• The Korean Journal of Financial Management
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• v.26 no.1
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• pp.221-263
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• 2009

This paper studies the behaviour of stock prices on the ex-dividend day in the Korean stock market. Since a majority of listed Korean firms are December firms whose fiscal year end in December and whose ex-dividend day falls on the same calendar day in the year, we use stock prices of Non-December firms to estimate the general stock price movements not related to cash dividends. We estimate excess returns on days around the ex-dividend day. Our major findings are (a) there is no tax clientele effect in Korea, (b) the opening price stock prices fell by the amount of the current cash dividend per share until 2001, but it does not fall as much as the current dividend per share since 2001. Furthermore, in contrast to the U.S. and the Japanese findings, (c) stocks earned negative excess returns on the ex-dividend day until 2001, after which all stocks are earning positive excess returns on the ex-dividend day, and (d) the closing stock price on the ex-dividend day that used to be even higher than the cum-dividend price until 2001 is lower than the opening stock price since 2001. The evidence suggests a structural break has happened around the year 2001.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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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.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.6
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• pp.354-367
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• 2010

There are many methods for analyzing patterns in time series data. Although stock data represents a time series, there are few studies on stock pattern analysis and prediction. Since people believe that stock price changes randomly we cannot predict stock prices using a scientific method. In this paper, we measured the degree of the randomness of stock prices using Kolmogorov complexity, and we showed that there is a strong correlation between the degree and the accuracy of stock price prediction using our semi-global alignment method. We transformed the stock price data to quantized string sequences. Then we measured randomness of stock prices using Kolmogorov complexity of the string sequences. We use KOSPI 690 stock data during 28 years for our experiments and to evaluate our methodology. When a high Kolmogorov complexity, the stock price cannot be predicted, when a low complexity, the stock price can be predicted, but the prediction ratio of stock price changes of interest to investors, is 12% prediction ratio for short-term predictions and a 54% prediction ratio for long-term predictions.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.239-252
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• 2004

It is known that the domestic and the U.S. stock prices tend to move together as those markets are closely interrelated. In this study, cointegration and causal relationships among the four stock price indexes of KOSPI, KOSDAQ, DOWJONES and NASDAQ are carefully investigated for the period of declining stock prices in the long run. When all indexes move in a similar fashion, cointegration does not exist and the causal linkages between the domestic and the U.S. stock prices appear relatively complex. On the other hand, when the domestic and the V.S. stock prices move in a different manner, cointegration exists and the causal relationships appear relatively simple. NASDAQ is apparently found to lead the domestic stock market in both periods, which is consistent with the actual market situation when the If industry is under recession.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.99-110
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• 2021

This study aims to examine empirically the industry and economic fundamental factors that affect the stock prices of the leading palm oil company in Indonesia. The dynamics of stock price are analyzed using the autoregressive distribution lag (ARDL) model both for symmetric and asymmetric effects. The data used in this study are monthly data for the period from 2008:01 to 2020:03. In the long run, the company stock price moves in line with the competitor company stock price at the current time. The palm oil price has a positive effect on the stock price. Meanwhile, inflation negatively affects the stock price in the short run. The estimated equilibrium correction coefficient indicates a reasonably quick correction of the distortion of the stock price equilibrium in monthly dynamics. However, fundamental factors have asymmetric effects, especially the response of stock price when these factors decrease rather than increase in the short run. Stock prices that are responsive to declines in fundamental performance should be of particular concern to both investors and management in their strategic decision making. The results of this study will contribute to the enrichment of literature related to stock prices from the viewpoint of economic analysis on firm-level data.

• The Journal of Industrial Distribution & Business
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• v.12 no.3
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• pp.43-49
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• 2021

