インスタンスベース政策最適化による市場の状態変化を考慮したポートフォリオのリバランス

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2016/03/01

タイトル(英語): Portfolio Rebalancing with a Consideration of Market Conditions Changes Using Instance-based Policy Optimization

著者名: 萩原　大地（東京理科大学大学院理工学研究科），原田　拓（東京理科大学理工学部）

著者名(英語): Daichi Hagiwara (Graduate School of Science and Technology, Tokyo University of Science), Taku Harada (Faculty of Science and Technology, Tokyo University of Science)

キーワード: ポートフォリオ最適化，リバランス，インスタンスベース政策，進化計算，遺伝的アルゴリズム　　portfolio optimization，rebalance，instance-based policy，evolutionary computation，genetic algorithms

要約(英語): The portfolio optimization problem involves decisions pertaining to the investment target and proportion of investment in a large number of assets in order to minimize risk and maximize returns. In recent years, metaheuristics methods have been actively applied to portfolio optimization. Under portfolio optimization, the portfolio is optimized for a fixed period of time so that its performance during that period is excellent. However, the optimized portfolio may not be able to sustain that performance later. Therefore, there is a need for recombining assets and changing the proportion of asset allocation by means of rebalancing. The rebalancing has to be done at an appropriate time. In this paper, we propose a technique for dynamic rebalancing of a portfolio at an appropriate time by applying instance-based policy optimization, with a consideration of market conditions changes.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.136 No.3 （2016） 特集：機械学習が拓くシステムイノベーション

本誌掲載ページ: 415-422 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/136/3/136_415/_article/-char/ja/