Technical analysis, spread trading, and data snooping control

Ioannis Psaradellis*, Jason Laws, Athanasios Pantelous, Georgios Sermpinis

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)
    11 Downloads (Pure)

    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.
    Original languageEnglish
    Pages (from-to)178-191
    Number of pages14
    JournalInternational Journal of Forecasting
    Volume39
    Issue number1
    Early online date24 Nov 2021
    DOIs
    Publication statusPublished - 1 Jan 2023

    Keywords

    • Technical trading rules
    • Spread trading predictability
    • False discovery rate
    • Bootstrap test
    • Portfolio performance

    Fingerprint

    Dive into the research topics of 'Technical analysis, spread trading, and data snooping control'. Together they form a unique fingerprint.

    Cite this