IDEAS home Printed from https://ideas.repec.org/a/nwe/eajour/y2017i2p237-250.html
   My bibliography  Save this article

Relationship Between Public Expectations and Financial Market Dynamics in South- East Europe Capital Markets

Author

Listed:
  • Ani Stoitsova-Stoykova

    (South-West University Neofit Rilski, Bulgaria)

Abstract

We examine the market efficiency, information asymmetry and the linkages between financial market dynamics and public expectations of the stock markets of South East Europe (SEE). Therefore, this study aims to answer the question of whether there is a difference between the stock market performance of the developed and emerging SEE stock exchanges. This paper employs GARCH models and uses the daily and monthly returns of eleven stock market indices of South East Europe (SEE) - Bulgaria, Banja Luka, Sarajevo, Croatia, Greece, Serbia, Slovenia, Turkey, Romania, Montenegro and Macedonia over the period from January 2005 to November 2015. The results reveal that SEE capital markets except Montenegro are not efficient in the context of the efficient market hypothesis (EMH). Moreover, the consumer sentiment information and inflation expectations affect the financial market dynamics of SEE stock indices. The analysis shows that there is no linkage between industrial expectations and the dynamics of the SEE capital markets. Test results potentially present that the consumer and inflation expectations have predictive power for the performance of SEE capital markets.

Suggested Citation

  • Ani Stoitsova-Stoykova, 2017. "Relationship Between Public Expectations and Financial Market Dynamics in South- East Europe Capital Markets," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 237-250, June.
    • Handle: RePEc:nwe:eajour:y:2017:i:2:p:237-250
    as

    Download full text from publisher

    File URL: http://www.unwe.bg/uploads/Alternatives/4-Alternativi_en_2_2017.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. GORMUS Sakir & GUNES, Sevcan, 2010. "Consumer Confidence, Stock Prices And Exchange Rates: The Case Of Turkey," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 10(2).
    2. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    3. Vladimir Tsenkov, 2015. "Crisis Influences between Developed and Developing Capital Markets – The Case of Central and Eastern European Countries," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 71-107.
    4. Maria Rosa Borges, 2010. "Efficient market hypothesis in European stock markets," The European Journal of Finance, Taylor & Francis Journals, vol. 16(7), pages 711-726.
    5. Kenourgios, Dimitris & Samitas, Aristeidis, 2011. "Equity market integration in emerging Balkan markets," Research in International Business and Finance, Elsevier, vol. 25(3), pages 296-307, September.
    6. Dragos Stefan Oprea & Laura Brad, 2014. "Investor Sentiment and Stock Returns: Evidence from Romania," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(2), pages 19-25, April.
    7. Peter Sellin, 2001. "Monetary Policy and the Stock Market: Theory and Empirical Evidence," Journal of Economic Surveys, Wiley Blackwell, vol. 15(4), pages 491-541, September.
    8. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    9. Victor Dragota & Dragos Stefan Oprea, 2014. "Informational Efficiency Tests on the Romanian Stock Market: A Review of the Literature," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 6(1), pages 015-028, June.
    10. Anton Gerunov, 2014. "Linkages between Public Sentiments and Stock Market Dynamics in the Context of the Efficient Market Hypothesis," Ikonomiceski i Sotsialni Alternativi, University of National and World Economy, Sofia, Bulgaria, issue 3, pages 58-71, July.
    11. Marin Geshkov, 2014. "The Effect of the World Economic Crisis on the Countries of the Balkan Region," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 1, pages 108-125, January.
    12. Daniel Stefan ARMEANU & Sorin-Iulian CIOACA, 2014. "Testing The Efficient Market Hypothesis On The Romanian Capital Market," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 8(1), pages 252-261, November.
    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. Tihana Škrinjarić & Branka Marasović & Boško Šego, 2021. "Does the Croatian Stock Market Have Seasonal Affective Disorder?," JRFM, MDPI, vol. 14(2), pages 1-16, February.

