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

Author

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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. 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.
    10. 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.
    11. 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.
    12. 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.
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    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.

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    Keywords

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    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

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