• Proceedings of the Korea Technology Innovation Society Conference
• /
• 1999.11c
• /
• pp.331-340
• /
• 1999

국제원유가격의 변동은 세계 각국의 경제에 상당한 영향을 미치고 있다. 이러한 원유가격의 변동을 정확히 예측하기 위해서는 원유가격 변동요인의 정립이 필히 요구된다. 본 연구에서는 전통적으로 원유가격의 중요한 변동요인으로 알려져 있는 OPEC의 원유생산량과 걸프전쟁 이후 주요한 국제원유가격 변동요인으로 알려져 있는 OPEC의 원유생산량과 걸프 전쟁 이후 주요한 국제원유가격 변동요인으로 주목받고 있는 미국의 원유재고량의 영향과 역할을 공적분(Cointegration) 모형과 오차수정모형(Error-Correction Model)을 통해 분석하였다. 분석결과, 원유생산량과 더불어 원유재고량도 원유가격의 중요한 변동요인으로 작용함을 알 수 있었다. 장·단기 탄력성의 경우, 원유생산량의 생산탄력성은 단기에 비해 장기에 더 탄력적으로 나타났으며 장기에는 원유재고량의 변동이 생산량의 변동보다 오히려 원유가격에 더 큰 영향을 미치는 것으로 나타났다. 또한, 원유가격은 첫해에서 나타난 불균형을 대략 12%의 조정속도로, 장기균형으로 조정됨을 알 수 있었다.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.10
• /
• pp.170-177
• /
• 2017

The price of land in Jeju is reaching a new high every day and this phenomenon not only causes real difficulties for the purchase of real estate by local residents, but also results in psychological deprivation. Therefore, this study analyzes the factors causing the increase of the land price in Jeju, in order to examine the measures required to stabilize the land price which is continuously rising. As a result of this study, we developed a land price prediction model including seven variables, including the 'inflation rate', 'interest rate', and 'population'. According to the model, land prices in Jeju are expected to rise steadily, and it is predicted that in 2020 the price will increase to 170% of that in 2015 and will triple by 2025. Based on the results of this study, this study suggested policy alternatives, such as 'Establishing a tourism policy for managing the number of tourists' and 'increasing the approval standards for development activities'. The two policies proposed in this study can be implemented as a regional initiative, which may be less effective than the changes in the national system, but it is meaningful that the efforts to stabilize the land price will continue at the regional level.

• Journal of the Korea Convergence Society
• /
• v.8 no.11
• /
• pp.383-391
• /
• 2017

This study examines the relationship between sentiment of speculators and price movements in the futures markets of WTI crude oil, copper, and wheat during the period 2003~2014 using Granger causality tests. The results indicate that speculative positions overall has no predictive power for returns in each futures market. Rather, returns seem to have effects on speculators' sentiment especially during periods of both economic expansion and recovery. During a recession, meanwhile, changes of speculators' sentiment index in the WTI crude oil and copper markets provide predictive power for returns in a positive direction, suggesting that speculators' pessimistic sentiment aggravates declines in commodity prices. Since the effects of speculative positions on market prices are ambiguous, tight regulations on speculative trading are not advisable. In a bearish market, however, regulatory bodies should consider raising speculative position limits because large speculative short positions and (or) liquidation of index traders' long positions may lead steep price declines.

• Journal of the Korean Society for information Management
• /
• v.24 no.2
• /
• pp.123-141
• /
• 2007

It has been known that predicting stock price is very difficult due to a large number of known and unknown factors and their interactions, which could influence the stock price. However, we started with a simple assumption that good news about a particular company will likely to influence its stock price to go up and vice versa. This assumption was verified to be correct by manually analyzing how the stock prices change after the relevant news stories were released. This means that we will be able to predict the stock price change to a certain degree if there is a reliable method to classify news stories as either favorable or unfavorable toward the company mentioned in the news. To classify a large number of news stories consistently and rapidly, we developed and evaluated a natural language processing based multi-stage news classification system, which categorizes news stories into either good or bad. The evaluation result was promising as the automatic classification led to better than chance prediction of the stock price change.

• Journal of Korean Society of Forest Science
• /
• v.98 no.3
• /
• pp.284-289
• /
• 2009

This study estimated the fiberboard demand using VAR and econometric model, and compared the prediction accuracy of the two models. And the variance decomposition and impulse response were analyzed using VAR model, and predicted the fiberboard demand. The VAR model was specified with lagged dependent variable, lagged own price, lagged construction product, dummy. The econometric model was specified with own price, substitute price, construction product, dummy. The dummy variable reflected the abrupt decrease in fiberboard demand in the late 1990's. The results showed that the fiberboard demand prediction can be performed more accurately by VAR model than by econometric model. In the VAR model of fiberboard demand, after twelve months, the construction product change accounts for about fifty percent of variation in the demand, and the own price change accounts for about thirty percent of variation in the demand. On the other hand, the impact of a shock to the construction product is significant for about twelve months on the demand of fiberboard, and the impact of a shock to the own price is significant for about six months on the demand of fiberboard.

