• Management & Information Systems Review
• /
• v.32 no.3
• /
• pp.83-105
• /
• 2013

There are many literatures about the herding behavior of institutional investors but there is lack of literatures about the relation among several investor groups consisting of institutional investors. So we investigate the relation among sub-institutional investor groups like bank, insurance companies, pension funds using KRX intraday trading data of 2009. As the result, we find that foreign, individual, and securities firm investors trade in the opposite direction of other investor groups including pension funds. And pension, insurance, asset management, private equity funds, other companies, government, and banks are cross-mimicking each other, so we conclude that these investors make herding behavior. In 2009 institutional investors except securities firms make herding in a short period, and insurance, asset management, pension funds and other companies make herding and self-mimicking in all period, but there is no herding and mimicking after foreign investors.

• The Korean Journal of Financial Management
• /
• v.26 no.4
• /
• pp.165-189
• /
• 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 Business Review
• /
• v.20 no.2
• /
• pp.15-27
• /
• 2007

This paper analyzed the relationship between Housing Purchase Price volatility of Seoul and Housing Purchase Price volatility of local large city. Other studies investigates the effect on the observed volatility Observed volatility consists of fundamental volatility and transitory volatility. Fundamental volatility is caused by information arrival and transitory volatility is caused by noise trading. Fundamental volatility is trend component and is modelled as a random walk with drift. Transitory volatility is cyclical component and is modelled as a stationary process. In contrast to other studies, this study investigates the effect on the fundamental volatility and transitory volatility individually. Observed volatility is estimated by GJR GARCH(1,1) model. We find that GJH GARCH model is superior to GARCH model and good news is more remarkable effect on volatility than bad news. This study decomposes the observed volatility into fundamental volatility and transitory volatility using Kalman filtering method. The findings in this paper is as follows. The correlation between Seoul housing price volatility and Busan housing price volatility is high. But, the correlation between Seoul and Daejeon is low. And the correlation between Daejeon and Busan is low. As a distinguishing feature, the correlation between fundamental volatilities is high in the case of all pairs. But, the correlation between transitory volatilities turns out low. The reason is as follows. When economic information arrives, Seoul, Daejeon, and Busan housing markets, all together, are affected by this information.

• Journal of Convergence for Information Technology
• /
• v.11 no.5
• /
• pp.94-103
• /
• 2021

This study aims to propose technical trading rules for Bitcoin futures and empirically analyze investment performance. Investment strategies include standard trading rules such as VMA, TRB, FR, MACD, RSI, BB, using Bitcoin futures daily data from December 18, 2017 to March 31, 2021. The trend-following rules showed higher investment performance than the comparative strategy B&H. Compared to KOSPI200 index futures, Bitcoin futures investment performance was higher. In particular, the investment performance has increased significantly in Sortino Ratio, which reflects downside risk. This study can find academic significance in that it is the first attempt to systematically analyze the investment performance of standard technical trading rules of Bitcoin futures. In future research, it is necessary to improve investment performance through the use of deep learning models or machine learning models to predict the price of Bitcoin futures.

• Smart Media Journal
• /
• v.10 no.3
• /
• pp.39-47
• /
• 2021

Enhanced index tracking is a problem of optimizing the objective function to generate returns above the index based on the index tracking that follows the market return. In order to avoid problems such as large transaction costs and illiquidity, we used a method of constructing a portfolio by selecting only some of the stocks included in the index. Commonly used enhanced index tracking methods tried to find the optimal portfolio with only one objective function in all tested periods, but it is almost impossible to find the ultimate strategy that always works well in the volatile financial market. In addition, it is important to improve generalization performance beyond optimizing the objective function for training data due to the nature of the financial market, where statistical characteristics change significantly over time, but existing methods have a limitation in that there is no direct discussion for this. In order to solve these problems, this paper proposes ensemble learning that composes a portfolio by combining several objective functions and a 3-stage portfolio selection algorithm that can select a portfolio by applying criteria other than the objective function to the training data. The proposed method in an experiment using the S&P500 index shows Sharpe ratio that is 27% higher than the index and the existing methods, showing that the 3-stage portfolio selection algorithm and ensemble learning are effective in selecting an enhanced index portfolio.

• The Journal of Society for e-Business Studies
• /
• v.17 no.4
• /
• pp.69-93
• /
• 2012

Various influence-related relationships in Social Network Services (SNS) among users, posts, and user-and-post, can be expressed using links. The current research evaluates the influence of specific users or posts by analyzing the link structure of relevant social network graphs to identify influential users. We applied the concept of mutual interactions proposed for ranking semantic web resources, rather than the voting notion of Page Rank or HITS, to blogosphere, one of the early SNS. Through many experiments with network models, where the performance and validity of each alternative approach can be analyzed, we showed the applicability and strengths of our approach. The weight tuning processes for the links of these network models enabled us to control the experiment errors form the link weight differences and compare the implementation easiness of alternatives. An additional example of how to enter the content scores of commercial or spam posts into the graph-based method is suggested on a small network model as well. This research, as a starting point of the study on identifying influential users in SNS, is distinctive from the previous researches in the following points. First, various influence-related properties that are deemed important but are disregarded, such as scraping, commenting, subscribing to RSS feeds, and trusting friends, can be considered simultaneously. Second, the framework reflects the general phenomenon where objects interacting with more influential objects increase their influence. Third, regarding the extent to which a bloggers causes other bloggers to act after him or her as the most important factor of influence, we treated sequential referencing relationships with a viewpoint from that of PageRank or HITS (Hypertext Induced Topic Selection).