• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.137-138
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• 2012

Domestic construction materials market was about 65 trillion won and it occupied 45% level of total construction cost by 2007. In addition, due to the recent rapid rise of crude oil and iron ore price, fluctuation of raw material cost has a great influence to the cost of construction industry. This means that smooth performance is closely related to construction materials. And among them, because of high putting rate of metal materials, it can be seen that the fluctuation of metal material prices is an important variables. So in this study, for the pre-study to analyze the impact of metallic material prices to construction cost, the researcher analyzed a causal relationship between metal material prices and construction cost.

• Journal of Distribution Science
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• v.13 no.8
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• pp.61-71
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• 2015

Purpose - Before the year 2000, the housing prices in Korea were increasing every decade. After 2000, for the first time, Korea experienced a decrease in housing prices, and the repetitive cycle of price fluctuation started. Such a "boom and bust cycle" is a worldwide phenomenon. The current study proposes a mathematical model to explain price fluctuation cycles based on the theory of consumer psychology. Specifically, the model incorporates the effects of buyer expectations of future prices on actual price changes. Based on the model, this study investigates various independent variables affecting the amplitude of price fluctuations in housing markets. Research design, data, and methodology - The study provides theoretical analyses based on a mathematical model. The proposed model uses the following assumptions of the pricing mechanism in housing markets. First, the price of a house at a certain time is affected not only by its current price but also by its expected future price. Second, house investors or buyers cannot predict the exact future price but make a subjective prediction based on observed price changes up to the present. Third, the price is determined by demand changes made in previous time periods. The current study tries to explain the boom-bust cycle in housing markets with a mathematical model and several numerical examples. The model illustrates the effects of consumer price elasticity, consumer sensitivity to price changes, and the sensitivity of prices to demand changes on price fluctuation. Results - The analytical results imply that even without external effects, the boom-bust cycle can occur endogenously due to buyer psychological factors. The model supports the expectation of future price direction as the most important variable causing price fluctuation in housing market. Consumer tendency for making choices based on both the current and expected future price causes repetitive boom-bust cycles in housing markets. Such consumers who respond more sensitively to price changes are shown to make the market more volatile. Consumer price elasticity is shown to be irrelevant to price fluctuations. Conclusions - The mechanism of price fluctuation in the proposed model can be summarized as follows. If a certain external shock causes an initial price increase, consumers perceive it as an ongoing increasing price trend. If the demand increases due to the higher expected price, the price goes up further. However, too high a price cannot be sustained for long, thus the increasing price trend ceases at some point. Once the market loses the momentum of a price increase, the price starts to drop. A price decrease signals a further decrease in a future price, thus the demand decreases further. When the price is perceived as low enough, the direction of the price change is reversed again. Policy makers should be cognizant that the current increase in housing prices due to increased liquidity can pose a serious threat of a sudden price decrease in housing markets.

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

• Korean System Dynamics Review
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• v.14 no.3
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• pp.135-163
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• 2013

This study aims to analyze the feedback loops and policy simulation of price fluctuation of Korean Cattle. The Korean Cattle market shows the 'Cycle of Beef' since 1970. In general, the market for agricultural commodities exhibit repeated cycles of prices and production. Why Beef products market in Korea shows the fluctuation of cattle and beef price repeatedly for forty years? To find an answer, this paper explores the feedback structure of the dynamics of the beef market by the systems thinking and build a stock-flow diagram model for the simulation of future behavior of the market sector of the Cattle. The dynamic simulation model was developed to identify and analyze the cyclical behavior among many variables, which is the number of cattle (calves, cow, etc.), the price of cattle, the demand for beef, the desirable number of cattle, slaughter, etc. The results of this study demonstrate that dominant feedback loops between the number of cattle and livestock prices. The demand for Beef and slaughter with time delay, also the results of the simulation to explain the persistence of future price fluctuations and actions meat market until 2025.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.16 no.6
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• pp.63-75
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• 2013

The purpose of the study is investigating the price fluctuation of landscaping plants in the Information on Commodity Prices(ICP) and the posted price fluctuation of landscaping plants of Public Procurement Service(PPS) recent 10 years. It also provides the basic information which can be applied to production and sales of landscaping plants, comparing with general price index. The major findings of the study are as follows. First, The price of investigated plants of PPS has increased about 4.56% in average recent 10 years. Among this increase, of evergreen tree was predominant. On the other hand, landscaping trees price of ICP has increased about only 2.34% in average. Secondly, The result shows that average price of investigated plants of PPS is positively related with the price of ICP. For this reason, we found that prices of ICP and of PPS move together in most case. However, we found that there are no relation between Consumer Price Index(CPI), Producer Price Index(PPI) and Agricultural Price Index(API). Therefore, price fluctuation of landscaping trees moves regardless of normal price fluctuation in general. Third, even though result shows that price index of evergreen trees, deciduous trees and shrubs are weakly related with normal price index partly, it was not high enough to be significant. According to the result, we found that price of landscaping plants is not related with market situation. For this reason, we thought that there are some difficulties for the reasonable production and sales of landscaping plants because the price is somewhat decided by rule of thumb. Therefore, understanding the composition of cost and making prediction by price fluctuation available are needed so that it can be practically conducive to reasonable production and sales.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.4
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• pp.33-53
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• 2010

