Evolutionary Computation in Financial Decision Making

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{Saks:thesis,
  author =       "Philip Saks",
  title =        "Evolutionary Computation in Financial Decision
                 Making",
  school =       "University of Essex",
  year =         "2008",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming, optimisation,
                 trading strategies, market efficiency, intraday data,
                 statistical arbitrage, portfolio construction, foreign
                 exchange and money management",
  URL =          "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495562",
  size =         "354 pages",
  abstract =     "This thesis considers genetic programming (GP) for
                 evolving financial trading strategies. The traditional
                 approach in the literature is to represent a trading
                 strategy, or a program, as a single decision tree. This
                 thesis proposes a general multiple tree framework for
                 dynamic decision making, where evaluation is contingent
                 on the previous output of the program. The conditional
                 multiple tree structure nests the single tree as a
                 special case. Theoretically, it is a superior
                 alternative, but in practice this is not always the
                 case. It depends on the underlying problem, and is
                 basically a manifestation of Ockham's razor (Occam).
                 The framework is validated on artificial data, and
                 hereafter it is applied to two real financial problems:
                 statistical arbitrage and high-frequency foreign
                 exchange trading. In contrast to a pure arbitrage, that
                 guarantees a sure profit, a statistical arbitrage
                 strategy only produces a risk less profit in the limit.
                 Both schemes are self-funding. In this thesis, single
                 and dual trees are used to evolve statistical arbitrage
                 strategies on banking stocks within the Euro Stoxx
                 index. Both single and dual trees are capable of
                 discovering significant statistical arbitrage
                 strategies, even in the presence of a realistic market
                 impact. A finding that points to weak form market
                 inefficiencies. Moreover, it is found that the dual
                 trees provide a more robust response, compared to the
                 single trees, when the market impact is increased. The
                 foreign exchange application considers a novel quad
                 tree structure for evolving trading strategies. Each of
                 the four trees serve different functions, i.e., long
                 entry, long exit, short entry and short exit. Within
                 this framework, the effects of money management are
                 investigated for investors with different utility
                 functions. Money management refers to the way in which
                 practitioners use stop orders to control risk and take
                 profits. Despite being widely used, it is found that
                 money management has a detrimental effect on utility.",
  notes =        "EThOS Persistent ID: uk.bl.ethos.495562 JEL
                 classifications: CO, CI5, C45, C53, C6I, C63, GIl",
}

Genetic Programming entries for Philip Saks

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