Evolutionary-based return forecasting with nonlinear STAR models: evidence from the Eurozone peripheral stock markets
Christos Avdoulas (),
Stelios Bekiros and
Sabri Boubaker ()
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Christos Avdoulas: Athens University of Economics & Business
Sabri Boubaker: ESC Troyes
Annals of Operations Research, 2018, vol. 262, issue 2, 307-333
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.
Keywords: Stock markets; Return forecasting; STAR models; Genetic algorithms (search for similar items in EconPapers)
JEL-codes: C32 C58 G10 G17 (search for similar items in EconPapers)
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