• The Korean Journal of Air & Space Law and Policy
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• v.32 no.2
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• pp.101-127
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• 2017

Even with the most advanced aviation technology benefits, aircraft accidents are constantly occurring while air passenger transportation volume is expected to double in the next 15 years. Since it is not possible to secure aviation safety only by the post aircraft accident safety action of accident investigations, it has been recognized and consensus has been formed that proactive and predictive prevention measures are necessary. In this sense, the aviation safety management system (SMS) was introduced in 2008 and has been carried out in earnest since 2011. SMS is a proactive and predictive aircraft accident preventive measure, which is a mechanism to eliminate the fundamental risk factors by approaching organizational factors beyond technological factors and human factors related to aviation safety. The methodology is to collect hazards in all the sites required for aircraft operations, to build a database, to analyze the risks, and through managing risks, to keep the risks acceptable or below. Therefore, the improper implementation of SMS indicates that the aircraft accident prevention is insufficient and it is to be directly connected with the aircraft accident. Reports of duty performance related hazards including their own errors are essential and most important in SMS. Under the policy of just culture for voluntary reporting, the guarantee of information providers' anonymity, non-punishment and non-blame should be basically secured, but to this end, under-reporting is stagnant due to lack of trust in their own organizations. It is necessary for the accountable executive(CEO) and senior management to take a leading role to foster the safety culture initiating from just culture with the safety consciousness, balancing between safety and profit for the organization. Though a Ministry of Land, Infrastructure and Transport's order, "Guidance on SMS Implementation" states the training required for the accountable executive(CEO) and senior management, it is not legally binding. Thus it is suggested that the SMS training completion certificates of accountable executive(CEO) and senior management be included in SMS approval application form that is legally required by "Korea Aviation Safety Program" in addition to other required documents such as a copy of SMS manual. Also, SMS related items are missing in the aircraft accident investigation, so that organizational factors in association with safety culture and risk management are not being investigated. This hinders from preventing future accidents, as the root cause cannot be identified. The Aircraft Accident Investigation Manuals issued by ICAO contain the SMS investigation wheres it is not included in the final report form of Annex 13 to the Convention on International Civil Aviation. In addition, the US National Transportation Safety Board(NTSB) that has been a substantial example of the aircraft accident investigation for the other accident investigation agencies worldwide does not appear to expand the scope of investigation activities further to SMS. For these reasons, it is believed that investigation agencies conducting their investigations under Annex 13 do not include SMS in the investigation items, and the aircraft accident investigators are hardly exposed to SMS investigation methods or techniques. In this respect, it is necessary to include the SMS investigation in the organization and management information of the final report format of Annex 13. In Korea as well, in the same manner, SMS item should be added to the final report format of the Operating Regulation of the Aircraft and Railway Accident Investigation Board. If such legal and institutional improvement methods are complemented, SMS will serve the purpose of aircraft accident prevention effectively and contribute to the improvement of aviation safety in the future.

• 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.