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Forecasting the investors behavior on the capital market in Romania: Trading strategies based on technical analysis versus Artificial Intelligence techniques

Author

Listed:
  • Gabriela Victoria Anghelache

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

  • Alina Lucia Trifan

    (Ph.D. Assistant, Department of Money and Banking,Bucharest University of Economic Studies, Romania)

Abstract

This research aims at characterizing and modelling the investors’ behaviours present on the Romanian capital market, by analyzing the behaviours proposed by the efficient markets theory and investigating the possibility of financial time series behaviour forecasting through artificial intelligence concepts and tools (artificial neural networks, fuzzy logic, neuro-fuzzy systems).The analysis of various forecasting strategies has been conducted using data sets on a daily basis, on a time horizon of nine years, for a total of 22 companies listed on BSE and for the BET and BET-C exchange indexes; the research is differentiating the pre-crisis period and the crisis period.

Suggested Citation

  • Gabriela Victoria Anghelache & Alina Lucia Trifan, 2013. "Forecasting the investors behavior on the capital market in Romania: Trading strategies based on technical analysis versus Artificial Intelligence techniques," International Journal of Business and Social Research, LAR Center Press, vol. 3(2), pages 114-121, February.
    • Handle: RePEc:lrc:larijb:v:3:y:2013:i:2:p:114-121
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    References listed on IDEAS

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    1. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt’s exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759.
    2. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
    3. Maia, André Luis Santiago & de Carvalho, Francisco de A.T., 2011. "Holt's exponential smoothing and neural network models for forecasting interval-valued time series," International Journal of Forecasting, Elsevier, vol. 27(3), pages 740-759, July.
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