• Environmental and Resource Economics Review
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• v.20 no.3
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• pp.489-529
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• 2011

When greenhouse gas mitigation policies are implemented, energy intensive manufacturing industries are influenced much due to an increase in cost. However, industries that have price setting power are damaged less by the policies. Therefore, this paper analyzes vulnerability of energy intensive manufacturing industries to the policies by measuring price setting power of the industries. We analyzed price setting power model through ECM, employing the import prices and wages as independent variables. The industries that their prices react to import prices are price takers, which their prices are determined by rival's ones. On the other hand, the industry that their prices react to wages that mean domestic cost are price setters, and they will be less vulnerable to the policies. In addition, fluctuation of energy prices would be reflected in import prices because it influences other countries than my one. Thus, we employed energy prices as control variable to measure the net effects of import prices. As empirical results, petroleum products, chemical products, non-metallic mineral products, textiles, and motor vehicles sector have price setting power, so the industries have competitiveness on greenhouse gas mitigation policies.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.125-141
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• 2020

This study investigated the time-series relationship between housing consumer sentiment and housing prices in the five major districts in Seoul and also analyzed the effect of the housing consumer sentiment on housing prices using Granger Causality and VEC (Vector Error Correction) models. To describe the key results, first of all, housing consumer sentiment and regional housing market prices were closely related to each other, and the consumer sentiment strongly affected the change of housing prices. Second, the housing consumer sentiment was confirmed to have a discriminatory effect on the housing prices among the districts in Seoul in the short term. Specifically, the housing price of the east southern district (ESD) was the main reason for the change in housing consumer sentiment in Seoul, and that the resulting impact was transferred to other districts. Third, it was analyzed that regions other than the ESD would increase the housing prices in the long term as the housing consumer sentiment turned positive, but that the ESD would see a steady tone. Fourth, in the case of relative influence by district, housing (apartment) price fluctuation in a district was generally found to be most affected by adjacent or competitive districts. Through these findings, this study confirmed that there is a clear causality between housing consumer sentiment and housing prices in each district of Seoul and that there is a discriminatory influence on housing consumer sentiment among the districts.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.105-113
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• 2006

Construction cost index is generally used to estimate the new project cost based on past construction data and to adjust the contract cost when the price change of various articles and items of expenditure composing the contract occurs. In Korea, it is mostly used for modulation of construction contract cost due to fluctuation of prices. However the method for making cost index had some problems in calculating cost index of each expenditure item that could not properly reflect the change of construction cost. To supplement these problems, the research of developing construction cost index has been executed. Through the precedent research, these problems were partially resolved but still remain. Therefore this research proposes the method for making cost index that utilizes representative items of labor, material, equipment by analyzing bill of quantity of road construction, through analysis and comparison of precedent studies. By using this method, it is expected to solve the problems which were not reflected in preceeding studies.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.221-233
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• 2014

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

• Journal of Korean Society of Forest Science
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• v.73 no.1
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• pp.70-75
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• 1986

The secular trend and seasonal variation of chestnut prices have been analyzed, and the production and price for the next two decades (1985-2004) have been forecasted by the derived equation model. The results of the study can be summarized as follows; 1) The chestnut prices went up at the rate of 10.95% per annum during 1965-1972, but, due to excessive supply of chestnuts, went down at the rate of 7.25% during 1973-1984. 2) In a year, the prices were lowest at the harvesting season, especially on October, and highest on July. Such a seasonal fluctuations of chestnut prices tend to be even with the passage of time, but the range of fluctuation is still wide. 3) It was forecasted under certain premises that the annual chestnut production will be increased by 99,000 tons in 1992, but the amount will fall rapidly to about 23,000 tons in 2004. The prices will be similar to the present level or have slightly upward Tendency until 1992, but this will be rapidly raised thereafter.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.289-296
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• 2022

The purpose of this study was to investigate the inter-correlation between crude oil prices and Dry Bulk Freight rates. Eco-friendly shipping fuels has being actively developed to reduce carbon emission. However, carbon neutrality will take longer than anticipated in terms of the present development process. Because of OVID-19 and the Russian invasion of Ukraine, crude oil price fluctuation has been exacerbated. So we must examine the impact on Dry Bulk Freight rates the oil prices have had, because oil prices play a major role in shipping fuels. By using the VAR (Vector Autoregressive) model with monthly data of crude oil prices (Brent, Dubai and WTI) and Dry Bulk Freight rates (BDI, BCI and (BP I) 2008.10~2022.02, the empirical analysis documents that the oil prices have an impact on Dry bulk Freight rates. From the analysis of the forecast error variance decomposition, WTI has the largest explanatory relationship with the BDI and Dubai ranks seoond, Brent ranks third. In conclusion, WTI and Dubai have the largest impact on the BDI, while there are some differences according to the ship-type.

• Korean Journal of Construction Engineering and Management
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• v.10 no.4
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• pp.50-57
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• 2009

The Korea Standard of Estimate which has been used as the only basis of Cost estimate of public construction projects is failed to reflect the fluctuation of current construction cost. Therefore, the government decided to gradually introduce historical construction cost into cost estimate of public construction projects from 2004 and to reduce the use of Korean Standard of Estimate. This paper presents a series of process and the methodology for computing Actual Cost and analyzing the fluctuation patterns based on not only previous contract prices which made a successful bid but also all of the other bid prices. Also, this paper mainly handles a device for extracting strategic bid price such as low price bid for assuring reliable data and for predicting the construction cost which is built by Wavelet Analysis of Time series Analysis data and Neural Network. It is anticipated that the effective use of the proposed process for estimating actual unit cost would make the cost estimation more current and reasonable.

• Korean Journal of Construction Engineering and Management
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• v.7 no.5
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• pp.167-176
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.4
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• pp.515-526
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• 2014

In this study, the 'preservation of Jeonju Hanok Village Improvement Project' in earnest in promoting itself as the official land price changes in Jeonju Hanok Village and surrounding area thereby affect land prices to some extent in order to identify the time series analysis, t-black dispersion analysis showed the following results were obtained. First, time series analysis, and the Hanok Village, but the average official land price rises, and the area has been stead ilyrising. Second, the time series of the Official price year-over-year change in the average rate of the Hanok Village(+)rising, and the area is a gentle rise sooner or later (+)is expected to be an increase in conversion. Third, the number of tourists visiting Jeonju Hanok Village and sharply increased since 2008, was. Fourth, in order to use local official land price rises in the commercial area of highest priority that requires strategy was analyzed.

• Smart Media Journal
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• v.7 no.1
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• pp.45-51
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• 2018

Avian influenza (AI) is notorious for its rapid infection rate, and has a serious impact on consumers and producers alike, especially in poultry farms. The AI outbreak, which occurred nationwide at the end of 2016, devastated the livestock farming industries. As a result, the prices of eggs and egg products had skyrocketed, and the event was reported by the media with heavy emphasis. The purpose of this study was to investigate the correlation between the egg price fluctuation and the keyword changes in online news articles reflecting social issues. To this end, we analyzed 682 cases of AI-related online news articles for fourteen weeks from November 2016 in South Korea. The results of this study are expected to contribute to understanding the relationship between the actual price of eggs and the keywords from news articles related to social issues.