Evolutionary Arbitrage For FTSE-100 Index Options and Futures

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Sheri Markose and Edward Tsang and Hakan Er and 
                 Abdel Salhi",
  title =        "Evolutionary Arbitrage For FTSE-100 Index Options and
  booktitle =    "Proceedings of the 2001 Congress on Evolutionary
                 Computation CEC2001",
  year =         "2001",
  pages =        "275--282",
  address =      "COEX, World Trade Center, 159 Samseong-dong,
                 Gangnam-gu, Seoul, Korea",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "27-30 " # may,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, FGP, Machine
                 Discovery, Arbitrage, Options, Futures",
  ISBN =         "0-7803-6658-1",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/TsangCEE2001.pdf",
  URL =          "http://privatewww.essex.ac.uk/~scher/eddieProj/TsangCEE2001.doc",
  DOI =          "doi:10.1109/CEC.2001.934401",
  abstract =     "The objective in this paper is to develop and
                 implement FGP-2 (Financial Genetic Programming) on
                 intra daily tick data for stock index options and
                 futures arbitrage in a manner that is suitable for
                 online trading when windows of profitable arbitrage
                 opportunities exist for short periods from one to ten
                 minutes. Our benchmark for FGP-2 is the textbook rule
                 for detecting arbitrage profits. This rule has the
                 drawback that it awaits a contemporaneous profitable
                 signal to implement an arbitrage in the same direction.
                 A novel methodology of randomised sampling is used to
                 train FGP-2 to pick up the fundamental arbitrage
                 patterns. Care is taken to fine tune weights in the
                 fitness function to enhance performance. As arbitrage
                 opportunities are few, missed opportunities can be as
                 costly as wrong recommendations to trade. Unlike
                 conventional genetic programs, FGP-2 has a constraint
                 satisfaction feature supplementing the fitness function
                 that enables the user to train the FGP to specify a
                 minimum and a maximum number of profitable arbitrage
                 opportunities that are being sought. Historical sample
                 data on arbitrage opportunities enables the user to set
                 these minimum and maximum bounds. Good FGP rules for
                 arbitrage are found to make a 3-fold improvement in
                 profitability over the textbook rule. This application
                 demonstrates the success of FGP-2 in its interactive
                 capacity that allows experts to channel their knowledge
                 into machine discovery",
  notes =        "CEC-2001 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 01TH8546C,

                 Library of Congress Number =",

Genetic Programming entries for Sheri M Markose Edward P K Tsang Hakan Er Abdel Salhi