%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 }