Adaptive efficiency of futures and stock markets : analysis and tests using a genetic programming

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

@PhdThesis{Miles:thesis,
  author =       "Stanley Miles",
  title =        "Adaptive efficiency of futures and stock markets :
                 analysis and tests using a genetic programming",
  school =       "York University",
  year =         "2006",
  address =      "TORONTO, ONTARIO, Canada",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-494-19800-1",
  URL =          "http://search.proquest.com/docview/304985250/F090C35B735040DEPQ/1?accountid=14511",
  URL =          "http://phdtree.org/pdf/25522336-adaptive-efficiency-of-futures-and-stock-markets-analysis-and-tests-using-a-genetic-programming-approach/",
  URL =          "https://www.library.yorku.ca/find/Search/Results?lookfor0[]=Miles&lookfor0[]=&type0[]=Author&join1=AND&lookfor1[]=Genetic+Programming&lookfor1[]=&type1[]=AllFields&mylang=en",
  size =         "328 pages",
  abstract =     "We propose a nonparametric method for finding
                 approximate solutions to dynamic portfolio choice
                 models: the use of genetic programming to directly
                 estimate the optimal trading strategy. After we
                 validate our methodology by conducting a simulation
                 exercise to demonstrate that genetic programming can
                 recover the true analytic solution to two models, we
                 apply it to the path-dependent problem of a futures
                 investor who is subject to initial and maintenance
                 margin constraints, a problem that is difficult to
                 solve using analytic methods. The resulting approximate
                 solution in functional form can be used to complement
                 the Monte Carlo numerical solution to this problem. We
                 proceed to evaluate the performance of our
                 nonparametric approach in the presence of estimation
                 risk and model risk. We apply the algorithm to evolve
                 trading strategies for 10 futures markets and 24 stock
                 markets. We extend the results of recent studies that
                 tested the efficient market hypothesis; these studies
                 investigated whether market participants can find
                 trading rules that use historical data as input that
                 consistently produce abnormally high out-of-sample
                 risk-adjusted returns (indicating that the markets are
                 not efficient). Previous studies were limited to
                 trading rules that returned simple buy/sell signals.
                 Our approach is broader, allowing the study of trading
                 strategies developed under a framework consistent with
                 the standard financial economics model, with a trading
                 strategy defined as the proportion of an investor's
                 total wealth invested into the risky asset (that is, a
                 strategy is a proportion rather than a simple buy/sell
                 signal). The trading strategies evolved by our
                 methodology demonstrate high out-of-sample
                 risk-adjusted fitness for most futures markets, but
                 strategies were produced for only a small fraction of
                 periods because strategies were accepted only if they
                 met criteria for in-sample fitness. Conversely, when
                 our methodology was applied to the stock markets, it
                 produced rules meeting the in-sample fitness criteria
                 for most periods, but the rules were in general
                 characterized by low out-of-sample risk-adjusted
                 fitness. Because of the difficulty of evolving trading
                 strategies that outperformed simple strategies, we
                 conclude that the 10 futures markets and the 24 stock
                 markets examined were adaptively efficient during the
                 1990's and the late 1980's.",
  notes =        "Adaptive efficiency of futures and stock markets:
                 Analysis and tests using a genetic programming
                 approach

                 NR19800",
}

Genetic Programming entries for Stan Miles

Citations