Process

How the book is built.

The portfolio is constructed from explicit, scenario-conditional views of the world. Each asset's expected return is derived from a fair-value model anchored to macro inputs. The allocation that emerges is the one that minimises regret across scenarios, subject to factor and risk constraints.

01 / Scenarios

Three states of the world, weighted by probability.

A small set of forward macro states drives everything downstream. Bear, Base, and Bull are not equal-weighted: the central case carries the bulk of probability mass, with tails sized to reflect current risk distribution around the central case.

Each scenario fixes a coherent 12-month forward state for growth, inflation, policy, and risk appetite. The portfolio is then optimised for blended performance across all three.

Forward macro states
Selected inputs, 12-month forward
Bear
25% probability
Mild recession. Growth negative, unemployment rising, policy easing, risk appetite shrinks.
Base
60% probability
Sub-trend growth, contained inflation, gradual policy normalisation. The central case.
Bull
15% probability
Above-trend growth, inflation tame, policy benign, risk appetite expansive.
Factor Bear Base Bull Unit VIX 32 17 13 level US 10Y Yield 5.1 4.1 3.4 % US GDP YoY -0.3 1.8 2.6 % US Unemployment 5.7 4.3 4.0 % US Policy Rate 2.3 3.4 3.6 % US CPI YoY 1.5 2.5 2.4 %
Illustrative 12-month forward macro inputs. Actual scenarios are refreshed on each portfolio review.
02 / Fair value

Each asset, conditional on the state of the world.

Macro scenarios feed asset-level fair-value models. Equity returns are predicted by an ElasticNet regression on macro levels, year-on-year changes, earnings yield and realised volatility. Credit spreads come from a three-sub-model ensemble: a cycle/labour panel, a rates/curve panel, and a stress panel with data-driven hockey-stick triggers.

The output is a 12-month expected return for every position under every scenario. This is what the optimiser sees, rather than historical averages.

Expected 12-month return by asset and scenario
Illustrative output
-20% -10% 0% +10% +20% US Equity -18% +6% +18% UK Equity -15% +7% +14% Europe Equity -16% +7% +15% US High Yield -7% +8% +10% US Investment Grade -2% +6% +7% UK Gilts +11% +5% -2% Expected 12-month total return Bear Base Bull
Bears reward duration; bulls reward equity beta; credit sits in between. The book's job is to be defensible in all three.
03 / Optimisation

A frontier shaped by regret.

One hundred thousand candidate portfolios are simulated under group and factor constraints. Each is evaluated by its regret (the gap between its outcome and the best feasible outcome) under every scenario. The chosen allocation balances worst-case regret against probability-weighted regret.

The frontier traced below is the locus of regret-best portfolios across CVaR buckets. The selected portfolio is the point on the frontier whose factor exposures, group caps, and target return all bind cleanly.

The regret-efficient frontier
Monte Carlo cloud and selected portfolio
2% 4% 6% 8% 10% 12% 5% 10% 15% 20% 25% 30% CVaR (5%), annualised tail loss Expected return (blended across scenarios) Selected portfolio vol 9.7% · expected return 9.3% Monte Carlo candidates Regret-efficient frontier Selected portfolio
Illustrative scatter. The selected portfolio sits inside the frontier, not on it: factor and group caps bind before the unconstrained maximum is reached.
Target return
cash + 3%
Mandate average
Realised vol
9.7%
Annualised
04 / Factor lens

Risk measured at the factor, not the instrument.

Two assets that look different on a holdings sheet (a high-yield bond and a small-cap equity, say) often share the same underlying risk. The optimiser decomposes every position into its factor exposures and caps how much portfolio variance any single factor can contribute.

This is what stops a "diversified" book from being a leveraged equity bet wearing different labels.

Factor contribution to total portfolio variance
Illustrative decomposition
Factor decomposition 38% 22% 14% 10% US EQUITY 38% US DURATION 22% US CREDIT HY 14% INTL EQUITY 10% INTL DURATION 8% CREDIT IG 5% RESIDUAL 3% No single factor may supply more than its budgeted share of total variance. The 60% cap on US Credit HY is the binding constraint that defines where the selected portfolio sits.
Roncalli-style factor budget. Constraints are linearised against an annualised vol target of 10% and re-checked at every review.

The numbers shown here are illustrative.