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Market Efficiency and Technical Analysis in Romania

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  • Dan Gabriel Anghel

Abstract

In this paper we make a detail evaluation of stock market efficiency in Romania. First, we employ 686,243 trading models derived from 44 technical analysis indicators and determine that significant inefficiencies exist for stock prices in this country. The time varying nature of these points out that market efficiency is not improving over time, but instead fluctuates in the way consistent to the Adaptive Market Hypothesis. We show that investor success does not depend on the target investment asset, slightly depends on specific prediction models and heavily depend on the size of the implemented rule universe. Next, we focus on finding out what are the determining factors for market efficiency. Contrary to what one might expect, we find that market liquidity has an almost insignificant impact on efficiency. The main determining factor for market efficiency in Romania is price momentum, and argues that the detected anomalies are due to investor behavioral biases.

Suggested Citation

  • Dan Gabriel Anghel, 2015. "Market Efficiency and Technical Analysis in Romania," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(2), pages 164-177, April.
    • Handle: RePEc:jfr:ijfr11:v:6:y:2015:i:2:p:164-177
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    References listed on IDEAS

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    1. Dan Anghel, 2013. "How Reliable is the Moving Average Crossover Rule for an Investor on the Romanian Stock Market?," 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. 5(2), pages 089-115, December.
    2. Xiaohong Chen & Norman R. Swanson (ed.), 2013. "Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis," Springer Books, Springer, edition 127, number 978-1-4614-1653-1, January.
    3. 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.
    4. Valentina Corradi & Norman Swanson, 2013. "A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance," Departmental Working Papers 201309, Rutgers University, Department of Economics.
    5. Dragota, Victor & Stoian, Andreea & Pele, Daniel Traian & Mitrica, Eugen & Bensafta, Malik, 2009. "The Development of the Romanian Capital Market: Evidences on Information Efficiency," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 6(2), pages 147-160, June.
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    Cited by:

    1. Shi, Huai-Long & Zhou, Wei-Xing, 2017. "Wax and wane of the cross-sectional momentum and contrarian effects: Evidence from the Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 397-407.
    2. Bley, Jorg & Saad, Mohsen, 2020. "An analysis of technical trading rules: The case of MENA markets," Finance Research Letters, Elsevier, vol. 33(C).
    3. Ruan, Yong-Ping & Song, Xin & Zheng, Kai, 2018. "Do large shareholders collude with institutional investors? Based on the data of the private placement of listed companies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 242-253.
    4. Ali Fayyaz Munir & Mohd Edil Abd. Sukor & Shahrin Saaid Shaharuddin, 2022. "Adaptive Market Hypothesis and Time-varying Contrarian Effect: Evidence From Emerging Stock Markets of South Asia," SAGE Open, , vol. 12(1), pages 21582440211, January.
    5. Tihana Škrinjarić, 2018. "Testing for Seasonal Affective Disorder on Selected CEE and SEE Stock Markets," Risks, MDPI, vol. 6(4), pages 1-26, December.

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