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Return prediction and stock selection from unidentified historical data

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  • Doron Sonsino
  • Tal Shavit

Abstract

The experimental approach was applied to test the value of historical return series in technical prediction. Return sequences were randomly drawn cross-sectionally and over time from S&P500 records and participants were asked to predict the 13th realization from 12 preceding returns. The hypothesis that predictions (nominal or real) are randomly assigned to historical sequences is rejected in permutation tests, and the best-stock portfolios by experimental predictions significantly outperform the worst-stock portfolios in joint examination of mean return and volatility. The participants dynamically adjust their predictions to the observed series and switch from momentum riding to contrarian extrapolation when recent trends get extreme. The implicit tuning of predictions to specific series captures variabilities that could not be inferred by schematic statistical forecasting.

Suggested Citation

  • Doron Sonsino & Tal Shavit, 2014. "Return prediction and stock selection from unidentified historical data," Quantitative Finance, Taylor & Francis Journals, vol. 14(4), pages 641-655, April.
  • Handle: RePEc:taf:quantf:v:14:y:2014:i:4:p:641-655
    DOI: 10.1080/14697688.2012.712210
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    1. Afik, Zvika & Lahav, Yaron, 2015. "Thinking near and far: Modeling the formation of traders’ beliefs in asset markets using experimental data," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 57(C), pages 73-80.

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