価値分配強化学習アルゴリズムに基づくポートフォリオ管理
Yan Yang1, Tian Wang1, Yiding Fu1
1Strategic Development Department Southern Power Grid Capital Holding Co., Ltd., Guangzhou, China.
Frontiers in artificial intelligence
|February 16, 2026
まとめ
この研究は,ポートフォリオ管理のための新しい強化学習アルゴリズムを導入し,リターンを向上させ,リスクを制御します. この新しいアプローチは,現実世界の市場データにおいて,従来の戦略を大幅に上回ります.
科学分野:
- 定量金融 定量金融とは
- 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.
- コンピュータ経済学 コンピュータ経済学
背景:
- 金融市場は,大きな不確実性と複雑さを持っています.
- リスクを管理しながらポートフォリオのリターンを最大化することは,絶え間ない課題です.
研究 の 目的:
- 強化学習を用いた新しいポートフォリオ管理フレームワークを導入する.
- ポートフォリオのリターンを向上させ,リスク管理を改善するために.
主な方法:
- 価値分布最大エントロピーアクター-クリティック (VD-MEAC) の強化学習アルゴリズムが提案されています.
- このフレームワークは,ポートフォリオの重み調整をアクションとして,ストックファクターをステートオブザーブメントとして利用しています.
- リスクマネジメントは,将来のリターンの完全な分布を学習することによって対処され,エントロピーの正規化はリターン向上のために使用されます.
主要な成果:
- 実験は,中国の株式市場の実際の市場データを用いて行われました.
- VD-MEAC戦略は,平均リターン2.490とシャープ比率2.978.8を達成しました.
- 業績は,ベンチマーク戦略を大幅に上回りました.
結論:
- 提案されたVD-MEACのアプローチは,実際のポートフォリオ管理に有効です.
- 結果は,複雑な金融市場におけるアルゴリズムの能力を検証しています.
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