Reducing Failures in Investment Recommendations using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{JinLi:2000:CEF,
  author =       "Jin Li and Edward P. K. Tsang",
  title =        "Reducing Failures in Investment Recommendations using
                 Genetic Programming",
  booktitle =    "Computing in Economics and Finance",
  year =         "2000",
  address =      "Universitat Pompeu Fabra, Barcelona, Spain",
  month =        "6-8 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/ReducingFailures.pdf",
  URL =          "http://cswww.essex.ac.uk/CSP/finance/papers/LiTsa-LowRF-Cef2000.ps",
  URL =          "http://ideas.repec.org/p/sce/scecf0/332.html",
  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.",
  notes =        "http://enginy.upf.es/SCE/index2.html",
}

Genetic Programming entries for Jin Li Edward P K Tsang

Citations