• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5403-5410
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• 2013

This study investigated the effect of symbolic meanings of the artwork objectives on auction price. Based on Buck(1948)'s House-Tree-People(H-T-P) model, we hypothesized that symbolic meanings of these objectives invoke preferences of bidders and increase the price. Using 402 auction price data from June 2010 to May 2011, we found that an artwork including house and people tend to be auctioned off at a higher prices than the artwork without them. This study confirmed that Buck's model can be used to determine artwork price, suggesting that symbolic objectives in the artwork do affect its price.

• Journal of Global Scholars of Marketing Science
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• v.15 no.1
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• pp.81-103
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• 2005

This study intends to examine whether consumers, especially buying apparel product postpone purchase in the beliefs of price break in the near future. For this research objectives, the dynamic price expectation effect model for apparel product is proposed and is applied to weekly sales data and price information of 43 women's apparel brands from a department store over the spring season. The main results are summarized as follows. 1. Dynamic price expectation effect model is developed and the model is more robust to apparel product than Gutenberg model, which is not considered these conditions. 2. By applying the model to empirical data, we find out the price expectation effect in the apparel product; concretely there are 23 brands out of 43 brands. This study confirms the price expectation effects in the apparel product theoretically and extends the research boundary of price expectation effects.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.63-79
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• 2022

Recently, various studies have been conducted on stock price prediction using machine learning and deep learning techniques. Among these studies, the latest studies have attempted to predict stock prices using limit order books, which contain buy and sell order information of stocks. However, most of the studies using limit order books consider only the trend of limit order books over the most recent period of a specified length, and few studies consider both the medium and short term trends of limit order books. Therefore, in this paper, we propose a deep learning-based prediction model that predicts stock price more accurately by considering both the medium and short term trends of limit order books. Moreover, the proposed model considers news headlines during the same period to reflect the qualitative status of the company in the stock price prediction. The proposed model extracts the features of changes in limit order books with CNNs and the features of news headlines using Word2vec, and combines these information to predict whether a particular company's stock will rise or fall the next day. We conducted experiments to predict the daily stock price fluctuations of five stocks (Amazon, Apple, Facebook, Google, Tesla) with the proposed model using the real NASDAQ limit order book data and news headline data, and the proposed model improved the accuracy by up to 17.66%p and the average by 14.47%p on average. In addition, we conducted a simulated investment with the proposed model and earned a minimum of $492.46 and a maximum of $2,840.93 depending on the stock for 21 business days.

• Defense and Technology
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• no.7 s.281
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• pp.38-49
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• 2002

이번 호에서는 COCOMO 모델, 기능점수모델, PRICE-S 모델, 일반 COSDES 모델 등의 무기체계 S/W 개발비용 산정모델들을 조사 및 분석하여 제시 하였다. 다음에 계속되는 7, 8월호에서는 무기체계 S/W 개발 비용 산정에 영향을 미치는 요소인 스텝수 산정, 환경요인 보정계수, 제경비 및 기술료를 산정하는데 있어서 각각의 영향요소들을 식별하여 제시하고, 무기체계 S/W 개발비용 산정 개선방안 및 이에 대한 정책적인 발전방향을 제시하고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.201-207
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• 2023

In this paper, we propose an optimal model for mid to long-term price prediction of agricultural products using LGBM, MLP, LSTM, and GRU to compare and analyze the three strategies of the Multi-Step Time Series. The proposed model is designed to find the optimal combination between the models by selecting methods from various angles. Prior agricultural product price prediction studies have mainly adopted traditional econometric models such as ARIMA and LSTM-type models. In contrast, agricultural product price prediction studies related to Multi-Step Time Series were minimal. In this study, the experiment was conducted by dividing it into two periods according to the degree of volatility of agricultural product prices. As a result of the mid-to-long-term price prediction of three strategies, namely direct, hybrid, and multiple outputs, the hybrid approach showed relatively superior performance. This study academically and practically contributes to mid-to-long term daily price prediction by proposing an effective alternative.

