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Financial time series analysis based on effective phase transfer entropy

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  • Yang, Pengbo
  • Shang, Pengjian
  • Lin, Aijing

Abstract

Transfer entropy is a powerful technique which is able to quantify the impact of one dynamic system on another system. In this paper, we propose the effective phase transfer entropy method based on the transfer entropy method. We use simulated data to test the performance of this method, and the experimental results confirm that the proposed approach is capable of detecting the information transfer between the systems. We also explore the relationship between effective phase transfer entropy and some variables, such as data size, coupling strength and noise. The effective phase transfer entropy is positively correlated with the data size and the coupling strength. Even in the presence of a large amount of noise, it can detect the information transfer between systems, and it is very robust to noise. Moreover, this measure is indeed able to accurately estimate the information flow between systems compared with phase transfer entropy. In order to reflect the application of this method in practice, we apply this method to financial time series and gain new insight into the interactions between systems. It is demonstrated that the effective phase transfer entropy can be used to detect some economic fluctuations in the financial market. To summarize, the effective phase transfer entropy method is a very efficient tool to estimate the information flow between systems.

Suggested Citation

  • Yang, Pengbo & Shang, Pengjian & Lin, Aijing, 2017. "Financial time series analysis based on effective phase transfer entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 398-408.
    • Handle: RePEc:eee:phsmap:v:468:y:2017:i:c:p:398-408
    DOI: 10.1016/j.physa.2016.10.085
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    References listed on IDEAS

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    Cited by:

    1. Qiu, Lu & Yang, Huijie, 2020. "Transfer entropy calculation for short time sequences with application to stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    2. Dai, Yimei & He, Jiayi & Wu, Yue & Chen, Shijian & Shang, Pengjian, 2019. "Generalized entropy plane based on permutation entropy and distribution entropy analysis for complex time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 217-231.
    3. Jian Huang & Junyi Chai & Stella Cho, 2020. "Deep learning in finance and banking: A literature review and classification," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-24, December.
    4. Zavala-Díaz, J.C. & Pérez-Ortega, J. & Hernández-Aguilar, J.A. & Almanza-Ortega, N.N. & Martínez-Rebollar, A., 2020. "Short-term prediction of the closing price of financial series using a ϵ-machine model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

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