Kazakov, Dimitar Lubomirov orcid.org/0000-0002-0637-8106 and Butler, Matthew Richard (2012) Testing Implications of the Adaptive Market Hypothesis via Computational Intelligence. In: Computational Intelligence for Financial Engineering & Economics (CIFEr), 2012 IEEE Conference on. 2012 IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr 2012), 29-30 Mar 2012 IEEE , USA , pp. 1-8.
Abstract
This study analyzes two implications of the Adaptive Market Hypothesis: variable efficiency and cyclical profitability. These implications are, inter alia, in conflict with the Efficient Market Hypothesis. Variable efficiency has been a popular topic amongst econometric researchers, where a variety of studies have shown that variable efficiency does exist in financial markets based on the metrics utilized. To determine if non-linear dependence increases the accuracy of supervised trading models a GARCH process is simulated and using a sliding window approach the series is tested for non-linear dependence. The results clearly demonstrate that during sub-periods where non-linear dependence is detected the algorithms experience a statistically significant increase in classification accuracy. As for the cyclical profitability of trading rules, the assumption that effectiveness waxes and wanes with the current market environment, is tested using a popular technical indicator, Bollinger Bands (BB), that are converted from static to dynamic using particle swarm optimization (PSO). For a given time period the parameters of the BB are fitted to optimize profitability and then tested in several out-of-sample time periods. The results indicate that on average a particular optimized BB is profitable, active and able to outperform the market index up to 35% of the time. These results clearly indicate the cyclical nature of the effectiveness of a particular trading model and that a technical indicator derived from historical prices can be profitable outside of its training period.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 16 Jul 2014 14:17 |
Last Modified: | 17 Oct 2024 09:00 |
Published Version: | https://doi.org/10.1109/CIFEr.2012.6327799 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/CIFEr.2012.6327799 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:76733 |