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Intraday Market Efficiency for a Typical Central and Eastern European Stock Market: The Case of Romania

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  • Dan Gabriel ANGHEL

    (Department of Money and Banking, Bucharest University of Economic Studies)

Abstract

Investors increasingly focus on high frequency data for fine-tuning portfolio management decisions in developed, emerging and frontier markets alike. However, the behavior of intraday price movements in the Central and Eastern European stock markets is insufficiently understood. We obtain a large sample of intraday prices in a typical Central and Eastern European stock market and we thoroughly investigate it for dependencies and economic profit opportunities. We determine that intraday price movements present important deviations from a random walk. Despite this, we find that investors are generally unable to use the dependencies imbedded in the price movements to gain economic profits when using trading strategies derived from three popular technical analysis indicators. Overall, we cannot reject the Efficient Market Hypothesis for intraday price movements in Romania. This implies that, because of the existing market frictions, trading on high frequency data is not feasible in the stock market of Romania, at least when using popular technical analysis indicators.

Suggested Citation

  • Dan Gabriel ANGHEL, 2017. "Intraday Market Efficiency for a Typical Central and Eastern European Stock Market: The Case of Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 88-109, September.
    • Handle: RePEc:rjr:romjef:v::y:2017:i:3:p:88-109
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    as
    1. Wood, Robert A & McInish, Thomas H & Ord, J Keith, 1985. "An Investigation of Transactions Data for NYSE Stocks," Journal of Finance, American Finance Association, vol. 40(3), pages 723-739, July.
    2. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    3. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    4. Rozeff, Michael S. & Kinney, William Jr., 1976. "Capital market seasonality: The case of stock returns," Journal of Financial Economics, Elsevier, vol. 3(4), pages 379-402, October.
    5. Webb, Robert I. & Ryu, Doojin & Ryu, Doowon & Han, Joongho, 2016. "The price impact of futures trades and their intraday seasonality," Emerging Markets Review, Elsevier, vol. 26(C), pages 80-98.
    6. Graham Smith, 2012. "The changing and relative efficiency of European emerging stock markets," The European Journal of Finance, Taylor & Francis Journals, vol. 18(8), pages 689-708, September.
    7. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    8. Steven L. Heston & Robert A. Korajczyk & Ronnie Sadka, 2010. "Intraday Patterns in the Cross‐section of Stock Returns," Journal of Finance, American Finance Association, vol. 65(4), pages 1369-1407, August.
    9. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
    10. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2005. "Evidence on the speed of convergence to market efficiency," Journal of Financial Economics, Elsevier, vol. 76(2), pages 271-292, May.
    11. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    12. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    13. Anderson, Robert M. & Eom, Kyong Shik & Hahn, Sang Buhm & Park, Jong-Ho, 2013. "Autocorrelation and partial price adjustment," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 78-93.
    14. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    15. Victor Dragotă & Elena Ţilică, 2014. "Market efficiency of the Post Communist East European stock markets," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(2), pages 307-337, June.
    16. Jensen, Michael C., 1978. "Some anomalous evidence regarding market efficiency," Journal of Financial Economics, Elsevier, vol. 6(2-3), pages 95-101.
    17. Todea, Alexandru & Pleşoianu, Anita, 2013. "The influence of foreign portfolio investment on informational efficiency: Empirical evidence from Central and Eastern European stock markets," Economic Modelling, Elsevier, vol. 33(C), pages 34-41.
    18. Alexandru Todea & Anita Pleşoianu, 2011. "Testing The Hypothesis Of Martingale On Intraday Data: The Case Of Bet Index," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 5(5(558)(su), pages 344-351, July.
    19. 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.
    20. Oleg Deev & Dagmar Linnertová, 2012. "Intraday and intraweek trade anomalies on the Czech stock market," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 60(4), pages 79-88.
    21. Harris, Lawrence, 1989. "A Day-End Transaction Price Anomaly," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(1), pages 29-45, March.
    22. Gibbons, Michael R & Hess, Patrick, 1981. "Day of the Week Effects and Asset Returns," The Journal of Business, University of Chicago Press, vol. 54(4), pages 579-596, October.
    23. Tālis J. Putniņš, 2012. "Market Manipulation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 26(5), pages 952-967, December.
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    Cited by:

    1. Dan Gabriel ANGHEL & Elena Valentina ŢILICĂ & Victor DRAGOTĂ, 2020. "Intraday Patterns in Returns on the Romanian and Bulgarian Stock Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 92-114, July.
    2. Tihana Škrinjarić & Zrinka Orlović, 2020. "Economic Policy Uncertainty and Stock Market Spillovers: Case of Selected CEE Markets," Mathematics, MDPI, vol. 8(7), pages 1-33, July.
    3. Claudiu Tiberiu Albulescu & Aviral Kumar Tiwari & Phouphet Kyophilavong, 2021. "Nonlinearities and Chaos: A New Analysis of CEE Stock Markets," Mathematics, MDPI, vol. 9(7), pages 1-13, March.
    4. Dan Gabriel Anghel, 2020. "What Can Machine Learning Tell Us About Intraday Price Patterns in a Frontier Stock Market?," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(5), pages 205-220, October.

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    More about this item

    Keywords

    Bootstrap; Central and Eastern Europe; Efficient Market Hypothesis; Hour of the Day Effect; Random Walk; Romanian Stock Market; Superior Predictive Ability; Technical Analysis;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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