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The use of the Hurst exponent to predict changes in trends on the Warsaw Stock Exchange

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  • Domino, Krzysztof

Abstract

The local properties of the time series of the evolution of share prices of 126 significant companies traded on the Warsaw Stock Exchange during the period between 1991–2008 have been investigated. The analysis was applied to daily financial returns. I have used the local DFA to obtain the Hurst exponent (diffusion coefficient) while searching for negative correlations by which changes of long-term trends would be effected. A certain evidence, proving that after the signature of anti-correlation–the drop in the Hurst exponent–the change in the trend and in the return rate of an investment is probable, was pointed out. Hence after further investigation this method may be useful as a part of an investment strategy. As the Warsaw Stock Exchange is relatively smaller and younger than other significant world Stock Exchanges–and as the developing market is less efficient–the generalization for others markets needs further investigation.

Suggested Citation

  • Domino, Krzysztof, 2011. "The use of the Hurst exponent to predict changes in trends on the Warsaw Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(1), pages 98-109.
  • Handle: RePEc:eee:phsmap:v:390:y:2011:i:1:p:98-109
    DOI: 10.1016/j.physa.2010.04.015
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    Citations

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    Cited by:

    1. Eder Lucio Fonseca & Fernando F. Ferreira & Paulsamy Muruganandam & Hilda A. Cerdeira, 2012. "Identifying financial crises in real time," Papers 1204.3136, arXiv.org, revised Nov 2012.
    2. Domino, Krzysztof & Błachowicz, Tomasz, 2014. "The use of copula functions for modeling the risk of investment in shares traded on the Warsaw Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 77-85.
    3. Zhuang, Xiaoyang & Wei, Yu & Ma, Feng, 2015. "Multifractality, efficiency analysis of Chinese stock market and its cross-correlation with WTI crude oil price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 101-113.
    4. Domino, Krzysztof & Błachowicz, Tomasz & Ciupak, Maurycy, 2014. "The use of copula functions for predictive analysis of correlations between extreme storm tides," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 489-497.
    5. da Fonseca, Eder Lucio & Ferreira, Fernando F. & Muruganandam, Paulsamy & Cerdeira, Hilda A., 2013. "Identifying financial crises in real time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1386-1392.
    6. Li, Daye & Nishimura, Yusaku & Men, Ming, 2016. "The long memory and the transaction cost in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 312-320.
    7. Caraiani, Petre, 2012. "Characterizing emerging European stock markets through complex networks: From local properties to self-similar characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(13), pages 3629-3637.
    8. Domino, Krzysztof, 2012. "The use of the Hurst exponent to investigate the global maximum of the Warsaw Stock Exchange WIG20 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 156-169.
    9. Ma, Pengcheng & Li, Daye & Li, Shuo, 2016. "Efficiency and cross-correlation in equity market during global financial crisis: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 163-176.
    10. Horta, Paulo & Lagoa, Sérgio & Martins, Luís, 2014. "The impact of the 2008 and 2010 financial crises on the Hurst exponents of international stock markets: Implications for efficiency and contagion," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 140-153.
    11. Domino, Krzysztof & Błachowicz, Tomasz, 2015. "The use of copula functions for modeling the risk of investment in shares traded on world stock exchanges," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 142-151.
    12. M. Fern'andez-Mart'inez & M. A S'anchez-Granero & Mar'ia Jos'e Mu~noz Torrecillas & Bill McKelvey, 2016. "A comparison among some Hurst exponent approaches to predict nascent bubbles in $500$ company stocks," Papers 1601.04188, arXiv.org.
    13. Martín-Montoya, L.A. & Aranda-Camacho, N.M. & Quimbay, C.J., 2015. "Long-range correlations and trends in Colombian seismic time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 124-133.
    14. Sukpitak, Jessada & Hengpunya, Varagorn, 2016. "Efficiency of Thai stock markets: Detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 204-209.
    15. Ladislav Kristoufek, 2012. "Fractal Markets Hypothesis And The Global Financial Crisis: Scaling, Investment Horizons And Liquidity," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1-13.
    16. Domino, Krzysztof, 2017. "The use of the multi-cumulant tensor analysis for the algorithmic optimisation of investment portfolios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 267-276.

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