• Asia pacific journal of information systems
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• v.24 no.2
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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.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.913-921
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• 2021

The objective of the study is to investigate the impact of the COVID-19 pandemic on Saudi Arabia stock market. The study relied on the data of the daily closing stock market price index Tadawul All Share Index (TASI), and the number of daily cases infected with COVID-19 during the period from March 15, 2020, to August 10, 2020. The study employs the Vector Auto-Regressive (VAR) model, the Impulse Response Function (IRF) and Autoregressive Conditional Heteroscedasticity (ARCH) models. The results of the correlation matrix and the Impulse Response Function (IRF) show that stock market returns responded negatively to the growth in COVID-19 infected cases during the pandemic. The results of ARCH model confirmed the negative impact of COVID-19 pandemic on KSA stock market returns. The results also showed that the negative market reaction was strong during the early days of the COVID-19 pandemic. The study concluded that stock market in KSA responded quickly to the COVID-19 pandemic; the response varies over time according to the stage of the pandemic. However, the Saudi government's response time and size of the stimulus package have played an important role in alleviating the impacts of the COVID-19 pandemic on Saudi Arabia Stock Market.

• Management & Information Systems Review
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• v.38 no.3
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• pp.1-19
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• 2019

This paper investigates the asymmetric effects of crude oil price uncertainty on industrial stock returns under different market conditions (bearish and bullish stock markets). We consider a quantile regression method using monthly oil volatility index, KOSPI and 22 industrial stock indices from May 2007 to February 2019. Especially, we take care of the positive and negative changes of the oil volatility index to analyze asymmetric effects of the oil price uncertainty for the bearish and bullish stock market conditions. During the bearish markets, the oil volatility index has relatively strong statistically significant negative effects on the industrial stock returns. These effects gradually decrease when the market conditions became more bullish markets. In particular, positive changes in the oil volatility index yields a further significant decrease in 12 industrial stock returns during the extreme bearish markets. Moreover, during the bullish markets, negative changes in the oil volatility index have statistically significant negative effects on the 12 industrial stock returns. From the empirical results, we see that participants of the Korean stock market are sensitive to bad news in a recession.

• East Asian Journal of Business Economics (EAJBE)
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• v.4 no.4
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• pp.1-7
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• 2016

This paper has studied the impacts of the exchange rate, government debt as a percent of GDP and other relevant macroeconomic variables on aggregate output in the Philippines. A simultaneous-equation model consisting of aggregate demand and short-run aggregate supply is applied. The dummy variable technique is employed to detect whether the slope and intercept of the real effective exchange rate may have changed. Real depreciation during 1998.Q1 - 2006.Q3, real appreciation during 2006.Q4 - 2016.Q1, a lower domestic debt as a percent of GDP, a lower real interest rate, a higher stock price or a higher lagged real oil price would raise aggregate output. Recent trends of real peso appreciation, declining domestic debt as a percent of GDP, lower real interest rates, and rising stock prices are in line with the empirical results and would promote economic growth. The authorities may need to continue to pursue fiscal prudence and maintain a stronger peso as the positive effect of real appreciation dominates its negative effect in recent years.

• International Commerce and Information Review
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• v.7 no.4
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• pp.325-349
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• 2005

One aspect of the globalization of the financial market after the 1980s is represented by the concurrent monetarization of the national stock markets. As the IR activity is regarded as a new financial productivity measure, the IR activity in the stock market is being emphasized domestically and internationally. This study analyzes domestic IR activities and compares them with foreign IR activities. Specifically the "road show", a typical IR activity, which is known to resolve the information asymmetry between the firm and the investors is analyzed to see the extent of the their value increase impact on the firm. The study employs domestic and international firms that publicly announced "road shows" after April 2004. Event studies are done to see the existence of abnormal return after the public announcement of road shows. Domestic firms were found to have positive IR impacts on the stock prices, but international firms were found to have negative IR impacts on the stock prices. Also it was found that international public announcement of the road show have stronger positive impact on the stock price than domestic public announcement. The investigation of the statistically significant difference of CAR before and after the fair public announcement enforcement rule showed that the positive CAR impact is strengthened after the adoption of the rule. The conclusion is that increase of the firm value after the road show implies that the information asymmetry is reduced by the active IR actions on the firm side. The policy implication is that we have to reassure the understanding of the role of the IR activities. Specifically Korean firms may have to encourage IR activities to share the information of the firms with the investors, which may result in the trustworthy relationship between the firms and investors.

