• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.552-560
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• 2021

With the recent development of the art distribution system, interest in art investment is increasing rather than seeing art as an object of aesthetic utility. Unlike stocks and bonds, the price of artworks has a heterogeneous characteristic that is determined by reflecting both objective and subjective factors, so the uncertainty in price prediction is high. In this study, we used LSTM Recurrent Neural Network deep learning model to predict the auction winning price by inputting the artist, physical and sales charateristics of the Korean artist. According to the result, the RMSE value, which explains the difference between the predicted and actual price by model, was 0.064. Painter Lee Dae Won had the highest predictive power, and Lee Joong Seop had the lowest. The results suggest the art market becomes more active as investment goods and demand for auction winning price increases.

• 한국IT서비스학회:학술대회논문집
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• 2003.05a
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• pp.577-583
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• 2003

Due to the widespread availability of the internet, large-scale and dynamic distributed projects in industry are becoming popular. We present a distributed, collaborative, and adaptive control approach for decentralized multiple projects, which is one of representative project environments in modern e-enterprises. In this paper we deal with short term scheduling and rescheduling of resources, which are shared by multiple projects. We in specific, address the dynamic nature of the situation. We model this as a dynamic economy, where the multiple local markets are established and cleared over time trading resource time slots(goods). Local markets are modeled using a combinatorial auction mechanism. Due to the dynamic and distributed nature of economy, through our approach we can achieve higher levels of flexibility, scalability and adaptability.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.49-54
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• 2009

One of the major challenges confronting a multiservice electric utility is the establishment of the right prices for its services. The key objectives of particular pricing schemes are reasonableness of company earnings, economic efficiency, the responsiveness of supply and of the allocation of sources to the desires of consumers, and maintenance of some degree of competition. This paper proposes a value-based pricing mechanism amenable to the current deregulation situation in electricity market allowing service differentiation. The proposed pricing mechanism can be implemented in a nodal auction model, and can also be applied to direct load control.

• The Journal of Economics, Marketing and Management
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• v.9 no.2
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• pp.33-43
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• 2021

Purpose: This study aims to verify sellers' economic incentives for voluntarily disclosing negative information in online markets and provide practical guidelines to online sellers in terms of whether, when, and how sharing low quality to buyers increase sales. Research design, data and methodology: Our model examines the number of bidders in Internet auctions to measure potential demand and uses count data analysis following previous studies that have also analyzed the number of bidders in auctions. After checking over-dispersion and zero-inflation in our data, we have run a Poisson regression to analyze the effect of sharing negative information on sales. Results: This study presents a counterintuitive result that low-quality sellers can increase their demand by fully disclosing negative information in an online market, if appropriate risk-reducing methods are employed. Our finding thus shows that there exists economic incentive for online sellers to voluntarily disclose negative information about their products, and that the context of transactions may affect this incentive structure as the incentive varies across product categories. Conclusions: As the positive impact of disclosing negative information has rarely been studied so far, this paper contributes to the literature by providing a unique empirical analysis on the impact of sellers' honesty on sales. By verifying economic incentives of disclosing low quality with actual online sales data, this study suggests practical implications on information disclosure strategy to many online sellers dealing with negative information.

• Asia-Pacific Journal of Business
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• v.14 no.2
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• pp.33-49
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• 2023

Purpose - The purpose of this study is to examine the option selection and optimal trading of informed traders in KOSPI 200 options market based on the PIN (probability of informed trading) model of Easley et al.(2002). Design/methodology/approach - This study uses TAQ (trade and quote) data provided by Korean Exchanges (KRX) which contains all the bids and trades recorded during the continuous auction trading hours for the KOSPI 200 options between May 2019 and September 2020. Findings - First, there was no difference in the PIN between call and put options in the 2019 data, but the PIN of put options was slightly higher in 2020. Second, regardless of the type of option, the PIN was higher for in-the-money (ITM) options, and the PIN of out-of-the-money (OTM) options was the same as or slightly higher than that of at-the-money (ATM) options. Third, we found that the PIN decreases as trading liquidity increases, and fourth, the PIN increased sharply as the expiration date approached, especially for OTM options, while ITM and ATM options showed relatively weak effects. Fifth, for foreign and institutional investors, the periodicity of orders was observed in milliseconds, especially for foreign investors, where the periodicity of orders was clear and frequent in OTM options. The results suggest that the purpose of option trading varies depending on the moneyness from the perspective of the informed trader.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.533-543
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• 2023

