Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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

Authors
Meng-Feng Yen 0, Tsung-Nan Chou, Ying-Yue Ho
Corresponding Author
Meng-Feng Yen
0National Cheng Kung University
Available Online October 2006.
DOI
https://doi.org/10.2991/jcis.2006.165How to use a DOI?
Keywords
BPNN, Genetic Programming, Inter-Commodity Spread, Momentum Strategy.
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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2006.165How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Meng-Feng Yen
AU  - Tsung-Nan Chou
AU  - Ying-Yue Ho
PY  - 2006/10
DA  - 2006/10
TI  - Inter-Commodity Spread Trading Using Neural Network and Genetic Programming Techniques
PB  - Atlantis Press
SP  - 636
EP  - 639
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.165
DO  - https://doi.org/10.2991/jcis.2006.165
ID  - Yen2006/10
ER  -