Bartosz Gebka
The benefits of combining seasonal anomalies and technical trading rules
Gebka, Bartosz; Hudson, Robert S.; Atanasova, Christina V.
Authors
Professor Robert Hudson Robert.Hudson@hull.ac.uk
Emeritus Professor of Finance
Christina V. Atanasova
Abstract
Although many seasonal anomalies and technical trading rules have been shown to have predictive ability, investigations have focused only on them operating individually. We study the benefits of trading based on combinations of three of the best known effects: the moving average rule, the turn of the month effect, and the Halloween effect. We show that the rules can be combined effectively, giving significant levels of returns predictability with low risk and offering the possibility of profitable trading. This new investment approach is especially beneficial for a typical individual investor, who faces high transaction costs and is poorly diversified.
Citation
Gebka, B., Hudson, R. S., & Atanasova, C. V. (2015). The benefits of combining seasonal anomalies and technical trading rules. Finance research letters, 14, 36-44. https://doi.org/10.1016/j.frl.2015.06.001
Journal Article Type | Article |
---|---|
Acceptance Date | Jun 19, 2015 |
Online Publication Date | Jun 26, 2015 |
Publication Date | 2015-08 |
Deposit Date | Jul 28, 2015 |
Publicly Available Date | Jul 28, 2015 |
Journal | Finance research letters |
Print ISSN | 1544-6123 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 14 |
Pages | 36-44 |
DOI | https://doi.org/10.1016/j.frl.2015.06.001 |
Keywords | Technical trading; Calendar anomalies; Stock market predictability; Market efficiency |
Public URL | https://hull-repository.worktribe.com/output/377039 |
Publisher URL | http://www.sciencedirect.com/science/article/pii/S1544612315000628 |
Additional Information | Author's accepted manuscript of article published in: Finance research letters, 2015, v.14 |
Contract Date | Jul 28, 2015 |
Files
Article.pdf
(372 Kb)
PDF
Copyright Statement
© 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
Does high frequency trading affect technical analysis and market efficiency? And if so, how?
(2013)
Journal Article
Automated Machine Learning and Asset Pricing
(2024)
Journal Article
Modelling credit and investment decisions based on AI algorithmic behavioral pathways
(2023)
Journal Article
Downloadable Citations
About Repository@Hull
Administrator e-mail: repository@hull.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search