• Journal of Service Research and Studies
• /
• v.14 no.1
• /
• pp.27-43
• /
• 2024

The market share of online platform services in the used car market continues to expand. And The used car online platform service provides service users with specifications of vehicles, accident history, inspection details, detailed options, and prices of used cars. SUV vehicle type's share in the domestic automobile market will be more than 50% in 2023, Sales of Hybrid vehicle type are doubled compared to last year. And these vehicle types are also gaining popularity in the used car market. Prior research has proposed a used car price prediction model by executing a Machine Learning model for all vehicles or vehicles by brand. On the other hand, the popularity of SUV and Hybrid vehicles in the domestic market continues to rise, but It was difficult to find a study that proposed a used car price prediction model for these vehicle type. This study selects a used car price prediction model by vehicle type using vehicle specifications and options for Sedans, SUV, and Hybrid vehicles produced by domestic brands. Accordingly, after selecting feature through the Lasso regression model, which is a feature selection, the ensemble model was sequentially executed with the same sampling, and the best model by vehicle type was selected. As a result, the best model for all models was selected as the CBR model, and the contribution and direction of the features were confirmed by visualizing Tree SHAP Value for the best model for each model. The implications of this study are expected to propose a used car price prediction model by vehicle type to sales officials using online platform services, confirm the attribution and direction of features, and help solve problems caused by asymmetry fo information between them.

• Proceedings of the Korea Technology Innovation Society Conference
• /
• 1999.11a
• /
• pp.333-340
• /
• 1999

국제원유가격의 변동은 세계 각국의 경제에 상당한 영향을 미치고 있다. 이러한 원유가격의 변동을 정확히 예측하기 위해서는 원유가격 변동요인의 정립이 필히 요구된다. 본 연구에서는 전통적으로 원유가격의 중요한 변동요인으로 알려져 있는 OPEC의 원유생산량과 걸프전쟁 이후 주요한 국제원유가격 변동요인으로 주목받고 있는 미국의 원유재고량의 영향과 역할을 공적분(Cointegration) 모형과 오차수정모형(Error-Correction Model)을 통해 분석하였다. 분석결과, 원유생산량과 더불어 원유재고량도 원유가격의 중요한 변동요인으로 작용함을 알 수 있었다. 장·단기 탄력성의 경우, 원유생산량의 생산탄력성은 장기에 비해 단기에 더 탄력적으로 나타났으며 원유재고량의 재고탄력성은 단기에 비해 장기에 더 탄력적으로 나타났으며 장기에는 원유재고량의 변동이 생산량의 변동보다 오히려 원유가격에 더 큰 영향을 미치는 것으로 나타났다. 또한, 원유가격은 첫해에서 나타난 불균형을 대략 12%의 조정속도로, 장기균형으로 조정됨을 알 수 있었다.

• Environmental and Resource Economics Review
• /
• v.14 no.2
• /
• pp.475-491
• /
• 2005

This study reviews various problems associated with the method of estimating the demand for industrial water that was employed in the Water Vision 2020 and it suggests an alternative econometric method. Comparing with the data cited in the Report on Industrial Census, estimates obtained by employing the concept of demand function are more exact compared to those offered by the Water Vision 2020. The amount of industrial water in 1998 was estimated at 2.8 billion tons decreasing by 2003. By employing the concept of demand function, this study shows that the amount of industrial water was 2.1 billion tons in 2003 while according to the Water Vision 2020 it amounted to 3.3 billion tons in 2001. Thus, it appears that the amount of industrial water in the Water Vision 2020 has been overestimated. This study also shows that the industrial water demand can be controlled by means of certain pricing policies. Finally, we argue that the demand for industrial water should be estimated by taking account of economic variables such as water price and output.

• Journal of the Korean Data Analysis Society
• /
• v.20 no.6
• /
• pp.2917-2932
• /
• 2018

Many researches are accomplished as a result of the efforts of developing the production predicting model to solve the supply imbalance of onions which are vegetables very closely related to Korean food. But considering the possibility of storing onions, it is very difficult to solve the supply imbalance of onions only with predicting the production. So, this paper's purpose is trying to build a sentiment dictionary to predict the price of onions by using the internet articles which include the informations about the production of onions and various factors of the price, and these articles are very easy to access on our daily lives. Articles about onions are from 2012 to 2016, using TF-IDF for comparing with four kinds of TF-IDFs through the documents classification of wholesale prices of onions. As a result of classifying the positive/negative words for price by k-means clustering, DBSCAN (density based spatial cluster application with noise) clustering, GMM (Gaussian mixture model) clustering which are partitional clustering, GMM clustering is composed with three meaningful dictionaries. To compare the reasonability of these built dictionary, applying classified articles about the rise and drop of the price on logistic regression, and it shows 85.7% accuracy.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.592-595
• /
• 2021

양자 알고리즘인 그루버나 쇼어 알고리즘에 의해 현존하는 암호 체계들이 무너질 수 있으며, 블록체인 네트워크를 기반으로 타원곡선 암호 및 타원곡선 전자서명을 사용하는 암호화폐의 안전성 또한 위협받고 있다. 따라서 암호화폐에도 양자 컴퓨터에 대한 대응책이 필요하다. 본 논문에서는 시계열 예측에 적합한 순환형 신경망을 활용하여 양자 저항성을 가지는 암호화폐들의 가격을 예측하고 분석한다. 데이터가 부족하였으나 학습 결과 0.005 이하의 손실을 달성하였으며, 최근 15일의 데이터를 통해 예측한 결과, 모두 소폭 상승할 것으로 나타났다. 향후에는 더 많은 데이터를 통해 더 정확한 예측이 가능한 신경망을 설계하고 다양한 양자 관련 이슈들을 참고하여 분석을 수행하고자 한다.