This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Reducing Failures In Investment Recommendations Using Genetic Programming

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Jin Li (University of Essex)
Edward P. K. Tsang (University of Essex)
Abstract

FGP (Financial Genetic Programming) is a genetic programming based system that specialises in financial forecasting. In the past, we have reported that FGP-1 (the first version of FGP) is capable of producing accurate predictions in a variety of data sets. It can accurately predict whether a required rate of return can be achieved within a user-specified period. This paper reports further development of FGP, which is motivated by realistic needs as described below: a recommendation "not to invest" is often less interesting than a recommendation "to invest". The former leads to no action. If it is wrong, the user loses an investment opportunity, which may not be serious if other investment opportunities are available. On the other hand, a recommendation to invest leads to commitment of funds. If it is wrong, the user fails to achieve the target rate of return. Our objective is to reduce the rate of failure when FGP recommends to invest. In this paper, we present a method of tuning the rate of failure by FGP to reflect the user's preference. This is achieved by introducing a novel constraint-directed fitness function to FGP. The new system, FGP-2, was extensively tested on historical Dow Jones Industrial Average (DJIA) Index. Trained with data from a seven-and-a-half-years period, decision trees generated by FGP-2 were tested on data from a three-and-a-half-years out-of-sample period. Results confirmed that one can tune the rate of failure by adjusting a constraint parameter in FGP-2. Lower failure rate can be achieved at the cost of missing opportunities, but without affecting the overall accuracy of the system. The decision trees generated were further analysed over three sub-periods with down trend, side-way trend and up trend, respectively. Consistent results were achieved. This shows the robustness of FGP-2. We believe there is scope to generalise the constrained fitness function method to other applications.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help file. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://fmwww.bc.edu/cef00/papers/paper332.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 332.

Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Length:
Date of creation: 05 Jul 2000
Date of revision:
Handle: RePEc:sce:scecf0:332

Contact details of provider:
Postal: CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain
Fax: +34 93 542 17 46
Email:
Web page: http://enginy.upf.es/SCE/
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords:

Statistics
Access and download statistics

Did you know? IDEAS also indexes book chapters.

This page was last updated on 2008-12-2.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.