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.
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.
| Original language | English |
|---|---|
| Title of host publication | Computational Intelligence for Financial Engineering & Economics (CIFEr), 2012 IEEE Conference on |
| Publisher | IEEE |
| Pages | 1-8 |
| ISBN (Electronic) | 978-1-4673-1801-3 |
| ISBN (Print) | 978-1-4673-1802-0 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 2012 IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr 2012) - New York, United States Duration: 29 Mar 2012 → 30 Mar 2012 |
Conference
| Conference | 2012 IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr 2012) |
|---|---|
| Abbreviated title | CIFEr 2012 |
| Country/Territory | United States |
| City | New York |
| Period | 29/03/12 → 30/03/12 |