• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1731-1737
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• 2007

The Korea electricity wholesale market is operated under the cost-based-pool system and the government regulation to the new generation capacities in order to insure the resource adequacy. The goal of government's regulation is the electricity market stability by attracting proper generation investment while keeping the reliability of system. Generation companies must mandatory observe that government plan by now. But if the restructuring is to be complete, generation companies should not bear any obligation to invest unless their profitability is guaranteed. Namely the investors' behavior will be affected by the market prices. In this paper, the system dynamics model for Korea wholesale electricity market to examine whether competitive market can help to stabilize is developed and analyzes the investors behavior. The simulation results show that market controlled by government will be operated stable without resulting in price spike but there is no lower price because of maintaining the reasonable reserve margin. However, if the competition is introduced and the new investment is determined by the investor's decision without government intervention, the benefits from lower wholesale price are expected. Nevertheless, the volatility in the wholesale market increases, which increases the investment risks.

• The Journal of Fisheries Business Administration
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• v.29 no.2
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• pp.97-110
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• 1998

This paper tries to estimate Korea's import demand function for fisheries using cointegration analysis. The estimation function consists of one dependent variable-import quantity of fisheries(FTIW) and two independent variables-relative price(RP) between importable and domestic products and real income(GDP). As it has been empirically found out that almost all of time series of macro-variables such as GDP, price index are nonstationary, existing studies which ignore this fact need to be reexamined. Conventional econometric method can not analyze nonstationary time series in level. To perform the analysis, time series should be differenciated until stationarity is guaranteed. Unfortunately, the difference method removes the long run element of data, and so leads to difficulties of interpretation. But according to new developed econometric theory, cointegration approach could solve these problems. Therefore this paper proceeds the estimation on the basis of cointegration analysis, because the quartly variables from 1988 to 1997 used in the model is found out to be nonstationary. The estimation results show that all of the variables are statistically significant. Therefore Korea's import demand for fisheries has been strongly affected by the variation of real income and the relative price.

• Journal of Integrative Natural Science
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• v.4 no.2
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• pp.121-129
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• 2011

The purpose of this paper is to estimate the fluctuation of an earning rate and risk management using the price index of Korea stocks. After an observation of conception of fluctuation, we can show volatility clustering and fluctuation phenomenon in the Korea stock price index using GARCH model with heteroscedasticity. In addition, the effects of fluctuation on the time-series was evaluated, which showed the heteroscedasticity. MCMC method and Winbugs as Bayesian computation were used for analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.4
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• pp.379-389
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• 2002

In this paper, we study the competition between two kinds of suppliers, a bricks and mortars(B&M) and a clicks and mortars(C&M). Using the circular spatial market model, we derive and analyze the Nash and Stackelberg equilibria as a function of offline market share and efficiency of online channel of the C&M supplier. The result can be summarized as follows: (1) Stackelberg equilibrium is always superior to the Nash equilibrium, (2) Under certain conditions, the price of online channel can be higher than that of offline channel, (3) It is impossible for the C&M supplier to encroach on all of the B&M supplier's market, (4) In some cases, the C&M supplier has incentive to lower the efficiency of its online channel for more profit.

• Korean Journal of Organic Agriculture
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• v.29 no.4
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• pp.523-538
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• 2021

The purpose of this paper is to analyze the difference of the value in main production areas affected through the hedonic price model and the conjoint analysis. In addition, the partial value of each attribute level, and the consumers' willingness to pay(WTP) for change in each attribute level are analyzed. For this, we compared the value of apple determined in Garak market with the value that consumers' WTP. The result showed that there is a gap between the market value and the consumers' preferences on apple. It means that it is necessary for the local branding to be more developed to receive higher sales. Furthermore, understanding the consumers' preferences on the apple attributes can enhance the consumer utility and the competitivity. As a result, this study provides an apple marketing direction for main production areas that has been changing due to climate change.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.96-102
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• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.45-53
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• 2020

Past literatures have not studied the impact of real-world events or information on the return and volatility of virtual currencies, particularly on the COVID-19 event, day-of-the-week effect, daily high-low price spreads and information flow rate. The study uses the ARMA-GARCH model to capture Bitcoin's return and conditional volatility, and explores the impact of information flow rate on conditional volatility in the Bitcoin market based on the Mixture Distribution Hypothesis (Clark, 1973). There were 3,064 samples collected during the period from 1^st of January 2012 to 20^th April, 2020. Empirical results show that in the Bitcoin market, a daily high-low price spread has a significant inverse relationship for daily return, and information flow rate has a significant positive relationship for condition volatility. The study supports a significant negative relationship between information asymmetry and daily return, and there is a significant positive relationship between daily trading volume and condition volatility. When Bitcoin trades on Saturday & Sunday, there is a significant reverse relationship for conditional volatility and there exists a day-of-the-week volatility effect. Under the impact of COVID-19 event, Bitcoin's condition volatility has increased significantly, indicating the risk of price changes. Finally, the Bitcoin's return has no impact on COVID-19 events and holidays (Saturday & Sunday).

• KIPS Transactions on Software and Data Engineering
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• v.7 no.10
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• pp.387-396
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• 2018

Stock price prediction has been a difficult problem to solve. There have been many studies to predict stock price scientifically, but it is still impossible to predict the exact price. Recently, a variety of types of cryptocurrency has been developed, beginning with Bitcoin, which is technically implemented as the concept of distributed ledger. Various approaches have been attempted to predict the price of cryptocurrency. Especially, it is various from attempts to stock prediction techniques in traditional stock market, to attempts to apply deep learning and reinforcement learning. Since the market for cryptocurrency has many new features that are not present in the existing traditional stock market, there is a growing demand for new analytical techniques suitable for the cryptocurrency market. In this study, we first collect and process seven cryptocurrency price data through Bithumb's API. Then, we use the gradient boosting model, which is a data-driven learning based machine learning model, and let the model learn the price data change of cryptocurrency. We also find the most optimal model parameters in the verification step, and finally evaluate the prediction performance of the cryptocurrency price trends.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.257-262
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• 2023

As part of their marketing policy, some suppliers allow retailers a period of credit in anticipation of increasing demand for the products they supply. The opportunity to defer payments on products through credit transactions has the effect of reducing retailers' inventory investment costs, and as a result, retailers determine selling prices in anticipation of increased demand from buyers. This study aims to analyze the inventory model that determines the retailer's selling price and EOQ(Economic Order Quantity) under the assumption that the buyer's demand is an exponentially decreasing function of the retailer's selling price in the credit transaction supply chain consisting of suppliers, retailers, and buyers. The products supplied for problem analysis include the case of deteriorating products that deteriorate over time, and the effect of the credit transaction period, the index of price elasticity and the degree of deterioration on the retailer's selling price and EOQ is analyzed.

• 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.