• Journal of Distribution Science
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• v.21 no.3
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• pp.93-100
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• 2023

Purpose: The purpose of this paper is to confirm whether stocks belonging to the distribution industry in Korea have reversals, following large daily stock price changes accompanied by large trading volumes. Research design, data, and methodology: We examined whether there were reversals after the event date when large-scale stock price changes appeared for the entire sample of distribution-related companies listed on the Korea Composite Stock Price Index from January 2004 to July 2022. In addition, we reviewed whether the reversals differed depending on abnormal trading volume on the event date. Using multiple regression analysis, we tested whether high trading volume had a significant effect on the cumulative rate of return after the event date. Results: Reversals were confirmed after the stock price shock in the Korean distribution industry and the return after the event date varied depending on the size of the trading volume on the event day. In addition, even after considering both company-specific and event-specific factors, the trading volume on the event day was found to have significant explanatory power on the cumulative rate of return after the event date. Conclusions: Reversals identified in this paper can be used as a useful tool for establishing a trading strategy.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.229-229
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• 1999

• Convergence Security Journal
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• v.8 no.3
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• pp.83-92
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• 2008

In respect complication of group and period, the sales of retail trade is composed of various factors. This paper studies focus on estimating the determinants of the sales of retail trade. The volume of analysis consist of 7 groups. Analyzing period be formed over a 36 point(2005. 1$\sim$2007. 12). In this paper dependent variable setting up sales of retail trade, explanatory(independent) variables composed of composite stock price index, the number of the consumer's online buying behavior company, the coincident composite index, the index of trading price of APT, employment rate, an average of the rate of operation(the manufacturing industry), the consumer price index. The result of estimating the determinants of sales of retail trade provides empirical evidences of significance positive relationships between the coincident composite index, the index of trading price of APT, employment rate, an average of the rate of operation(the manufacturing industry). However this study provides empirical evidences of significance negative relationships between the consumer price index. The explanatory variables, that is, composite stock price and the number of the consumer's online buying behavior company, are non-significance variables. Implication of these findings are discussed for content research and practices.

• Journal of Navigation and Port Research
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• v.43 no.2
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• pp.134-141
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• 2019

The objective of this study was to establish whether global macroeconomic indicators affect the profitability of Korean shipping companies by using panel regression analysis. OROA (operating return on assets) and ROA (ratio of net profit to assets) were selected as proxy variables for profitability. OROA and ROA were used as dependent variables. The world GDP growth rate, interest rate, exchange rate, stock index, bunker price, freight, demand and supply of the world shipping market were set as independent variables. The size of the firm was added to the control variable. For small-sized firms, OROA was not affect by macroeconomic indicators. However, ROA was affected by variables such as interest rates, bunker prices, and size of firms. For medium-sized firms, OROA was affected by demand, supply, GDP, freight, and asset variables. However, macroeconomic indicators did not affect ROA. For large-sized firms, freight, GDP, and stock index (SCI; Shanghai Composite Index) have an effect on OROA. ROA was analyzed to be influenced by bunker price and SCI.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2038-2045
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• 2010

Households' monthly average income is composed of various factors. This study paper studies focuses on estimating the determinants of a households' monthly average income. The region for analysis consist of three groups, that is, the whole country, a metropolitan city(such as Busan, Daegu, Incheon, Gwangiu, Daejeon, Ulsan.) and Seoul. Analyzing period be formed over a 57 time points(2005. 01~2009. 09). In this paper the dependent variable setting up the households' monthly average income, explanatory (independent) variables are composed of the consumer price index, employment to population ratio, Index of housing sale price, the preceding composite index, loans of housing mortgage, spending rate for care medical expense and the composite stock price index. In looking at the factors which determine the monthly average income, evidence was produced supporting the hypothesis that there is a significant positive relationship between the composite index and housing loans. The study also produced evidence supporting the view that there is a significant negative relationship between employment ratios, the house sale pricing index and spending rates for care or medical needs. The study found that the consumer price index and composite stock price index were not significant variables. The implications of these findings are discussed for further research.

• The Journal of Bigdata
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• v.3 no.1
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• pp.13-20
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• 2018

Stock market prediction has been long dream for researchers as well as the public. Forecasting ever-changing stock market, though, proved a Herculean task. This study proposes a novel stock market sentiment lexicon acquisition system that can predict the growth (or decline) of stock market index, based on economic news. For this purpose, we have collected 3-year's economic news from January 2015 to December 2017 and adopted Word2Vec model to consider the context of words. To evaluate the result, we performed sentiment analysis to collected news data with the automated constructed lexicon and compared with closings of the KOSPI (Korea Composite Stock Price Index), the South Korean stock market index based on economic news.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.28 no.3
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• pp.71-87
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• 2024

In this paper, we adopt the MF-DCCA (Multifractal Detrended Cross-Correlation Analysis) method to study the nonlinear correlation between the returns of financial stock markets and investors' sentiment index (SI). The return series of Shanghai Securities Composite Index (SSEC) of China, Shenzhen Securities Component Index (SZI) of China, Nikkei 225 Index (N225) of Japan, and Standard & Poor's 500 Index (S&P500) of the United States are adopted. Firstly, we preliminarily analyze the correlation between SSEC and SI through the Pearson correlation coefficient. In addition, by MF-DCCA, we observe a power-law correlation between investors' sentiment index and SSEC stock market returns, with a significant multifractal correlation. Besides, SI series and SSEC return series have positive persistence. We compare the differences in multifractal cross-correlation between SI and stock return sequences in different markets. We found that the values of SZI-SI in terms of cross-correlation persistence and cross-correlation strength are relatively close to those of SSEC-SI, while the H_xy(2), ∆H_xy, and ∆α_xy of N225-SI and S&P500 are much smaller than those of SSEC-SI and SZI-SI. This reason is related to the fact that the investors' sentiment index originated from the Shanghai Composite Index Tieba. The SI is obtained through natural language processing method. Finally, we study the rolling of H_xy(2) and ∆α_xy. Results indicate that the macroeconomic environment may cause fluctuations in two sequences of H_xy(2) and ∆α_xy.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.95-101
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• 2008

Modeling of stock prices forecast has been considered as one of the most difficult problem to develop accurately since stock prices are highly correlated with various environmental conditions including economics and political situation. In this paper, we propose a agent system approach to predict Korea Composite Stock Price Index (KOSPI) using neural network and statistical methods. To minimize mean of prediction error and variation of prediction error, agent system includes sub-agent modules for feature extraction, variables selection, forecast engine selection, and forecasting results analysis. As a first step to develop agent system for KOSPI forecasting, twelve economic indices are selected from twenty two basic standard economic indices using principal component analysis. From selected twelve economic indices, prediction model input variables are chosen again using best-subsets regression method. Two different types data are tested for KOSPI forecasting and the Prediction results showed 11.92 points of root mean squared error for consecutive thirty days of prediction. Also, it is shown that proposed agent system approach for KOSPI forecast is effective since required types and numbers of prediction variables are time-varying, so adaptable selection of modeling inputs and prediction engine are essential for reliable and accurate forecast model.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.807-814
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• 2021

As economic activities decrease, and the stock market decline due to COVID-19, many people are jumping into stock investment as an alternative source of income. As people's interest increases, many stock price analysis studies are underway to earn more profits. Due to the variance observed in the stock markets, it is necessary to analyze each stock independently and consistently. To solve this problem, we designed and implemented models and services that analyze stock prices using a reinforcement learning technique called Asynchronous Advantage Actor-Critic(A3C). Stock market data reflected external factors such as government bonds and KOSPI (Korea Composite Stock Price Index) as well as stock prices. Our proposed work provides a web service with a visual representation of predictions of stocks and stock information through which directions are given to investors to make safe investments without analyzing domestic and foreign stock market trends.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.272-277
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• 2010

In respect complication of group, rate of arrearage in domestic bank is composed of various factors. This paper studies focus on estimating the determinants of the rate of arrearage in domestic bank using panel data model. The volume of analysis consist of 3 groups(loaned patterns of enterprise, housekeeping, credit card). Analyzing period be formed over a 54 point(2005. 1~ 2009. 06). In this paper dependent variable setting up rate of arrearage in domestic bank, explanatory(independent) variables composed of the consumer price index, composite stock price index, rate of exchange, the coincident composite index, national housing bonds and employment rate. The result of estimating the rate of arrearage in domestic bank provides empirical evidences of significance positive relationships between the consumer price index However this study provides empirical evidences of significance negative relationships between the coincident composite index and the composite stock price index. The explanatory variables, that is, rate of exchange, national housing bonds and the employment rate are non-significance variables of negative factor. Implication of these findings are discussed for content research and practices.