• Journal of Navigation and Port Research
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• v.43 no.2
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• pp.134-141
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• 2019

The objective of this study was to establish whether global macroeconomic indicators affect the profitability of Korean shipping companies by using panel regression analysis. OROA (operating return on assets) and ROA (ratio of net profit to assets) were selected as proxy variables for profitability. OROA and ROA were used as dependent variables. The world GDP growth rate, interest rate, exchange rate, stock index, bunker price, freight, demand and supply of the world shipping market were set as independent variables. The size of the firm was added to the control variable. For small-sized firms, OROA was not affect by macroeconomic indicators. However, ROA was affected by variables such as interest rates, bunker prices, and size of firms. For medium-sized firms, OROA was affected by demand, supply, GDP, freight, and asset variables. However, macroeconomic indicators did not affect ROA. For large-sized firms, freight, GDP, and stock index (SCI; Shanghai Composite Index) have an effect on OROA. ROA was analyzed to be influenced by bunker price and SCI.

• Journal of Families and Better Life
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• v.7 no.1
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• pp.15-25
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• 1989

The purpose of this study is to examine the factors affecting the consumer's search for information and the relationship between the amount of search and the final price paid. The model indicates the demand for search is affected by the market price of each durable good purchased, the tim available for search, family income, direct cost of search, the initial stock of information, effectiveness of search, and shopping attitudes. The final price savings are a function of search, price of dispersion in the market, the initial stock of information, and effectiveness of search. Data from the Pane Study on Consumer Decisions and Asset Management were used for the empirical testing of the theoretical model. The amount of information search as dependent variable is represented by two different measures, the level of discussion with others and the number of stores visited. The amount of discussion with others depends mainly on the respondent's shopping attitude. The higher the wife's desire to search, the higher the degree of husband's comparison shopping, the less the husband's perception of price-quality relationship, the higher the level of discussions with others. The number of stores visited depends on the average market price of product purchased and the level of family income. The higher the average market price and he higher the level of family income, the greater the number of stores visited. The final savings depend upon the level of information search. The greater the number of store visited, but the less the purchase is discussed with stores, the higher the final savings are.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6B
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• pp.390-401
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• 2007

This paper is to estimate and analyze the price elasticities of demand for mobile calls. We used the data for the period from January 2000 to December 2005 on a monthly basis. Data used are call minutes to mobile-originating(ML+MM), tariff for dispatch of fixed and mobile calls($P_L,P_M$), income(Y), and subscriber for mobile(N). In order to provide robust estimates of price elasticities, we have used two different econometric models. One is a Dynamic model which includes a lagged dependent variable and so can differentiate between long-un and short-run price elasticities using the Generalized Method of Moments(GMM). The other is a Box-Cox transformation model which is one of the most useful methods. Box-Cox transformation model shows that elasticity changes with the lapse of time. The results are as follow : Not including the price indices for land-originating, the estimate is overestimated otherwise. In Box-Cox transformation case, price elasticity had been steadily declining. And this result shows that mobile services had been changed necessities increasingly in Korea.

• Proceedings of the KIEE Conference
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• summer
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• pp.698-700
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• 2004

Electric power restructuring offers a major change to the vertically integrated utility monopoly. Deregulation has had a great impact on the electric power industry in various countries. Bidding competition is one of the main transaction approaches after deregulation. The energy trading levels between market participants is largely dependent on the short-term price forecasts. This paper presents the short-term System Marginal Price (SMP) forecasting implementation using backpropagation Neural Network in competitive electricity market. Demand and SMP that supplied from Korea Power Exchange (KPX) are used by a input data and then predict SMP. It needs to analysis the input data for accurate prediction.

• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.354-363
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• 2016

Due to uncertain environment, various parameters such as price, queuing length, warranty, and so on influence on inventory models. In this paper, an inventory-queuing-pricing problem with continuous review inventory control policy and batch arrival queuing approach, is presented. To best of our knowledge, (I) demand function is stochastic and price dependent; (II) due to the uncertainty in real-world situations, a fuzzy programming approach is applied. Therefore, the presented model with goal of maximizing total profit of system analyzes the price and order quantity decision variables. Since the proposed model belongs to NP-hard problems, Pareto-based approaches based on non-dominated ranking and sorting genetic algorithm are proposed and justified to solve the model. Several numerical illustrations are generated to demonstrate the model validity and algorithms performance. The results showed the applicability and robustness of the proposed soft-computing-based approaches to analyze the problem.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.493-499
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• 2023

Trade credit refers to a transaction where a product supplier allows an distributor to defer payment for a certain period of time for the purchase cost of the products. This practice is generally permitted as a means of differentiation between competing companies. Such trade credit is commonly granted based on the volume of transactions, aiming to increase customer orders. From the perspective of the distributor, trade credit allows for a deferred payment period for the purchase cost, leading to cost savings in inventory investment. These cost savings in inventory investment can be a factor in reducing selling prices with the aim of increasing customer demand. In this study, we analyze a model that determines the optimal selling price and order quantity from the perspective of the distributor, assuming that the supplier allows a deferred payment period dependent on the transaction volume. We assume that the final customer's annual demand exhibits an exponential decrease with respect to the distributor's selling price, using a constant price elasticity function. To analyze the problem, we assume that the product deteriorates at a constant rate over time and aim to establish an inventory model for the intermediate distributor. We also want to analyze the impact of deterioration on the inventory policies of the intermediate distributor.

• Environmental and Resource Economics Review
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• v.16 no.1
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• pp.27-63
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• 2007

Using the CGE model, this paper investigates economic impacts of a shortage in crude oil resulting from voluntary export restraints, OPEC's agreement of a cut in oil production, and/or a storing on speculation. Unlike most previous studies considering oil price as the unpredictable variable, this study constructs the model to determine the oil price endogenously under the condition of an insufficient supply of crude oil. According to IEA's extraordinary steps for a shortage of crude oil, we investigate an economic impact of 7~12% shortage below the level of business as usual. The results show that oil price soars by 17.3~33.5%, the rate of economic growth falls by 0.52~0.96%p, and the consumer price index(CPI) rises by 0.8~1.51%p. These results imply that increasing in 1%p of oil price results in decreasing in 0.03%p of economic growth and increasing in 0.045%p of consumer price index. The production of electricity declines because of the increase in production cost. A shortage of crude oil has an effect on sources of electricity. Most reduction in electricity generation occurs from the reduction in the thermal power generation which is highly dependent on crude oil. The shortage of crude oil causes demand for petroleum to significantly decline but demand for coal and heat to increase because of the substitution effect with petroleum. Demand for gas rise in the first year but falls from the second year.

• Journal of Service Research and Studies
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• v.6 no.4
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• pp.75-85
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• 2016

Coal can be divided into thermal coal and coking coal. The price of thermal coal is basically affected by demand and supply. However, many other factors with regard to economic condition such as exchange rate, economy growth rate also make an influence on the price. This study is targeted to develop a forecast model for thermal coal price by using System Dynamics Method. System dynamics provides results that better reflect the real world by employing an inter-dependent system of variables. This study found out that 8 factors have important influence on the thermal coal price. Most of the data of the variables were acquired from the Bloomberg Database. The period extends to 2 years and 4 months, from May of 2011 to August of 2013. The causal relations among the variables were acquired by regression analysis

• Journal of IKEEE
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• v.28 no.3
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• pp.451-457
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• 2024

This paper proposes a method for predicting agricultural product prices by utilizing various variables such as price, climate factors, demand, and import volume as data, and applying the Long Short-Term Memory (LSTM) model. The analysis of prediction performance using the LSTM model, which learns the long-term dependencies of time series data, showed that integrating diverse data improved performance compared to traditional methods. Furthermore, even when predicting without price data as a dependent variable, meaningful results were achieved using only independent variables, indicating the potential for further model development. Moreover, it was found that using a multi-variable model could further enhance prediction performance, suggesting that this complex approach is effective in improving the accuracy of cabbage price predictions.