• 수산경영론집
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• 제40권1호
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• pp.27-49
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• 2009

Recently, the Korea's economy concerns the second money crisis because of the rapid increase of the exchange rate. The Korea's economy which is very dependent on the foreign trade is more sensitive to the change of exchange rates. There are many literatures which analyze the effects of variations of the exchange rates on the secondary and tertiary industries such as the manufacturing industry and IT(Information Technology). But there have been no studies which try to figure out the effects of variations of exchange rate on the primary industries, especially, fisheries' industry. Therefore this paper tries to analyze the effect of price competition structure and the change of exchange rate on foreign fisheries exporting prices in Korea's fisheries import market. This study utilizes OLS(Ordinary Least Squares Analysis) for the analysis in the market of frozen yellow corvina, hairtail, angler fish which are major fisheries importable in Korea. The results show that the exporting country which has the highest market share is more sensitive to the change of the exchange rates itself than that of the other exporting countries' price when it starts to set up its exporting price. And the exporting countries which have low market share are more sensitive to the change of price which country has the highest market share than that of price whose countries have low market share and those of their exchange rate. Also we can find out that the countries which have similar market share try to set up price-setting strategy in the opposite direction. In other words, one country tries to bid up its price, other countries response to rival country by lowering their prices. In the consideration of the fact that most exporting countries aren't affected by Korea's fisheries' prices, the exporting countries in Korea's fisheries import market are more sensitive to the prices of other exporting countries than that of Korea's. This result indicates that the price leader-follower model could be applicable to the Korea's fisheries import market.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.107-115
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• 2020

This study is concerned with the relationship between firm's ownership structure and the co-movement of the stock return with the market return. Four different types of firm ownership, including managerial ownership, state ownership, foreign ownership, and concentrated ownership, are among the main features of the company's governance mechanism and have been separately documemented in the previous research to understand their impact on stock price synchronicity. We constructed the regression model, using stock price synchronicity as the dependent variable and the above four components of ownership structure as explanantory variables. The pooled OLS, the fixed effects model, and the random effects are employed to investigate the outcome of the study. Data used in the reserch are of public firms listed on the Ho Chi Minh City Stock Exchange (HOSE) during the five-year period term from 2015 to 2019. The data sample contains 235 companies from 10 industries with 1135 observations. The results revealed by the fixed effects model, the large ownership and the managerial ownership are found to have adverse effect on the stock price synchronicity, whereas the foreign ownership model is revealed to have positive influence on the stock return co-movement. The effect of the state ownership on the stock price synchronicity is not confirmed.

• 정보처리학회논문지D
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• 제12D권5호
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• pp.737-744
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• 2005

국방 관련 시스템 구축에 있어 소프트웨어의 비중이 커짐에 따라 국방 소프트웨어 개발비용 산정의 정확성에 대한 요구는 점점 높아가고 있다. 소프트웨어의 개발 초기단계에서 신속하고 합리적인 비용 산정을 하는데 적용할 수 있는 PRICE S는 미국 환경의 매개변수형 산정법으로 국내 실정에 다소 적합하지 않은 부분이 있다. 본 연구는 소프트웨어 개발비용 산정을 위해 국방 소프트웨어 비용 산정에 적용되는 PRICE S의 기존 적용방법을 국내 소프트웨어 개발비용 기준인 한소협 모델과의 비교를 통하여 수정 및 보완한다. 또한, 기능 점수 방식의 소프트웨어 개발비용 산정을 위한 백파이어링 절차를 제시함으로써 향후에 계획된 소프트웨어 개발 사업에 기능 점수 방식의 소프트웨어 개발비용 산정 기법을 적용하는 방안을 제시하여 개발비용 산정의 정확성을 향상시키고 기능 점수 방식의 적용에 대한 대비책을 제공한다.

• 한국국방경영분석학회지
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• 제25권2호
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• pp.44-61
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• 1999

Cost estimation has posed a significant challenge to estimators, planners, and managers in both government and military. Considerable historical evidence shows that accurate cost estimation has been difficult to achieve across a wide range of projects, including weapon systems. This paper introduces new cost estimating concept, CAIV(Cost As an Independent Variable) and a cost estimating case study using PRICE model, computer aided parametric estimating models(CAPE) for K1 tank cost estimate. CAIV concept is to set realistic but aggressive cost objectives easily in each acquisition program and to achieve cost, schedule, and performance objectives considering various managing risks with a project manager and industry teams. The Price model is one of computer aided cost estimating models and widely used in U.S. defense system analysis as a tool for CAIV. We analyze theories, inputs, outputs of the PRICE model and present a case study for K1 tank to estimate costs in requirement and concept phase, program and budgeting phase, and life cycle phase. Finally we obtain results that the Price model can be used in various phases of PPBEES depending upon available data and time.

• 한국국방경영분석학회지
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• 제27권1호
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• pp.10-27
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• 2001

This paper deals with the application of PRICE model in estimating the proper acquisition cost for weapon budgeting phase. The PRICE(Parametric Review of Information for Costing and Evaluation) Hardware model is a computerized method for deriving cost estimates of electronic and mechanical hardware assemblies and systems. The model can be used in obtaining not only initial cost estimates in conceptual phase, but also detailed cost estimates in budgeting phase depending on available historical and empirical data. We analyzed first step cost estimate parameters and derived cost equations using PRICe output dta. Using weight and complexity, We can find cost variation. Sensitivity analysis shows that cost increases exponentially as complexity increases exponentially as complexity increases. We estimated KAAV\`s (Korea Amphibious Assault Vehicle) production cost using the PRICE model and compare with engineering cost estimates which is based on actual production data submitted by the production company. The result shows that tow estimates are close within $\pm2%$ differences.

• ETRI Journal
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• 제6권4호
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• pp.9-20
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• 1984

Communications is becoming a vital sector to lead Information Society in the nearfuture. In that sense the price system of its services is now being discussed seriously among the people who are directly and indirectly related to communications development. Input-Output analysis model developed by W. Leontief in early 1930's is a very useful tool to measure the price linkage effects in the national economy, which would be induced from the shift of a certain sector's price. 1980's I-0 table with 19 sectors is rear-ranged and applied to price analysis model, in which communications is treated as a exogenous sector, to see the price impacts generated from a change of telephone rate. With the results of this study, the authorities concerned with price policy can make their decisions more reasonable.

• 가정과삶의질연구
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• 제7권1호
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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.

• International Journal of Control, Automation, and Systems
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• 제6권5호
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• 자원ㆍ환경경제연구
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• 제27권3호
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• pp.521-544
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• 2018

This paper investigates the impact of ethanol mandate on the price relationship between corn and beef using the monthly time-series data from January 2003 through December 2013. In addition, we examine the non-linearity in ethanol, corn, and beef markets. Based on the threshold cointegration test, we find the symmetric relationship in pairs with ethanol production-corn price and ethanol production-beef price whereas there is the asymmetric relationship between prices of corn and beef. Employing the threshold vector error correction and vector error correction models, we also find that the corn price in the U.S is caused by both ethanol production and beef price in a long-run when the beef price is relatively high. On the other hand, the corn price does not cause both ethanol production and beef price in the long run. Findings from this study imply that demanders for corn such as ethanol and beef producers have price leadership on corn producers.