• Journal of Navigation and Port Research
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• v.41 no.5
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• pp.251-258
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• 2017

Shortage of trained seafarers is an issue, which many ship-owners are facing and, according to recent studies, the shortfall of both officers and rating will worsen in the next few years. The key role of seafarers is of fundamental importance in international trade, as they are the ones responsible for safely manning and operating ships. In developing countries, they also perform a strategic aspect in terms of contribution to GDP, mainly by earning foreign currency and increasing national consumption of goods and services. Myanmar is still considered a developing country with an economy, which has only recently started growing steady. It is also one of the major seafarers supplying nations and the contribution, which seafarers have on the national GDP may be particularly significant in comparison to other countries. This study aims at investigating seafarers' impact towards the Myanmar national economy. The paper describes the status of Myanmar seafarers, and the seafarers' current and forecasted impact towards the Myanmar economy through a regression model. The study concludes with recommendations to make Myanmar seafarers more competitive internationally and increase their economic contribution nationally.

• Proceedings of the Technology Innovation Conference
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• 1997.12a
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• pp.177-196
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• 1997

The engineering industry plays an important role for national competitive, since it has an high impact on other industries. With its importance, the engineering industry development largely depends on its technical manpower ather than capital factor. This study aims at estimating the additional requirement on technical manpower based on the forecasted market size which represents the structure change corresponding to economic growth in related industry. Research scope includes the twelve of fifteen field except three with insufficient historical data and technical manpower above bachelor degree. Specialty, we forecast market size with determinants resulted from historical data analysis on each field. The demand on technical manpower is derived from the forecasted market. We also estimate an additional requirement with the supply analysis. The research results show different patterns over time period. The relative ratio on chemical and construction to total market will steadily grow over short term, while applied, environment, electronic and information will rapidly grow This pattern will be stabilized over mid or long term. The additional requirement on technical manpower represents the similar pattern to market growth. The research result implies manpower policy for having high inflow of technical engineer from educational institute and the related industries through the image improvement.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2689-2694
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• 2014

This study analyze the impact of omija(maximowiczia chinensis) market by Korea-China FTA and review the change of mid and long term supply-demand from 2014 to 2018. A scenario is also imported to simulate and measure the impacts of the Korea-China FTA. The scenario is that tariff rates for Chinese product(omija) will be zero after 5 years from 2014. A partial equilibrium model of Omija is specified to forecast mid and long term supply-demand and prices. Equations in the model were estimated by using econometric techniques. The results based on scenario are compared with the results by the baseline case(maintenance of current situation). Our study show that when the tariff rates for Chinese product(Omija) will be zero after 5 years from 2014, the cultivated area of Omija is forecasted to decline until 3,370ha in 2018, and the consumption is forecasted to increase up to 12,040.8MT in 2018, and also total revenue of about 9.8 billion korean won will be decreased during 5 years(2014-2018).

• Journal of Environmental Policy
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• v.10 no.3
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• pp.49-71
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• 2011

Water is an essential element required for the survival of 6 billion human beings around the world, and it has limited mobility. The importance of water industry has grown considerably, as its forecasted market size is expected to increase from $336 billion in 2007 to $865 billion in 2025, respectively. In 2003, the domestic water market was estimated to be worth 11 trillion won. However, according to the estimates of this research, the result above was overestimated by 2 trillion won. The economic ripple effects of newly defined water industry based on the input-output data from 2000 to 2008 were trivial, as the price effects of water industry was considerably minor at only 0.12%. To successfully implement policies to enhance the water industry, the market price mechanism must operate and function properly. Nevertheless, the impact of water's market prices on the economy upon application is projected to be trivial.

• Korea journal of population studies
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• v.34 no.2
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• pp.113-133
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• 2011

The generation of babyboomers has a significant impact on the socio-economic development of the society in general. The Korean Babyboomers will soon leave from their workforce as they reach the retirement age. This, coupled with the low fertility rate, may cause a serious social problem in the society at large as well as the social welfare issues among the Korean elderly population. The Central Statistical Systems have estimated the future projection which plays critical role to establish fundamental basis for the social and economic policies of the nation. This study examined the effect of the babyboomers on the life expectancy by comparing forecasted life expectancies provided by the statistical office and the previous studies in the related areas. The study also suggested a future population projection based on fertility rates provided, along with the changes of the number of babyboomers as they become ageing. The study results with the comparison between the population projection by the statistical office are provided.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.4
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• pp.1-15
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• 2016

Startups in Korea are experiencing for themselves the laws of survival through competition in the local and international market, and are performing active business movements based on these. Korea's economic growth rate is 2.6% due to the slump in the domestic demand and reduced exports brought by the MERSC incident in 2015. The Korea Development Institute has estimated the economic growth rate in 2016 to be around 3.0%. South Korea's economy is facing the crisis of low-growth solidification due to the decrease in economic growth, and it is forecasted that growth without employment and polarization will worsen. Startups in the high-tech industrial generation of a particular field wherein the market environment is rapidly changing must maintain a competitive advantage with the capabilities and functions exclusive to them. It is very important that they maintain a competitive edge by utilizing the capabilities exclusive to startup companies. Likewise, the accumulation of resources is also crucial in determining the success of a startup business. In a poor local startup ecosystem, majority of the startup companies are performing their business activities while striving for survival, rather than success. About 80% are struggling to survive and are failing to overcome the "Death Valley" faced 3-5 years after establishing the company. Since majority of the startups fail to achieve results during the initial stages of foundation, the importance of research on business activities and achievement during the early stages of establishment is being raised. In accordance to this, this research has performed an actual analysis on how the activities of startups during their preparation phase and early stages affect their achievements. A survey was done on the CEOs or executives (people in a position to make decisions) of local small and medium-sized enterprises that are considered start-ups, and 203 valid data were collected and analyzed. Results showed that the discoveries and utilized activities necessary for the businesses of startups have a significant impact on their achievement through the entrepreneur resources and external partners' cooperation; additionally, the related implications were discussed.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.