• Journal of the Korean Home Economics Association
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• v.37 no.4
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• pp.41-58
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• 1999

The purposes of this study were 1) to segment the market based on purchase frequency of products such as apparel, food, home electronics, life commodity in department store and low-price retailing, 2) to identify differences among segments in belief and attitude toward each store, purchase frequency of apparel items in each store and demographic variables. The data were collected via a self-administered questionnaire from 274 married women living in Seoul, Korea and analyzed by factor analysis, cluster analysis, one-way ANOVA and x$^2$-test. The results of this study were as follows: First, using cluster analysis on purchase frequency of products in each store, four groups were identified and labeled as department store patronage/ non-purchasers of apparel in low-price retailing(25.2%), purchasers of apparel in department store and low-price retailing(16.8%), low-price retailing patronage(30.3%) and non-purchasers of products in department store and low-price retailing(27.0%). Second, a series of one-way ANOV As revealed significant differences among four segments on beliefs of low-price retailing(four store attributes: price and variety of apparel product, facilities for convenient shopping, promotion, brand-reputation and fashionability of apparel product) and department store(three store attributes: price and variety of apparel product, facilities for convenient shopping and promotion) and attitude toward low-price retailing and department store. Attitude toward each store was yielded using Fishbein's multiattributes model. There were also significant differences among groups in purchase frequency of seven apparel items in low-price retailing and six apparel items in department store, and six demographic and personal variables(age, educational status, type of husband's occupation, monthly income and housing). Finally, the papers discussed manageral implications for each segments as well as theoretical implications.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.1-6
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• 2022

This paper uses the gravity model estimated by the random effect method to analyze the factors affecting Vietnam's coffee export turnover for the period 2007-2020 major markets according to statistics from the General Statistics Office and the General Department of Customs. Coffee export turnover was collected from the General Statistics Office, General Department of Customs, and Vietnam Cacao Coffee Association. The authors calculated the price of coffee based on output and export value from data on coffee export turnover; the authors calculated the economic gap based on population and Gross Domestic Product data (reference: geographic distance metrics on the website: http://www.distancefromto.net/countries.php) and other data was collected based on the databases of the Food and Agriculture Organization of the United Nations, the International Monetary Fund, and World Bank organizations. The results of the study show that from 2007 to 2020, the factors of Vietnam's export price of coffee, geographical distance, Gross Domestic Product of the importing country and Gross Domestic Product of Vietnam, the population of Vietnam, the economic gap between Vietnam and the importing country, the openness of the economy, all have an impact on Vietnam's coffee export turnover. Finally, some conclusions about the policy's impact are made based on the empirical results of the paper.

• The Journal of Industrial Distribution & Business
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• v.9 no.6
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• pp.15-24
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• 2018

Purpose - The adjustment of one country's monetary policy can cause the macroeconomic change of other countries. Due to this, this paper attempts to analyze the impact of China's monetary policy on South Korea's exchange rate. Research design, data, and methodology - Based on the flexible-price monetary model, sets of annual time series from 1980 to 2017 are employed to perform an empirical estimation. The vector error correction model is also used to exploit the short-run relationship between both of them. Of course, the South Korea's real GDP, the China's real GDP, South Korea's interest rate, the South Korea's interest rate and the South Korea's monetary supply are treated as independent variables in this paper. Result - The long-run findings reveal that the China's money supply has a negative effect on South Korea's exchange rate. Respectively, the short-run findings depicts that the China's money supply has negative a effect on South Korea's exchange rate. Of course, other variables selected in this paper also have an effect on South Korea's exchange rate whatever positive or negative. Conclusions - As the empirical evidence shows, the China's monetary policy has a negative effect on South Korea's exchange rate whenever in the long run or in the short run.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7B
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• pp.550-564
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• 2012

In this work we propose a new pricing scheme for the wireless Internet services over WiMAX system. First, let us review the characteristics of wireless network which is based on multi-hop relay WiMAX system. Next, we show why usage-based and QoS-aware pricing scheme is needed in the wireless Internet. After that, we propose a theoretical model for the price of multimedia services called a DAP (DiffServ-aware pricing) scheme for the WiMAX multimedia network which takes into account the consumed radio resource of WiMAX system as well as the supported QoS in the IP backbone network. Finally, we present explicit formulae for the packet price, price of consumed radio resource, and price of consumed bytes.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.822-823
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• 2007

It is easily can be expected that Korea cannot be free under the regulation, because Korea is one of the major $CO_2$ emitter in the world. Even though Korea currently doesn't have any obligation to mitigate the carbon emission, power industry needs to study the effect of that. this paper aims to analyze the change of economic loading order for generation dispatch by various carbon price, looking at each plant's or generator's variable generation cost per unit electricity(kWh) that consists of basic generation price calculated by automatic generation system planning model, WASP 4.0, and $CO_2$ price per unit electricity generation.

