• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.367-374
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• 2021

Stock price prediction is a subject of research in various fields such as economy, statistics, computer engineering, etc. In recent years, researches on predicting the movement of stock prices by learning artificial intelligence models from various indicators such as basic indicators and technical indicators have become active. This study proposes a deep learning model that predicts the ups and downs of KOSPI from overseas indices such as S&P500, past KOSPI indices, and trading trends by KOSPI investors. The proposed model extracts a latent variable using a stacked auto-encoder to predict stock price fluctuations, and predicts the fluctuation of the closing price compared to the market price of the day by learning an LSTM suitable for learning time series data from the extracted latent variable to decide to buy or sell based on the value. As a result of comparing the returns and prediction accuracy of the proposed model and the comparative models, the proposed model showed better performance than the comparative models.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.3
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• pp.262-268
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• 2020

The IMO (International Maritime Organization) has mandated the restriction of SOx emissions to 0.5 % for all international sailing vessels since January 2020. And, a number of countries have designated emission control areas for stricter environmental regulations. Three representative methods have been suggested to cope with these regulations; using low-sulphur oil, installing a scrubber, or using LNG (Liquefied Natural Gas) as fuel. In this paper, economic analysis was performed by comparing the method of installing a scrubber with the method of using low-sulphur oil without installing additional equipment. We suggested plausible layouts and compared the pros and cons of dif erent scrubber types for retrofitting. We selected an international sailing ship as the target vessel and estimated payback time and benefits based on navigation route, fuel consumption, and installation and operation costs. Two case of oil prices were analyzed considering the uncertainty of fuel oil price fluctuation. We found that the expected payback time of investment varies from 1 year to 3.5 years depending on the operation ratio of emission control areas and the fuel oil price change.

• Journal of Fisheries and Marine Sciences Education
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• v.6 no.1
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• pp.1-10
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• 1994

This study attempted to investigate the demands of fishing village people and the actual status of their living, to diagnose the problems in fishing villages and to present some basic data for fishing village development. The major findings of this study were as follows ; 1) The problems in fishing village of Korea included fishing village, household debt, big fluctuation of fish prices, children's education, cultural and welfare facilities need to investment for equipment, marriage and health. 2) The respondents said that fishing village household debt was mainly caused by lack of income from fishery(37%), educational expenses(28.5%), excessive fishery expenses(20%), excessive fishery equipment(15%), and government policy(12%). 3) The outlook of fishery development is considered bright by the respondents. They said that fishery would be a prospective industry if the appropriate conditions are given. Considerable research efforts for government policies will be needed to develop the fishery industry.

• Korean Journal of Construction Engineering and Management
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• v.7 no.5
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• pp.159-166
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• 2006

It was found that Korean Standard of Estimate which has been used as the only basis of cost estimate of public construction projects had some side effects such as jerry-build construction and over-estimation because it failed to reflect the current price and the state-of-the-art construction methods in a changing construction environment. Therefore, the government decided to gradually introduce historical construction cost into cost estimate of public construction projects from 2004. This paper presents analytic criteria and a process model for deducing more current and reasonable historical construction cost for contract items from not only previous contract prices but also all of the other bid prices that were not contracted. The procedure of estimating actual unit cost proposed in this paper focuses on the removal of abnormal values including strategically too low or high prices and the time correction. In addition, basic research is conducted for the correction of actual unit cost through the analysis of fluctuation of bid price depending on bidding types and rates of successful bid. It is anticipated that the effective use of the proposed process model for estimating actual unit cost would make the cost estimation more current and reasonable.

• Journal of the Korea Institute of Building Construction
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• v.12 no.1
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• pp.108-114
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• 2012

Escalation method examining the changes in price index has been widely utilized in public construction projects. The previous escalation method determined estimated changes in price based on the average unit price of contract items over a period of time. In relation to this method, the issue has been raised that the fluctuation rates of previous method show different trends compared with other related price indices, as the influence of the small group of contract items with large unit prices overwhelms the others. This research suggested an improved escalation method which estimates the fluctuation rate by examining the changes in CCI (construction cost index) and applies it to the total amount or the partial amount deducted for labor cost in price escalation for previous bid price method. To verify the improvement, a case study is conducted on an educational facility, and the fluctuation rate was estimated in two different base periods (short term, long term). The results over the long term showed similar tendencies to those of related price indices, as well as significant differences in fluctuation rates compared to those of the previous method.

