• 제41권12호
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• pp.1066-1074
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• 2014

본 논문에서는 시공간 정보를 포함하는 트윗 스트림에서 비정상적인 이벤트에 대한 상관관계를 사용자에게 시각적으로 분석하는 방법을 다양한 실험을 통하여 제안한다. 제안하는 방법으로는 트윗에서 토픽 모델링을 수행한 다음 계절요인과 추세요인을 반영한 시계열 분석 기법을 이용하여 비정상적인 이벤트 후보군을 추출한다. 추출된 토픽이 포함되어 있는 데이터를 대상으로 다시 한 번 토픽을 추출하여 시계열 분석을 수행한 다음 앞서 추출한 토픽과의 상관관계를 분석하여 비정상적인 이벤트를 탐지할 수 있도록 하였다. 비정상 이벤트를 탐지하는 모든 과정에 시각적 분석 방법을 이용하여 단순한 수치 정보가 아닌 시각적 패턴 형태로 나타냄으로써 사용자는 직관적으로 비정상 이벤트의 동향과 주기적인 패턴을 분석할 수 있도록 하였다. 실험은 2014년 1월 1일부터 2014년 6월 30일까지 국내에서 발생한 트윗을 대상으로 2개의 사건[경주 마우나 리조트 붕괴 사건(2014.02.17.), 진도 여객선 침몰 사건(2014.04.16.)]에 대해 시각적 분석 시스템을 적용하여 사용자는 쉽게 데이터를 분석하고 이해할 수 있음을 보였다.

• 유통과학연구
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• 제13권9호
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• pp.5-11
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• 2015

Purpose - The purpose of this research is to provide insights that can be used for deliberate decision making around challenging big data investments by measuring the economic value of such big data implementations. Research design, data, and methodology - We perform empirical research through an event study. To this end, we measure actual abnormal returns of companies that are triggered by their investment announcements in big data, or firm size information, during the three-year research period. The research period targets a timeframe after the introduction of big data at Korean firms listed on the Korea stock markets. Results - Our empirical findings discover that on the event day and the day after, the abnormal returns are significantly positive. In addition, our further examination of firm size impacts on the abnormal returns does not show any evidence of an effect. Conclusions - Our research suggests that an event study can be useful as an alternative means to measure the return on investment (ROI) for big data in order to lessen the difficulties or decision making around big data investments.

• 한국산업융합학회 논문집
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• 제26권2_1호
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• pp.217-223
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• 2023

With the rapid advancement of technologies, need for different research fields where this technology can be used is also increasing. One of the most researched topic in computer vision is object detection, which has widely been implemented in various fields which include healthcare, video surveillance and education. The main goal of object detection is to identify and categorize all the objects in a target environment. Specifically, methods of object detection consist of a variety of significant techniq ues, such as image processing and patterns recognition. Anomaly detection is a part of object detection, anomalies can be found various scenarios for example crowded places such as subway stations. An abnormal event can be assumed as a variation from the conventional scene. Since the abnormal event does not occur frequently, the distribution of normal and abnormal events is thoroughly imbalanced. In terms of public safety, abnormal events should be avoided and therefore immediate action need to be taken. When abnormal events occur in certain places, real time detection is required to prevent and protect the safety of the people. To solve the above problems, we propose a modified YOLOv5 object detection algorithm by implementing dilated convolutional layers which achieved 97% mAP50 compared to other five different models of YOLOv5. In addition to this, we also created a simple mobile application to avail the abnormal event detection on mobile phones.

• The Journal of Asian Finance, Economics and Business
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• 제8권5호
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• pp.523-531
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• 2021

Vietnam's Oil and gas industry make a significant contribution to the Gross Domestic Product of Vietnam. The ongoing COVID-19 pandemic has hit every industry hard, but perhaps the one industry which has taken the biggest hit is the global oil and gas industry. The purpose of this article is to examine how the COVID-19 pandemic affects the share price of the Vietnam Oil and Gas industry. The event study method applied to Oil and Gas industry index data around three event days includes: (i) The date Vietnam recognized the first patient to be COVID-19 positive was January 23, 2020; (ii) The second outbreak of COVID-19 infection in the community began on March 6, 2020; (iii) The date (30/3/2020) when Vietnam announced the COVID-19 epidemic in the whole territory. This study found that the share price of the Vietnam Oil and Gas industry responded positively after the event (iii) which is manifested by the cumulative abnormal return of CAR (0; 3] = 3.8% and statistically significant at 5 %. In the study, event (ii) has the most negative and strong impact on Oil and Gas stock prices. Events (i) favor negative effects, events (iii) favor positive effects, but abnormal return change sign quickly from positive to negative after the event date and statistically significant shows the change on investors' psychology.

• 인터넷정보학회논문지
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• 제20권6호
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• pp.137-142
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• 2019

본 논문은 이상 상황을 탐지하고 모니터링하는 다양한 서비스가 존재한다. 하지만 대부분의 서비스는 화재, 가스누출에 초점을 맞추어 진행되고 있으며, 독거노인과 중증장애인들의 사망 혹은 심정지 등 위급상황에 대하여 사전 예방 및 위급상황 대응이 불가능하다. 본 연구에서는 여러 생체신호 중 가장 위중하다고 판단되는 심박 신호의 이상 상태를 탐지하기 위하여 인공지능 모델을 설계하는 과정에서 적합한 데이터 변형과 모델을 비교한다. 세부적으로는 오픈 의료 데이터 PhysioNet의 MIT-BIH Arrhythmia Database를 이용하여 심전도(ECG) 데이터를 수집하고, 수집한 데이터를 각각 다른 방법으로 데이터를 변형한 후 학습하여 기본 심전도 데이터를 이용해 학습한 인공지능 모델과 비교한다.

• 유통과학연구
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• 제10권7호
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• pp.13-22
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• 2012

The effect of the September 2008 global financial crisis weighed heavily on stock markets around the world. The purpose of this study is to empirically investigate the impact of the crisis on Amman Stock Exchange. Event study methodology has been adopted on a period of 24 months, from January 2008 to December 2009. Monthly average abnormal returns across a sample of 52 industrial and services companies have been tested separately. The results reveal that Amman Stock Exchange experienced significant negative abnormal returns in the fourth quarter of the year 2008. However, there were no significant abnormal returns observed thereafter. This means that Amman Stock Exchange managed to overcome its adverse consequences. Since the event study tests for market efficiency, as well, the results show that Amman Stock Exchange reaction is consistent with the semi-strong form of the efficient market hypothesis.

• 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.