This article uses genetic programming to construct
risk-adjusted, ex ante, optimal, trading rules for the
S&P500 Index and then characterizes the predictive
content of these rules. These results extend previous
results by using risk adjustment selection criteria to
generate ex ante rules with improved performance. There
is, however, no evidence that the rules significantly
outperform the buy-and-hold strategy on a risk-adjusted
basis. Therefore, the results are consistent with
market efficiency. Nevertheless, risk-adjustment
techniques should be seriously considered when
evaluating trading strategies.
%0 Journal Article
%1 neely:2003:IREF
%A Neely, Christopher J.
%D 2003
%J International Review of Economics and Finance
%K Equity Stock Technical Trading algorithms, analysis, genetic price price, programming, rule,
%N 1
%P 69--87
%R doi:10.1016/S1059-0560(02)00129-6
%T Risk-adjusted, ex ante, optimal technical trading
rules in equity markets
%U http://www.sciencedirect.com/science/article/B6W4V-45Y6NP2-2/2/509ab197233d25f28135f96076539edf
%V 12
%X This article uses genetic programming to construct
risk-adjusted, ex ante, optimal, trading rules for the
S&P500 Index and then characterizes the predictive
content of these rules. These results extend previous
results by using risk adjustment selection criteria to
generate ex ante rules with improved performance. There
is, however, no evidence that the rules significantly
outperform the buy-and-hold strategy on a risk-adjusted
basis. Therefore, the results are consistent with
market efficiency. Nevertheless, risk-adjustment
techniques should be seriously considered when
evaluating trading strategies.
@article{neely:2003:IREF,
abstract = {This article uses genetic programming to construct
risk-adjusted, ex ante, optimal, trading rules for the
S&P500 Index and then characterizes the predictive
content of these rules. These results extend previous
results by using risk adjustment selection criteria to
generate ex ante rules with improved performance. There
is, however, no evidence that the rules significantly
outperform the buy-and-hold strategy on a risk-adjusted
basis. Therefore, the results are consistent with
market efficiency. Nevertheless, risk-adjustment
techniques should be seriously considered when
evaluating trading strategies.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Neely, Christopher J.},
biburl = {https://www.bibsonomy.org/bibtex/2f8f6d52cf92b0b3d2261f393a735eee2/brazovayeye},
doi = {doi:10.1016/S1059-0560(02)00129-6},
interhash = {e3733b279f2cfedb2a4089d6021a268c},
intrahash = {f8f6d52cf92b0b3d2261f393a735eee2},
journal = {International Review of Economics and Finance},
keywords = {Equity Stock Technical Trading algorithms, analysis, genetic price price, programming, rule,},
month = {Spring},
notes = {JEL classification codes: G0; G14 Also available as
working paper 1999-015D},
number = 1,
pages = {69--87},
timestamp = {2008-06-19T17:48:10.000+0200},
title = {Risk-adjusted, ex ante, optimal technical trading
rules in equity markets},
url = {http://www.sciencedirect.com/science/article/B6W4V-45Y6NP2-2/2/509ab197233d25f28135f96076539edf},
volume = 12,
year = 2003
}