Technical analysis, spread trading, and data snooping control

Ioannis Psaradellis, Jason Laws, Athanasios A. Pantelous and Georgios Sermpinis

International Journal of Forecasting, 2023, vol. 39, issue 1, 178-191

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.

Keywords: Technical trading rules; Spread trading predictability; False discovery rate; Bootstrap test; Portfolio performance (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:1:p:178-191

DOI: 10.1016/j.ijforecast.2021.10.002

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