• 제목/요약/키워드: price down

검색결과 205건 처리시간 0.039초

의복구매 의사결정과정의 가격관련반응에 따른 단계적 구분과 특성에 관한 질적 연구 (The Qualitative Study on Consumers' Price Related Response in Clothing Purchase Decision-Making Process)

  • 윤남희;이은영
    • 한국의류산업학회지
    • /
    • 제11권4호
    • /
    • 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.

분류 알고리즘 기반 주문 불균형 정보의 단기 주가 예측 성과 (Classification Algorithm-based Prediction Performance of Order Imbalance Information on Short-Term Stock Price)

  • 김선웅
    • 지능정보연구
    • /
    • 제28권4호
    • /
    • pp.157-177
    • /
    • 2022
  • 투자자들은 증권회사가 제공하는 시세표인 Limit Order Book 정보를 통해 국내외 투자자들이 제출하는 주문 정보를 실시간으로 파악하면서 거래에 참여하고 있다. Limit Order Book에 실시간으로 공개되고 있는 주문 정보가 주가 예측에서 유용성이 있을까? 본 연구는 장 중 투자자들의 매수와 매도 주문이 어느 한쪽으로 쏠리면서 주문 불균형이 나타나는 경우 미래 주가 등락의 예측 변수로서 유의성이 있는지를 분석하는 것이다. 분류 알고리즘을 이용하여 주문 불균형 정보의 당일 종가 등락에 대한 예측 정확도를 높이고, 예측 결과를 이용한 데이트레이딩 전략을 제안하며 실증분석을 통해 투자 성과를 분석한다. 자료는 2004년 1월 19일부터 2022년 6월 30일까지의 4,564일 동안의 코스피200 주가지수선물 5 분 봉 주가를 분석하였다. 실증분석 결과는 다음과 같다. 첫째, 총매수 주문량과 총매도 주문량의 불균형 정도로 측정하는 주문 불균형지수와 주가는 유의적 상관성을 보인다. 둘째, 주문 불균형 정보는 당일 종가까지의 미래 주가 등락에 대해서도 유의적인 영향력이 나타났다. 셋째, 주문 불균형 정보를 이용한 당일 종가 등락의 예측 정확도는 Support Vector Machines 알고리즘이 54.1%로 가장 높게 나타났다. 넷째, 하루 중 이른 시점에서 측정한 주문 불균형지수가 늦은 시점에서 측정한 주문 불균형지수보다 예측 정확성이 더 높았다. 다섯째, 종가 등락 예측 결과를 이용한 데이트레이딩 전략의 투자 성과는 비교모형의 투자 성과보다 높게 나타났다. 여섯째, 분류 알고리즘을 이용한 투자 성과는 K-Nearest Neighbor 알고리즘을 제외하면 모두 비교모형보다 총수익 평균이 높게 나타났다. 일곱째, Logistic Regression, Random Forest, Support Vector Machines, XGBoost 알고리즘의 예측 결과를 이용한 데이트레이딩 전략의 투자 성과는 수익성과 위험성을 동시에 평가하는 샤프비율에서도 비교모형보다 높은 결과를 보여주었다. 본 연구는 Limit Order Book 정보 중 총매수 주문량과 총매도 주문량 정보의 경제적 가치가 존재함을 밝혔다는 점에서 기존의 연구와 학술적 차별점을 갖는다. 본 연구의 실증분석 결과는 시장 참여자들에게 투자 전략적 측면에서 함의가 있다고 판단된다. 향후 연구에서는 최근 활발히 연구가 진행되고 있는 딥러닝 모형 등으로의 확장을 통해 주가 예측의 정확도를 높임으로써 데이트레이딩 투자전략의 성과를 개선할 필요가 있다.

PCB 산업의 환경변화와 기술적 대응 (Environmental Changes & Technical Responses in Printed Circuit Board Industry)

  • 이진호
    • 마이크로전자및패키징학회지
    • /
    • 제6권4호
    • /
    • 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.

  • PDF

준지도 학습 및 신경망 알고리즘을 이용한 전기가격 예측 (Electricity Price Prediction Based on Semi-Supervised Learning and Neural Network Algorithms)

  • 김항석;신현정
    • 대한산업공학회지
    • /
    • 제39권1호
    • /
    • 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.

분양가 자율화이후 공동주택 단위평면의 변화경향에 관한 연구 (A Study of Transformation tendency of an Apartment Unit Plan after The Enforcement of Price Deregulation)

  • 고영석;권영;김용성
    • 한국실내디자인학회:학술대회논문집
    • /
    • 한국실내디자인학회 2003년도 춘계학술발표대회 논문집
    • /
    • 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.

  • PDF

모선별 한계가격의 구성요소 산정 기법 (A New Approach to Calculation of the Components of Locational Marginal Price)

  • 이기송;정윤원;신중린;김진호;박종배
    • 대한전기학회논문지:전력기술부문A
    • /
    • 제55권8호
    • /
    • 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.

Prediction of the price for stock index futures using integrated artificial intelligence techniques with categorical preprocessing

  • Kim, Kyoung-jae;Han, Ingoo
    • 한국경영과학회:학술대회논문집
    • /
    • 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
    • /
    • 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.

  • PDF

초경량 섬유 소재를 사용한 다운점퍼에 대한 제품 평가 (Down jumpers using Ultra-light Fiber Materials about to Product Evaluation)

  • 류신아;박길순
    • 한국생활과학회지
    • /
    • 제24권5호
    • /
    • 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'.

Stock Price Predictability of Financial Ratios and Macroeconomic Variables: A Regulatory Perspective

  • Kwag, Seung Woog;Kim, Yong Seog
    • Industrial Engineering and Management Systems
    • /
    • 제12권4호
    • /
    • 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.