書誌事項
- タイトル別名
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- Generating Stock Trading Rules Using Genetic Network Programming with Flag Nodes and Adjustment of Importance Indexes
- フラグ ノード オヨビ ジュウヨウド シヒョウ ノ チョウセイ オ モチイタ イデンテキ ネットワーク プログラミング ニ ヨル カブシキ バイバイ ルール ノ セイセイ
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抄録
Genetic Network Programming (GNP) is an evolutionary algorithm which represents its solutions using graph structures. Since GNP can create quite compact programs and has an implicit memory function, GNP works well especially in dynamic environments. In addition, a study on creating trading rules on stock markets using GNP with Importance Index (GNP-IMX) has been done. IMX is one of the criterions for decision making. However, the values of IMXs must be deteminined by our experience/knowledge. Therefore in this paper, IMXs are adjusted appropriately during the stock trading in order to predict the rise and fall of the stocks. Moreover, newly defined flag nodes are introduced to GNP, which can appropriately judge the current situation of the stock prices, and also contributes to the use of many kinds of nodes in GNP program. In the simulation, programs are evolved using the stock prices of 20 companies. Then the generalization ability is tested and compared with GNP without flag nodes, GNP without IMX adjustment and Buy&Hold.
収録刊行物
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- 電気学会論文誌Ｃ（電子・情報・システム部門誌）
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電気学会論文誌Ｃ（電子・情報・システム部門誌） 128 (9), 1462-1469, 2008
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390001204605816064
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- NII論文ID
- 10024267090
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 9635721
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可