A stock price prediction model by using genetic network programming

Shigeo Mori, Kotaro Hirasawa, Takayuki Furuzuki

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

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 languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
Pages1477-1482
Number of pages6
Publication statusPublished - 2004
EventSICE Annual Conference 2004 - Sapporo
Duration: 2004 Aug 42004 Aug 6

Other

OtherSICE Annual Conference 2004
CitySapporo
Period04/8/404/8/6

Fingerprint

Evolutionary algorithms

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Mori, S., Hirasawa, K., & Furuzuki, T. (2004). A stock price prediction model by using genetic network programming. In Proceedings of the SICE Annual Conference (pp. 1477-1482). [FAI-1-3]

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 proceedingConference contribution

Mori, S, Hirasawa, K & Furuzuki, T 2004, A stock price prediction model by using genetic network programming. in Proceedings of the SICE Annual Conference., FAI-1-3, pp. 1477-1482, SICE Annual Conference 2004, Sapporo, 04/8/4.
Mori S, Hirasawa K, Furuzuki T. A stock price prediction model by using genetic network programming. In Proceedings of the SICE Annual Conference. 2004. p. 1477-1482. FAI-1-3
Mori, Shigeo ; Hirasawa, Kotaro ; Furuzuki, Takayuki. / A stock price prediction model by using genetic network programming. Proceedings of the SICE Annual Conference. 2004. pp. 1477-1482
@inproceedings{0e642423b27d4a188f4c2e6d1882ff10,
title = "A stock price prediction model by using genetic network programming",
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.",
author = "Shigeo Mori and Kotaro Hirasawa and Takayuki Furuzuki",
year = "2004",
language = "English",
pages = "1477--1482",
booktitle = "Proceedings of the SICE Annual Conference",

}

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.

UR - http://www.scopus.com/inward/record.url?scp=12744269435&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=12744269435&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:12744269435

SP - 1477

EP - 1482

BT - Proceedings of the SICE Annual Conference

ER -