Most portfolios today are built using a mix of models, heuristics, and manual adjustments. They often look well-diversified, but there’s limited visibility into whether they are truly efficient.

This creates two practical challenges:

• Uncertainty in decisions - even experienced advisers may not know if a better allocation exists
• Difficult client conversations - hard to clearly justify why this portfolio vs alternatives

This case study shows how a different optimisation approach can address both.

This type of optimisation approach is most relevant for:

• client portfolio reviews
• rebalancing decisions
• model portfolio adjustments
• improving existing allocations (not rebuilding from scratch)

Instead of asking “Is this portfolio acceptable?" it allows you to ask “Is this portfolio as efficient as it could be?”

✅ Based on data available at the time

✅ Same investment universe and constraints

✅ Forward performance tracked over time

❌ Not a retrofitted backtest

❌ Not a guarantee of future performance