• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Korean Security Journal
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• no.22
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• pp.91-111
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• 2010

Around the middle of the ninth century the strict bone-rank system of Silla frustrated many people who had political ambition but lacked nobility. They had to seek other ways, including maritime trade. Such an undertaking reflected and also increased their economic and military power. Trade prospered with T'ang China and with Japan as well. The threat of piracy to Silla's thriving maritime trade caused to create a succession of garrisons at important coastal points. Chonghae Jin (Chonghae garrison) was regarded as the most important of these. It was established in 828 by Chang Pogo. Chonghae Jin was on Wando, an island just east of the southwestern tip of Korea and a key place at this time in the trade between China, Korea, and Japan. From this vantage point Chang Pogo became a merchant-prince with extensive holdings and commercial interests in China and with trade contacts with Japan. Although piracy was rampant in East Asia at that time, either the Chinese or Silla government was not able to control it due to inner political strife and lack of policing resources. Infuriated by the piracy and the government's inability to control it, Chang Pogo came back to Silla to fight against the pirates and to protect maritime trade. He persuaded the king of Silla and was permitted to control the private armed forces to sweep away the pirates. In 829 he was appointed Commissioner of Chonghae-Jin with the mission of curbing piracy in that region. Chang's forces were created to protect people from pirates, but also developed into traders among Silla Korea, T'ang China, and Japan in the 9th century. This was geographically possible because the Chonghae Garrison was situated at the midpoint of Korea, China, and Japan, and also because Chang's naval forces actually dominated the East Asia Sea while patrolling sea-lanes. Based on these advantages, Chang Pogo made a great fortune, which might be collected from a charge for protecting people from pirates and the trades with China and Japan. Chang's forces could be termed the first private security company in the Korean history, at least in terms of historical documents. Based on historical documents, the numbers of private soldiers might be estimated to exceed tens of thousands at least, since Chang's forces alone were recorded to be more than ten thousand. Because local powers and aristocratic elites were said to have thousands of armed forces respectively, the extent of private forces was assumed to be vast, although they were available only to the privileged class. In short, the domination of Chang's forces was attributable to the decline of central government and its losing control over local powers. In addition it was not possible without advanced technologies in shipbuilding and navigation.