Using neural network and genetic programming techniques to forecast inter-commodity spreads

Meng Feng Yen, Tsung Nan Chou, Hung Chih Li, Ying Yue Ho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this article, we use both neural network (hereafter NN) and genetic programming (hereafter GP) 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). A variety of technical indicators are used 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.

Original languageEnglish
Title of host publicationSecond International Conference on Innovative Computing, Information and Control, ICICIC 2007
PublisherIEEE Computer Society
ISBN (Print)0769528821, 9780769528823
DOIs
Publication statusPublished - 2007 Jan 1
Event2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007 - Kumamoto, Japan
Duration: 2007 Sept 52007 Sept 7

Publication series

NameSecond International Conference on Innovative Computing, Information and Control, ICICIC 2007

Other

Other2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007
Country/TerritoryJapan
CityKumamoto
Period07-09-0507-09-07

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Mechanical Engineering

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