By the same authors

Testing Implications of the Adaptive Market Hypothesis via Computational Intelligence

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Publication details

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


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

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