Structural Evolution of Stock Networks

Alkan, Serkan; Khashanah, Khaldoun

doi:10.1109/sitis.2015.118

Cited by 2 publications

(2 citation statements)

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“…To analyze dynamic properties of S&P 500 Index, we divide the whole period by the moving-window method. Following Majapa et al [26], Alkan et al [35], and Onnela et al [40], we set the width of each window as t = 250 days and the moving step as τ = 5 days. With these choices, the overall number of windows is 253.…”

Section: Application To Sandp 500 Stocksmentioning

confidence: 99%

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Efficient construction of threshold networks of stock markets

Kuo

Zhu

et al. 2018

Physica A: Statistical Mechanics and its Applications

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Although the threshold network is one of the most used tools to characterize the underlying structure of a stock market, the identification of the optimal threshold to construct a reliable stock network remains challenging. In this paper, the concept of dynamic consistence between the threshold network and the stock market is proposed. The optimal threshold is estimated by maximizing the consistence function. The application of this procedure to stocks belonging to Standard & Pool's 500 Index from January 2006 to December 2011 yields the threshold value 0.28. In analyzing topological characteristics of the generated network, three globally financial crises can be distinguished well from the evolutionary perspective.

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Section: Application To Sandp 500 Stocksmentioning

confidence: 99%

“…Among research, a fundamental question is to identify accurate threshold to construct reliable stock networks. Recent work suggested statistical text method to find the solution [33,34,35,36,37], but this methodology usually doesn't account for multiple hypothesis testing corrections [38].…”

Section: Introductionmentioning

confidence: 99%