• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.925-937
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• 2008

In this study, we considered the clustering analysis for stock return traded in the stock market. Most of financial time-series data, for instance, stock price and exchange rate have conditional heterogeneous variability depending on time, and, hence, are not properly applied to the autoregressive moving-average(ARMA) model with assumption of constant variance. Moreover, the variability is font and center for stock investors as well as academic researchers. So, this paper focuses on the generalized autoregressive conditional heteroscedastic(GARCH) model which is known as a solution for capturing the conditional variance(or volatility). We define the metrics for similarity of unconditional volatility and for homogeneity of model structure, and, then, evaluate the performances of the metrics. In real application, we do clustering analysis in terms of volatility and structure with stock return of the 11 Korean companies measured for the latest three years.

• Journal of the Korea Safety Management & Science
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• v.1 no.1
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• pp.231-239
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• 1999

CAPM explains the rate of return for the risk asset by $\beta$, systematic risk. There are some assumption in CAPM. But CAPM can not explain the movement of stock price sufficiently due to limitation of the assumptions. Therefore many scholars study which variables with the exception of $\beta$ effect on the rate of return of risk asset for supplementing this limitation by using PER, size of firm etc.. But it will be natural that PER, size of firm etc. to be determinant factors of $\beta$ also effect on the abnormal rate of return, because PER, size of firm etc. used in their studies already effect on determination of $\beta$, . That is, the determinant factors of $\beta$ effect on determination of abnormal rate of return according as $\beta$, effects on abnormal rate of return. Therefore, this study tests empirically how the determinant factors of $\beta$, effect on determination of$\beta$, ,and how $\beta$ and the determinant factor of $\beta$ effect on the abnormal rate of return in CAPM.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.201-234
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• 2003

This paper examines the day of the week effect focusing on the effect of the settlement procedures on the stock price in seven major international stock markets. Settlement dates or procedures may have an effect on rate of return distributions in international stock markets. Those Settlement procedures are different among various international stock markets. Furthermore, several international stock markets change their systems of settlement procedures. On the New York stock exchanges, stock transactions are settled in five business days after the transaction. However, they changed settlement procedures from five business days to three business days from 1995. Those settlement procedures on the London stock exchanges and the Paris stock exchanges were changes from the fixed settlement date systems to the fixed settlement lag systems. Thus, this paper examines the effect of the changes in settlement procedures on the stock price in several stock markets. I found that changes of settlement dates or procedures have an effect on the rate of return distributions for specific days in some stock markets. This paper also examines the day of the week effect for seven international stock markets. I found that strong weekend effect before the period of 1990. However, the weekend effect has disappeared during the period from 1990 to 2002 in international stock markets.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.675-685
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• 2021

The purpose of this study is to examine the reaction of stock prices on the implementation of Extensible Business Reporting Language (XBRL) in companies listed on the Indonesia Stock Exchange (IDX). Using the event study method and calculating abnormal returns of the 2015 financial statements of 462 companies listed on the IDX, findings showed that 49 companies have not applied the XBRL format in their financial statements. Based on the results of the Average Abnormal Return (AAR) and Cumulative Average Abnormal Return (CAAR) values, using the one-sample test, investors react to shares in companies that have not implemented XBRL and who have implemented XBRL; however, based on the independent t-test based on average values there are differences between companies that have not applied XBRL and those who have implemented XBRL. This research only looks at the one-year implementation of XBRL in financial reporting (2015), then the research does not separate which companies are on time in the delivery of financial statements to the public through the IDX website. Our research contributes to the understanding of the use of XBRL in corporate financial reporting because before the XBRL financial reporting format was published, the company had published a financial statement format based on the legal provisions of financial statements in Indonesia.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.153-158
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• 2021

Convergence is the process of gradual adoption of a certain accounting standard issued by different regulatory bodies. The aim is to achieve uniformity and standardization across borders to open opportunities for international investment and collaboration. The implementation of IFRS, in theory, encourages more transactions by presenting financial statements in a simple and understandable manner for all investors and other businesses interested in the company. Using event study methodology, this study investigates whether Malaysian companies' adoption of IFRS is recognized by the investment community. A total of 89 public listed companies in Bursa Malaysia are involved in this study. The results show that about 62.8 percent of the companies that adopted IFRS-based financial statements experienced an increase in their average abnormal return after the announcement. However, the paired sample test results show that only 5.6 percent out of 89 companies studied experience a significant difference in abnormal return before and after the announcement. The inexistence of the average abnormal return difference between before and after the announcement may indicate that IFRS-based financial statements do not have any new market informational content. This study found little evidence to show that convergence with IFRS affects the company's stock price in Malaysia.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.203-215
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• 2022

Bayesian Networks are multivariate probabilistic factor graphs that are used to assess underlying factor relationships. From January 2005 to December 2018, the study examines how Dynamic Bayesian Networks can be utilized to estimate portfolio risk and return as well as determine inter-factor relationships among reversal profit-generating components in Pakistan's emerging market (PSX). The goal of this article is to uncover the factors that cause reversal profits in the Pakistani stock market. In visual form, Bayesian networks can generate causal and inferential probabilistic relationships. Investors might update their stock return values in the network simultaneously with fresh market information, resulting in a dynamic shift in portfolio risk distribution across the networks. The findings show that investments in low net profit margin, low investment, and high volatility-based designed portfolios yield the biggest dynamical reversal profits. The main triggering aspects related to generation reversal profits in the Pakistan market, in the long run, are net profit margin, market risk premium, investment, size, and volatility factor. Investors should invest in and build portfolios with small companies that have a low price-to-earnings ratio, small earnings per share, and minimal volatility, according to the most likely explanation.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.81-93
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• 2022

The stock investing is one of the most popular investment techniques. However, since it is not easy to obtain a return through actual investment, various strategies have been devised and tried in the past to obtain an effective and stable return. Among them, the volatility breakout strategy identifies a strong uptrend that exceeds a certain level on a daily basis as a breakout signal, follows the uptrend, and quickly earns daily returns. It is one of the popular investment strategies that are widely used to realize profits. However, it is difficult to predict stock prices by understanding the price trend pattern of stocks. In this paper, we propose a method of buying and selling stocks by predicting the return in trading based on the volatility breakout strategy using a bi-directional long short-term memory deep neural network that can realize a return in a short period of time. As a result of the experiment assuming actual trading on the test data with the learned model, it can be seen that the results outperform both the return and stability compared to the existing closing price prediction model using the long-short-term memory deep neural network model.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.45-55
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• 2015

This study used for KOSPI, KOSDAQ, entertainment culture and digital contents index that is related to cultural contents industry. There was investigated the each stock price index and return trends for a total 597 weeks to July 2015 from March 2004. They looked the content-related stocks about investment worth to comparative analysis the return, volatility, correlation, synchronization phenomena etc. of each stock index. When we saw the growth potential of the cultural contents industry forward, looked forward to the investment possibility of related stocks. Analysis Result cultural content related stocks showed a higher rate after the last 2008 global financial crisis. Recent as high interest in the cultural contents industry, we could see that the investment merit increases slowly. In the future, the cultural content industry is expected to continue to evolve. The increase of investments value in the cultural content related businesses is much expectation.

• 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.

• Journal of Distribution Science
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• v.21 no.5
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• pp.11-18
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• 2023

Purpose: The purpose of this article is to determine whether there are differences in the level of return and risk of the conventional and Islamic capital markets. Research design, data and methodology: This study takes data on the Jakarta Islamic Index (JII) and the Liquid-45 (LQ45) stock groups in the 2017 to 2020 period. The research approach used is quantitative research with a type of comparison. The data used secondary data sourced from the closing price of shares on the Indonesia Stock Exchange. The statistical method used to test the hypothesis is a different test or independent sample t-test. Results: There is a significant difference between the rate of return and investment risk in JII and LQ-45. The rate of return and risk of investing in LQ-45 is higher than that of JII. Conclusions: There is a significant difference in the rate of return on investment in Jakarta Islamic Index (JII) and LQ-45, including conventional stock Liquid-45 (LQ-45) is higher than the rate of return on shares of JII shares. There is a significant difference in the level of investment risk in the Jakarta Islamic Index (JII) and the Liquid-45 (LQ-45), where the risk level for the LQ-45 is higher than that of the JII shares.