FGP: A genetic programming based tool for financial forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{JinLi:thesis,
  author =       "Jin Li",
  title =        "FGP: A genetic programming based tool for financial
                 forecasting",
  school =       "Department of Computer Science, University of Essex",
  year =         "2000",
  address =      "UK",
  month =        "7 " # oct,
  keywords =     "genetic algorithms, genetic programming, Artificial
                 intelligence Artificial intelligence Finance Taxation",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.5643.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.5643",
  URL =          "http://phdtree.org/pdf/25391795-fgp-a-genetic-programming-based-tool-for-financial-forecasting/",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=20&uin=uk.bl.ethos.343550",
  size =         "190 pages",
  abstract =     "Computers-aided financial forecasting has been made
                 possible following continuous increase in machine power
                 at reduced price, increasingly easy access to financial
                 information, and advances in artificial intelligence
                 (AI) techniques. In this thesis, we present a genetic
                 programming based machine-learning tool called FGP
                 (Financial Genetic Programming). We apply FGP to
                 financial forecasting. Two versions of FGP, namely,
                 FGP-1 and FGP-2, have been designed and implemented to
                 address two research goals that we set. FGP-1 is
                 intended to improve prediction accuracy over the
                 predictions given. FGP-2 is aimed at improving
                 prediction precision.

                 Predictions are available to users from different
                 sources. We investigate whether FGP-1 has the
                 capability of improving on them by combining them
                 together. Based on the experiments presented in this
                 thesis, we conclude that FGP-1 is capable of improving
                 the given predictions in terms of prediction accuracy.
                 This partly attributes the capability of FGP-1 of
                 finding positive interactions between the predictions
                 given. However, caution should be excised for choosing
                 its parameters in such applications.

                 Improving prediction precision is highly demanded in
                 financial forecasting. Our investigation is based on a
                 set of specific prediction problems: to predict whether
                 a required rate of return can be achieved within a
                 user-specified period. In order to improve prediction
                 precision, without affecting the overall prediction
                 accuracy much, we invent a novel constrained fitness
                 function, which is used by FGP-2. The effectiveness of
                 FGP-2 is demonstrated and analysed in a variety of
                 prediction tasks and data sets. The constrained fitness
                 function provides the user with a handle to improve
                 prediction precision at the price of missing
                 opportunities.

                 This thesis reports the utility of FGP applications to
                 financial forecasting to a certain extent. As a tool,
                 FGP aims to help users make the best use of information
                 available to them. It may assist the user to make more
                 reliable decisions that would otherwise not be achieved
                 without it.",
  notes =        "Feb 2015 uk.bl.ethos.343550 This thesis is not
                 available from the EThOS service. Please contact the
                 current institution's library directly if you wish to
                 view the thesis.",
}

Genetic Programming entries for Jin Li

Citations