Can a corporate network and news sentiment improve portfolio optimization using the Black–Litterman model?

DOI10.1080/14697688.2015.1039865zbMath1406.91406OpenAlexW1944496791MaRDI QIDQ4619505

Germán G. Creamer

Publication date: 6 February 2019

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2015.1039865

zbMATH Keywords

portfolio optimizationsocial networkmachine learningboostingtext analysislink miningblack-litterman model

Mathematics Subject Classification ID

Social networks; opinion dynamics (91D30) Learning and adaptive systems in artificial intelligence (68T05) Portfolio theory (91G10)

Related Items (1)

Editors' foreword

Uses Software

• Pajek
• AdaBoost.MH

Cites Work

• Unnamed Item
• A decision-theoretic generalization of on-line learning and an application to boosting
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Network-based marketing: identifying likely adopters via consumer networks
• Markov logic networks
• 10.1162/jmlr.2003.3.4-5.993
• 10.1162/153244303322753670
• Trading networks
• Collective dynamics of ‘small-world’ networks
• Logistic regression, AdaBoost and Bregman distances

This page was built for publication: Can a corporate network and news sentiment improve portfolio optimization using the Black–Litterman model?