Optimality problem of network topology in stocks market analysis

Djauhari, Maman A.; Gan, Siew Lee

doi:10.1016/j.physa.2014.09.060

Cited by 36 publications

(13 citation statements)

References 39 publications

(96 reference statements)

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“…For example, principal component analysis (Loretan, 1997), neural network (Fu and Kara, 2011;Kara et al, 2011), minimum spanning tree (Sharif et al, 2012;Rostrup et al, 2013;Setiawan, 2014) and optimal minimum spanning tree (Djauhari, 2013;Djauhari and Gan, 2015). In this case, every stock is considered as a complex network system consisting of 77 stocks as nodes connected by several numbers of links.…”

Section: Optimal Minimum Spanning Treementioning

confidence: 99%

See 1 more Smart Citation

Validation of Global Financial Crisis on Bursa Malaysia Stocks Market Companies via Covariance Structure

Sharif¹,

Ismail²,

Omar³

et al. 2016

American Journal of Applied Sciences

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Section: Optimal Minimum Spanning Treementioning

confidence: 99%

“…Subsequently, the SDU of D is the min-max transitive closure denoted by D *K for an integer; 2≤K≤n (Djauhari and Gan, 2015).…”

Section: Omst Algorithmmentioning

confidence: 99%

Validation of Global Financial Crisis on Bursa Malaysia Stocks Market Companies via Covariance Structure

Sharif¹,

Ismail²,

Omar³

et al. 2016

American Journal of Applied Sciences

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“…Kwapień et al [15] introduced a family of q-dependent minimum spanning trees (qMSTs) that are selective for cross-correlations between different fluctuation amplitudes and different time scales of multivariate data. Djauhari et al [16] introduced a set of optimality criteria and proposed the process of selecting the optimal MST. Through the analysis of the planar maximally-filtered graphs (PMFG) of the portfolio of the 300 stocks traded, Tumminello et al [17] confirmed that the selected stocks composed a hierarchical system progressively structuring as the sampling time horizon increases.…”

Section: Introductionmentioning

confidence: 99%

Construction of Complex Network with Multiple Time Series Relevance

Huang

Wang³

et al. 2018

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Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method.

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“…In order to obtain the embodying economic information from the stock market analysis, many researchers have risen up to investigate in this area. There are various approaches being used in filtering the information from the stock network structure such as minimum spanning tree (MST) [1][2][10][11], planar maximally filtered graph [12][13], average linkage-based MSTs [14] and Directed Bubble Hierarchical Tree [15]. MST is the simplest and well-known method for filtering the information that contained in the stock network structure among these approaches.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Minimal Spanning Tree: The Bursa Malaysia

Yee

Salleh

Asrah

2018

JST

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The stock market has constituted a complex system since the interrelationships among the stocks are complicated and unpredictable. Moreover, the stock price does not stagnate at a certain price all the time but the price keeps changing from minute to minute during the transaction hours. Thus, it is quite difficult to indicate which stock influences the performances of other stocks as well as the behaviours of the stocks in a network. The economic information might be misleading and incomplete if the analysis applies with univariate time series of stock price only as each stock is represented by four features of the price. To obtain the complete information of the Bursa Malaysia stock network as well as the interrelationships among the stocks, multivariate time series of stocks are measured by using RV coefficient. Besides, minimum spanning tree and centrality measures are applied in this paper in order to construct the stock network virtually and determine the behaviours of the stocks by using the recent data of top 100 stocks in Bursa Malaysia.

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Optimality problem of network topology in stocks market analysis

Cited by 36 publications

References 39 publications

Validation of Global Financial Crisis on Bursa Malaysia Stocks Market Companies via Covariance Structure

Validation of Global Financial Crisis on Bursa Malaysia Stocks Market Companies via Covariance Structure

Construction of Complex Network with Multiple Time Series Relevance

Multidimensional Minimal Spanning Tree: The Bursa Malaysia

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