• 한국멀티미디어학회논문지
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• 제19권7호
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• pp.1107-1115
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• 2016

Efficient Market Hypothesis (EMH), states that at any point in time in a liquid market security prices fully reflect all available information. This paper presents a study of proving the hypothesis through daily Twitter sentiments using the hybrid approach of the lexicon-based approach and the naïve Bayes classifier. In this research we analyze the currency exchange rate movement of Indonesia Rupiah vs US dollar as a way of testing the Efficient Market Hypothesis. In order to find a correlation between the prediction sentiments from Twitter data and the actual currency exchange rate trends we collect Twitter data every day and compute the overall sentiment to label them as positive or negative. Experimental results have shown 69% correct prediction of sentiment analysis and 65.7% correlation with positive sentiments. This implies that EMH is semi-strong Efficient Market Hypothesis, and that public information provide by Twitter sentiment correlate with changes in the exchange market trends.

• 재무관리연구
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• 제26권1호
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• pp.189-220
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• 2009

본 연구는 약형 EMH에 대한 반론으로 제기되어 왔던 모멘텀전략과 반대전략의 우월성에 대하여 검정하였다. 모멘텀전략과 반대전략이 우월한 전략이라면 이는 약형 EMH에 대한 중대한 비판이 된다. 하지만 몬테카를로 시뮬레이션 결과에 따르면 기존의 검정방법은 유의수준왜곡이라는 오류를 갖고 있는 것으로 나타났다. 이에 본 연구는 데이터 스누핑 편의를 해결하는 것으로 알려진 White (2000)의 사실성 체크검정을 이용하여 모멘텀전략과 반대전략의 우월성을 검정하였다. 검정결과는 다음과 같다. 종합주가지수에 대한 정액정기매입전략을 벤치마크 포트폴리오로 정하였을 때 평균수익률을 이용하면 모멘텀전략 중 최선의 전략은 벤치마크 포트폴리오보다 우월한 것으로 나타났다. 하지만 위험을 고려한 성과측정치인 샤프비율을 이용할 경우 모멘텀전략과 반대전략 중 최선의 전략은 우월한 전략이라고 볼 수 없었다. 따라서 위험을 고려한다면 모멘텀전략과 반대전략의 우월성을 근거로 약형 EMH를 기각할 수는 없다.

• 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.

• The Journal of Asian Finance, Economics and Business
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• 제7권8호
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• pp.185-195
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• 2020

This study examines the association between governance quality at country level and stock market performance. Specifically, the study investigates the influence of control of corruption, government effectiveness, political stability and absence of violence, rule of law, regulatory quality, and voice and accountability on all-share index of the stock markets of the six Gulf Cooperation Council (GCC) countries. This study is anchored on two theories - the Efficient Market Hypothesis (EMH) and Institutional Theory. The study employs panel data spanning from 2006 to 2017. The findings show that political stability and absence of violence and rule of law exhibit a significant positive impact on stock market performance, while regulatory quality and voice and accountability have a significant, but negative relationship with stock market performance. The results imply that quality of governance in terms of rule of law and political stability devoid of violence have strong impact on stock market returns. Similarly, improved stock market returns are largely dependent on the efficiency of the institutional environment of market as investors are always wary of the inherent risks associated with the uncertainty of the market. This study has crucial policy implications for the government of the GCC countries and stock market participants.

• Asia pacific journal of information systems
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• 제24권2호
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• pp.233-253
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• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.