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Correlations of stock price fluctuations under multi-scale and multi-threshold scenarios

Author

Listed:
  • Sui, Guo
  • Li, Huajiao
  • Feng, Sida
  • Liu, Xueyong
  • Jiang, Meihui

Abstract

The multi-scale method is widely used in analyzing time series of financial markets and it can provide market information for different economic entities who focus on different periods. Through constructing multi-scale networks of price fluctuation correlation in the stock market, we can detect the topological relationship between each time series. Previous research has not addressed the problem that the original fluctuation correlation networks are fully connected networks and more information exists within these networks that is currently being utilized. Here we use listed coal companies as a case study. First, we decompose the original stock price fluctuation series into different time scales. Second, we construct the stock price fluctuation correlation networks at different time scales. Third, we delete the edges of the network based on thresholds and analyze the network indicators. Through combining the multi-scale method with the multi-threshold method, we bring to light the implicit information of fully connected networks.

Suggested Citation

  • Sui, Guo & Li, Huajiao & Feng, Sida & Liu, Xueyong & Jiang, Meihui, 2018. "Correlations of stock price fluctuations under multi-scale and multi-threshold scenarios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1501-1512.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:1501-1512
    DOI: 10.1016/j.physa.2017.08.141
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    Cited by:

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    4. Yajie Qi & Huajiao Li & Sui Guo & Sida Feng, 2019. "Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks," Complexity, Hindawi, vol. 2019, pages 1-15, December.
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    6. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    7. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Zhu, You & Uddin, Gazi Salah, 2023. "Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).

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