• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.289-291
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• 2000

In a competitive electricity power market, the price of electricity changes instantly, that of conventional market is predetermined and hardly changes. In such a new environment, customers' behaviors change instantly according to the changing electricity prices. If we develop a electricity load model that well describes the behavior of electricity consumers, we can utilize that model in forecasting the amount of future load, solving the load flow problem and finding the weak point of the system. In this paper new electricity model that considers the price of electricity and power factor of the load is presented. While conventional load model, which is demand function of electricity, uses the price of real and reactive power as the independent variable of the demand function. this new load model uses price of real power and penalty factor according to the power factor for the calculation of amount of electricity demand.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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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.

• Journal of the Korean Geographical Society
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• v.43 no.1
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• pp.104-116
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• 2008

Overheated speculation areas which have high potential of becoming speculative are the target of many real estate policies. This paper proposes a model for spatial patterns of house price volatility and suggests a spatial pattern of overheated speculation areas. House prices are determined by economic behaviors of sellers and buyers who have rational or adaptive expectations. Spatial patterns of house price volatility are formed by tendencies of their economic behavior. If there is a majority of adaptive sellers and buyers in an area, it may appear as a "hotspot" by showing high volatility of house prices and simultaneous price increases. Overheated speculation areas are formed by adaptive sellers and buyers who want to realize maximum expectation profit, therefore these areas patterns are defined as hotspot patterns of price volatility.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.41-46
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• 2022

Chinese stock market often rises and falls sharply. The impact of the stock price crash risk has become a hot research field to maintain financial stability. This study starts from the perspective of the proportion of largest shareholders holding shares, and studies whether largest shareholders have more incentive to supervise management and reduce self-interest behavior of management. We use the data of Chinese listed companies from 2009 to 2019 as a sample, and study the relationship between largest shareholders and share price crash risk. Empirical research shows that the higher the proportion of largest shareholders of state-owned enterprise, the company's stock price crash risk can be significantly reduced. This study suggests that the higher the share of the largest shareholder, the lower the opportunistic behavior of managers and that information asymmetry between the company and the shareholders can be alleviated.

• Asia-Pacific Journal of Business
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• v.10 no.4
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• pp.77-86
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• 2019

The purpose of this study is to examine how the characteristics of family firms affect stock price crash risk. Prior studies argued that the opacity of information due to agency problem causes a plunge in stock prices. The governance characteristics of family firms can increase information opacity which leads to crash risk. Therefore, this study verifies whether family firms have a high possibility of stock price crash risk. We use a logistic regression model to test the relationship between family firms and stock price crash risk using listed firms listed on the Korean Stock Exchange during the fiscal years 2011 through 2017. The family firm is defined as the case where the controlling shareholder is the chief executive officer or the registered executive. If the controlling shareholder's share is less than 5%, it is not considered a family business. We found that family firms are more likely to experience a plunge in stock prices. This supports the hypothesis of this study that passive information disclosure behavior and information opacity of family firms increase stock price crash risk.

• Korean System Dynamics Review
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• v.12 no.2
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• pp.5-36
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• 2011

Seoul citizens are currently suffering from high housing price. Home prices have risen more rapidly than salaries so owning a housing unit (apartment, condominium, or single-family home) in Seoul is becoming more difficult than ever. Therefore, this research examines the behavior of average Seoul citizen in owning housing unit in Seoul, Korea, particularly in terms of the length of time required to afford a house unit. This research estimates that it will take about 18.75 years in maximum after getting a job (12.75 years after purchasing the housing unit) to own housing unit in Seoul that is currently valued at $300,000 where the growth rate of income is 2.97% and consumption price increases at a rate of 2.95% per annum. Finally in this research, the optimal growth rate of housing price is estimated ranged from 3.5 to 4.0% minimizing the loan payoff period.

