EDDIE for Stock Index Options and Futures Arbitrage

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Sheri Markose and Edward Tsang and Hakan Er",
  title =        "EDDIE for Stock Index Options and Futures Arbitrage",
  booktitle =    "Genetic Algorithms and Genetic Programming in
                 Computational Finance",
  publisher =    "Kluwer Academic Press",
  year =         "2002",
  editor =       "Shu-Heng Chen",
  chapter =      "14",
  pages =        "281--308",
  keywords =     "genetic algorithms, genetic programming, Machine
                 Learning, Genetic Decision Trees, Arbitrage, Options
                 Futures, Constraint Satisfaction",
  ISBN =         "0-7923-7601-3",
  URL =          "http://www.springer.com/economics/economic+theory/book/978-0-7923-7601-9",
  DOI =          "doi:10.1007/978-1-4615-0835-9_14",
  abstract =     "EDDIE-ARB (EDDIE stands for Evolutionary Dynamic Data
                 Investment Evaluator) is a genetic program (GP) that
                 implements a cross market arbitrage strategy in a
                 manner that is suitable for online trading. Our
                 benchmark for EDDIE-ARB is the Tucker (1991)
                 put-call-futures (P-C-F) parity condition for detecting
                 arbitrage profits in the index options and futures
                 markets. The latter presents two main problems, (i) The
                 windows for profitable arbitrage opportunities exist
                 for short periods of one to ten minutes, (ii) Prom a
                 large domain of search, annually, fewer than 3percent
                 of these were found to be in the lucrative range of
                 500-800 profits per arbitrage. Standard ex ante
                 analysis of arbitrage suffers from the drawback that
                 the trader awaits a contemporaneous signal for a
                 profitable price misalignment to implement an arbitrage
                 in the same direction. Execution delays imply that this
                 naive strategy may fail. A methodology of random
                 sampling is used to train EDDIE-ARB to pick up the
                 fundamental arbitrage patterns. The further novel
                 aspect of EDDIE-ARB is a constraint satisfaction
                 feature supplementing the fitness function that enables
                 the user to train the GP how not to miss opportunities
                 by learning to satisfy a minimum and maximum set on the
                 number of arbitrage opportunities being sought. Good GP
                 rules generated by EDDIE-ARB are found to make a 3-fold
                 improvement in profitability over the naive ex ante
  notes =        "part of \cite{chen:2002:gagpcf}

                 Also known as: Evolutionary Decision Trees in FTSE-100
                 Index Options and Futures Arbitrage",

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