• Title/Summary/Keyword: Abnormal event

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Sharia Stock Reaction Against COVID-19 Pandemic: Evidence from Indonesian Capital Markets

  • RYANDONO, Muhamad Nafik Hadi;MUAFI, Muafi;GURITNO, Agung
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.2
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    • pp.697-710
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    • 2021
  • The purpose of this study is to explore the reaction of sharia stock in the Indonesian capital market to the global Covid-19 pandemic. The method used in this study is an event study with a Market Adjusted Model (MAM) approach. The population of this study is shares listed on the Indonesian Stock Exchange (IDX), with the sample chosen from the Jakarta Sharia (Islamic) Index. The result of this study found that the global Covid-19 pandemic is bad news, with the indicators as follows: a) the average expected return is negative; b) the average actual return is negative; c) the average abnormal return is negative, and d) the increase selling action of stock as a cut loss strategy. There is a negative abnormal return and significant Trading Volume Activity (TVA) before, during, and after the announcement of the global Covid-19 pandemic. However, this study found no difference in abnormal return and TVA before and after the announcement of the global Covid-19 pandemic. From these results, this study indicates that the sharia stocks in the capital market in Indonesia can respond quickly to the information that existed. Therefore, the capital market of Indonesia is a capital market with a semi-strong efficient form.

Proposing a New Approach for Detecting Malware Based on the Event Analysis Technique

  • Vu Ngoc Son
    • International Journal of Computer Science & Network Security
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    • v.23 no.12
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    • pp.107-114
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    • 2023
  • The attack technique by the malware distribution form is a dangerous, difficult to detect and prevent attack method. Current malware detection studies and proposals are often based on two main methods: using sign sets and analyzing abnormal behaviors using machine learning or deep learning techniques. This paper will propose a method to detect malware on Endpoints based on Event IDs using deep learning. Event IDs are behaviors of malware tracked and collected on Endpoints' operating system kernel. The malware detection proposal based on Event IDs is a new research approach that has not been studied and proposed much. To achieve this purpose, this paper proposes to combine different data mining methods and deep learning algorithms. The data mining process is presented in detail in section 2 of the paper.

A Study on the Prediction of Stock Return in Korea's Distribution Industry Using the VKOSPI Index

  • Jeong-Hwan LEE;Gun-Hee LEE;Sam-Ho SON
    • Journal of Distribution Science
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    • v.21 no.5
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    • pp.101-111
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    • 2023
  • Purpose: The purpose of this paper is to examine the effect of the VKOSPI index on short-term stock returns after a large-scale stock price shock of individual stocks of firms in the distribution industry in Korea. Research design, data, and methodology: This study investigates the effect of the change of the VKOSPI index or investor mood on abnormal returns after the event date from January 2004 to July 2022. The significance of the abnormal return, which is obtained by subtracting the rate of return estimated by the market model from the rate of actual return on each trading day after the event date, is determined based on T-test and multifactor regression analysis. Results: In Korea's distribution industry, the simultaneous occurrence of a bad investor mood and a large stock price decline, leads to stock price reversals. Conversely, the simultaneous occurrence of a good investor mood and a large-scale stock price rise leads to stock price drifts. We found that the VKOSPI index has strong explanatory power for these reversals and drifts even after considering both company-specific and event-specific factors. Conclusions: In Korea's distribution industry-related stock market, investors show an asymmetrical behavioral characteristic of overreacting to negative moods and underreacting to positive moods.

A Study on Reversals after Stock Price Shock in the Korean Distribution Industry

  • Jeong-Hwan, LEE;Su-Kyu, PARK;Sam-Ho, SON
    • Journal of Distribution Science
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    • v.21 no.3
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    • pp.93-100
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    • 2023
  • Purpose: The purpose of this paper is to confirm whether stocks belonging to the distribution industry in Korea have reversals, following large daily stock price changes accompanied by large trading volumes. Research design, data, and methodology: We examined whether there were reversals after the event date when large-scale stock price changes appeared for the entire sample of distribution-related companies listed on the Korea Composite Stock Price Index from January 2004 to July 2022. In addition, we reviewed whether the reversals differed depending on abnormal trading volume on the event date. Using multiple regression analysis, we tested whether high trading volume had a significant effect on the cumulative rate of return after the event date. Results: Reversals were confirmed after the stock price shock in the Korean distribution industry and the return after the event date varied depending on the size of the trading volume on the event day. In addition, even after considering both company-specific and event-specific factors, the trading volume on the event day was found to have significant explanatory power on the cumulative rate of return after the event date. Conclusions: Reversals identified in this paper can be used as a useful tool for establishing a trading strategy.

Risk Evaluation of Slope Using Principal Component Analysis (PCA) (주성분분석을 이용한 사면의 위험성 평가)

  • Jung, Soo-Jung;Kim, -Yong-Soo;Kim, Tae-Hyung
    • Journal of the Korean Geotechnical Society
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    • v.26 no.10
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    • pp.69-79
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    • 2010
  • To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.