This study explores the influence of the COVID-19 pandemic on the Korean art market and contrasts the classic hedonic method of art price prediction with the Artificial Neural Network technique. The empirical analysis of this paper utilizes 14,639 observations of Korean art auction data from 2015 to 2021. There are three types of variables in this study: artist-related, artwork-related, and sales-related. Previous studies have suggested that these three types of variables influence art prices. The empirical findings in this research are in twofold. First, in terms of RMSE and R², the Artificial Neural Network outperforms the hedonic model. Both techniques discover that sales and artwork variables have a greater impact than artist-related attributes. Second, when the primary factors of art price are controlled, Korean art prices are found to fall dramatically in 2020, shortly following the onset of COVID-19, but to rebound in 2021. The main lesson in this study is that the Artificial Neural Network enhances art price prediction and reduces information asymmetry in the Korean art market even in the face of unanticipated turmoil such as the COVID-19 outbreak.

• Journal of Internet Computing and Services
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• v.11 no.5
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• pp.105-117
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• 2010

In recent years, dynamic collaboration (DC) among cloud providers (CPs) is becoming an inevitable approach for the widely use of cloud computing and to realize the greatest value of it. In our previous paper, we proposed a combinatorial auction (CA) based cloud market model called CACM that enables a DC platform among different CPs. The CACM model allows any CP to dynamically collaborate with suitable partner CPs to form a group before joining an auction and thus addresses the issue of conflicts minimization that may occur when negotiating among providers. But how to determine optimal group bidding prices, how to obtain the stability condition of the group and how to distribute the winning prices/profits among the group members in the CACM model have not been studied thoroughly. In this paper, we propose to formulate the above problems of cooperative negotiation in the CACM model as a bankruptcy game which is a special type of N-person cooperative game. The stability of the group is analyzed by using the concept of the core and the amount of allocationsto each member of the group is obtained by using Shapley value. Numerical results are presented to demonstrate the behaviors of the proposed approaches.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.61-70
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• 2019

The The government has recently announced its mid- to long-term plans for promoting art. With the advent of the 4th industrial revolution, contemporary art contents that are integrated with Intelligent Information Technologies such as Artificial Intelligence (AI), Virtual Reality (VR), and Big Data are being introduced, and social interest in humanities and creative convergence is rising. In addition, the industrialization of the art market is expanding amid the rising popularity of art among the general public and the growing interest of art as an investment replacement system, along with the strengthening of the creative personality education of our Education Ministry. Therefore, it is necessary to establish a strategy for transparency and revitalization of the art market by providing comprehensive information such as search functions, analysis data, and criticism by writer and price. This paper has established an information system plan for the establishment of an online supply system for art transaction information, providing auction transaction information for art market, providing report and news for art market, providing public relations platform, and providing art market analysis service and membership relationship management service. To this end, the future model was established through environmental analysis and focus analysis of the art market, and strategic tasks and implementation plans were established accordingly.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.501-512
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• 2012

Social welfare maximization in a double-sided auction market is performed by implementing an aggregation-based particle swarm optimization (CAPSO) algorithm for optimal placement and sizing of one Static Synchronous Series Compensator (SSSC) device. Dallied simulation results (without/with line flow constraints and without/with SSSC) are generated to demonstrate the impact of SSSC on the congestion levels of the modified IEEE 14-bus test system. The proposed CAPSO algorithm employs conventional quadratic smooth and augmented quadratic nonsmooth generator cost curves with sine components to improve the accurate of the model by incorporating the valve loading effects. CAPSO also employs quadratic smooth consumer benefit functions. The proposed approach relies on particle swarm optimization to capture the near-optimal GenCos and DisCos, as well as the location and rating of SSSC while the Newton based load flow solution minimizes the mismatch equations. Simulation results of the proposed CAPSO algorithm are compared to solutions obtained by sequential quadratic programming (SQP) and a recently implemented Fuzzy based genetic algorithm (Fuzzy-GA). The main contributions are inclusion of customer benefit in the congestion management objective function, consideration of nonsmooth generator characteristics and the utilization of a coordinated aggregation-based PSO for locating/sizing of SSSC.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.474-477
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• 2013

As the market-based spectrum policy is introduced, an interest in the economic value of the frequency is increasing. Research and practical applications concerning the methodology for the estimation of the economic value of the frequency and its determinants are actively engaged, which are used for setting a reserve price and bid price of spectrum auction and a spectrum clearance cost. In this study, by the analysis of the spectrum valuation methodology, we derive the changes in the factors affecting the valuation and propose to apply improved. In the model frequency value is consist of technical value, commercial value and strategic value, we find the dynamics of fluctuation factors and suggest how to apply them to spectrum policy.