When purchase prices of a raw material fluctuate over time, the total purchasing cost is mainly affected by reordering time. Existing researches focus on deciding the right time when the demand for each period is replenished at the lowest cost. However, the decision is based on expected future prices which usually turn out to include some error. This discrepancy between expected prices and actual prices deteriorates the performance of inventory models dealing with fluctuating purchase prices. In this paper, we propose a new inventory model which incorporates not only cost but also risk into making up a replenishment schedule to meet each period's demand. For each replenishment schedule, the risk is defined to be the variance of its total cost. By introducing the risk into the objective function, the variability of the total cost can be mitigated, and eventually more stable replenishment schedule will be obtained. According to experimental results from crude oil inventory management, the proposed model showed better performance over other models in respect of variability and cost.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.1112-1117
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• 2005

Construction cost index is generally used to estimate the new project cost based on past construction data and to adjust contract cost when the price change of various articles and items of expenditure composing the contract occurs. In Korea, it is mostly used for adjustment of construction contract cost due to fluctuation of prices. However index adjustment rate which is used for adjustment of construction contract cost had some problems in calculating cost index of each expenditure item that could not reflect properly the change of construction cost. For supplementing 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 method of making cost index that utilizes representative items of labor, material, and equipment by analyzing bill of quantity of road construction, through analysis and comparison of precedent study. By using this method, it is expected to solve problems which were not reflected in precedent studies.

• Journal of the Korea Safety Management & Science
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• v.6 no.2
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• pp.211-229
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• 2004

The basis of cyber trading has been sufficiently developed with innovative advancement of Internet Technology and the tendency of stock market investment has changed from long-term investment, which estimates the value of enterprises, to short-term investment, which focuses on getting short-term stock trading margin. Hence, this research shows a Short-term Stock Price Forecasting System on Learning Agent System using DTA(Decision Tree Algorithm) ; it collects real-time information of interest and favorite issues using Agent Technology through the Internet, and forms a decision tree, and creates a Rule-Base Database. Through this procedure the Short-term Stock Price Forecasting System provides customers with the prediction of the fluctuation of stock prices for each issue in near future and a point of sales and purchases. A Human being has the limitation of analytic ability and so through taking a look into and analyzing the fluctuation of stock prices, the Agent enables man to trace out the external factors of fluctuation of stock market on real-time. Therefore, we can check out the ups and downs of several issues at the same time and figure out the relationship and interrelation among many issues using the Agent. The SPFA (Stock Price Forecasting System) has such basic four phases as Data Collection, Data Processing, Learning, and Forecasting and Feedback.

• Korean Management Science Review
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• v.28 no.1
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• pp.53-60
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• 2011

Time series analysis methods have been traditionally used in stock price prediction. However, most of the existing methods represent some methodological limitations in reflecting influence from external factors that affect the fluctuation of stock prices, such as oil prices, exchange rates, money interest rates, and the stock price indexes of other countries. To overcome the limitations, we propose a network based method incorporating the relations between the individual company stock prices and the external factors by using a graph-based semi-supervised learning algorithm. For verifying the significance of the proposed method, it was applied to the prediction problems of company stock prices listed in the KOSPI from January 2007 to August 2008.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.7-14
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• 2012

To analyze any given situation, it is necessary to have information on elements which affect the situation. Particularly, there is greater variability in both frequency and magnitude of agricultural product prices as they are affected by various unpredictable factors such as weather conditions etc. This is the reason why it is difficult for the farmers to maintain their stable income through agricultural production and marketing. In this research, attempts are made to quantify the entropy of various situations inherent in the price changes so that the stability of farmers' income can be increased. Through this research, we developed an entropy model which can quantify the uncertainties of price changes using the probability distribution of price changes. The model was tested for its significance by comparing its simulation outcomes with actual ranges and standard deviations of price variations of the past using monthly agricultural product prices data. We confirmed that the simulation results reflected the features of the ranges and standard deviations of actual price variations. Also, it is possible for us to predict standard deviations for changed prices which will occur after a certain time using the information entropy obtained from relevant agricultural product price data before the time.