• Asian Journal of Business Environment
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• v.7 no.4
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• pp.5-9
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• 2017

Purpose - Many countries rely on currency depreciation or debt-financed government spending to stimulate their economies. Currency depreciation tends to increase net exports and aggregate demand but reduce short-run aggregate supply due to higher import costs. Debt-financed government spending increases aggregate demand, but the crowding-out effect due to a higher real interest rate may reduce private spending and aggregate demand. Therefore, the net impact of currency depreciation or debt-financed government spending on equilibrium real GDP is unclear. Research design, data, and methodology - This paper examines potential impacts of real depreciation of the ringgit, more government debt as a percent of GDP and other relevant macroeconomic variables on aggregate output in Malaysia. Results - Applying the AD/AS model, this paper finds that aggregate output in Malaysia is positively associated with real appreciation during 2005.Q3-2010.Q3, real depreciation during 2010.Q4-2016.Q1, the debt-to-GDP ratio and the real stock price, negatively affected by the real lending rate and inflation expectations, and is not influenced by the real oil price. Conclusions - Real depreciation of the ringgit after 2010. Q3 or sustainable expansionary fiscal policy would be beneficial to the economy.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• The Korean Journal of Financial Management
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• v.25 no.3
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• pp.79-109
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• 2008

According to asymmetric information hypothesis (for example, Ross (1977), Myers and Majluf (1984)), the impact of seasoned equity offering (SEO) announcement on the stock price depends mainly on the informational market efficiency. Despite of the importance of this fact, most of the previous SEO-related studies have done under the assumption of equal informational market efficiency among sample firms. This study intends to solve this problematic assumption and explores the real impact of SEO announcement on the stock prices. For this purpose, we divide 122 SEO firms into two subgroups; one with firms from KOSPI200 and the other including firms from the rest of KOSPI, assuming the former is more informationally efficient than the latter. Different from the US market-based study demonstrating short-and long-term negative price impacts of SEO announcement, most of the Korean market-based ones show price increases up until the announcement and decreases just after the announcement and in the long run. These previous studies attribute this difference to the different market system and regulation between them. Our results indicate that this discrepancy can be attributed to the different degree of market efficiency as well as the different market system and regulation.

• Journal of Information Technology Applications and Management
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• v.21 no.1
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• pp.177-184
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• 2014

This research analyzes the effects of factors on the demands for outbound to the countries such as Japan, China, the United States of America, Thailand, Philippines, Hong Kong, Singapore and Australia, the countries preferred by many Koreans. The factors for this research are (1) economic variables such as Korea Composite Stock Price Index (KOSPI), which could have influences on outbound tourism and exchange rate and (2) unpredictable events such as diseases, financial crisis and terrors. Regression analysis was used to identify relationship based on the monthly data from January 2001 to December 2010. The results of the analysis show that both exchange rate and KOSPI have impacts on the demands for outbound travel. In the case of travels to the United States of America and Philippines, Korean tourists usually have particular purposes such as studying, visiting relatives, playing golf or honeymoon, thus they are less influenced by the exchange rate. Moreover, Korean tourists tend not to visit particular locations for some time when shock reaction happens. As the demands for outbound travels are different from country to country accompanied by economic variables and shock variables, differentiated measure to should be considered to come close to the target numbers of tourists by switching as well as creating the demands. For further study we plan to build outbound tourism forecasting models using Artificial Neural Networks.