By the same authors

Testing Implications of the Adaptive Market Hypothesis via Computational Intelligence

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Full text download(s)

Published copy (DOI)

Author(s)

Department/unit(s)

Publication details

Title of host publicationComputational Intelligence for Financial Engineering & Economics (CIFEr), 2012 IEEE Conference on
DatePublished - 2012
Pages1-8
PublisherIEEE
Original languageEnglish
ISBN (Electronic)978-1-4673-1801-3
ISBN (Print)978-1-4673-1802-0

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.

Bibliographical note

© 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.

???prize-relations???

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations