• The Journal of Asian Finance, Economics and Business
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• v.9 no.9
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• pp.351-360
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• 2022

This study aims to determine the factors that can influence investors in selecting stocks in the Indonesian capital market to establish an optimal portfolio, and find phenomena that occurred during the COVID-19 pandemic so that buying interest / the number of investors increased in the Indonesian capital market. This study collection technique uses primary data obtained from the survey questionnaire and secondary data which is market data, stock price movement data sourced from the Indonesia Stock Exchange, Indonesian Central Securities Depository, and Bank Indonesia, as well as empirical literature on behavior finance, investment decision, and interest in buying stock. The method used in this research is the survey questionnaire analysis with the SEM (statistical approach). The results of the analysis using SEM show that investor behavior influences the stock-buying interest, investor behavior, and the stock-buying interest influences investor decision-making. However, risk management does not influence investor-decision making. This occurs when the investigator's psychological capacity produces more decision information by decreasing all potential biases, allowing the best stock selection model to be selected. When the investigator's psychological capacity creates more decision information by reducing biases, the optimum stock selection model can be chosen.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.173-183
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• 2022

The objective of this research is to examine the influence of the COVID-19 pandemic on stock markets in a few developing and developed countries. This study uses daily data from January 2020 to May 2021 and obtained from World Health Organization and Thomson Reuters. The secondary data was evaluated through panel econometric methodology that includes different unit root tests, and to analyze the long-run relationship between variables, panel cointegration techniques were applied. The long-run causality among variables was examined through Panel Vector Error Correction Model. The overall findings of this study suggest a long-run association exists between several cases and death with the stock returns of the GCC and other stock markets. Furthermore, the VECM model also identified a long-run causality running from COVID cases and death towards the stock rerun of both sets of stock markets. However, a subsequent Wald test yielded mixed results, indicating no short-run causality between cases and deaths and stock returns in both groups; however, in the case of GCC, several COVID-19 cases are having a causal impact on stock markets, which is notable in light of the fact that the death rate in GCC is significantly lower than in many developed and developing countries.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.99-108
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• 2022

This study aims to examine the influence of capital structure on the business efficiency of joint stock commercial banks listed on the Vietnamese stock market. The article uses data collected from the financial statements of 15 prominent joint-stock commercial banks out of 27 joint-stock commercial banks listed in Vietnam from 2011 to 2021. The research uses E-view software in quantitative analysis to build regression models to determine the relationship and the impact of capital structure factors on the business efficiency of listed joint stock commercial banks. Research results show that ROA is affected by 2 variables of capital structure. It is the sum of customer deposits to total assets and total liabilities to total equity. Total debt to total equity and total customer deposits to total assets both have a negative effect on ROA. For the regression results of ROA with all control variables, the control variables have a positive relationship with the dependent variable. The article has provided recommendations based on the research findings to determine the proper capital structure. Managers must solve the outstanding amount of mobilized capital in previous years, combined with the bad debt handling activities that have arisen.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.337-346
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• 2022

This study explores the impact of the COVID-19 pandemic on the stock prices of state-owned enterprises listed on the Indonesia Stock exchange. The impact of the pandemic is analyzed based on different pandemic phases and the corresponding government pandemic interventions to curb the disease. This study analyzes 6 pandemic event dates, covering the time period from January 2020 to February 2021. A total of 20 state-owned enterprises are included as the sample of this study. Test of difference is employed to compare the stock prices of the state-owned enterprises before and after each pandemic event date. In general, this study confirms the adverse impact of the COVID-19 pandemic on the stock prices, especially the event in 2020, although some variations do exist. The results of the study reveal a significant decrease in the stock prices of the state-owned enterprises after the announcement of the first confirmed COVID-19 cases, the announcement of COVID-19 as a global pandemic, the imposing of Large Scale Social Restriction (PSBB I and PSBB II). In contrast, the stock prices increase after the imposing of a new normal policy and the imposing of Public Activity Restriction (PPKM). This study also documents that the effect of the pandemic may vary based on the pandemic phase.

• 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 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.

• Information Systems Review
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• v.18 no.2
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• pp.23-38
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• 2016

Previous studies on information systems (IS) and finance suggest that information on stock message boards influence the investment decisions of individual investors. However, how information on online stock message boards influences an individual investor's buy or sell decisions is unclear. To address this research question, we investigate the relationship between a number of posts on stock message boards and order imbalance in stock markets. Order imbalance is defined as the difference between the daily sum of buy-side shares traded and the daily sum of sell-side shares traded. Therefore, order imbalance can suggest the direction of trades and the strength of the direction with trading volumes. In this regard, this study examines how the number of posts (information on stock message boards) influences order imbalance (stock trading behavior). We collected about 46,077 messages of 40 companies on the Korea Composite Stock Price Index from Paxnet, the most popular Korean online stock message board. The messages we collected were divided based on in-trading and after-trading hours to examine the relationship between the numbers of posts and trading volumes. We also collected order imbalance data on individual investors. We then integrated the balanced panel data sets and analyzed them through vector regression. We found that the number of posts on online stock message boards is positively related to prior order imbalance. We believe that our findings contribute to knowledge in IS and finance. Furthermore, this study suggests that investors should carefully monitor information on stock message boards to understand stock market sentiments.

• Culinary science and hospitality research
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• v.16 no.5
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• pp.287-298
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• 2010

This study aims to develop chicken stock, which is the base of sauce, soup, etc., using various nourishing elements in chicken bones. For this purpose, we prepared chicken stock with varying the amounts of tomato added in order to produce basic data for enhancing the taste and nutrition of chicken stock, improving the quality of stock-based dishes, and developing stock. Sensory characteristics of tomato chicken stock such as water, ash, color, sugar, pH and sensory tests were studied by adding tomatoes for finding out the effect on free amino acid and various nutrients. The total free amino acid content and general acceptance were highest when 7.4% of tomato added. Based on the results of this study, the optimal tomato content for maximizing the overall quality of chicken stock was 7.4%.

• KDI Journal of Economic Policy
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• v.14 no.4
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• pp.63-77
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• 1992

This paper examined interactions between stock price and key macroeconomic variables over the period of 1975-1992. It has been found that more than 60% of real stock price changes can be well explained by movements in key macroeconomic variables, particularly in net exports and industrial production. On the other hand, real stock price changes were found to have a significant explanatory power for plant and equipment investments for the sample period of 1975-1985 during which the stock market was stable. In contrast, no significant linkage between stock price changes and investments emerged over the subsample period of 1986-92 despite the sharp expansion of the stock market in terms of trade volume. Based on such findings, two major policy implications were derived; (i) the government's intervention in the stock market to stabilize stock prices would be ineffective unless the stable economic growth supports the market fundamental, and (ii) the stock price stability is a precondition for the stock market to play a key role in mobilizing resources to finance the firm's long-term capital.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.223-228
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• 2020

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.