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A Machine Learning Approach in Regime-Switching Risk Parity Portfolios
A. Sinem Uysal,
John M. Mulvey
Operations Research & Financial Engineering
Bendheim Center for Finance
Center for Statistics & Machine Learning
Research output
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Contribution to journal
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Article
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peer-review
19
Scopus citations
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Keyphrases
Machine Learning Approach
100%
Regime Switching Risk
100%
Risk Parity Portfolio
100%
Random Forest
50%
United States
50%
Stock Market
50%
Probability Estimates
50%
Asset Allocation
50%
Macroeconomics
50%
Interest Rates
50%
Dynamic Investment
50%
Supervised Learning Algorithms
50%
Out-of-sample Performance
50%
Risk-adjusted Returns
50%
Multi-asset
50%
Risk Parity
50%
Nominal Risk
50%
Forest-based
50%
Asset Tests
50%
Computer Science
Machine Learning Approach
100%
Random Decision Forest
50%
Learning Algorithm
50%
Supervised Learning
50%
Substantial Amount
50%
Asset Allocation
50%
Economics, Econometrics and Finance
Regime Switching
100%
Machine Learning
100%
Macroeconomics
50%
Portfolio Selection
50%
Engineering
Learning Approach
100%
Learning Algorithm
50%
Test Sample
50%
Random Forest
50%