IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v450y2016icp462-465.html
   My bibliography  Save this article

Stock market stability: Diffusion entropy analysis

Author

Listed:
  • Li, Shouwei
  • Zhuang, Yangyang
  • He, Jianmin

Abstract

In this article, we propose a method to analyze the stock market stability based on diffusion entropy, and conduct an empirical analysis of Dow Jones Industrial Average. Empirical results show that this method can reflect the volatility and extreme cases of the stock market.

Suggested Citation

  • Li, Shouwei & Zhuang, Yangyang & He, Jianmin, 2016. "Stock market stability: Diffusion entropy analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 462-465.
    • Handle: RePEc:eee:phsmap:v:450:y:2016:i:c:p:462-465
    DOI: 10.1016/j.physa.2016.01.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116000765
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2016.01.037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. McCauley, Joseph L., 2003. "Thermodynamic analogies in economics and finance: instability of markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 329(1), pages 199-212.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Kalev, Petko S. & Liu, Wai-Man & Pham, Peter K. & Jarnecic, Elvis, 2004. "Public information arrival and volatility of intraday stock returns," Journal of Banking & Finance, Elsevier, vol. 28(6), pages 1441-1467, June.
    4. Koulakiotis, Athanasios & Babalos, Vasillios & Papasyriopoulos, Nicholas, 2015. "Liquidity matters after all: Asymmetric news and stock market volatility before and after the global financial crisis," Economics Letters, Elsevier, vol. 127(C), pages 58-60.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    6. Oh, Gabjin & Kim, Ho-yong & Ahn, Seok-Won & Kwak, Wooseop, 2015. "Analyzing the financial crisis using the entropy density function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 464-469.
    7. Amalia Morales-Zumaquero & Sim�n Sosvilla-Rivero, 2015. "Temporary ban on short positions and financial market volatility: evidence from the Madrid Stock Market," Applied Economics Letters, Taylor & Francis Journals, vol. 22(11), pages 854-859, July.
    8. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Kumar, Sushil & Kumar, Sunil & Kumar, Pawan, 2020. "Diffusion entropy analysis and random matrix analysis of the Indian stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    2. Ha, Le Thanh, 2022. "Effects of digitalization on financialization: Empirical evidence from European countries," Technology in Society, Elsevier, vol. 68(C).
    3. Zhang, Junhuan, 2018. "Influence of individual rationality on continuous double auction markets with networked traders," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 353-392.
    4. Phuc Nguyen, Canh & Dinh Su, Thanh & Doytch, Nadia, 2020. "The drivers of financial development: Global evidence from internet and mobile usage," Information Economics and Policy, Elsevier, vol. 53(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Massimiliano Caporin & Francesco Poli, 2017. "Building News Measures from Textual Data and an Application to Volatility Forecasting," Econometrics, MDPI, vol. 5(3), pages 1-46, August.
    2. Pavel Ciaian & d'Artis Kancs & Miroslava Rajcaniova, 2018. "The Price of BitCoin: GARCH Evidence from High Frequency Data," EERI Research Paper Series EERI RP 2018/14, Economics and Econometrics Research Institute (EERI), Brussels.
    3. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2019. "Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 180-197, February.
    4. repec:ipg:wpaper:2014-405 is not listed on IDEAS
    5. Smales, L.A., 2021. "Investor attention and global market returns during the COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 73(C).
    6. Mauro Bernardi & Leopoldo Catania & Lea Petrella, 2014. "Are news important to predict large losses?," Papers 1410.6898, arXiv.org, revised Oct 2014.
    7. K. Lebedeva, 2015. "An Empirical Analysis of the Russian Financial Markets’ Liquidity and Returns," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 3(3), pages 5-31.
    8. Semen Son Turan, 2014. "Internet Search Volume and Stock Return Volatility: The Case of Turkish Companies," Information Management and Business Review, AMH International, vol. 6(6), pages 317-328.
    9. Jawadi, Fredj & Louhichi, Waël & Ameur, Hachmi Ben & Cheffou, Abdoulkarim Idi, 2016. "On oil-US exchange rate volatility relationships: An intraday analysis," Economic Modelling, Elsevier, vol. 59(C), pages 329-334.
    10. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.
    11. Afkhami, Mohamad & Cormack, Lindsey & Ghoddusi, Hamed, 2017. "Google search keywords that best predict energy price volatility," Energy Economics, Elsevier, vol. 67(C), pages 17-27.
    12. Massimo PERI & Daniela VANDONE & Lucia BALDI, 2012. "Internet, noise trading and commodity prices," Departmental Working Papers 2012-07, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    13. Shen, Dehua & Zhang, Wei & Xiong, Xiong & Li, Xiao & Zhang, Yongjie, 2016. "Trading and non-trading period Internet information flow and intraday return volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 519-524.
    14. Taylor, Nicholas, 2008. "Can idiosyncratic volatility help forecast stock market volatility?," International Journal of Forecasting, Elsevier, vol. 24(3), pages 462-479.
    15. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    16. Yen-Ju Hsu & Yang-Cheng Lu & J. Jimmy Yang, 2021. "News sentiment and stock market volatility," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1093-1122, October.
    17. Zhao, Xiaojun & Zhang, Na & Zhang, Yali & Xu, Chao & Shang, Pengjian, 2024. "Equity markets volatility clustering: A multiscale analysis of intraday and overnight returns," Journal of Empirical Finance, Elsevier, vol. 77(C).
    18. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    19. Shi, Yanlin & Ho, Kin-Yip & Liu, Wai-Man, 2016. "Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 291-312.
    20. Taner Sekmen, 2015. "Effect of the Subprime Crisis on Return and Volatility of the Turkish Stock Market," Journal of Economics and Behavioral Studies, AMH International, vol. 7(3), pages 23-29.
    21. Yarovaya, Larisa & Brzeszczyński, Janusz & Lau, Chi Keung Marco, 2017. "Asymmetry in spillover effects: Evidence for international stock index futures markets," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 94-111.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:450:y:2016:i:c:p:462-465. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.