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
As portfolio construction becomes
increasingly driven by models and artificial intelligence, traditional
assumptions about diversification are being challenged.
What once appeared to be a reliable
outcome of optimization is now being exposed as fragile under stress, prompting
risk and investment teams to rethink how resilience is built into portfolios.
According to Alejandro Rodriguez
Dominguez, Head of Quantitative Analysis and Artificial Intelligence at
Miraltabank, the problem lies in how diversification has historically been
treated.
“Traditionally, portfolio
construction is framed as an optimisation problem where diversification is an
indirect outcome of estimated inputs,” he explained.
“From a risk management perspective,
that’s problematic because those inputs are noisy and unstable, and
diversification often looks good in sample but disappears out of sample.”
Instead, Rodriguez Dominguez
advocates reframing portfolio construction as an ensemble learning problem.
In this framework, each asset
represents a hypothesis, while the portfolio itself becomes the aggregation of
multiple predictive views.
“By reframing portfolio construction
as a supervised ensemble learning problem, we make diversification explicit
rather than incidental,” he said.
This shift has significant
implications for risk management. Rather than relying on correlations and
covariance matrices to imply diversification, practitioners can design it
directly into the decision-making process.
“This perspective shifts
diversification from something we estimate to something we engineer,” he noted.
“That makes it much easier to govern, monitor, and explain.”
The distinction becomes particularly
important during periods of market stress. Many portfolios that appear
diversified on paper fail precisely when diversification is needed most.
Rodriguez Dominguez attributes this
to a lack of diversity in decision pathways rather than asset holdings.
“Many portfolios look diversified
because they hold many assets, sectors, or factors, but they are not
diversified in how decisions are made,” he said.
When markets shift into stressed
regimes, correlations tend to converge, and portfolios behave as if they were
concentrated exposures. The ensemble framework offers a different lens.
“The question becomes not ‘Are my
assets uncorrelated?’ but ‘Do my models and signals fail differently under
stress?’” he explained. “That distinction is critical.”
Central to this approach is what
Rodriguez Dominguez describes as the quality–diversity trade-off.
Highly accurate models often rely on
fragile structures that may break down under changing conditions, while more
diverse signals can provide greater stability even if they appear less precise.