• The Journal of Fisheries Business Administration
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• v.47 no.4
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• pp.65-75
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• 2016

The purpose of this study was to analyze the production and marketing control effects of aqua-cultured flounder required for stable income growth of aqua-cultured household. We analyzed the supply and demand structure of cultured flounder using the partial equilibrium model approach. And we estimated the optimal yield of cultured flounder and analyzed the effect of marketing control through constructed model. The main results of this study are summarized as follows. First, the fitness and predictive power of the estimated model showed that the RMSPE and MAPE values were less than 5% and Theil's inequality coefficient was very close to 0 rather than 1. It was evaluated that the prediction ability of the aqua-cultured flounder supply and demand model by dynamic simulation was excellent. Second, dynamic simulation based on policy simulation was conducted to analyze the price increase effect of production and shipment control of cultured flounder. As a result, if the annual production volume is reduced by 1%, 5%, and 10% among 32,852~37,520 tons, it is analyzed that the price increase effect is from 1.2% to 12.5%. Finally, this study suggests that the production and marketing control can increase the price of aqua-cultured flounder in the market. In this paper, we propose a policy implementation of the total supply system instead of conclusions.

• Journal of the Korean Geographical Society
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• v.49 no.5
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• pp.761-778
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• 2014

There has been relatively little study to model price for commercial property because of its low transaction volume in the market. Despite of this thin market character, this paper tried to estimate prices for commercial lots as accurate as possible. We constructed a model whose components consist of mean structure(global trend), exponential covariance function and a pure error term, and applied it to actual sales price data of Seoul. We explicitly took account of spatial autocorrelation of land price by utilizing a kriging technique, a representative method of spatial interpolation, because the land price of commercial lots has feature of differential price forming pattern depending on submarkets they belong to. In addition, we chose to apply a bayesian kriging to overcome data scarcity by incorporating experts' knowledge into prior probability distribution. The chosen model's excellent performance was verified by the result from validation data. We confirmed that the excellence of the model is attributed to incorporating both autocorexperts' knowledge and spatial autocorrelation in the model construction. This paper is differentiated from previous studies in the sense that it applied the bayesian kriging technique to estimate price for commercial lots and explicitly combined experts' knowledge with data. It is expected that the result of this paper would provide a useful guide for the circumstances under which property price has to be estimated reliably based on sparse transaction data.

• Korean Journal of Construction Engineering and Management
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• v.6 no.6 s.28
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• pp.98-106
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• 2005

The construction cost index and the forecasting model of apartment house can be efficient for evaluating the validness of the fluctuating price, and for making guidelines for construction firms when calculating their profit. In this study the previous construction cost index of apartment house was improved, and the forecasting model based on X-12 ARIMA was developed. According to the result, during the last five years the construction cost, excluding labor expense, has risen approximately to 22.7%. And during next three years, additional 16.8% rise of construction cost is expected. Those quantitative results can be utilized for evaluating the apartment house's selling price in an indirection, and be helpful to understand the variation pattern of the price.

• Journal of the Korean housing association
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• v.18 no.3
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• pp.63-72
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• 2007

The purpose of this study is to identify the impact of the building, site, and region characteristic factors on the annual average price rising rate of apartment housing in Seoul. The data were consisted of 272 apartment units in Seoul. A survey included checking the drawing documents and interview with apartment maintenance staffs and real estate agencies from October 2006 to February 2007. Data were analyzed with descriptives, frequency, crosstabs, and linear regression by SPSS/PC for Window. The linear regression model was employed to evaluate the price rising rate in apartment housing. Following results were obtained. The price rising rate for pyeong ($3.3m^2$) of apartment housing was determinated by the district zone, the construction company's brand name, the building age, the building stories, the floor space index, the building-to-land ratio, the green space rate, and the distance from the downtown. Especially, the district zone was the most important factor that affected the price rising of apartment housing in Seoul. Therefore, the policy has to focus to solve the imbalance between autonomous districts with the collaborated tax.

• Asia pacific journal of information systems
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• v.30 no.1
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• pp.31-52
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• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.