Return Prediction and Stock Selection from Unidentified Historical Data

Forthcoming, Quantitative Finance

42 Pages Posted: 20 Oct 2009 Last revised: 20 Jul 2018

See all articles by Doron Sonsino

Doron Sonsino

Ben-Gurion University of the Negev; Center for Academic Studies

Tal Shavit

College of Management (Israel)

Date Written: October 18, 2009

Abstract

The experimental approach is applied to explore the value of unidentified historical information in stock-return prediction. Return sequences were randomly drawn cross section and time from historical S&P500 data. Subjects were requested to predict returns or select stocks from 12 preceding realizations. The hypothesis that predictions are randomly assigned to historical sequences is rejected in permutation tests and prediction-errors decrease with expertise. The best-stock portfolios by experimental predictions significantly outperform worst-stock portfolios in joint examination of mean-return and volatility. Actual predictions are significantly more effective than various statistical rules in separating the “best” stock from the “worst” in random 6-stock menus.

Keywords: return forecasting, predictability, expertise, prediction regime

JEL Classification: D8, G1, C9

Suggested Citation

Sonsino, Doron and Shavit, Tal, Return Prediction and Stock Selection from Unidentified Historical Data (October 18, 2009). Forthcoming, Quantitative Finance. Available at SSRN: https://ssrn.com/abstract=1490625 or http://dx.doi.org/10.2139/ssrn.1490625

Doron Sonsino (Contact Author)

Ben-Gurion University of the Negev ( email )

1 Ben-Gurion Blvd
Beer-Sheba 84105, 84105
Israel

Center for Academic Studies ( email )

Ha-Yotsrim 2
Or Yehuda, 6021816
Israel

Tal Shavit

College of Management (Israel) ( email )

7 Rabin Blvd.
Rishon Lezion
Rishon Lezion, 75190
Israel

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