Evolution of Worldwide Stock Markets, Correlation Structure and Correlation Based Graphs

Song, Dong-Ming; Tumminello, Michele; Zhou, Wei‐Xing; Mantegna, Rosario N.

doi:10.2139/ssrn.1899476

Cited by 56 publications

(84 citation statements)

References 27 publications

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“…Community detection is a task to find communities for a graph, and it has many applications in different fields, such as: (1) mining sets of highly correlated stocks [27]; (2) detecting DNA motif in bioinformatics [25]; (3) finding Web sites communities sharing similar topics [23]. Numerous works, which are based on different community models, have been developed to detect communities [13,32].…”

Section: Introductionmentioning

confidence: 99%

Query Optimal k-Plex Based Community in Graphs

et al. 2017

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Community search problem, which is to find good communities given a set of query nodes in a graph, has attracted increasing research interest recently. Though various measurement models have been proposed to define and solve community search problem. Few of them could define a community concisely and have good quality of query results. They either involve additional constraints for modeling communities, such as size and diameter, or suffer from the free rider effect, i.e., include irrelevant subgraphs. In this paper, we propose a new k-plex based community model for community search. We show that our model not only is simple and clear, but also meets with basic requirements of defining a community search problem. We formulate the maximum k-plex community query (MCKPQ) problem, that is, given a set of query nodes Q, searching for optimal k-plex containing Q. We prove that MCKPQ is NP-hard, and it is hard to approximate in any constant factor. We first give exact solutions. Then, we propose an efficient branch-and-bound (B&B) method and design an effective upper bound function and a pruning strategy. Furthermore, we optimize the basic B&B by fast candidate generation. We also give a fast heuristic solution, which produces high-quality results in practice. The effectiveness of our model of community and the efficiency of our methods are verified by elaborate experiments.

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Section: Introductionmentioning

confidence: 99%

Query Optimal k-Plex Based Community in Graphs

et al. 2017

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“…For this reason, cluster analysis make us more aware of the internal relationships of complex systems, like the variation trend of stock price which is a complex system. Some researchers use clustering algorithm to understand discreteness and corelationship of different stock [6]. The different stock in the same cluster indicate that they are very likely to have the same variation trend in the future.…”

Section: Background Knowledge and Problem Analysismentioning

confidence: 99%

“…Researchers use cluster analysis to obtain the correlationship between different stock index [6], or to determine the period of the stock price [3]. Furthermore some researchers predict the future trend of stock price based on the cluster analysis, such as fuzzy rule based clustering prediction [4].…”

Section: Introductionmentioning

confidence: 99%

A Data Preprocessing Method Applied to Cluster Analysis on Stock Data by Kmeans

Xiong¹,

Zhongneng²

2016

Proceedings of the 2016 International Conference on Intelligent Control and Computer Application

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Abstract-Recent years, more and more data mining methods are involved in applications like stock price analysis or predication, etc. Kmeans is one commonly used algorithm in those applications. However, those applications only take the technical indices (indicators) as features of data, where may make some important information lost, like the cross of different curves formed by the same technical index with different parameters. In this paper, we propose one way to quantify the variation trend of different curves, which can make kmeans clustering algorithm more effective on stocks analysis.

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“…is calculated over the non-diagonal elements [29,30] to study the collective behavior of the market. By using the sliding window over the whole study period, we calculate the eigenvalues λ 1 , λ 2 , .…”

Section: T)mentioning

confidence: 99%

Impact of monetary policy changes on the Chinese monetary and stock markets

Tang

Luo

Xiong

et al. 2013

Physica A: Statistical Mechanics and its Applications

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The impact of monetary policy changes on the monetary market and stock market in China is investigated in this study. The changes of two major monetary policies, the interest rate and required reserve ratio, are analyzed in a study period covering seven years on the interbank monetary market and Shanghai stock market. We find that the monetary market is related to the macro economy trend and we also find that the monetary change surprises both of lowering and raising bring significant impacts to the two markets and the two markets respond to the changes differently. The results suggest that the impact of fluctuations is much larger for raising policy changes than lowering changes in the monetary market on policy announcing and effective dates. This is consistent with the ''sign effect'', i.e. bad news brings a greater impact than good news. By studying the event window of each policy change, we also find that the ''sign effect'' still exists before and after each change in the monetary market. A relatively larger fluctuation is observed before the event date, which indicates that the monetary market might have a certain ability to predict a potential monetary change, while it is kept secret by the central bank before official announcement. In the stock market, we investigate how the returns and spreads of the Shanghai stock market index respond to the monetary changes. Evidences suggest the stock market is influenced but in a different way than the monetary market. The climbing of returns after the event dates for the lowering policy agrees with the theory that lowering changes can provide a monetary supply to boost the market and drive the stock returns higher but with a delay of 2 to 3 trading days on average. While in the bear market, the lowering policy brings larger volatility to the market on average than the raising ones. These empirical findings are useful for policymakers to understand how monetary policy changes impact the monetary and stock markets especially in an emerging market like China where * Corresponding author at: Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg, Switzerland. Tel.: +86 13880206370.E-mail address: yong.tang@unifr.ch (Y. Tang).the economy is booming and the policy changes impact the markets as surprises by the central bank without a pre-decided schedule. This is totally different from previous studies on FED, which follows pre-decided schedules for monetary policy changes.

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Evolution of Worldwide Stock Markets, Correlation Structure and Correlation Based Graphs

Cited by 56 publications

References 27 publications

Query Optimal k-Plex Based Community in Graphs

Query Optimal k-Plex Based Community in Graphs

A Data Preprocessing Method Applied to Cluster Analysis on Stock Data by Kmeans

Impact of monetary policy changes on the Chinese monetary and stock markets

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