Adaptive efficiency of futures and stock markets : analysis and tests using a genetic programming
Created by W.Langdon from
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 @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 = "9780494198001",

URL = "http://search.proquest.com/docview/304985250/F090C35B735040DEPQ/1?accountid=14511",

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 pathdependent 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 outofsample
riskadjusted 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 outofsample
riskadjusted 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 insample fitness. Conversely, when
our methodology was applied to the stock markets, it
produced rules meeting the insample fitness criteria
for most periods, but the rules were in general
characterized by low outofsample riskadjusted
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