• The Journal of Industrial Distribution & Business
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• v.11 no.3
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• pp.39-50
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• 2020

Purpose: Agriculture, which is heavily influenced by climate conditions, is one of the industries most affected by climate change. In this respect, various studies on the impact of climate change on the agricultural market have been conducted. Since climate change is a long-term phenomenon for more than a decade, long-term projections of agricultural prices as well as climate variables are needed to properly analyze the impact of climate change on the agricultural market. However, these long-term price projections are often major constraints on studies of climate changes. The purpose of this study is to analyze the impacts of climate changes on the Korean onion market using ex-post analysis approach in order to avoid the difficulties of long-term price projections. Research design, data and methodology: This study develops an annual dynamic partial equilibrium model of Korean onion market. The behavioral equations of the model were estimated by OLS based on the annual data from 1988 to 2018. The modelling system is first simulated to have actual onion market conditions from 2014 to 2018 as a baseline and then compared it to the scenario assuming the climatic conditions under RCP8.5 over the same period. Scenario analyses were simulated by both comparative static and dynamic approach to evaluate the differences between the two approaches. Results: According to the empirical results, if the climate conditions under RCP8.5 were applied from 2014 to 2018, the yield of onion would increase by about 4%, and the price of onion would decrease from 3.7% to 17.4%. In addition, the average price fluctuation rate over the five years under RCP8.5 climate conditions is 56%, which is more volatile than 46% under actual climate conditions. Empirical results also show that the price decreases have been alleviated in dynamic model compared with comparative static model. Conclusions: Empirical results show that climate change is expected to increase onion yields and reduce onion prices. Therefore, the appropriate countermeasures against climate change in Korean onion market should be found in the stabilization of supply and demand for price stabilization rather than technical aspects such as the development of new varieties to increase productivity.

• Environmental and Resource Economics Review
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• v.30 no.4
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• pp.535-555
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• 2021

This study empirically analyzes the effects of international oil prices on domestic stock market volatility. The data used for the analysis are 10-minute high-frequency data of the KOSPI index and WTI futures price from January 2, 2015, to July 30, 2021. For using the high-frequency data, a heterogeneous autoregression (HAR) model is employed. The analysis model utilizes the advantages of high frequency data to observe the impact of international oil prices through realized volatility, realized skewness, and kurtosis as well as oil price return. In the estimation, the Box-Cox transformation is applied in consideration of the distribution of realized volatility with high skewness. As a result, it finds that the daily return fluctuation of the WTI price has a statistically significant positive (+) effect on the volatility of the KOSPI return. However, the volatility, skewness, and kurtosis of the WTI return do not appear to affect the volatility of the KOSPI return. This result is believed to be because the volatility of the KOSPI return reflects the daily change in the WTI return, but does not reflect the intraday trading behavior of investors.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.67-74
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• 2023

Stock price prediction research in the financial sector aims to ensure trading stability and achieve profit realization. Conventional statistical prediction techniques are not reliable for actual trading decisions due to low prediction accuracy compared to randomly predicted results. Artificial intelligence models improve accuracy by learning data characteristics and fluctuation patterns to make predictions. However, predicting stock prices using long-term time series data remains a challenging problem. This paper proposes a stable and reliable stock price prediction method using K-means clustering-based data augmentation and normalization techniques and LSTM models specialized in time series learning. This enables obtaining more accurate and reliable prediction results and pursuing high profits, as well as contributing to market stability.

• International Commerce and Information Review
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• v.7 no.3
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• pp.269-286
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• 2005

The exchange rate volatility has been increased since the time when the floating exchange rate system was introduced in Korea. As a result, the increase of the exchange rate volatility raised the risk in international trades in Korea. The purpose of this study in to study the feature of exchange rate volatility and the main sources of its increase and to confirm whether the exchange rate volatility influence export volume and price of Korea. In the first place, I measured exchange rate volatility with two methods. The one is descriptive statistic method such as the width of daily exchange rate fluctuation and the rate of exchange rate devaluation. The other is the time varying conditional variance of exchange rate. Then, I studied the sources of exchange rate volatility. In the second place, I defined the exchange rate volatility as the time varying conditional variance and estimated it by using elastic a approach model which shows exchange rate is affected by itself and its conditional variance, I estimated its effects on export volumes and prices of electric home appliances, information & communication equal and semi-conductor. The result of this study is as follows. With presumed result EU and Korea because is not the goods which is to substantial competition relationship, The effect where the relative value change of presumed result expression anger and the dollar of import and export function goes mad to the import and export of Korea the income compared to is to export and it is appearing a lot. The EU goods is sold more expensively the Korean goods than from about length being caused by American market of the dollar and the balance of trade of Korea is visible like being visible the improvement of single breadth. Because the relationship of competition is weak but substantially there is to a short term and expression - the effect where the dollar rate fluctuation is big in Korean trade there is a possibility of saying that widely known it is not.

• International Area Studies Review
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• v.15 no.2
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• pp.77-100
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• 2011

The Korean economy have experienced the remarkable decreases in factor prices such as bond yields, real wage since the IMF foreign exchange crisis. This paper investigates the effects of the price changes in the factor markets on determining the level and cyclicality of industrial markups in the manufacturing industry. For this purpose, we construct a markup equation in the small open economy based on the production function including foreign intermediate goods and assuming constant returns to scale technology and AR(1) process of technological coefficient. Empirical results are summarized as the followings. The empirical results shows that the increased markups after the IMF crisis can be explained by the price decreases in the factor markets which result in lowering marginal costs. And we also observed counter cyclicality of markup, labor share and interest rates while real wages, technical coefficients, and production price index proved to be pro-cyclical. In conclusion, the price changes in factor market have contributed to the stickiness in markup fluctuation in the manufacturing industry.