• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.37-46
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• 2023

The main aim of the article is to solve the problem of automating price monitoring using marketing forecasting methods and Excel functionality under martial law. The study used the method of algorithms, trend analysis, correlation and regression analysis, ANOVA, extrapolation, index method, etc. The importance of monitoring consumer price developments in market pricing at the macro and micro levels is proved. The introduction of a Dummy variable to account for the influence of martial law in market pricing is proposed, both in linear multiple regression modelling and in forecasting the components of the Consumer Price Index. Experimentally, the high reliability of forecasting based on a five-factor linear regression model with a Dummy variable was proved in comparison with a linear trend equation and a four-factor linear regression model. Pessimistic, realistic and optimistic scenarios were developed for forecasting the Consumer Price Index for the situation of the end of the Russian-Ukrainian war until the end of 2023 and separately until the end of 2024.

• 경영과학
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• 제33권2호
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• pp.75-87
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• 2016

Quality function deployment (QFD) is a widely adopted customer-oriented product development methodology by analyzing customer requirements. It is a main activity in QFD planning process to determine the optimal values of the technical attributes (TAs) so as to achieve the customer requirements (CRs) from the House of Quality (HoQ). In most of the previous research, all the TAs in QFD are assumed to have either continuous or discrete values. In the real world applications, the continuous TAs and the discrete TAs are often mixed in QFD. In this paper, a mixed integer linear programming model is formulated to obtain the optimal values for the continuous TAs and the discrete TAs in QFD planning as well as Branch and Bound (B and B) algorithm is proposed as the solution approach. Finally, the proposed model and solution approach are illustrated with an office chair under multi-segment market, and the sensitivity analysis is performed to study how the proposed model and its solutions respond to the variation for the two elements which are budget and CRs' weights.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.201-208
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• 2021

This study aimed to identify the key factors that affect the financial market performance (Price-Earnings Model) through a sample of 35 public shareholding industrial companies on the Amman Stock Exchange for the period 2010-2019, using statistical models and methods, such as the Simple Linear Regression Model, Correlation Coefficient, and dispersion board. The study results showed the nonexistence of a statistically significant effect between the intellectual capital and market value added (MVA) and market performance. Results also showed a statistically significant positive effect between financial leverage (FL) and the market performance, where the interpreted variation reached 64%. It showed from the analysis results that the relationship between (MVA) and market performance (P/E) agrees with the study hypotheses, while the result related to (FL) disagrees with the study hypotheses. The study recommends that public shareholding industrial companies should focus more on intellectual capital and show its value in the annual financial statements and reports, and those companies that have high profitability and the chance to hold gains and profits should rely less on debt and more on retained earnings, due to the high risk of debt and in line with the present unstable circumstances in Jordan, especially in light of the global Covid-19 crisis.

• The Journal of Asian Finance, Economics and Business
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• 제7권8호
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• pp.33-40
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• 2020

The main aim of the study is to test a house pricing model by combining hedonic and asset-based pricing models. An understanding of the relationship between house pricing and its return (the rental income) helps to establish houses as a significant asset class. The model tested the relationship between house pricing (dependent variable) and the house attributes (independent variables) derived from Freeman's framework of housing attributes. This study uses a large data-set of 1,899 sample of new, high-end houses purchased between 2016 and 2019 collected from the national capital region of India (Delhi-NCR). The algorithm was built in R-Script, and stepwise multiple linear regression was used to analyze the model. The analysis of the model proves that the three significant variables, namely, carpet area, pay-off, and annual maintenance charges explain the price function. Further, the model is statistically fit. The major contribution of the study is to understand the key factors and their influence on the house pricing. The model will be helpful in risk assessment in the housing investment and enhance the chances of investment. Policy-makers can use information about the underlying valuation drivers of the house prices to stabilize the market and also in framing the tax policies.

• 한국경영과학회지
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• 제31권3호
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• pp.97-105
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• 2006

Brand Pricing is the most important issue for the brand manager in the dynamic market. in the typical dynamic pricing model, a linear function has been used based on the assumption that the non-Price Influences and the price influences were independent. However, to incorporate the characteristics of the dynamic market, it is natural to consider the multiplicative relationship. We are going to try the multiplicative linkage between the non-price Influences and the price influences and suggest a new dynamic pricing model with e multiplicative functional form. An empirical study of 19 brands in the Korean cigarette market shows the feasibility of the suggested model.

• 정보기술과데이타베이스저널
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• 제6권2호
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• pp.115-130
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• 1999

The price structure of housing market varies depending upon market price policy rather than low or high price policy because of IMF. The object of this study is to develop an analysis system for analyzing housing market and its demand. The analysis system consists of four major categories: macro index analysis, market decision analysis, housing market analysis, and consumer analysis. We model each category by using a variety of techniques such as generalized linear model, categorical analysis, bubble analysis, drill-down analysis, price sensitivity meter analysis, optimum price index analysis, profit index measurement analysis, correspondence analysis, conjoint analysis, and multidimensional scaling analysis. Seoul apartment data is analyzed to demonstrate the practical usefulness of the system.

• 한국경영과학회지
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• 제38권1호
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• pp.45-59
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• 2013

A long-term forecasting method for a new product in early stage of diffusion is proposed. The method includes a constrained non-linear least square estimation with the logistic diffusion model. The constraints would be critical market informations such as market potential, peak point, and take-off. Findings on 20 cases having almost full life cycle are that (i) combining any market information improves the forecasting accuracy, (ii) market potential is the most stable information, and (iii) peak point and take-off information have negative effect in case of overestimation.

• 한국건축시공학회지
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• 제14권6호
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• pp.631-637
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• 2014

최근 모듈러주택 시장은 주거시설 뿐만 아니라 업무시설등에 적용되면서 시장영역이 확대되고 있다. 해외 선진국의 경우 성숙단계로 접어들고 있으며, 국내의 경우 시장이 형성되어 있지 않기 때문에 중 장기 시장 전망을 위한 추세 파악에 어려움이 있다. 이에 본 연구는 시계열 분석을 기반으로 비선형 예측모형을 활용하여 국내 모듈러주택의 시장수요를 전망하였다. 모듈러주택 시장수요 전망은 신규 주택 건설에 대한 수요량 추정 결과를 기반으로 주택 공급량을 파악하고 주택공급량의 일부를 모듈러주택 수요로 가정하여 시나리오분석을 하였으며, 비선형 예측모형을 활용하여 모듈러주택 시장 전망을 하였다.

• 전기학회논문지P
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• 제67권4호
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• pp.183-190
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• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제16권4호
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• pp.427-441
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• 2009

The method of Lattice Dynamical System is used to establish a global model on an infinite chain of many local markets interacting each other through a diffusion of prices between them. This global model extends the Walrasian evolutionary cobweb model in an independent single local market to the global market evolution. We assume that each local market has linear decreasing demands and quadratic supplies with naive predictors, and investigate the stationary behaviors of global price dynamics and show that their dynamics are conjugate to those of $H{\acute{e}}non$ maps and hence can exhibit complicated behaviors such as period-doubling bifurcations, chaos, and homoclic orbits etc.