• Journal of Distribution Science
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• v.10 no.3
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• pp.43-50
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• 2012

This study sets out to examine how a brand image that matches the advertising model has a positive impact on brand attitude and price fairness perceptions. We reviewed the constructs on the basis of previous studies and each of the concepts has been redefined. One such concept, "image congruence," refers to the harmony, fitness, and matching quality of images. For example, how well celebrity advertising model is matches the brand image shows image congruence. Results are summarized as follows: First, the congruence of brand image and sports advertising model has no significant impact on brand attitude certainty and persistence. Second, the individual's brand attitude certainty and brand attitude persistence has a positive impact on the perceptions of price fairness. Third, the congruence of brand image and sports advertising model has a positive impact on the perceptions of price fairness. The first and the third results suggest that the positive impact on the price fairness perceptions is temporary but it has insignificant effects on the formation of brand attitude causing ongoing purchases. In other words, in order to influence consumers' long-term confidence on the brand, improving the quality of products or services has to precede promotional strategies such as advertising. When an advertising model is inappropriate for the brand image, consumers perceive product price changes as a negative issue in the short term. However, in the long term, attitude formation such as consumers' repurchase intentions and word of mouth will be not affected. The second result suggests that an already existing positive brand attitude can contribute more positively to change the perceptions of price fairness. In particular, attitude persistence has greater influence than attitude certainty on the price fairness. It suggests that persistence issues such as the trading period and the frequency of transactions must be managed and controlled because they are more important than the certainty issues such as strength of belief or trust. For example, when a commercial model for expensive sporting goods matches up with the brand image, consumer feels less pressure on the price changes. However, it does not determine the consumer's repeated purchases or sustainable transactions and it also has no absolute impact on the brand trust. In other words, consumer brand attitude should be recognized and approached as a routine strategy in view of the result that it is of great value as a causal variable in the process of consumer decision-making.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.27-43
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• 2024

The market share of online platform services in the used car market continues to expand. And The used car online platform service provides service users with specifications of vehicles, accident history, inspection details, detailed options, and prices of used cars. SUV vehicle type's share in the domestic automobile market will be more than 50% in 2023, Sales of Hybrid vehicle type are doubled compared to last year. And these vehicle types are also gaining popularity in the used car market. Prior research has proposed a used car price prediction model by executing a Machine Learning model for all vehicles or vehicles by brand. On the other hand, the popularity of SUV and Hybrid vehicles in the domestic market continues to rise, but It was difficult to find a study that proposed a used car price prediction model for these vehicle type. This study selects a used car price prediction model by vehicle type using vehicle specifications and options for Sedans, SUV, and Hybrid vehicles produced by domestic brands. Accordingly, after selecting feature through the Lasso regression model, which is a feature selection, the ensemble model was sequentially executed with the same sampling, and the best model by vehicle type was selected. As a result, the best model for all models was selected as the CBR model, and the contribution and direction of the features were confirmed by visualizing Tree SHAP Value for the best model for each model. The implications of this study are expected to propose a used car price prediction model by vehicle type to sales officials using online platform services, confirm the attribution and direction of features, and help solve problems caused by asymmetry fo information between them.

• Journal of the military operations research society of Korea
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• v.36 no.1
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• pp.29-38
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• 2010

In Korea weapon system acquisition processes, it's required a cost estimation report obtained from a commercial cost model. The PRICE model is generally used as a cost estimation model in Korea. However, the model uses American historical R&D data and it's output cost component is different from our cost component of defense accounting system. Also, we found that estimating results show about 10% of difference when we comparing with actual costs in 44 finished weapon acquisition projects. There are some limitations in calibration to increase an accuracy of the PRICE model because it's difficult obtain good real input data, detailed cost and technical data in low level WBS. So, only 8% of the defense R&D projects are calibrated and validation of calibration results is more difficult. Therefore, we studied the standard calibration process and performed the calibration about the MCPLXS/E parameters of the PRICE model based on actual cost data. In order to obtain a good calculation result, we collected the actual material costs from the defense industry companies. Our results can be used for an reference in similar weapon system R&D and production cost estimation cases.

• Journal of the military operations research society of Korea
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• v.25 no.2
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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.

• Journal of the military operations research society of Korea
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• v.27 no.1
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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.