IDEAS home Printed from https://ideas.repec.org/r/arx/papers/1401.2548.html
   My bibliography  Save this item

Mutual Information Rate-Based Networks in Financial Markets

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Hosseini, Seyed Soheil & Wormald, Nick & Tian, Tianhai, 2021. "A Weight-based Information Filtration Algorithm for Stock-correlation Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
  2. Xiurong Chen & Aimin Hao & Yali Li, 2020. "The impact of financial contagion on real economy-An empirical research based on combination of complex network technology and spatial econometrics model," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.
  3. Zhong, Tao & Peng, Qinke & Wang, Xiao & Zhang, Jing, 2016. "Novel indexes based on network structure to indicate financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 583-594.
  4. Xue Guo & Hu Zhang & Tianhai Tian, 2018. "Development of stock correlation networks using mutual information and financial big data," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
  5. James, Nick & Menzies, Max & Gottwald, Georg A., 2022. "On financial market correlation structures and diversification benefits across and within equity sectors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
  6. Musmeci, Nicoló & Aste, Tomaso & Di Matteo, T., 2015. "Relation between financial market structure and the real economy: comparison between clustering methods," LSE Research Online Documents on Economics 61644, London School of Economics and Political Science, LSE Library.
  7. Arthur Matsuo Yamashita Rios de Sousa & Hideki Takayasu & Misako Takayasu, 2017. "Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-18, May.
  8. Będowska-Sójka, Barbara & Kliber, Agata, 2021. "Information content of liquidity and volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
  9. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
  10. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
  11. Yong Kheng Goh & Haslifah M Hasim & Chris G Antonopoulos, 2018. "Inference of financial networks using the normalised mutual information rate," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-21, February.
  12. Weibo Li & Wei Liu & Lei Wu & Xue Guo, 2021. "Risk spillover networks in financial system based on information theory," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
  13. Nick James & Max Menzies & Georg A. Gottwald, 2022. "On financial market correlation structures and diversification benefits across and within equity sectors," Papers 2202.10623, arXiv.org, revised Jun 2022.
  14. Tao You & Paweł Fiedor & Artur Hołda, 2015. "Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information," JRFM, MDPI, vol. 8(2), pages 1-19, June.
  15. Lukun Zheng, 2019. "Using mutual information as a cocitation similarity measure," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1695-1713, June.
  16. Jonathan E. Ogbuabor & Anthony Orji & Gladys C. Aneke & Oyun Erdene-Urnukh, 2016. "Measuring the Real and Financial Connectedness of Selected African Economies with the Global Economy," South African Journal of Economics, Economic Society of South Africa, vol. 84(3), pages 364-399, September.
  17. 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).
  18. A. Q. Barbi & G. A. Prataviera, 2017. "Nonlinear dependencies on Brazilian equity network from mutual information minimum spanning trees," Papers 1711.06185, arXiv.org, revised May 2019.
  19. khoojine, Arash Sioofy & Han, Dong, 2019. "Network analysis of the Chinese stock market during the turbulence of 2015–2016 using log-returns, volumes and mutual information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1091-1109.
  20. Yajie Yang & Longfeng Zhao & Lin Chen & Chao Wang & Jihui Han, 2021. "Portfolio optimization with idiosyncratic and systemic risks for financial networks," Papers 2111.11286, arXiv.org.
  21. Gao, Hai-Ling & Mei, Dong-Cheng, 2019. "The correlation structure in the international stock markets during global financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
  22. Liu, Xueyong & Jiang, Cheng, 2020. "The dynamic volatility transmission in the multiscale spillover network of the international stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
  23. Peng Yue & Yaodong Fan & Jonathan A. Batten & Wei-Xing Zhou, 2020. "Information transfer between stock market sectors: A comparison between the USA and China," Papers 2004.07612, arXiv.org.
  24. Eduard Baitinger, 2021. "Forecasting asset returns with network‐based metrics: A statistical and economic analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1342-1375, November.
  25. Sun, Qingru & Gao, Xiangyun & Zhong, Weiqiong & Liu, Nairong, 2017. "The stability of the international oil trade network from short-term and long-term perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 345-356.
  26. Fiedor, Paweł, 2014. "Sector strength and efficiency on developed and emerging financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 180-188.
  27. Nicoló Musmeci & Tomaso Aste & T Di Matteo, 2015. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.
  28. Barbi, A.Q. & Prataviera, G.A., 2019. "Nonlinear dependencies on Brazilian equity network from mutual information minimum spanning trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 876-885.
  29. Ferreira, Paulo & Almeida, Dora & Dionísio, Andreia & Bouri, Elie & Quintino, Derick, 2022. "Energy markets – Who are the influencers?," Energy, Elsevier, vol. 239(PA).
  30. Dong, Keqiang & Long, Linan & Zhang, Hong & Gao, You, 2018. "The mutual information based minimum spanning tree to detect and evaluate dependencies between aero-engine gas path system variables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 248-253.
  31. Zeng, Zhi-Jian & Xie, Chi & Yan, Xin-Guo & Hu, Jue & Mao, Zhou, 2016. "Are stock market networks non-fractal? Evidence from New York Stock Exchange," Finance Research Letters, Elsevier, vol. 17(C), pages 97-102.
  32. Songtao Wu & Jianmin He & Chao Wang, 2017. "Effects of Common Factors on Dynamics of Stocks Traded by Investors with Limited Information Capacity," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-15, September.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.