• Journal of the Korea Safety Management & Science
• /
• v.13 no.1
• /
• pp.189-193
• /
• 2011

The current standard cost for recycling applied under the Extended Producer Responsibility(EPR) institution, is not coping with continuously increased number of obligatory subject items as well as a variety of variable cost changing factors regarding the recycling treatment cost caused by price fluctuation such as increased material and labor cost entirely across the society; changes in recycling treatment process following the developing technologies; and changes in the required work forces and equipments followed by the trends of automated facilities. Despite such various cost fluctuation factors, the current EPR is not coping with the trends, making the re-calculation process difficult, which causes differences between the real treatment cost for recycling. In this study, the analysis was made on main factors affecting on the related cost and the related price changing index was calculated, by conducting the influence evaluation on the standard cost factors of the current standard cost for recycling. Through theses results, more objective standard will be set for the re-calculation of standard cost for recycling to greatly contribute to setting up the midterm and long-term strategies in the future towards efficient institution.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.1
• /
• pp.19-28
• /
• 2022

Recently, cryptocurrency has attracted much attention, and price prediction studies of cryptocurrency have been actively conducted. Especially, efforts to improve the prediction performance by applying the deep learning model are continuing. LSTM (Long Short-Term Memory) model, which shows high performance in time series data among deep learning models, is applied in various views. However, it shows low performance in cryptocurrency price data with high volatility. Although, to solve this problem, new input features were found and study was conducted using them, there is a lack of study on input features that drop predictive performance. Thus, in this paper, we collect the recent trends of six cryptocurrencies including Bitcoin and Ethereum and analyze effects of input features on the cryptocurrency price predictive performance through statistics and deep learning. The results of the experiment showed that cryptocurrency price predictive performance the best when open price, high price, low price, volume and price were combined except for rate of closing price fluctuation.

• Asia-Pacific Journal of Business
• /
• v.14 no.1
• /
• pp.265-279
• /
• 2023

Purpose - The purpose of this paper is to compare and review behavioral economics models that explain stock price changes after large-scale price shocks in the Korean stock market and to find a suitable model. In this paper, among the theories reviewed, it was confirmed that the anchoring heuristics theory has high explanatory power for stock prices after large-scale stock price fluctuations. Design/methodology/approach - This paper conducts an event study on stock price shocks in which the individual stocks that make up the KOSPI200 index show more than 10% fluctuation on a daily basis. In order to materialize the abstract predictions of heuristics theories in a varifiable form, this paper uses the daily stock price index change as a reference point for availability heuristics, and uses the 52-week highest and lowest price as reference point for anchoring heuristics. Research implications or Originality - As a result of the empirical analysis, the stock price reversals did not consistently appear for changes in the daily index. On the other hand, the stock price drifts consistently appeared around the 52-week highest and the 52-week lowest price. And in the multiple regression analysis that controlled for company-specific and event-specific variables, the results that supported the anchoring heuristics were more evident. These results suggest that it is possible to establish an investment strategy using large-scale price change in Korean stock market.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.38 no.2
• /
• pp.95-115
• /
• 2013

In traditional inventory models, purchase prices of raw materials are assumed to be fixed and have no effect on the optimal choice of inventory policies. However, when purchase prices fluctuate continuously over time, inventory costs are heavily affected by purchasing prices. Risk-averse inventory model decides order quantity and ordering time by considering not just purchase prices but also the risk from the discrepancy between estimated prices and realized prices. In this paper, we propose a myopic inventory policy which incorporates price risk into deciding ordering time and quantities. While the existing risk-averse model has no mechanism to reallocate inventories already purchased for a specific future period, the revised one reallocates initial inventories of each period to other future periods so that it can avoid purchasing raw materials at high prices. Experimental results demonstrate that the revised model outperforms the existing one in respect of total cost and variability.

