Evolutionary algorithms for financial trading

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Dome Lohpetch",
  title =        "Evolutionary algorithms for financial trading",
  school =       "Mathematical and Computer Sciences, Heriot-Watt
  year =         "2011",
  address =      "UK",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, grammatical
  URL =          "http://www.ros.hw.ac.uk/bitstream/handle/10399/2510/LohpetchD_1111_macs.pdf",
  URL =          "http://hdl.handle.net/10399/2510",
  size =         "273 pages",
  abstract =     "Genetic programming (GP) is increasingly popular as a
                 research tool for applications in finance and
                 economics. One thread in this area is the use of GP to
                 discover effective technical trading rules. In a
                 seminal article, Allen & Karjalainen (1999) used GP to
                 find rules that were profitable, but were nevertheless
                 outperformed by the simple buy and hold trading
                 strategy. Many succeeding attempts have reported
                 similar findings. This represents a clear example of a
                 significant open issue in the field of GP, namely,
                 generalization in GP [78]. The issue of generalisation
                 is that GP solutions may not be general enough,
                 resulting in poor performance on unseen data. There are
                 a small handful of cases in which such work has managed
                 to find rules that outperform buy and hold, but these
                 have tended to be difficult to replicate. Among
                 previous studies, work by Becker & Seshadri (2003) was
                 the most promising one, which showed outperformance of
                 buy-and-hold. In turn, Becker & Seshadri's work had
                 made several modifications to Allen & Karjalainen's
                 work, including the adoption of monthly rather than
                 daily trading. This thesis provides a replicable
                 account of Becker & Seshadri's study, and also shows
                 how further modifications enabled fairly reliable
                 outperformance of buy-and-hold, including the use of a
                 train/test/validate methodology [41] to evolve trading
                 rules with good properties of generalization, and the
                 use of a dynamic form of GP [109] to improve the
                 performance of the algorithm in dynamic environments
                 like financial markets. In addition, we investigate and
                 compare each of daily, weekly and monthly trading; we
                 find that outperformance of buy-and-hold can be
                 achieved even for daily trading, but as we move from
                 monthly to daily trading the performance of evolved
                 rules becomes increasingly dependent on prevailing
                 market conditions. This has clarified that robust
                 outperformance of B&H depends on, mainly, the adoption
                 of a relatively infrequent trading strategy (e.g.
                 monthly), as well as a range of factors that amount to
                 sound engineering of the GP grammar and the validation
                 strategy. Moreover, v we also add a comprehensive study
                 of multiobjective approaches to this investigation with
                 assumption from that, and find that multiobjective
                 strategies provide even more robustness in
                 outperforming B&H, even in the context of more frequent
                 (e.g. weekly) trading decisions. Last, inspired by a
                 number of beneficial aspects of grammatical evolution
                 (GE) and reports on the successful performance of
                 various kinds of its applications, we introduce new
                 approach for (GE) with a new suite of operators
                 resulting in an improvement on GE search compared with
                 standard GE. An empirical test of this new GE approach
                 on various kind of test problems, including financial
                 trading, is provided in this thesis as well.",
  notes =        "Supervisor David Wolfe Corne",

Genetic Programming entries for Dome Lohpetch