Abstract
A new stock price prediction model is proposed based on Genetic Network Programming (GNP), i.e., an evolutionary computation recently developed. In the proposed prediction model, GNP is applied to searching for an optimal combination of two or more appropriate stock price indices, which is different from a conventional GA or GP based stock price prediction model, where GA or GP is usually used as an optimization technique to search for an optimal value of parameters in the stock price index. In this paper, a combination of several indices is shown to be more effective than a single index, because the most effective index usually differs from one brand to another. A series of simulation studies are carried out to confirm the effectiveness of the proposed new model.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the SICE Annual Conference |
| Pages | 1477-1482 |
| Number of pages | 6 |
| Publication status | Published - 2004 |
| Event | SICE Annual Conference 2004 - Sapporo Duration: 2004 Aug 4 → 2004 Aug 6 |
Other
| Other | SICE Annual Conference 2004 |
|---|---|
| City | Sapporo |
| Period | 04/8/4 → 04/8/6 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
A stock price prediction model by using genetic network programming. / Mori, Shigeo; Hirasawa, Kotaro; Furuzuki, Takayuki.
Proceedings of the SICE Annual Conference. 2004. p. 1477-1482 FAI-1-3.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A stock price prediction model by using genetic network programming
AU - Mori, Shigeo
AU - Hirasawa, Kotaro
AU - Furuzuki, Takayuki
PY - 2004
Y1 - 2004
N2 - A new stock price prediction model is proposed based on Genetic Network Programming (GNP), i.e., an evolutionary computation recently developed. In the proposed prediction model, GNP is applied to searching for an optimal combination of two or more appropriate stock price indices, which is different from a conventional GA or GP based stock price prediction model, where GA or GP is usually used as an optimization technique to search for an optimal value of parameters in the stock price index. In this paper, a combination of several indices is shown to be more effective than a single index, because the most effective index usually differs from one brand to another. A series of simulation studies are carried out to confirm the effectiveness of the proposed new model.
AB - A new stock price prediction model is proposed based on Genetic Network Programming (GNP), i.e., an evolutionary computation recently developed. In the proposed prediction model, GNP is applied to searching for an optimal combination of two or more appropriate stock price indices, which is different from a conventional GA or GP based stock price prediction model, where GA or GP is usually used as an optimization technique to search for an optimal value of parameters in the stock price index. In this paper, a combination of several indices is shown to be more effective than a single index, because the most effective index usually differs from one brand to another. A series of simulation studies are carried out to confirm the effectiveness of the proposed new model.
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M3 - Conference contribution
AN - SCOPUS:12744269435
SP - 1477
EP - 1482
BT - Proceedings of the SICE Annual Conference
ER -