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Evolutionary-based return forecasting with nonlinear STAR models: evidence from the Eurozone peripheral stock markets

Author

Listed:
  • Christos Avdoulas

    () (Athens University of Economics & Business)

  • Stelios Bekiros

    () (IPAG Business School
    European University Institute (EUI))

  • Sabri Boubaker

    () (ESC Troyes
    IRG - Université Paris Est)

Abstract

Abstract Traditional linear regression and time-series models often fail to produce accurate forecasts due to inherent nonlinearities and structural instabilities, which characterize financial markets and challenge the Efficient Market Hypothesis. Machine learning techniques are becoming widespread tools for return forecasting as they are capable of dealing efficiently with nonlinear modeling. An evolutionary programming approach based on genetic algorithms is introduced in order to estimate and fine-tune the parameters of the STAR-class models, as opposed to conventional techniques. Using a hybrid method we employ trading rules that generate excess returns for the Eurozone southern periphery stock markets, over a long out-of-sample period after the introduction of the Euro common currency. Our results may have important implications for market efficiency and predictability. Investment or trading strategies based on the proposed approach may allow market agents to earn higher returns.

Suggested Citation

  • Christos Avdoulas & Stelios Bekiros & Sabri Boubaker, 2018. "Evolutionary-based return forecasting with nonlinear STAR models: evidence from the Eurozone peripheral stock markets," Annals of Operations Research, Springer, vol. 262(2), pages 307-333, March.
  • Handle: RePEc:spr:annopr:v:262:y:2018:i:2:d:10.1007_s10479-015-2078-z
    DOI: 10.1007/s10479-015-2078-z
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    References listed on IDEAS

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    More about this item

    Keywords

    Stock markets; Return forecasting; STAR models; Genetic algorithms;

    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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