Interpretable Machine Learning for Diversified Portfolio Construction | The Journal of Financial Data Science

Interpretable Machine Learning for Diversified Portfolio Construction | The Journal of Financial Data Science

1. Markus Jaeger 1. is a senior investment expert at Munich Re Markets in Munich, Germany. (majaeger{at}munichre.com) 2. Stephan Krügel 1. is a senior investment expert at Munich Re Markets in Munich, Germany. (skruegel{at}munichre.com) 3. Dimitri Marinelli 1. is a financial data scientist and Marie Skłodowska-Curie Fellow at Munich Re Markets in Munich, Germany. (dmarinelli{at}munichre.com) 4. Jochen Papenbrock 1. is the CEO of Firamis GmbH in Oberursel, Germany. (jp{at}firamis.de) 5. Peter Schwendner 1. is the director of the Institute of Wealth & Asset Management at Zurich University of Applied Sciences in Winterthur, Switzerland. (scwp{at}zhaw.ch) <!-- --> 1. To order reprints of this article, please contact David Rowe at d.rowe{at}pageantmedia.com or 646-891-2157. In this article, the authors construct a pipeline to benchmark hierarchical risk parity (HRP) relative to equal risk contribution (ERC) as examples of diversification strategies al

1 mentions: @Maxwell_110
Date: 2021/10/14 22:37

Referring Tweets

@Maxwell_110 資産ポートフォリオ間のカルマーレシオスプレッドを XGB + SHAP で解釈 📝 t.co/BWsFKSJThm MunichRe の研究で,ポートフォリオ戦略間(ERC vs HRP)のスプレッドを利回り等から得られる特徴量を使い XGB で学習し SHAP で解釈 特に Maxdrowdown 関連の特徴量がスプレッドに影響すると報告 t.co/p7WQCNQDwO

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