• Proceedings of KOSOMES biannual meeting
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• 2009.06a
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• pp.105-111
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• 2009

Marine pollution prevention manager system has been operated for the purpose of preventing marine pollution from ships and marine facilities in Korea. As a new "marine environment management act" replacing an old "marine pollution prevention act" had entered into force from January 20, 2008, the works and business of the education and training courses for marine pollution prevention manager are scheduled to transfer from KIMFT(Korea Institute of Maritime and Fisheries Technology) and FHRDI(Fisheries Human Resource Development Institute) to KOEM(Korea Marine Environment Management Corporation) on January 1, 2011. The present state of the education and training for marine pollution prevention managers on board ships was surveyed and analyzed In order to prospect trainee demands for the education and training and to provide KOEM with basic data for establishing a new system for the education and training. The existing education and training courses of KIMFT for marine pollution prevention manager on board ships are classified into a regular course of 21 classes for 3 days, a reeducation course of 18 classes for 2 days and a NLS course of 17 classes for 2days. The annual numbers of trainees in 2008 were 516 persons for regular course, 1085 persons for reeducation course and 135 persons for NLS course in KIMFT. The trainee demands for the education and training were attempted to be predicted on a basis on annual increase rate of ships. The predicted demands for the education and training are changeable depending on the major factors such shipping market, naval architecture industry, etc.

• Journal of Korea Port Economic Association
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• v.40 no.1
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• pp.151-163
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• 2024

This study sought to confirm the impact of analytical methods and behavioral economic theory factors on decision-making when making chartering decisions in the dry bulk shipping market. This study on chartering decision-making model was began to verify why shipping companies do not make rational decision-making and behavior based on analytical methods such as freight prediction and process of alternative selection in the same market situation. To understand the chartering decision-making model, it is necessary to study the impact of behavioral economic theory such as heuristics, loss aversion, and herding behavior on chartering decision-making. Through AHP analysis, the importance of the method factors relied upon in chartering decision-making. The dependence of the top factors in chartering decision-making was in the following order: market factors, heuristics, internal factors, herding behavior, and loss aversion. Market factors, heuristics, and internal factors. As for detailed factors, spot freight index and empirical intuition were confirmed as the most important factors relied on when making decisions. It was confirmed that empirical intuition is more important than internal analysis, which is an analytical method. This study can be said to be meaningful in that it academically researched and proved the bounded rationality of humans, which cannot be fully rational, and sometimes relies on experience or psychological tendencies, by applying it to the chartering decision-making model in the dry bulk shipping market. It also suggests that in the dry bulk shipping market, which is uncertain and has a high risk of loss due to decision-making, the experience and insight of decision makers have a very important impact on the performance and business profits of the operation part of shipping companies. Even though chartering are a decision-making field that requires judgment and intuition based on heuristics, decision-makers need to be aware of this decision-making model in order to reduce repeated mistakes of deciding contrary to market situation. It also suggests that there is a need to internally research analytical methods and procedures that can complement heuristics such as empirical intuition.

• 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.