Purpose: This study attempted to examine the risk of stock price plunge according to the firm's management strategy. Prospector firms value innovation and have high uncertainties due to rapid growth. There is a possibility of lowering the quality of financial reporting in order to meet market expectations while withstanding the uncertainty of the results. In addition, managers of prospector firms enter into compensation contracts based on stock prices, thus creating an incentive to withhold negative information disclosure to the market. Prospector firms' information opacity and delays in disclosure of negative information are likely to cause a sharp decline in share prices in the future. Research design, data and methodology: This study performed logistic analysis of KOSPI listed firms from 2014 to 2017. The independent variable is the strategic index, and is calculated by considering the six characteristics (R&D investment, efficiency, growth potential, marketing, organizational stability, capital intensity) of the firm. The higher the total score, the more it is a firm that takes a prospector strategy, and the lower the total score, the more it is a firm that pursues a defender strategy. In the case of the dependent variable, a value of 1 was assigned when there was a week that experienced a sharp decline in stock prices, and 0 when it was not. Results: It was found that the more firms adopting the prospector strategy, the higher the risk of a sharp decline in the stock price. This is interpreted as the reason that firms pursuing a prospector strategy do not disclose negative information by being conscious of market investors while carrying out venture projects. In other words, compensation contracts based on uncertainty in the outcome of prospector firms and stock prices increase the opacity of information and are likely to cause a sharp decline in share prices. Conclusions: This study's analysis of the impact of management strategy on the stock price plunge suggests that investors need to consider the strategy that firms take in allocating resources. Firms need to be cautious in examining the impact of a particular strategy on the capital markets and implementing that strategy.

• 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.7
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• pp.11-19
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• 2021

The study aims to investigate the impact of seasonality in Gulf Cooperation Council (GCC) countries' share market during the month of Ramadan. It helps in finding the opportunities for stock market investors to earn abnormal (returns) gain by investing during Ramadan in GCC stock markets. This study uses stock returns data of GCC countries (Saudi Arabia, Bahrain, Qatar, Kuwait, Dubai, and UAE) from January 2004 to November 2019. Stock prices indexes of GCC stock markets have been obtained from Datastream. The ARCH-GARCH model is used to study the impact of the Ramadan month on the return and volatility of the stock market in GCC countries. The results showed that the Ramadan month has a significant impact on share market prices in Saudi Arabia and the United Arab Emirates. However, Ramadan has an insignificant impact on share market prices in Bahrain and Oman. The study found no evidence of serial correlational between residuals in Kuwait; meaning that stock return was not dependent on the prior stock returns in Kuwait, therefore, we cannot go for forecasting. The ARCH-LM test statistic for Qatar does not fulfill the requirement of a good regression model; therefore, we cannot go for forecasting or testing the hypothesis of Qatar.

• The Journal of Asian Finance, Economics and Business
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• v.4 no.1
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• pp.25-38
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• 2017

This study investigates the causal relationship between stock prices and exchange rates for six Middle Eastern countries, namely, Egypt, Iran, Jordan, Kuwait, Oman, and Saudi Arabia before and during (after) the 2007 global financial crisis for the period between January 2004 and September 2015. The sample is divided into two sub-periods, that is, the period from January 1, 2004 to September 30, 2007 and the period from October 1, 2007 to September 30, 2015, to represent the pre-crisis period and the post-crisis period, respectively. Using Vector Autoregressive (VAR) model in a multivariate framework (including two control variables, inflation rates and oil prices) the results suggest that in the case of Jordan, Kuwait and Saudi Arabia, there exists bidirectional causalities after the crisis period but not the before. The opposite status is available for the case of Iran. In the case of Oman, there is bidirectional causality between the variables of interest in both periods. The results also reveal that the relationship between stock prices and exchange rates has become stronger after the 2007 global financial crisis. Overall, the results of this study indicate that fluctuations in foreign exchange markets can significantly affect stock markets in the Middle East.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.101-106
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• 2020

The research was conducted with the aim of knowing the effect of the CAMELS ratio either partially or simultaneously on stock prices. The CAMELS ratio (Capital, Asset Quality, Management, Earning, Liquidity) is used to measure the soundness of a bank, where by the better the soundness of the bank, the more profitable the bank will be for potential investors and other interested parties. The population of this research consists of the four state banks documented on the Indonesia Stock Exchange over the 2012-2019 period. The sample selection technique is a saturated sampling. This study provides the results that partially CAR has a significant effect on the share price of government banks listed on the IDX. Meanwhile, NPL, NPM, ROA, and LDR do not have a significant effect on stock prices of state banks listed on the IDX. The results of the regression analysis show that, together the CAMELS ratio, which is proxied by CAR, NPLS, NPM, ROA, and LDR has a positive and significant influence on the share price of state-owned banks documented on the Indonesia Stock Exchange, so this can be used as a reference for investors in predicting the share price of a state-owned bank before investing in shares.