• JOURNAL OF ELECTRICAL WORLD
• /
• no.10 s.154
• /
• pp.16-22
• /
• 1989

• Fashion & Textile Research Journal
• /
• v.11 no.4
• /
• pp.537-548
• /
• 2009

Consumers' price related response in the clothing purchase decision-making process includes their expectation of price, price perception, attitude toward price and consequent behaviors. The purposes of this research are to systematically organize consumers' price related responses in the clothing purchase decision-making process, and to explain the effect of price on their purchasing. The qualitative research including shopping observation and in-depth interview was conducted. The result identified stages that showed different price related responses in clothing purchase decision-making process, and clarified each stage's characteristics. In the internal search stage, consumers recalled price information from memory and had a specific expectation about the price. This set a direction for the external search. In the external search stage, consumers selected brands or stores by a non-compensatory evaluating with an expectation of the price, and narrowed these down to several determinant alternatives by actively evaluating the products. In case a sufficient amount of price information was not recalled, the consumer established reference price through the external search. Finally, in the purchasing stage, consumers evaluated the determinant alternatives based on their compensatory evaluation. When perception of price was negative, consumers evaluate price combined with the higher criteria of clothing benefits, such as symbolic value and usability. The research is expected to contribute to predicting consumers' responses to price, and to establishing an effective pricing strategy.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.157-177
• /
• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of the Microelectronics and Packaging Society
• /
• v.6 no.4
• /
• pp.73-77
• /
• 1999

Revolutionary changes on multimedia, network and PDA(Personal digital assistants) causes PCB(Printed circuit beard) manufacturers to change their attitudes to product. Traditional idea for current market such as price, market, and service has collapsed down and new digitalization urges PCB manufacturers to deal with new technologies, shorter lead time with reasonable price, high qualities. Therefore PCB manufacturers have an effort to develop new marketing, products, processes for low cost to keep up pace with assembly makers.

• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.1
• /
• pp.30-45
• /
• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• Proceedings of the Korean Institute of Interior Design Conference
• /
• 2003.05a
• /
• pp.74-77
• /
• 2003

After the Enforcement of Price Deregulation of Apartment, Apartment house get down to originality goods, The Housing Market have reorganized the nucleus by a user, have demanding the development for discriminative unit plan. The purpose of this study is that before and after the Price Decontrol of Apartment take part a variety of unit plan, search for transformation factor and analyze into the tendency of the distinction plan of Housing Goods. Before and after the Price Decontrol of Apartment, Apartment unit have analyzed from 85 $m^2$ till 152 $m^2$ private area; ten corporations of civil construction' unit in Seoul and The national capital region supply apartment, will supply apartment. For selected examples, first, unit plan is normalized from the ratio of front to side wall, bay, a Room' organization and a kind of Room, number, and for examples of unit plan of apartment, the examples were analyzed with respect to change of a Room' organization and the number of a room and the ratio of front wall to side wall for item investigated. Finally, I search out course of transformation tendency of an apartment unit plan after Enforcement of Price Deregulation and analyzed a factor. The results of the study are follows, after Enforcement of Price Deregulation, unit plan of apartment lead to change lay out, to secure each family's privacy, to secure feeling for open hearted, tendency of flexibility.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.55 no.8
• /
• pp.341-350
• /
• 2006

This paper presents a new methodology to draw the components of locational marginal price (LMP) in electricity market. Recently, the changing environments surrounding electricity industries resulted in the unbundled services provided by electricity market players, which may require the new pricing mechanisms based on the LMP. The changed pricing mechanisms will provide the price signals of time and location to the market participants. Most of the existing studies of LMP are based on the Lagrangian multipliers as shadow prices to evaluate the equivalent values of constraints or factors for security, reliability and quality. However, the shadow prices cannot provide enough information for components of LMP. In this paper, therefore, we proposed a new approach that LMP is divided into three components. To do this, we first present the method for shadow prices calculation and then break down LMP into a variety of parts corresponding to the concerned factors. The proposed approach is applied to 5-bus and modified IEEE 14-bus sample system in order to verify its validity.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1997.10a
• /
• pp.105-108
• /
• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• Korean Journal of Human Ecology
• /
• v.24 no.5
• /
• pp.677-686
• /
• 2015

This study is to survey the concept of ultralight down jumpers examine customers' knowledge about ultralight down jumpers, factor effect when purchasing them, and satisfaction level. The research method is to examine a survey of consumer evaluation about ultralight down jumpers using a questionnaire targeting 240 men and women in their 30s and 40s. The results of the study are as follows. The knowledge Customers have about ultralight down jumpers appeared low scores in most items; 62.1%9(2.28)) answered 'does not know' in the item of 'knows about the mixed composition rate of filler', 54.6%(2.49) answered 'does not know' in the item of 'knows about ultralight materials', and 52.5%(2.56) answered 'does not know' in the item of 'knows about filling rate'. The important factors to consider when purchasing were 'size and pattern'(4.34), 'color'(4.32), 'design and price'(4.30). About satisfaction, 66.7%(3.69) answered 'most satisfied' in the item of 'well-fitting(wearing) sensation' and 60.0%(3.63) answers 'satisfied' in the item of 'activity and easy-to-wear'.

• Industrial Engineering and Management Systems
• /
• v.12 no.4
• /
• pp.406-415
• /
• 2013

The present study examines a set of financial ratios in predicting the up or down movements of stock prices in the context of a securities law, the Sarbanes-Oxley Act of 2002 (SOA), controlling for macroeconomic variables. Using the logistic regression with proxy betas to alleviate the incompatibility problem between the firm-specific financial ratios and macroeconomic indicators, we report evidence that financial ratios are meaningful predictors of stock price changes, which subdue the influence of macroeconomic indicators on stock returns, and more importantly that the SOA truly improves the stock price predictability of financial ratios for the markup sample. The empirical results further suggest that industry and time effects exist and that for the markdown sample the SOA actually deteriorates the predictive power of financial ratios.