    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. Mariya Paskaleva & Ani Stoitsova-Stoykova, 2017. "Linkages and Efficiency Between iTraxx Europe and Financial Market Dynamics in South-East Europe Capital Markets in Post-crisis Period," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 172-179.
    2. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2019. "Return spillovers around the globe: A network approach," Economic Modelling, Elsevier, vol. 77(C), pages 133-146.
    3. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    4. Baumohl, Eduard & Lyocsa, Stefan, 2013. "Volatility and dynamic conditional correlations of European emerging stock markets," MPRA Paper 49898, University Library of Munich, Germany.
    5. Mariya Paskaleva & Ani Stoykova, 2021. "The Influence of Uncertainty on Market Efficiency: Evidence from Selected European Financial Markets," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 175-198.
    6. Baumöhl, Eduard & Lyócsa, Štefan, 2014. "Volatility and dynamic conditional correlations of worldwide emerging and frontier markets," Economic Modelling, Elsevier, vol. 38(C), pages 175-183.
    7. Czudaj Robert L., 2020. "The role of uncertainty on agricultural futures markets momentum trading and volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(3), pages 1-39, June.
    8. Christoph S. Weber, 2018. "Central bank transparency and inflation (volatility) – new evidence," International Economics and Economic Policy, Springer, vol. 15(1), pages 21-67, January.
    9. Charles, Amélie, 2010. "The day-of-the-week effects on the volatility: The role of the asymmetry," European Journal of Operational Research, Elsevier, vol. 202(1), pages 143-152, April.
    10. He, Changli & Teräsvirta, Timo, 1999. "Higher-order dependence in the general Power ARCH process and a special case," SSE/EFI Working Paper Series in Economics and Finance 315, Stockholm School of Economics.
    11. Tak Siu & John Lau & Hailiang Yang, 2007. "On Valuing Participating Life Insurance Contracts with Conditional Heteroscedasticity," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 14(3), pages 255-275, September.
    12. Ender Su & John Bilson, 2011. "Trading asymmetric trend and volatility by leverage trend GARCH in Taiwan stock index," Applied Economics, Taylor & Francis Journals, vol. 43(26), pages 3891-3905.
    13. Rituparna Sen & Pulkit Mehrotra, 2016. "Modeling Jumps and Volatility of the Indian Stock Market Using High-Frequency Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(1), pages 137-150, June.
    14. Christophe Chorro & Dominique Guegan & Florian Ielpo, 2010. "Option pricing for GARCH-type models with generalized hyperbolic innovations," Post-Print halshs-00469529, HAL.
    15. Chalamandaris, Georgios & Rompolis, Leonidas S., 2012. "Exploring the role of the realized return distribution in the formation of the implied volatility smile," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1028-1044.
    16. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    17. Dogus Emin, 2016. "Effects of Global Incidents on Dynamic Correlations of Emerging European Countries," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 4(1), pages 1-23.
    18. Gavriilidis, Konstantinos & Kambouroudis, Dimos S. & Tsakou, Katerina & Tsouknidis, Dimitris A., 2018. "Volatility forecasting across tanker freight rates: The role of oil price shocks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 376-391.
    19. Stavros Stavroyiannis, 2017. "A note on the Nelson Cao inequality constraints in the GJR-GARCH model: Is there a leverage effect?," Papers 1705.00535, arXiv.org.
    20. Francq, Christian & Makarova, Svetlana & Zakoi[diaeresis]an, Jean-Michel, 2008. "A class of stochastic unit-root bilinear processes: Mixing properties and unit-root test," Journal of Econometrics, Elsevier, vol. 142(1), pages 312-326, January.

    More about this item

    Keywords

    Efficient Market Hypothesis; market efficiency and information asymmetry; public expectations; capital markets; GARCH models;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    Statistics

    Access and download statistics

    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:nwe:eajour:y:2017:i:2:p:237-250. 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: Vanya Lazarova (email available below). General contact details of provider: https://edirc.repec.org/data/unweebg.html .

    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.