• Journal of Distribution Science
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• v.11 no.10
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• pp.63-72
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• 2013

Purpose - This study analyzes the relationship and dynamic interactions between stock price index, interest rate, price index, and housing price indices using Korean monthly data from 2000 to 2013, based on a VAR model. This study also examines Granger causal relationships among these variables in order to determine whether the time series of one is useful in forecasting another, or to infer certain types of causal dependency between stochastic variables. Research design, data, and methodology - We used Korean monthly data for all variables from 2000: M1 to 2013: M3. First, we checked the correlations among different variables. Second, we conducted the Augmented Dickey-Fuller (ADF) test and the co-integration test using the VAR model. Third, we employed Granger Causality tests to quantify the causal effect from time series observations. Fourth, we used the impulse response function and variance decomposition based on the VAR model to examine the dynamic relationships among the variables. Results - First, stock price Granger affects interest rate and all housing price indices. Price index Granger, in turn, affects the stock price and six metropolitan housing price indices. However, none of the Granger variables affect the price index. Therefore, it is the stock markets (and not the housing market) that affects the housing prices. Second, the impulse response tests show that maximum influence on stock price is its own, and though it is influenced a little by interest rate, price index affects it negatively. One standard deviation (S.D.) shock to stock price increases the housing price by 0.08 units after two months, whereas an impulse shock to the interest rate negatively impacts the housing price. Third, the variance decomposition results report that the shock to the stock price accounts for 96% of the variation in the stock price, and the shock to the price index accounts for 2.8% after two periods. In contrast, the shock to the interest rate accounts for 80% of the variation in the interest rate after ten periods; the shock to the stock price accounts for 19% of the variation; however, shock to the price index does not affect the interest rate. The housing price index in 10 periods is explained up to 96.7% by itself, 2.62% by stock price, 0.68% by price index, and 0.04% by interest rate. Therefore, the housing market is explained most by its own variation, whereas the interest rate has little impact on housing price. Conclusions - The results of the study elucidate the relationship and dynamic interactions among stock price index, interest rate, price index, and housing price indices using VAR model. This study could help form the basis for more appropriate economic policies in the future. As the housing market is very important in Korean economy, any changes in house price affect the other markets, thereby resulting in a shock to the entire economy. Therefore, the analysis on the dynamic relationships between the housing market and economic variables will help with the decision making regarding the housing market policy.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.267-272
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• 2020

Macroeconomics are one of the indicators that are preceded and analyzed when analyzing stocks because it shows the movement of a country's economy as a whole. The overall economic situation at the national level, such as national income, inflation, unemployment, exchange rates, currency, interest rates, and balance of payments, has a great affect on the stock market, and economic indicators are actually correlated with stock prices. It is the main source of data for analysts to watch with interest and to determine buy and sell considering the impact on individual stock prices. Therefore, economic indicators that impact on the stock price are analyzed as leading indicators, and the stock price prediction is predicted through deep learning-based prediction, after that the actual stock price is compared. If you decide to buy or sell stocks by analysis of stock prediction, then stocks can be investments, not gambling. Therefore, this research was conducted to enable automated stock trading by using macro-indicators and deep learning algorithms in artificial intelligence.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.3015-3024
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• 2018

This study investigates the recent asymmetric effect of news on stock return volatility in Asian five stock markets - Japan, Korea, Singapore, Taiwan, and Malaysia - since 2000. This study uses the GJR-M model which shows a different effect of a good and bad news on volatility. Empirical results show that the unexpected negative return has a more crucial effect on stock return volatility than the unexpected positive one does in all five stock markets. This implies that the bad news of the stock markets gives a more remarkable effect on volatility than good news does. This study finds that it is very important for market participants and regulation practitioners to distinguish between positive and negative return shocks in the stock markets since bad news might have a larger impacts on volatility than good news.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.134-136
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• 2017

The stock price of a companies may be changed according to not only the result of business performance but also the information and trends created by various investors. In this point, this study is to suggest a way to understand the relationship between big-data on each companies and its stock price, and to perform a simulation to examine it.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.543-549
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• 2021

The stock market plays a crucial role in the growth of industry and trade, which eventually affects the economy. This paper studies the determinants of stock market development in Jordan using yearly time-series data (1978-2019). The autoregressive distributed lag approach is applied to examine co-integration, while the vector error correction model is employed to estimate (long-run and short-run) causal relationships. The results show that macroeconomic determinants such as gross domestic product, gross domestic savings, investment rate, credit to the private sector, broadest money supply, stock market liquidity, and inflation rate are important determinants of stock market development. These findings provide vital implications for policymakers in developed and emerging stock markets. First, economic development plays an imperative role in stock market development. Second, developing the banking sector is mandatory because it can significantly promote stock market development. Third, domestic investment is a significant determinant of stock market development, especially in emerging countries. However, it is vital to launch policies that lead to encourage investment and promote stock market development, and this could be done through (1) encouraging competition, (2) improving the institutional framework, and (3) removing trade blocks by establishing a mutual connection between foreign private investment entities and government authorities.

