• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• The Korean Journal of Financial Management
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• v.23 no.2
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• pp.109-141
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• 2006

The central factor in the pecking order theory of financial structure is the asymmetric distribution of information between managers and less-informed outside investors. Myers and Majluf (1984) show that this asymmetry leads managers to prefer internal funds to external funds. Funds are raised through equity issues only after the capacity to issue debt has been exhausted. In contrast, according to static tradeoff theory, an optimum financial structure exists by the tradeoff between tax saving by debt and bankruptcy costs. This study examines the recent changes of Korean firms' financial structure and financing behavior and the determinants of financial structure. The sample of firms comes from the period of $1996{\sim}2004$, and the number of firms is 32,003. The major findings are as follows. First, in contrast with previous studies using US firms as sample, Korean firms have been using debt financing as their major financing instrument. Especially, the firms in the fund deficit situation relies much more on $long{\sim}term$ and $short{\sim}term$ debts rather than on equity issues. Second, as is the case with previous studies using US firms sample indicates, the financing deficit variable can not explain perfectly the net debt issue. However, compared with net equity issue variable, net debt issue variable is more closely related to the financing deficit variable. Third, when financing deficit variable is added to the current list of explanatory variables of financial structure determinants model, it has a significant and positive explanatory power. In addition, the coefficients of determinants are much improved. Thus, it is concluded that although pecking order theory is not perfect, it appears to be more useful compared to static tradeoff theory, at least in explaining the recent financing behavior of Korean manufacturing firms.

• 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.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.222-235
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• 2020

This study examines the poverty progress and its factors which drove the lives of the middle-aged men in Chokbang area. The observed examples are the retired government officials and the self-employed who have been classified as the ones in the economically-middle class but currently as the welfare recipients. According to the results of in-depth interview and observation, the poverty of the observed has undergone the progress of trigger, worsening, breakup, desperation and stabilizing stages. The poverty factors found in this study could be categorized into two factors; circumstantial factors(bankruptcy after IMF, debt guarantee for relatives) and inner factors(the participants' behavior and characteristics). The circumstantial factors worked mainly in the trigger stage and the inner factors contributed to worsening economic crisis and facilitating the progress. According to the result, this study suggests not only individual-scale measures such as encouragement of familial bond or medical treatment of the alcoholism but also social measures including proper regulation of shark loan and opportunity supply to exit from poverty.

• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.261-277
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• 2003

The Korean Stock Market Surveillance System is desinged to control the volatility of stocks by drawing investor's attention and suppressing disguised demand, when stocks run up so rapidly in short period of time. Yet the Surveillance System has not been under empirical examination about its role and evolved in line with the Price Limit System. This study looks at the security returns under surveillance designation for 1995 -2001 period. The results indicate that the volatility of stocks has not been affected after surveillance designation. The constraints against the disguised demand, however, seems to limit the security returns rather than volatilities. These findings raises a question about the role of The Korean Stock Market Surveillance System for the control of volatility. The Surveillance System needs to be examined thoroughly about its role, function, and its conditions. Otherwise, the shareholders with less information could be placed at a disadvantage. This paper suggests that the system should be amended in an effort to make the volatility of stocks under control.

• Proceedings of the KAIS Fall Conference
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• 2001.05a
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• pp.62-65
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• 2001

