Technical analysis, spread trading, and data snooping control
Abstract
This paper utilizes a large universe of 18,410 technical trading rules (TTRs) and adopts a technique that controls for false discoveries to evaluate the performance of frequently traded spreads using daily data over 1990–2016. For the first time, the paper applies an excessive out-of-sample analysis in different subperiods across all TTRs examined. For commodity spreads, the evidence of significant predictability appears much stronger compared to equity and currency spreads. Out-of-sample performance of portfolios of significant rules typically exceeds transaction cost estimates and generates a Sharpe ratio of 3.67 in 2016. In general, we reject previous studies’ evidence of a uniformly monotonic downward trend in the selection of predictive TTRs over 1990–2016.
Citation
Psaradellis , I , Laws , J , Pantelous , A & Sermpinis , G 2023 , ' Technical analysis, spread trading, and data snooping control ' , International Journal of Forecasting , vol. 39 , no. 1 , pp. 178-191 . https://doi.org/10.1016/j.ijforecast.2021.10.002
Publication
International Journal of Forecasting
Status
Peer reviewed
ISSN
0169-2070Type
Journal article
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