• 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.

• Information Systems Review
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• v.9 no.1
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• pp.161-189
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• 2007

The purposes of this dissertation are to identify various factors affecting the outcomes of feasibility analysis and investment decision makings of new IT project plans and empirically analysis the relationships among them. 9 variables which have been drawn from prior studies and industry practices are the amount of the necessary resource such as development budget and time, the expect financial benefits, the degree of alignments between IT projects and the business strategy, the estimated risk, and the investment priority as the dependent variable. Data from 125 IT projects of K bank, the leading commercial bank in Korea, have been collected and Regression Analysis and ANOVA have been performed. As results, 5 out of 8 hypothesis have been accepted partially or totally.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.472-482
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• 2012

There are many investors in the stock market, and more and more people get interested in the stock investment. In order to avoid risks and make profit in the stock investment, we have to determine several aspects using various information. That is, we have to select profitable stocks and determine appropriate buying/selling prices and holding period. This paper proposes a data mining tool for the investors' decision support. The data mining tool makes stock investors apply machine learning techniques and generate stock price prediction model. Also it helps determine buying/selling prices and holding period. It supports individual investor's own decision making using past data. Using the proposed tool, users can manage stock data, generate their own stock price prediction models, and establish trading policy via investment simulation. Users can select technical indicators which they think affect future stock price. Then they can generate stock price prediction models using the indicators and test the models. They also perform investment simulation using proper models to find appropriate trading policy consisting of buying/selling prices and holding period. Using the proposed data mining tool, stock investors can expect more profit with the help of stock price prediction model and trading policy validated on past data, instead of with an emotional decision.

• East Asian Journal of Business Economics (EAJBE)
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• v.7 no.4
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• pp.41-49
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• 2019

Purpose - This paper empirically investigates what factors contribute to corporate investments under financial constraint condition in the Korean stock market. In the paper, tangible assets' growth rate and fixed assets' growth rate were employed as investment performance and total assets were also used for comparison purpose. Research design and methodology - Samples are constructed by manufacturing firms listed on the stock market of Korea as well as those who settle accounts in December from 2001 to 2018. Financial institutions are excluded from the sample as their accounting procedures, governance and regulations differ. This study adopted a fixed panel regression model to assess the sample construction including yearly and cross-sectional data. Results - This results support the literatures that major shareholders showed positive significance to investment in financially unconstrained firms and no significance to investment in financially constrained firms. ROA showed positive significance to investment in financially unconstrained and constrained firms, whereas firm size showed negative significance to investment in financially unconstrained and constrained firms. Debt showed no positive significance to investment in financially unconstrained firms and negative significance to investment in financially constrained firms. Conclusions - This paper documented evidence that ROA and firm size are important factors to investment irrespective of firms' financial constraints. And this paper also supports that major shareholders give positive impact to investments in financially unconstrained firms. This means that financial constraints itself rule corporate' investment decision in financially constrained firms.

• Journal of The Korean Association of Information Education
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• v.9 no.2
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• pp.167-176
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• 2005

It has been an important agenda to acquire effective decision making procedure for various issues occurred in education area. As an example, when it comes for the ministry of education to make a decision on such an issue that proper investment, to enhance information of education area, in national wide elementary schools, an effective decision making procedure will aid to establish right way of investment. Currently, the questionnaires gathered from school teachers or the related professional consultants are the only resources in order for making such a critical and important decision. Recently, however, educational, medical, and financial industries are looking forward the best decision making method integrated with rapidly upgraded modern IT technologies using the various resources and tools which they already possess. With this subject in mind, in this paper we present a generic decision making model applying ADALINE neural network. The model can be easily adapted to various problems arising in education area. We proved the model through simulations with realistic sample data.

• Korea Trade Review
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• v.46 no.1
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• pp.1-21
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• 2021

The main purpose of this research is to analyze the changes in investment motivation by year through time series and cross-sectional analysis of the factors and investment decisions of Korean manufacturing companies. According to the investment pattern for Asean from the 1980s to the 19th, the first expansion period was 82 to 86, the average increase in overseas investment for securing foreign raw materials due to the second oil shock, and the second expansion period was a gradual increase in exports to the U.S. in 1987 to 1996. During the first stagnation period, direct investment in Asean stagnated in the aftermath of the 1998-05 Asian crisis, and in the third expansion period, part of the production facilities invested in China were relocated to Asean, increasing Asean's investment to become Korea's largest manufacturing investment in 17. Korea's proportion of investment in Asean surpassed that of mass investment since 10 years ago, and the proportion of investment in manufacturing sector has been transferred from China to Asean, and after 17 years, it has served as an overseas production base connecting China. As such, The main purpose of the research will be to extract the determinant factors and key factors for overseas direct investment and investment patterns in conjunction with global manufacturing companies' production base relocation and investment trends through empirical analysis. This research paper gave basic reference to the motivation and determinant of investment 16 years ago, and analyzed the changes in investment motivation by year and content through empirical analysis, contributing some reasonable purpose to the decision of companies and policy makers interested in overseas direct investment.

• The Journal of Information Systems
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• v.17 no.4
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• pp.129-156
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• 2008

This study empirically tested a research model and hypothesis extracted and based on IT governance and knowledge sharing theories. In this study we targeted CIO and IS managers to find the effects of the degree of knowledge sharing between CEO and CIO regarding five areas of IT governance decision-making: IT principles, IT architecture, IT infrastructure, business application needs, and IT investment and prioritization, on IT and business alignment. Additionally we studied the effects of business alignment on the effectiveness of information systems. Results showed that the degree of knowledge sharing in CEO and CIO on IT principles, IT infrastructure, IT investment and prioritization had a positive influence on IT and business alignment, ultimately showing a positive influence on the effectiveness of information systems. This research has shown that fording the preferable relationship between IT and business affected by performing high quality decision making based on knowledge sharing and consequently it also is a basis to provide a positive influence to the effectiveness of the information system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.73-82
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• 2012

Currently, R&D investment of government is increased dramatically. However, the budget of the government is different depending on the size of ministry and priorities, and then it is difficult to obtain consensus on the budget. They did not establish decision support systems to evaluate and execute R&D budget. In this paper, we analyze factors affecting research funds by linear regression and decision tree analysis in order to increase investment efficiency in national research project. Moreover, we suggested strategies that budget is estimated reasonably.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1460-1470
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• 2015

This paper presents an approach to solve the long-term generation expansion planning problem of the restructured electricity industry using an agent-based environment. The proposed model simulates the generation investment decisions taken by a particular agent (i.e. a generating company) in a market environment taking into account its competitors’ strategic investment. The investment decision of a particular company is modeled taking into account that such company has imperfect foresight on the future system development hence electricity prices. The delay in the construction of new plants is also explicitly modeled, in order to compute accurately the yearly revenues of each agent. On top of a conventional energy market, several capacity incentive mechanisms including capacity payment and capacity market are simulated, so as to assess their impact on the investment promotion for generation expansion. Results provide insight on the investment cycles as well as dynamic system behavior of long-term generation expansion planning in a competitive electricity industry.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1205-1210
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• 2007

Several mechanisms are introduced for the procurement of capacity adequacy. In the competitive electricity market, however, it is a GENCO that makes generation investment decision. A GENCO will invest a new generator when it can get more profit than cost. There requires a model to evaluate profit with respect to a new generation investment. In the view of long-term investment, evaluation of a profit of a generator in the electricity market is quite different from that of short-term operation. In this paper, a new profit-evaluation model is proposed for the long-term generation investment. It can treat the probabilistic characteristics of generators, ie, forced-outage-rates, which affect profit of generators.