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Sector strength and efficiency on developed and emerging financial markets

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  • Fiedor, Paweł

Abstract

In this paper we analyse the importance of sectors and market efficiency on developed and emerging financial markets. To perform this we analyse New York Stock Exchange between 2004 and 2013 and Warsaw Stock Exchange between 2000 and 2013. To find out the importance of sectors we construct minimal spanning trees for annual time series consisting of daily log returns and calculate centrality measures for all stocks, which we then aggregate by sectors. Such analysis is of interest to analysts for whom the knowledge of the influence of particular groups of stocks to the market behaviour is crucial. We also analyse the predictability of price changes on those two markets formally, using the information-theoretic concept of entropy rate, to find out the differences in market efficiency between a developed and an emerging market, and between sectors themselves. We postulate that such analysis is important to the study of financial markets as it can contribute to the profitability of investments, particularly in the case of algorithmic trading.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:phsmap:v:413:y:2014:i:c:p:180-188
    DOI: 10.1016/j.physa.2014.06.066
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    1. A. Sienkiewicz & T. Gubiec & R. Kutner & Z. R. Struzik, 2013. "Dynamic structural and topological phase transitions on the Warsaw Stock Exchange: A phenomenological approach," Papers 1301.6506, arXiv.org.
    2. G. Bonanno & F. Lillo & R. N. Mantegna, 2001. "High-frequency cross-correlation in a set of stocks," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 96-104.
    3. M. Wili'nski & A. Sienkiewicz & T. Gubiec & R. Kutner & Z. R. Struzik, 2013. "Structural and topological phase transitions on the German Stock Exchange," Papers 1301.2530, arXiv.org, revised Jul 2013.
    4. Carlos Eduardo León Rincón & Jhonatan Pérez Villalobos, 2013. "Authority Centrality and Hub Centrality as metrics of systemic importance of financial market infrastructures," Borradores de Economia 754, Banco de la Republica de Colombia.
    5. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    6. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    7. Pawe{l} Fiedor, 2013. "Frequency Effects on Predictability of Stock Returns," Papers 1310.5540, arXiv.org, revised Nov 2013.
    8. Kristin J. Forbes & Roberto Rigobon, 2002. "No Contagion, Only Interdependence: Measuring Stock Market Comovements," Journal of Finance, American Finance Association, vol. 57(5), pages 2223-2261, October.
    9. Paweł Fiedor, 2014. "Information-theoretic approach to lead-lag effect on financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(8), pages 1-9, August.
    10. M. Tumminello & T. Di Matteo & T. Aste & R. N. Mantegna, 2007. "Correlation based networks of equity returns sampled at different time horizons," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 209-217, January.
    11. Pierre Cizeau & Marc Potters & Jean-Philippe Bouchaud, 2000. "Correlation structure of extreme stock returns," Science & Finance (CFM) working paper archive 0006034, Science & Finance, Capital Fund Management.
    12. Kuzubaş, Tolga Umut & Ömercikoğlu, Inci & Saltoğlu, Burak, 2014. "Network centrality measures and systemic risk: An application to the Turkish financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 203-215.
    13. J. Barkley Rosser, 2008. "Econophysics And Economic Complexity," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(05), pages 745-760.
    14. Tumminello, Michele & Lillo, Fabrizio & Mantegna, Rosario N., 2010. "Correlation, hierarchies, and networks in financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 75(1), pages 40-58, July.
    15. H. Jeong & S. P. Mason & A.-L. Barabási & Z. N. Oltvai, 2001. "Lethality and centrality in protein networks," Nature, Nature, vol. 411(6833), pages 41-42, May.
    16. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    17. Pawe{l} Fiedor, 2014. "Mutual Information Rate-Based Networks in Financial Markets," Papers 1401.2548, arXiv.org.
    18. Caprio, Gerard (ed.), 2012. "The Evidence and Impact of Financial Globalization," Elsevier Monographs, Elsevier, edition 1, number 9780123978745.
    19. Krzysztof Kompa & Aleksandra Matuszewska-Janica, 2009. "Efficiency of the Warsaw Stock Exchange: Analysis of Selected Properties," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 15(1), pages 59-70, February.
    20. Michael C. Munnix & Rudi Schafer & Thomas Guhr, 2010. "Impact of the tick-size on financial returns and correlations," Papers 1001.5124, arXiv.org, revised Jul 2010.
    21. Li, Hong & Majerowska, Ewa, 2008. "Testing stock market linkages for Poland and Hungary: A multivariate GARCH approach," Research in International Business and Finance, Elsevier, vol. 22(3), pages 247-266, September.
    22. Münnix, Michael C. & Schäfer, Rudi & Guhr, Thomas, 2010. "Impact of the tick-size on financial returns and correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4828-4843.
    23. P. Cizeau & M. Potters & J-P. Bouchaud, 2001. "Correlation structure of extreme stock returns," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 217-222.
    24. repec:kap:iaecre:v:15:y:2009:i:1:p:59-70 is not listed on IDEAS
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