Firm's Market Value Trends after Information Security Management System(ISMS) Certification acquisition (정보보호 관리체계 인증 취득 후 기업가치의 변화에 관한 연구)

  • Jo, Jung-Gi;Choi, Sang-Hyun
    • Journal of the Korea Convergence Society
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    • v.7 no.6
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    • pp.237-247
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    • 2016
  • This study analyzed quantitative effects of ISMS certification. To measure the company value change the stock data was used and the methodology of event study was also applied. Event study methodology is a method of analyzing the effects of information or public announcement about certain events on the stock market through abnormal return of stock price. First, ISMS certification was acquired followed by the measurement of abnormal excess return of company. Based on the increase or decrease of abnormal excess return, the group was classified. There are 3 types of groups("Increase", "Reduce", "Maintain"). Next, the cluster analysis was performed for each group. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups(clusters). The purpose of this study is to have a quantitative measurement of performance of ISMS certification. So, the result of this study will be promoted a company's ISMS certification acquisition. And it would further be beneficial to your company's information security activities.

Detection of Abnormal Signals in Gas Pipes Using Neural Networks

  • Min, Hwang-Ki;Park, Cheol-Hoon
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.669-670
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    • 2008
  • In this paper, we present a real-time system to detect abnormal events on gas pipes, based on the signals which are observed through the audio sensors attached on them. First, features are extracted from these signals so that they are robust to noise and invariant to the distance between a sensor and a spot at which an abnormal event like an attack on the gas pipes occurs. Then, a classifier is constructed to detect abnormal events using neural networks. It is a combination of two neural network models, a Gaussian mixture model and a multi-layer perceptron, for the reduction of miss and false alarms. The former works for miss alarm prevention and the latter for false alarm prevention. The experimental result with real data from the actual gas system shows that the proposed system is effective in detecting the dangerous events in real-time with an accuracy of 92.9%.

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Real-time information effect of patent listing disclosure (특허권 취득 공시와 한국유가증권시장의 실시간 정보효율성에 관한 연구)

  • Lee, Jong-Wook;Kim, Jong-Yoon
    • Management & Information Systems Review
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    • v.35 no.3
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    • pp.195-212
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    • 2016
  • Utilizing intra-day volume weighted average price (VWAP) based on 1 minute return data of stocks traded on the Korean Stock Exchange, this paper examines and analyzes abnormal returns in reaction to patent listing disclosures as well as the cumulative abnormal returns, traded volumes, the interaction of VWAP spreads, the reaction of volumes, the reaction of VWAP spreads and the realized returns obtained from trading using an event driven arbitrage strategy. The results of the aforementioned research topics are follows. First, our analysis suggests that on average, 0.92% positive cumulative returns arise 1 minute after the patent listing disclosure announcement with high statistical significance, thereby reconfirming that the Korean stock market is a semi-strong form of the efficient market. Employing 3 separate panel tests differentiated by the size factor, we find that the abnormal returns of small sized stocks were less than the returns of medium sized stocks, which goes to support recent research findings suggesting that the size premium is no longer existent in the Korean stock market. Secondly, we show that among the event driven type strategies, the most outstanding realized returns are from the market making strategies. Furthermore, placing market order trades only at the bid or ask price resulted in negative returns. This implies that strategies utilizing a combination of market orders and limit orders, order cancelations ratios and order flows can enhance realized returns.

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ISO9000 Certification Effect: Evidence from China

  • Liu, Yumin
    • International Journal of Quality Innovation
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    • v.9 no.3
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    • pp.15-27
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    • 2008
  • As a sign of international quality system, ISO9000 certification has been adopted by more and more enterprises. In recent five years, there have been 560,000 certified companies in Europe and America, and there have been more than 390,000 ones in China. It has being attracted many quality scholars' attentions whether ISO9000 certification can bring more benefits to certified companies or not. This paper investigates the ISO9000 certification effect on market performance by the samples from Chinese list companies in shanghai stock exchange. Considering ISO9000 certification as an event, a certification effect model will be set up by means of the event study method, which takes abnormal return rate as a basic indicator to measure the ISO9000 certification effect on Chinese market performance in different event times. Investigation results show that the Chinese certified companies have some positive effects on market performance in the short term. From a long standpoint, the relation between certification and performance has a positive trend.

Classification Abnormal temperatures based on Meteorological Environment using Random forests (랜덤포레스트를 이용한 기상 환경에 따른 이상기온 분류)

  • Youn Su Kim;Kwang Yoon Song;In Hong Chang
    • Journal of Integrative Natural Science
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    • v.17 no.1
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    • pp.1-12
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    • 2024
  • Many abnormal climate events are occurring around the world. The cause of abnormal climate is related to temperature. Factors that affect temperature include excessive emissions of carbon and greenhouse gases from a global perspective, and air circulation from a local perspective. Due to the air circulation, many abnormal climate phenomena such as abnormally high temperature and abnormally low temperature are occurring in certain areas, which can cause very serious human damage. Therefore, the problem of abnormal temperature should not be approached only as a case of climate change, but should be studied as a new category of climate crisis. In this study, we proposed a model for the classification of abnormal temperature using random forests based on various meteorological data such as longitudinal observations, yellow dust, ultraviolet radiation from 2018 to 2022 for each region in Korea. Here, the meteorological data had an imbalance problem, so the imbalance problem was solved by oversampling. As a result, we found that the variables affecting abnormal temperature are different in different regions. In particular, the central and southern regions are influenced by high pressure (Mainland China, Siberian high pressure, and North Pacific high pressure) due to their regional characteristics, so pressure-related variables had a significant impact on the classification of abnormal temperature. This suggests that a regional approach can be taken to predict abnormal temperatures from the surrounding meteorological environment. In addition, in the event of an abnormal temperature, it seems that it is possible to take preventive measures in advance according to regional characteristics.