شناسایی ساختار همبستگی گروهی در بازار مالی کره ای
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|14293||2011||11 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Physica A: Statistical Mechanics and its Applications, Volume 390, Issue 11, 1 June 2011, Pages 1991–2001
We investigate the structure of the cross-correlation in the Korean stock market. We analyze daily cross-correlations between price fluctuations of 586 different Korean stock entities for the 6-year time period from 2003 to 2008. The main purpose is to investigate the structure of group correlation and its stability by undressing the market-wide effect using the Markowitz multi-factor model and the network-based approach. We find the explicit list of significant firms in the few largest eigenvectors from the undressed correlation matrix. We also observe that each contributor is involved in the same business sectors. The structure of group correlation can not remain constant during each 1-year time period with different starting points, whereas only two largest eigenvectors are stable for 6 years 8–9 eigenvectors remain stable for half-year. The structure of group correlation in the Korean financial market is disturbed during a sufficiently short time period even though the group correlation exists as an ensemble for the 6-year time period in the evolution of the system. We verify the structure of group correlation by applying a network-based approach. In addition, we examine relations between market capitalization and businesses. The Korean stock market shows a different behavior compared to mature markets, implying that the KOSPI is a target for short-positioned investors.
The correlation-based approach used in the study of financial markets is highly relevant among the various methodologies in the econophysics field . Cross-correlation between the stock entities of financial markets provides not only a physical interpretation of the market but also empirical or real information about the correlation structure between market entities , , , , , , , , , , ,  and . Basically, financial market fluctuations result from the correlated decision making between buy and sell orders of various stock entities participating in the market. Because the conceptions of the market are reflected in price fluctuations, we can obtain the structure of a given financial market by investigating financial cross-correlation data. Random matrix theory has provided highly meaningful information about financial market structures, especially that of the U.S. market , , , , , ,  and . As reported in previous works, the seemingly complicated structure of stock market cross-correlation can be divided into three categories: a bulk random part, a market-wide part and the group correlation between firms in the same business sector  and . This indicates that investors engaged in the stock market manage their stock portfolios to be optimized in relation to the structure of a particular business cluster. Clearly decomposed group correlation is one of the key features of a mature stock market. Does this hold true for the Korean stock market? Korea has a distinctive economic structure and background compared with other typical mature markets . Although several papers studying the cross-correlation of the Korean stock market have been published recently, no work has investigated the explicit structure of group correlation  and . Genuine information about group correlation was obtained by undressing market-wide effects using the Markowitz multifactor models. In the first part of this paper, we found the structure of business clusters in the Korean stock market by applying multifactor models. We verified the business structures by a network approach. Time stability of the market structure is another major issue in this research. We investigated the time stability of the obtained group correlation by separating the time series into several parts. We ultimately tested whether the structure of group correlation in the Korean stock market was maintained consistently during a sufficiently long time period or whether it showed a different pattern in the evolution of the system.
نتیجه گیری انگلیسی
We successfully identified the explicit group correlation of the Korean stock market. We extracted a list of eigenvector components of the largest few eigenvectors. Those eigenvector components belonging to each eigenvector represent firms in the same business sectors. We verified the structure of the group correlation by applying a network-based approach. However, We also found several characteristics of the Korean stock market distinguished them from previous analyses on the US market. First, the structure of group correlation is not stable for a long time. Among the few largest eigenvectors, only the largest eigenvector shows clear stability for the time period of 6 years. The 4 largest eigenvectors are stable for 4 years on average. Secondly, not only the temporally averaged eigenvectors, but also the contribution of eigenvector components also varied significantly according to the evolution of group structure with time. This result indicates that financial market structure can be variable over a sufficiently short period even though the structure of group correlation as an ensemble was observed for the 6-year time period of the evolution of the stock market. In other words, each factor represented by eigenvectors corresponding to the largest eigenvalues shows a greatly varying characteristic according to entries. We also detected a weak relation between businesses and market capitalization for the KOSPI. Comparing developed markets such as NYSE, we did not check an exponential function of market capitalization. Proceeding from this fact, we could logically assume that the Korean market has very many short-term portfolios and is a conglomerate structure. With this evidence at hand we conclude that investors do not make a long-term investment strategy based on industrialized classifications. In short, we see that they change their strategies very easily according to the domestic or/and global economic environment (see Fig. 10 and Fig. 11).