• Journal of Distribution Science
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• v.20 no.12
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• pp.81-87
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• 2022

Purpose: This study aims to explore the relationship between stock liquidity and skewness risk-tail risk (stock price crash risk) in an emerging market, in which problems on liquidity are more severe than in developed markets. Research design, data, and methodology: Based on the Thai market stock exchange over the period of 2000 to 2019, our sample include 13,462 firm-period observations. We employ a panel regression models regarding to five liquidity measures. These five liquidity measures cover three dimensions of liquidity namely the volume-based, price-based, and transaction cost-based measures for the liquidity-tail risk relationship. Results: We find a positively significant relationship between stock liquidity and tail risk in all cases. The finding here shows that the higher the stock liquidity, the larger the tail risk is. Conclusion: As the prior studies show inconclusive effect of stock liquidity on stock price crash risk, we demonstrate that mixed results found in prior studies are probably driven from the type of liquidity measure. The stock liquidity-tail risk association is present in the Stock Exchange of Thailand. The results remain the same regardless of the definition of tail risk and liquidity factors. An endogeneity issue is addressed by employing the two-stage least squares regression.

• Journal of Distribution Science
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• v.21 no.9
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• pp.103-114
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• 2023

Purpose: This paper aims to assess psychological heuristics' effectiveness on cumulative returns after significant stock price changes. Specifically, it compares availability and anchoring heuristics' empirical validity due to conflicting stock return predictions. Research Design, Data, and Methodology: This paper analyzes stock price changes of Korean distribution industry stocks in the KOSPI market from January 2004 to July 2022, where daily fluctuations exceed 10%. It evaluates availability heuristics using daily KOSPI index changes and tests anchoring heuristics using 52-week high and low stock prices as reference points. Results: As a result of the empirical analysis, stock price reversals did not consistently appear alongside changes in the daily KOSPI index. By contrast, stock price drifts consistently appeared around the 52-week highest stock price and 52-week lowest stock price. The result of the multiple regression analysis which controlled for both company-specific and event-specific variables supported the anchoring heuristics. Conclusions: For stocks related to the Korean distribution industry in the KOSPI market, the anchoring heuristics theory provides a consistent explanation for stock returns after large-scale stock price fluctuations that initially appear to be random movements.

• Journal of Distribution Science
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• v.21 no.12
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• pp.83-89
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• 2023

Purpose: Research on spin-off and treasury stock is necessary because the market has realized that this can be utilized for major shareholder private interest. Considering the unique characteristic of a spin-off and treasury stock in the Korean stock market, this study contributes to the literature by examining the effects on shareholder value in the Korean distribution industry. Research design, data, and methodology: The present study investigates literature, analyst reports, and news articles to examine the spin-off process and analyze how treasury stock magic happens. Results: Setting the exchange ratio favoring Spin-Co in the spin-off is the leading cause for reducing the minor shareholders' value. Moreover, treating treasury stock as an asset is also problematic, allowing the allocation of Spin-Co shares. This leads to an increase in the major shareholder controls of Spin-Co without any contribution from the major shareholders. Therefore, the exchange ratio should be calculated reasonably, and treasury stock from the stock repurchase should be treated as stock retirement. Conclusion: By analyzing the spin-off and how treasury stock magic occurs, this study provides recommendations to improve shareholder value. Moreover, it contributes to the maturation of the Korean capital market by promoting a discussion on the revision of spin-off and treasury stock.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.896-899
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• 1991

This paper presents the neural network method to predict the Korea composition stock price index. The error back-propagation method is used to train the multi-layer perceptron network. Ten of the various economic indices of the past 7 Nears are used as train data and the monthly transition of the composition stock price index is represented by five output neurons. Test results of this method using the data of the last 18 months are very encouraging.