1997년과 1998년 4월 1일자의 M&A제한규정이었던 증권거래법 제200조의 철폐와 적대적 M&A의 전면허용 그리고 외국자본의 투자제한의 간소화 등 규제완화조치와, 향후 외국인에 대해서 100%까지 주식을 매입할 수 있는 적대적 M&A가 허용될 것으로 보여 우리나라에서도 M&A활동이 보다 본격화 될 것으로 전망되고 있다. 우리나라에서는 1980년대부터 M&A에 대한 관심과 연구가 시작되면서 지금까지 기업합병ㆍ인수의 동기와 효과에 대해서 많은 연구가 이루어져 왔다. 지금까지의 연구내용은 대부분 기업의 M&A동기는 시너지효과를 얻기 위함이며, 또한 공시정보가 합병공시일 전후동안에 주가수익율에 반영되어 합병 당사기업의 주주의 부에 영향을 미치는지의 주가변화 유무에 국한된 것이었다. 또한 1985년부터 1991년 초 사이에 이루어졌던 연구는 당시 국내 M&A 환경이 매우 열악하고 극히 제한된 비정상적인 자본시장 상황 하에서 이루어진 사례에 의한 연구이었기 때문에 연구결과의 문제점과 한계성이 있었고, 실효성 측면에서도 다소 미흡하였다고 할 수 있다. 우리나라 M&A의 성장발전과정을 크게 2단계로 분류한다면, 제1단계는 1975년부터 시작하여 1990년까지로써 이 기간동안의 M&A환경은 그 여건조성과 성숙준비단계였으며, 제2단계인 1990년 초부터 비로소 선진국형 시장경제원리에 근거한 보다 경쟁적이고 자율적인 M&A 시장구조가 형성되면서 활성화 단계로 진입하였다고 볼 수 있을 것이다. 본 연구의 주된 목적은 1990년부터 1996년 사이에 이루어진 상장기업의 M&A 사례를 표본으로 하여 한국에서의 기업합병과 기업인수(주식취득)가 기업가치에 미치는 영향을 구명하는데 있다. 이를 위하여 먼저, M&A 당사기업의 합병성과 발생과 차이 유무를 주가수익율을 측정하여 실증분석하고 다음으로 M&A에 따른 시너지의 잠재적 원천이 어디에 있는지 재무성과분석을 통해서 이를 실증하는데 목적이 있다.PA-designated retailers ("sellers") must accept end-of-life items returned to them by the consumers. At the local level, Taipei City implements a pay-as-you-throw program, whereby citizens pay waste collection and treatment fees through the purchase of special trash bags approved by the Taipei City Government. However. recyclables that are separated by citizens are collected free-of-charge by the City. Taichung City and Kaohsiung City, on the other hand, enforce mandatory sorting schemes, whereby citizens face penalties if they don't separate recyclables from the trash before pick-up. These programs have resulted in a significant reduction in municipal waste. Per capita waste collected per day has dropped from 1.143 kg in 1997 to 0.978 kg in 2000. Targeting a 10% recycling rate for municipal waste in 2001. EPA plans to research and develop new recycling techniques, expand the scope of producer responsibilities, and strengthen existing mu

• Journal of Korean Philosophical Society
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• v.126
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• pp.213-233
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• 2013

The aim of this paper is to reveal the ethical problem of insider trading. 'Insider trading' refer to obtaining information from non-public sources such as private acquaintances about trade secret, using it purposes of enhancing insider's financial advantages. And sometimes such a practice can be conducted fraudulently. Therefore, the focus of this paper will be on fairness or justice arguments against insider trading. And all kinds of discussion this paper are to focus the underlying consideration behind these arguments, that is, the underlying consideration about violation of ethical standards of fairness. First, one of these arguments argues that insider trading does necessarily involve defrauding general investors such as general employees, general stockholders. And economic power and unjust advantage of insider can be exercised to the detriment of this non-insider's interests. Second, another argument argues that insider trading undermines competition which is the principle of any free market. And insider trading is not only a complication in the free market mechanism, but also thwarts free competition which free markets depend. Third, the final argument argues that insider trading will be made something unfair about the concept of equal access to information. This argument argues, therefore, that to permit insider trading would be to set up stock market trading rules that are unfair to non-insiders.

• International Area Studies Review
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• v.21 no.1
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• pp.221-243
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• 2017

In this study, I examine overall conditions and problems of personal asset management processes by the old age people in Korea from the global perspectives. Major recommended policy implications for those are as follows.. First, the IRR (income replacement ratio) of public pensions in Korea is found to rank nearly the lowest among the OECD member countries. The relatively low fund performance compared to that of developed countries as well as this low IRR can be pointed out as major problems of public pension in Korea. It is recommended to reinforce specialty in fund management as a top priority to solve out these problems related with public pensions in Korea. Second, it is needed to set retirement pensions to be mandatory for almost all the firms in Korea to substitute for the above lower IRR of public pensions and to recover from the highest elderly poverty ratio among the OECD countries. Third, it is required to discuss about the expansion of tax refund policy application in the individual pension sector and many financial investment products under the correction of current budget control to motivate voluntary subscription for individual pension planning and to stabilize elderly lives of ordinary people in Korea. Fourth, it is required to induce market mechanism in controling price and longevity risk of reverse mortgages for the long-run sustainability.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.711-719
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• 2024

This study examined the policy of opening up the Chinese A-share market and its performance in four aspects: institutional investors system, cross-trading system with overseas stock markets, inclusion of A-shares into global indices, and establishment of a new board. Then, the impact of these policies on the Stock Index was empirically analyzed, and it was confirmed that institutional investors system such as QFII and RQFII, cross-trading system with overseas stock markets such as Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect, inclusion of A-shares into global indices such as the MSCI EM index and FTSE Russell index, and the establishment of a new board of the Science Innovation Board all had statistically significant positive impacts on the stock index. Based on the results of these analysis, we conclude that China should further expand its stock market opening to the outside world, that mutual efforts are needed to alleviate political conflicts and improve understanding, and that easing industry regulations, including real estate, will help China's economic recovery and foreigners' investment in the A-share market.