Inter-Commodity Spread Trading Using Neural Network and Genetic Programming Techniques

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

  author =       "Meng-Feng Yen and Tsung-Nan Chou and Ying-Yue Ho",
  title =        "Inter-Commodity Spread Trading Using Neural Network
                 and Genetic Programming Techniques",
  booktitle =    "Proceedings of the 9th Joint Conference on Information
                 Sciences (JCIS)",
  year =         "2006",
  editor =       "Heng-Da Cheng and Shu-Heng Chen and Ren-Yih Lin",
  publisher =    "Atlantis Press",
  keywords =     "genetic algorithms, genetic programming, BPNN,
                 Inter-Commodity Spread, Momentum Strategy",
  ISBN =         "90-78677-01-5",
  URL =          "",
  DOI =          "doi:10.2991/jcis.2006.165",
  size =         "4 pages",
  abstract =     "We employ the methods of neural network (hereafter NN)
                 and genetic programming (hereafter GP) in this paper to
                 construct a spread trading system, respectively, to
                 forecast the trend of the price spread between Taiwan
                 Stock Exchange Electronic Index Futures (hereafter TE)
                 and Taiwan Stock Exchange Finance Sector Index Futures
                 (hereafter TF). To forecast the trend of the spread, we
                 use a variety of technical indicators as the inputs to
                 our two models. We tend to long one contract and short
                 another if the next-day return of the former is
                 predicted to be larger than the latter. If the spread
                 trend is predicted to change its direction, we close
                 off the position and open a new position completely
                 contrary to the closed one. We compare the trading
                 performances of this momentum strategy to the day trade
                 strategy, i.e. closing off our positions before the
                 market close ever day. We find that the momentum
                 strategy tends to outperform the day trade strategy and
                 that the BPNN model is superior to the GP model under
                 both strategies whilst both are profitable.",
  notes =        "

                 c Atlantis Press. This is an open-access article
                 distributed under the terms of the Creative Commons
                 Attribution License, which permits non-commercial use,
                 distribution and reproduction in any medium, provided
                 the original work is properly cited.",
  bibdate =      "2007-06-08",
  bibsource =    "DBLP,

Genetic Programming entries for Stephane Meng-Feng Yen Tsung-Nan Chou Ying-Yue Ho