Technical analysis, spread trading, and data snooping control

Psaradellis, I., Laws, J., Pantelous, A. A. and Sermpinis, G. (2023) Technical analysis, spread trading, and data snooping control. International Journal of Forecasting, 39(1), pp. 178-191. (doi: 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: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Laws, Mr Jason and Sermpinis, Professor Georgios and Psaradellis, Mr Ioannis
• Authors: Psaradellis, I., Laws, J., Pantelous, A. A., and Sermpinis, G.
• College/School: College of Social Sciences > Adam Smith Business School; College of Social Sciences > Adam Smith Business School > Economics
• Journal Name: International Journal of Forecasting
• Publisher: Elsevier
• ISSN: 0169-2070
• ISSN (Online): 1872-8200
• Published Online: 24 November 2021
• Copyright Holders: Copyright © 2021 Crown Copyright
• First Published: First published in International Journal of Forecasting 39(1): 178-191
• Publisher Policy: Reproduced in accordance with the publisher copyright policy