Technical analysis, spread trading, and data snooping control

Ioannis Psaradellis; Jason Laws; Athanasios Pantelous; Georgios Sermpinis

doi:10.1016/j.ijforecast.2021.10.002

Technical analysis, spread trading, and data snooping control

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

^*Corresponding author for this work

Finance (Business School)

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	178-191
Number of pages	14
Journal	International Journal of Forecasting
Volume	39
Issue number	1
Early online date	24 Nov 2021
DOIs	https://doi.org/10.1016/j.ijforecast.2021.10.002
Publication status	Published - 1 Jan 2023

Keywords

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

Access to Document

10.1016/j.ijforecast.2021.10.002

Psaradellis_2021_IJF_Technicalanalysis_AAM
Crown Copyright © 2021 Published by Elsevier B.V. on behalf of International Institute of Forecasters. All rights reserved. This work has been made available online in accordance with publisher policies or with permission. Permission for further reuse of this content should be sought from the publisher or the rights holder. This is the author created accepted manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1016/j.ijforecast.2021.10.002
Accepted author manuscript, 409 KBLicence: CC BY-NC-ND

Cite this

@article{bd79cb04e6524674a2dce82c3dae8e12,

title = "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{\textquoteright} 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",

author = "Ioannis Psaradellis and Jason Laws and Athanasios Pantelous and Georgios Sermpinis",

year = "2023",

month = jan,

day = "1",

doi = "10.1016/j.ijforecast.2021.10.002",

language = "English",

volume = "39",

pages = "178--191",

journal = "International Journal of Forecasting",

issn = "0169-2070",

publisher = "Elsevier",

number = "1",

}

TY - JOUR

T1 - Technical analysis, spread trading, and data snooping control

AU - Psaradellis, Ioannis

AU - Laws, Jason

AU - Pantelous, Athanasios

AU - Sermpinis, Georgios

PY - 2023/1/1

Y1 - 2023/1/1

N2 - 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.

AB - 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.

KW - Technical trading rules

KW - Spread trading predictability

KW - False discovery rate

KW - Bootstrap test

KW - Portfolio performance

UR - https://www.scopus.com/pages/publications/85119923059

U2 - 10.1016/j.ijforecast.2021.10.002

DO - 10.1016/j.ijforecast.2021.10.002

M3 - Article

SN - 0169-2070

VL - 39

SP - 178

EP - 191

JO - International Journal of Forecasting

JF - International Journal of Forecasting

IS - 1

ER -

Technical analysis, spread trading, and data snooping control

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this