• Journal of Distribution Science
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• v.17 no.4
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• pp.5-16
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• 2019

Purpose - This study examines whether price cartel firms perform downward earnings management to avoid or minimize penalty surcharges levied by the Korea Fair Trade Commission and analyzes such earnings management in distribution industry. Research design, data, and methodology - We use 247 firms from 64 price cartel cases in the period of 2011-2016, and collect data from 3 years before to 3 years after the start of price cartel. Earnings management is measured by discretionary accruals. Three discretionary accrual estimation models are employed; modified Jones model, ROA adjusted modified Jones model and CFO-adjusted modified Jones model. For pre- and post-cartel periods, one year, two year, and three year windows are used. Additional empirical analyses are performed for distribution industry sub-sample of 25 cartel firms. Result - The regression results show that cartel firms' discretionary accruals are significantly lower in the period after the start of price cartel than before. And discretionary accruals are lower in cartel firms than in non-cartel firms during the cartel period. Cartel firms in distribution industry also show the earnings management similar to those in other industries. Conclusions - These two findings lead to the conjecture that managers of cartel firms manage their earnings downward. This behavior is indistinguishable between firms in distribution industry and other industries.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.151-157
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• 2013

Since the sub-prime mortgage crisis from the US in 2008, the Korean housing market has plummeted. However, the deposit prices of the Korean local lease contract, Chonsei, had been increasing. This increase of Chonsei prices can be a threat to low-income people, most of whom prefer to live in houses with a Chonsei contract. In the housing and Chonsei market, there are many stakeholders with their own interest, hence, simple thoughts about housing and Chonsei market, such as more house supply, will decrease house price, would not work in a real complex housing market. In this research, we suggests system dynamics conceptual model which consists of causal-loop-diagrams for the Chonsei market as well as the housing market. In conclusion, the Chonsei price has its own homeostasis characteristics and different price behavior with housing price in the short and long term period. We found that unless government does not have a structural causation mind in implementing policies in the real estate market, the government may not attain their intended effectiveness on both markets.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.445-456
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• 2021

The purpose of this study is to identify the importance of variables affecting women's golf wear sales with macroeconomic variables and consumer selling prices that affect consumers' purchasing behavior, and to propose a price strategy to increase sales of golf wear. Data of domestic women's golf wear brands were analyzed using decision tree algorithms and ensemble. Consumer selling price is the most significant factors in terms of sales volume for T-shirt, pants and knit, while categories were found to be the most important factors in addition to consumer sales prices for skirt and one piece dress. These findings suggest that items have different economic variables that affect consumers' purchasing behavior, suggesting that sales and profits can be maximized through appropriate price strategies.

• The Research Journal of the Costume Culture
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• v.13 no.1
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• pp.161-173
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• 2005

The Main objective of this study was to investigate the relationship between shopping orientations and Knit wear Buying Behavior of female college students in the U.S. age 18 to 33. The questionnaires for this survey were developed to measure knitwear purchasing behavior, including sources of information about knitwear, evaluative criteria of knit wear product, attributes f store preference for knitwear, and shopping orientation. The questionnaire was administered to 119 female college students in the University of California. The data was analyzed by percentage, frequency, mean, factor analysis, Cluster Analysis and ANOVA, Duncan Multiple Range test. The female college students were classified into five subdivisions by cluster analysis; cautious shopping group, recreational shopping group, self-confident shopping group, shopping indifferent group, price conscious shopping group. In the case of fashion information sources of knit wear, significant differences were found according to shopping orientation subdivision in observation of others' and famous people's clothing, fashion shows, fashion articles in magazines, newspapers, and on the Internet, and shop displays. The evaluation criteria of knit wear product were significantly different depending on shopping orientation subdivision in fashionable, brand and store name, appropriate for different occasion, prestige. The store attributes of knitwear were significantly different depending on shopping orientation subdivision in product knowledge of sales personnel, store atmosphere, display of merchandise, layaway payment plan, price level, ease of parking and access, and new fashion.