Portfolio optimisation is often presented as a mathematical exercise: feed expected returns, volatilities and correlations into a model, and out comes an “optimal” portfolio.
In theory, Modern Portfolio Theory (MPT) offers a clean, elegant framework for allocating capital efficiently. In practice, professional portfolio construction looks very different.
Real portfolios must operate within real-world constraints including regulatory limits, liquidity rules, client preferences, risk budgets, or other operational realities. The result is a persistent gap between academic optimisation and what advisers, portfolio managers and asset allocators can actually implement.
This is why professional portfolio construction is less about finding a mathematically optimal solution, and more about trying different scenarios and finding a usable one. The challenge is not whether optimisation works, but how to apply it in a way that respects the constraints that define real investment mandates.
Why Unconstrained Portfolio Optimisation Does not Work
Theoretical optimisation vs professional reality
Unconstrained optimisation assumes a world that does not exist, like:
- Frictionless markets with no transaction costs
- Unlimited liquidity
- No regulatory or mandate restrictions
- Continuous rebalancing without operational overhead
When applied mechanically, these models often produce portfolios that look elegant on paper but are impossible to implement.
For example, a mean-variance model may allocate 30–40% to a single low-volatility ETF because it dominates the efficient frontier. In most institutional settings, internal governance would cap that position at 5-10%, immediately invalidating the solution.
The cost of ignoring constraints
Ignoring constraints is not just impractical, but it creates business risks.
- Portfolios that can’t be traded, due to liquidity or market impact
- Client pushback, when allocations conflict with stated preferences
- Reputational risk for advisers, who must justify why an “optimal” portfolio cannot be implemented
In real firms, unconstrained optimisation often generates outputs that require so much manual adjustment that the optimisation step becomes largely symbolic.
The Most Common Constraints in Practice
Several types of constraints often met in advisory practices are:
Asset-level constraints
Typical examples include:
- Minimum and maximum weights (e.g. no holding below 2%, none above 10%)
- Liquidity limits, often expressed as a percentage of average daily volume
- Position size rules, linked to internal risk or governance policies
Without these, optimisation naturally drifts toward extreme concentration.
Portfolio-level constraints
These operate at the aggregate level and include:
- Risk budgets, such as volatility caps or Value-at-Risk limits
- Turnover constraints, to control transaction costs and tax leakage
- Tracking error, particularly for benchmark-aware strategies
- Income targets, common in retirement and liability-driven portfolios
For example, an optimiser may recommend a large shift into small-cap equities for expected return, but a turnover constraint of 15% per rebalance makes that allocation infeasible.
Business & client-driven constraints
These are often the hardest to formalise but the most binding in practice:
- ESG exclusions or tilts
- Client preferences, such as avoiding specific sectors
- Regulatory and mandate limits, particularly in managed funds
This is where professional judgement overrides theoretical efficiency.
How Professionals Approach Constrained Portfolio Optimisation
Once constraints are acknowledged, optimisation becomes a different discipline.
Step 1: Define constraints before optimisation
In professional workflows, constraints are defined first because they shape the solution space entirely. An optimisation problem with 5 constraints and one with 25 constraints are not variations of the same problem, they are fundamentally different problems.
This is why portfolio construction in institutional settings often begins with governance documents, not models.
Step 2: Model trade-offs, not perfect outcomes
There is no single “best” portfolio in a constrained environment. There are only trade-offs:
- Higher return vs higher turnover
- Lower risk vs higher concentration
- ESG compliance vs diversification
Professionals work within acceptable ranges, not theoretical frontiers. Scenario testing matters not because it finds perfection, but because it reveals where portfolios are fragile.
Step 3: Iterate, don’t optimise once
Real portfolios evolve as markets shift, clients update preferences, regulations change. Constrained optimisation is therefore an iterative process, not a one-off calculation. The role of the professional is to continuously rebalance trade-offs as inputs and constraints evolve.
Why Excel Struggles with Constrained Optimisation at Scale
Excel remains deeply embedded in finance for good reason.
What Excel does well
- Small models
- One-off analysis
- Transparent calculations
Where Excel breaks
As constraints increase, Excel becomes fragile:
- Multiple constraints become hard to manage
- Scenario analysis requires manual duplication of work
- Repeatability and auditability deteriorate
- Performance collapses as asset universes grow
At scale, Excel shifts from being a modelling tool to being a source of operational risk.
What to Look for in a Professional Portfolio Optimisation Platform
Professional platforms focus on capabilities, not formulas:
- Flexible constraint handling
- Fast iteration and scenario testing
- Clear visual outputs for governance and clients
- Consistent, auditable workflows
This is exactly the gap professional portfolio optimisation platforms are designed to address: making constrained optimisation usable at scale without turning portfolio construction into spreadsheet engineering.
When Portfolio Optimisation as a Service Makes Sense
Not every firm needs to build optimisation infrastructure internally.
Portfolio Optimisation as a Service (POS) is most useful when:
- Teams are small or capacity-constrained
- Workloads are cyclical
- Firms want outcomes, not tooling
For many firms, the economic question is not “can we optimise?” but “where is our professional time best spent?”
Key Takeaways for Portfolio Professionals
- Constraints are the starting point, not the problem
- Optimisation is about trade-offs, not perfection
- Tools and services exist to reduce time drag
- The real objective is better decisions, faster, not mathematical elegance
Not sure if Portfolio Optimisation as a Service is right for you? Take the Portfolio Time Audit – in less than 5 minutes assess the time drag in your portfolio construction process.
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