Technical analysis, spread trading, and data snooping control

Psaradellis, Ioannis, Laws, Jason ORCID: 0000-0002-0277-2018, Pantelous, Athanasios A ORCID: 0000-0001-5738-1471 and Sermpinis, Georgios (2022) Technical analysis, spread trading, and data snooping control. INTERNATIONAL JOURNAL OF FORECASTING, 39 (1). pp. 178-191. ISSN 0169-2070, 1872-8200

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Official URL: https://doi.org/10.1016/j.ijforecast.2021.10.002

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.

Item Type:	Article
Additional Information:	Source info: International Journal of Forecasting, Volume 39, Issue 1, pp 178-191, January–March 2023, DOI: 10.1016/j.ijforecast.2021.10.002
Uncontrolled Keywords:	Bootstrap test, False discovery rate, Portfolio performance, Spread trading predictability, Technical trading rules
Depositing User:	Symplectic Admin
Date Deposited:	19 Jul 2024 14:37
Last Modified:	06 Dec 2024 19:38
DOI:	10.1016/j.ijforecast.2021.10.002
Open Access URL:	https://eprints.gla.ac.uk/253683/
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3183016