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Article
Rethinking Diversification for a Model Driven Investment World
As AI reshapes portfolio construction, traditional diversification is proving fragile. Alejandro Rodriguez Dominguez argues that embedding diversity directly into decision-making frameworks can improve resilience, governance, and out-of-sample performance.
Apr 03, 2026
Alejandro Rodriguez Dominguez, Head of Quantitative Analysis and Artificial Intelligence, Miraltabank
Tags:
Model risk
The views and opinions expressed in this content are those of the thought leader as an individual and are not attributed to CeFPro or any other organization
- Traditional
optimisation often produces fragile diversification that fails under
stress
- Ensemble learning
reframes diversification as something designed, not assumed
- True diversification
depends on diversity of decisions, not just asset holdings
- Quality–diversity
trade-off allows better control of robustness versus accuracy
- Portfolios can
improve out-of-sample performance even with lower predicted returns
- AI models risk
synchronized failure without enforced diversity
- Diversity should be
treated as a measurable governance parameter
- Key risks include
diversity collapse and excessive noise from over-diversification
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