市場動向のダイナミクスを有する深層のモメンタルネットワーク
1Graduate School of Data Science, Chonnam National University, Gwangju, Republic of Korea.
PloS one
|September 2, 2025
まとめ
この研究は,市場動向データを統合することによって,タイムシリーズ・モメンタム (TSMOM) の取引戦略を強化します. 改善されたモデルは,長期短期記憶 (LSTM) とMTDPスコアを使用して,市場のシフト時により良いパフォーマンスを示しています.
科学分野:
- 定量金融
- 金融における機械学習
- アルゴリズム取引
背景:
- タイムシリーズ・モメンタム (TSMOM) 戦略は,リターン・トレンドの持続性に基づいています.
- ロング・ショート・ターム・メモリー (LSTM) ネットワークはTSMOMを向上させることができるが,市場動向の変化に苦しんでいる.
研究 の 目的:
- TSMOMのパフォーマンスを向上させる 市場動向の大きな変化
- 取引決定の改善のための市場ダイナミクスを組み込むモデルを開発する.
主な方法:
- 短期と長期のシグナルを統合して 監督学習を用いた市場状態の表現にします
- エクストリーム・グラデーション・ブースト (XGBoost) による市場動向予測 (MTDP) スコア.
- LSTMベースの取引戦略のMTDPスコアを適用する.
主要な成果:
- 99のフューチャー (1995-2021) のバックテストは,MTDPスコア統合がシャープ比率を改善したことを示しました.
- 8週間のモメンタルウィンドウは安定期 (1995年−2019年) に優れている.
- 20週間のウィンドウは,極端な低迷期 (例えばCOVID-19) で優れたパフォーマンスを示し,より迅速に回復しました.
結論:
- 市場状況情報 (MTDPスコア) を統合することで,TSMOM戦略が効果的に強化されます.
- 変動する市場での一貫した収益性を確保するために,ダイナミックな動きの調整が不可欠です.
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