• Journal of Korean Society of Rural Planning
• /
• v.10 no.3 s.24
• /
• pp.53-58
• /
• 2004

In this study the effect caused by limited storage lift of agricultural products for determining shipping amount can be analyzed by $l^{st}$ order autoregressive model based on cobweb theorem. Carrying capacity and auction price of upland-grown cabbage and garlic from 2000 to 2003 in wholesale markets were used for analysis. In result regression models of cabbage can not be used in verification periods although those of garlic approximately predicted shipping amounts in verification periods. It can be inferred that it is hard to control shipping amounts depending on price fluctuation for agricultural products which have limited storage life so cultivated areas and meteorological risk should be managed for stable price.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.35 no.1
• /
• pp.217-227
• /
• 2015

This study conducted case studies in order to suggest the improvement of GDP (Gross Domestic Product) deflator method which is adopted on calculating fluctuation rate on BTL (Build-Transfer-Lease) sewer rehabilitation projects in Jeju. As a result, because GDP deflator method calculates fluctuation rate by each quarterly, the fluctuation rate of GDP deflator method is higher than it of index adjustment rate method. And GDP deflator method cannot reflect real price because of applying fixed index in whole construction cost for calculating fluctuation rate. Especially, the notification day - the base point influences fluctuation rate and fluctuation amount strongly in GDP deflator method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.8 no.8
• /
• pp.1670-1678
• /
• 2004

Currently, Price comparison sites receive inputs of product information and price manually after being joined by releted shopping mall companies, and can't support automatically price fluctuation of products in real time. Therefore, this paper designs and implements web service system based on SOAP for interactive product order and price comparison dynamically using web service technologies in real time. This web service system composes web service client module including functions of product search, sort and order, and server module including functions of remote procedure call for product search and order. This web service system implements interchanging product information based on SOAP messages and can support independence of platform and flexible potability in environment conforming to SOAP, WSDL and UDDI standards.

• Land and Housing Review
• /
• v.10 no.1
• /
• pp.9-18
• /
• 2019

This study examined the dynamic change in the co-movement between the house price rates with the network methods of Mantegna (1999). First, Capital area and non-capital area form independent clusters which have the heterogeneous co-movement pattern. In other words, Capital and non-capital areas have low connectivity in the housing market. Also, if the co-movement between capital areas have been strengthened, the co-movement between non-capital areas have been weakened. The results of the dynamic analysis show that the degree of the co-movement in the housing market is continuously increased. The members of the co-movement group in the capital area are strongly steadied by all periods. However, the members in the non-capital area have been changed according to the period. Accordingly, it is necessary to establish policies based on various information for the housing market of the non-capital area rather than policies targeting the capital area. In addition, Apartments in Korea are more likely to be used as investment or speculative assets than other types of houses. It has been confirmed that this is Gangbuk, which is locatied in the northern part of Seoul, appears to be a region where the Spillover Effects of price fluctuation can be triggered in the housing and apartment market. However, the housing market in Gangnam, which is locatied in the southern part of Seoul, was divided into low systematic risk.

• KIISE Transactions on Computing Practices
• /
• v.23 no.11
• /
• pp.625-631
• /
• 2017

The stock price prediction for stock markets remains an unsolved problem. Although there have been various overtures and studies to predict the price of stocks scientifically, it is impossible to predict the future precisely. However, stock price predictions have been a subject of interest in a variety of related fields such as economics, mathematics, physics, and computer science. In this paper, we will study fluctuation patterns of stock prices and predict future trends using the Deep learning. Therefore, this study presents the three deep learning models using Tensorflow, an open source framework in which each learning model accepts different input features. We expand the previous study that used simple price data. We measured the performance of three predictive models increasing the number of priced-based input features. Through this experiment, we measured the performance change of the predictive model depending on the price-based input features. Finally, we compared and analyzed the experiment result to evaluate the impact of the price-based input features in stock price prediction.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.47 no.5
• /
• pp.779-803
• /
• 2023

The growth of digital news media and the stock price index has resulted in economic fluctuations in the fashion industry. This study examines the impact of fashion industry news and macroeconomic changes on the Textile & Wearing Apparel KOSPI over the past five years. An auto-regressive integrated moving average exogenous time series model was conducted using the fashion industry stock market index, the news topic index, and macro-economic indicators. The results indicated the topics of "Cosmetic business expansion" and "Digital innovation" impacted the Textile & Wearing Apparel KOSPI after one week, and the topics of "Pop-up store," "Entry into the Chinese fashion market," and "Fashion week and trade show" affected it after two weeks. Moreover, the topics of "Cosmetic business expansion" and "Entry into the Chinese fashion market" were statistically significant in the macroeconomic environment. Regarding the effect relation of Textile & Wearing Apparel KOSPI, "Cosmetic business expansion," "Entry into the Chinese fashion market," and consumer price fluctuation showed negative effects, while the private consumption change rate, producer price fluctuation, and unemployment change rate had positive effects. This study analyzes the impact of media framing on fashion industry business cycles and provides practical insights into managing stock market risk for fashion companies.