Investment Decision Making Using FGP: A Case
Study
J. Li, and E. Tsang. Proceedings of the Congress on Evolutionary
Computation, 2, page 1253--1259. Mayflower Hotel, Washington D.C., USA, IEEE Press, (6-9 July 1999)
Abstract
Financial investment decision making is extremely
difficult due to the complexity of the domain. Many
factors could influence the change of share prices. FGP
(Financial Genetic Programming) is a genetic
programming based forecasting system, which is designed
to help users evaluate impact of factors and explore
their interactions in relation to future prices. Users
channel into FGP factors that they believe are relevant
to the prediction. Examples of such factors may include
fundamental factors such as "price-earning ratio",
"inflation rate" or/and technical factors such as
"5-days moving average", "63-days trading range
breakout", etc. FGP uses the power of genetic
programming to generate decision trees through
combination of technical rules with self-adjusted
thresholds. In earlier papers, we have reported how FGP
used well-known technical analysis rules to make
investment decisions. This paper tests the versatility
of FGP by testing it on shorter-term investment
decisions. To evaluat...
%0 Conference Paper
%1 li:1999:IDMUFACS
%A Li, Jin
%A Tsang, Edward P. K.
%B Proceedings of the Congress on Evolutionary
Computation
%C Mayflower Hotel, Washington D.C., USA
%D 1999
%E Angeline, Peter J.
%E Michalewicz, Zbyszek
%E Schoenauer, Marc
%E Yao, Xin
%E Zalzala, Ali
%I IEEE Press
%K algorithms, forecasting genetic programming,
%P 1253--1259
%T Investment Decision Making Using FGP: A Case
Study
%U http://citeseer.ist.psu.edu/237547.html
%V 2
%X Financial investment decision making is extremely
difficult due to the complexity of the domain. Many
factors could influence the change of share prices. FGP
(Financial Genetic Programming) is a genetic
programming based forecasting system, which is designed
to help users evaluate impact of factors and explore
their interactions in relation to future prices. Users
channel into FGP factors that they believe are relevant
to the prediction. Examples of such factors may include
fundamental factors such as "price-earning ratio",
"inflation rate" or/and technical factors such as
"5-days moving average", "63-days trading range
breakout", etc. FGP uses the power of genetic
programming to generate decision trees through
combination of technical rules with self-adjusted
thresholds. In earlier papers, we have reported how FGP
used well-known technical analysis rules to make
investment decisions. This paper tests the versatility
of FGP by testing it on shorter-term investment
decisions. To evaluat...
%@ 0-7803-5537-7 (Microfiche)
@inproceedings{li:1999:IDMUFACS,
abstract = {Financial investment decision making is extremely
difficult due to the complexity of the domain. Many
factors could influence the change of share prices. FGP
(Financial Genetic Programming) is a genetic
programming based forecasting system, which is designed
to help users evaluate impact of factors and explore
their interactions in relation to future prices. Users
channel into FGP factors that they believe are relevant
to the prediction. Examples of such factors may include
fundamental factors such as {"}price-earning ratio{"},
{"}inflation rate{"} or/and technical factors such as
{"}5-days moving average{"}, {"}63-days trading range
breakout{"}, etc. FGP uses the power of genetic
programming to generate decision trees through
combination of technical rules with self-adjusted
thresholds. In earlier papers, we have reported how FGP
used well-known technical analysis rules to make
investment decisions. This paper tests the versatility
of FGP by testing it on shorter-term investment
decisions. To evaluat...},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Mayflower Hotel, Washington D.C., USA},
author = {Li, Jin and Tsang, Edward P. K.},
biburl = {https://www.bibsonomy.org/bibtex/2438252f72d397242408309768526e6ca/brazovayeye},
booktitle = {Proceedings of the Congress on Evolutionary
Computation},
editor = {Angeline, Peter J. and Michalewicz, Zbyszek and Schoenauer, Marc and Yao, Xin and Zalzala, Ali},
interhash = {d09a1aa44632a4edc46fd35372053e08},
intrahash = {438252f72d397242408309768526e6ca},
isbn = {0-7803-5537-7 (Microfiche)},
keywords = {algorithms, forecasting genetic programming,},
month = {6-9 July},
notes = {CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143},
organisation = {Congress on Evolutionary Computation, IEEE / Neural
Networks Council, Evolutionary Programming Society,
Galesia, IEE},
pages = {1253--1259},
publisher = {IEEE Press},
publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA},
timestamp = {2008-06-19T17:45:30.000+0200},
title = {Investment Decision Making Using {FGP}: {A} Case
Study},
url = {http://citeseer.ist.psu.edu/237547.html},
volume = 2,
year = 1999
}