• The Korean Journal of Financial Management
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• v.27 no.2
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• pp.145-167
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• 2010

We investigate the dynamic relationship between stock returns and investors' behavior. For the putpose of the paper, daily KOSPI returns are decomposed into two parts: overnight returns and daytime returns. Overnight return is measured by the closing price of the previous day and the opening price of the current day. And daytime return is measured by the opening and closing prices of the current day. Qvernight returns are assumed to reflect global economic information, and daytime returns, domestic or local information. Major results are as follows: Foreign investors' behavior has an effect on the overnight returns more than the daytime returns. Individual investors' behavior, however, has little effect on the overnight returns, but not the daytime returns. Consequently, forecast error variance decomposition shows that the variance explanation power of foreign investors is higher in overnight returns rather than in the daytime returns. And the variance explanation power of individual investors is higher in daytime returns rather than in overnight returns. It implies that foreign investors employ dynamic hedging strategies and give more weight to global economic information rather than to domestic information. We conclude that investment behavior of foreign investors and domestic individuals is based on different economic information. This paper's findings are consistent with the economic situation that the Korean capital markets have faced since the global financial crisis of August 2008.

• Archives of design research
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• v.16 no.2
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• pp.385-394
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• 2003

Modem market's tendency is displaying brand strategy of Corporation sells product and consumer buys rand'.Brand acts by intermediate that reflect consumer's request and experience and attach product and consumer hat produce. Successful brand intended strategy empirical esthetics enemy, and consumer recognizes brand to novel experience and think by one fixed idea (awareness). any corporations wish to get maximum effect into minimum investment expense that reduce huge public elations and advertisement charge. Do to incuse impression for product brand through direct interview with consumer at purchase visual point, and do function hat drive purchase and the importance great rawly. Therefore, sight expression of brand image must be able to from immobile one impression in consumer's spirit being inked nearly with brand .Brand that situate by affirmative impression once can arrive in purchase by brand without alternative information being withdrawn by consumer's emory. Solution of visual expression for this being city cornification laying stress on logo and mark of brand as central element of brand image, speak as communication actor who back this. This to be Dija as direction that product and connection are deep before meaning that is sight language enemy who allow fetters special quality and the seniority in age sex language enemy of brand as ell as have direction of by methods method for problem solution present can .Therefore, relation with brand image analysis of sports equipment and consumer product purchase that this research forms market economy theoretical investigating to be effective and present direction that is image-making to be consistency this the purpose be.

• Journal of Korea Technology Innovation Society
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• v.9 no.4
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• pp.842-864
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• 2006

This paper deals with an empirical study to statistically analyse various financial performances of the selected IPO firms using their investments on research and development(R&D) as an independent variables. The major results of statistical analyses have come up with the followings: 1) The regression analyses for change in average annual total market stock value/total assets over that of R&D expenditures showed the positive relationship, However, those of sales volume and net assets per share showed negative without statistical significances. 2) The statistical analyses in effect of the 3-year average total market stock value/total assets over the 3-year average R&D expenditures resulted in the positive coefficients what are statistically significant at 95% level. 3) Another statistical analysis showed that the financial performances of the IPO finns with deferred assets were better than those of the firms without them. In sum, the degree of investment on R&D by the IPO firms are expected to positively affect their financial performances except the Finns without having proper original technologies.

• Information Systems Review
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• v.20 no.4
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• pp.43-57
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• 2018

We propose network effects upon the investment decision of cloud-based ERP. Using the survey data collected from 82 companies in 2015, we examine whether IT managers have an intention to adopt real options in order to manage the risk of cloud-based ERP investments and how the network effects influence upon the intention to adopt real options. Based on prior literature, we propose a research model with 4 hypotheses. We find partial support of the hypotheses from the empirical analysis: technological risks has a positive impact upon the adoption of real options such as defer, contract, and abandon. In contrast, we find no significant impact of security risks upon real options. We validate positive network effects upon the adoption of real options such as defer, contract, and abandon. This work empirically find that IT managers in Korean middle and small sized firms have an intention to adopt real options when the managers realize economic, technological, and relationship risks and when they expect network effects.

• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.189-210
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• 2022

Cities around the world have paid attention to public transportation as an alternative to reducing traffic congestion caused by automobile usage, excessive energy consumption, and environmental pollution. This study measures accessibility to subway stations in Seoul using a supply-demand-based accessibility technique. Then, the impacts were analyzed through land prices by use and segment. As a result of analysis using the multilevel hedonic price models, accessibility considering both supply and demand for the subway had a positive effect on both residential and non-residential land prices. The effect was stronger for residential than for non-residential. Further, among the accessibility measured by the three functions, the accessibility by the Exponential function was most suitable for the residential land price, and the accessibility measured by the Power function for the non-residential land price had the highest explanatory power. Also, looking at the impacts by land price segments, it was found that higher access to metro stations had the greatest positive impacts on the most expensive segment of residential and non-residential land prices. The results of this study can be applied not only to identify the impacts of public investment on neighborhoods, but also to support real estate valuation.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.151-180
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• 2003

We examine the interdependence of the major Asia-Pacific stock markets including S&P 500, FTSE 100, Kualar Lumpur Composite, Straits Times, Hang Seng, NIKKEI 225 and KOSPI 200 from October 4, 1995 to March 31,2000. The analysis employs the vector-auto-regression, Granger causality, impulse response function and variance decomposition using daily returns on the national stock market indices. The findings in this paper indicate that the volatilities of all countries has grown after IMF crisis, while there is no significance in cointegration test of both total period and sub-periods. This result implies that investors are able to get abnormal returns by investment diversification according to the portfolio theory. We find that while the effect from NIKKEI 225 to others is relatively weak, the interdependence from S&P 500 to other countries is strong. Also we find that the strong effect from Straits Times to Hang Seng exists. This study suggests that there is slight feedback relation between KOSPI 200 and Kualar Lumpur Composite, Straits Times, Hang Seng stock market.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.1 s.24
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• pp.21-28
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• 2003

This study was aimed at suggest a proper planning method to select a optimal forest road network in mountains forest using GIS(Geographic Information System). To examine the field applicability, the method was applied to the National Forest at Mt. Kumsan in Namhae-gun, Gyungsangnam-do. The main results from altogether six alternative road route plans were derived from these criteria obtained the alternative route plan No.2 has two layout criteria, longitudinal gradient and earth work volume, and it showed similar pattern of existing forest road network which was designed mainly ground slope and longitudinal gradient. The alternative route plan No.6 has four criteria, longitudinal gradient, earth work volume, investment effect and landscape impact. It was different for the lowest forest road density among the alternatives and the pattern of the forest road layout was radial form, which was also quite different to other alternatives. For optimal forest road network planning, GIS provide the efficient and resonable solutions for decision making to provide the support for evaluation about various alternative road networks. If detailed inventory and relevant data are provided and also clear and objective indicators for evaluations are set up, it could be applied to preliminary analysis and detail planning stage to prevent undesirable effect such the land slide and soil erosion due to inadequate planning for forest road network.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Knowledge Management Research
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• v.17 no.4
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• pp.27-56
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• 2016

Korean traditional markets have been struggling of late as big-sized superstores and SSM(Super Supermarkets) are thriving in the market. They have therefore upgraded their facilities and undertaken management modernization actively to overcome the threat to traditional markets and ensure their competitiveness; however, the effect does not appear to be verifiable. The purpose of this study is to analyze the impact of the organizational characteristics of the traditional market merchant association on social capital and organizational performance. In other words, this paper investigates a merchant association's organizational characteristics in terms of the modernization of business activities of the traditional markets and the influence on their social capital and organizational performance. This study analyzes the traditional market by evaluating the impact of these factors. This study consists of four hypotheses: The first hypothesis relates to the causal relationship between the characteristics of a merchant association and social capital. The second and third hypotheses, respectively, relate to the causal relationships between the social capital of a merchant association and the merchant's satisfaction and that between the social capital of a merchant association and organizational commitment. The last hypothesis relates to the relationship between the organizational commitment of a merchant association and the merchant's satisfaction. This study conducts a reliability and validity analysis of the above factors and analyzes the causal relationships between them by using the PLS(Partial Least Squares) path model as one of the structural equation models. The results of the empirical analysis are summarized as follows: First, the organizational characteristics of the traditional market merchant association have a significant influence on social capital. However, only two sub-hypotheses are not significant; these insignificant hypotheses relate to the relationship between a merchant's entrepreneurship and structural capital and that between a merchant's entrepreneurship and cognitive capital. Second, the social capital of a merchant association influences organizational commitment significantly. Third, the relationship between the social capital of a merchant association and the merchant's satisfaction is mostly significant. However, one of the sub-hypotheses, that is, the relationship between relational capital and a merchant's satisfaction is not exceptionally significant. Lastly, the organizational commitment of a merchant association affect the merchant's satisfaction significantly. Through our extensive study, this paper found that a merchant association's organizational characteristics of the traditional market significantly affect social capital, organizational commitment, and satisfaction through the mediation of social capital. Therefore, in order to activate the key traditional market, an understanding of organizational characteristics and social capital is primarily required. Systematic management and investment pertaining to these two factors will be the first consideration for revitalizing traditional markets.