• The Korean Journal of Financial Management
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• v.26 no.4
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• pp.165-189
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• 2009

According to disclosure regulation, insider can hide their trading until disclosure day, because there be interval between trading time and disclosure time. To accommodate strategic trade, they have an incentive to be brought disclosure interval as long as possible. This research investigate whether strategical behaviour of informed traders using disclosure intervals exists in domestic stock market.ls xt, we aney he whether they can get abnormal return through stealth strategy after announcement date. We also evaluate the effect of mimicking trading on price impact with the assumption of existence of mimicking trading. Our major research results are as follows: In case of main shareholder without having no prompt disclosure duty, the frequency of trading started at the beginning of month is shown significantly higher than others. This result shows a direct evidence that informed traders buy or sell their equity strategically using disclosure intervals. Also, we find the result that the coefficient of strategic variables has highest value in middle size information. However, the empirical evidence that informed trader get abnormal return through strategic trading was not shown in this study. Meanwhile, stock price over-reacts for selling transaction on trading point and is recovered after disclosure date., so we assume possibility of mimicking trading exists in domestic stock market.

• Korean Association of Arts Management
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• no.58
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• pp.179-204
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• 2021

Not only the consumption trend after the COVID-19 pandemic but also low financial interest rates have stimulated people to invest artworks. With the recent noticeable growth, art investments that mainly conducted by younger generation through online platform can be characterized by a fractional ownership in art market which means several people share one piece of artwork. This study explores 4 fractional ownership platforms in the domestic art market including Art Together, Art & Guide, Tessa, and Pica projects, using a business model canvas that describes nine key elements: Customer Segments, Value Proposition, Channels, Customer Relationships, Revenue Streams, Key Resources, Key Activities, Key Partners and Cost Structure. The four cases have similar business models, but the details of revenue streams are different. The key sources of revenue are the profit and commission of the work. Thus, maximizing the profit margin of artworks is the core of revenue streams, so selecting and purchasing highly profitable artworks are significant. Based on the analysis, there are 3 suggestions to continue fractional ownership platform businesses in art market successfully. First, it is required to have a long-term perspective on art investments, as a way to diverse asset portfolio. Second, business confidence should be increased to maintain customer loyalty. Third, the role of platforms as competent experts is important.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.111-113
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• 2002

본 논문에서는 경쟁적 전력시장에서 송전 계약에 관한 비용을 모선에서 한계비용 만감도를 이용해서 분할하는 방법을 제시하였다. 구조개편된 전력사장에서는 단순하면서도 분명한 전력 요금 시스템이 요구된다. Zonal pricing은 이런 면에서 좋은 pricing 시스템이 요구된다. 하지만, zone을 효과적으로 나누는 것은 상당히 어렵다. 기존의 방법에서는 오직 부하만을 고려하여 분류를 하였지만, 에너지 시장에서의 전력의 매매행위를 고려할 때, 시스템 내부의 모든 사업자에 대한 고려가 필요하다. 그래서, 본 논문에서는 한계비용 민감도를 이용해서 모든 에너지 시장 참여자를 고려하여, 네트워크를 분할하였다. 모든 노드에서의 민감도의 패턴이 분석되었고 동일한 패턴을 가진 노드들을 같은 zone으로 분류하였다. 6-모선 전력시스템을 이용해서 제안한 방법을 설명하였다.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.419-430
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• 2023

Currently, NFTs have no dominant application except for the proof of ownership for digital content, and it also have small liquidity problem, which makes their price difficult to predict. Real estate usually has very high barriers to investment due to its high pricing. Real estate can be converted into NFTs and also divided into small value fungible tokens (FTs), and it can increase the the volume of the investor community due to more liquidity and better accessibility. In this document, we implement and design a system that allows ordinary users can invest on high priced real estate utilizing Black Litterman (BL) model-based Portfolio investment interface. To this end, we target a set of real estates pegged as collateral and issue NFT for the collateral using blockchain. We use oracle to get the current real estate information and to monitor varying real estate prices. After tokenizing real estate into NFTs, we divide the NFTs into easily accessible price FTs, thereby, we can lower prices and provide large liquidity with price volatility limited. In addition, we also implemented BL based asset portfolio interface for effective portfolio composition for investing in multiple of real estates with small investments. Using BL model, investors can fix the asset portfolio. We implemented the whole system using Solidity smart contracts on Flask web framework with public data portals as oracle interfaces.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.317-332
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• 2020

Purpose - This study examines the information effect and trading behavior of investors for the 430 stock split data from January 2004 to June 2018 in the Korean stock market. Design/methodology/approach - The stock split samples are classified into two groups by split ratio as well as three groups by price level prior to split. We also investigate the trading behavior of investors categorized by institutional versus individual investors. Findings - First, we find a significantly positive information effect on the announcement day. In particular, the information effect is more distinct in the group of larger split ratio and higher price level of stocks. Second, we find a huge increase in turnover following the stock splits, which mainly results from the trading by individual investors. Also, the increase in turnover by individual investors is evident in the group of larger split ratio and higher price level of stocks. Third, the stock splits have a negative impact on the long-term stock performance. The negative buy-and-hold abnormal return(BHAR) makes no difference in the groups by split ratio as well as price level of stocks. Lastly, we find individual investors tend to buy splitted stocks, which exhibit the long-term under-performance. Research implications or Originality - The results in this paper suggest that the liquidity hypothesis is not supported in the Korean stock splits. In addition, we observe that individual investors are exposed to losses due to their unfavorable trading behavior following the stock split.