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, liquidity and risk constraints, with a rules-based overlay that moves it towards cash in stress.
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 the current distribution of risk around it.
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, and the scenarios are refreshed at each review.
Macro scenarios feed asset-level fair-value models. Equity returns are estimated from macro levels and changes, valuation and realised volatility. Credit spreads come from an ensemble of sub-models covering the cycle and labour market, the rates curve, and a stress component with data-driven triggers.
The output is a 12-month expected return for every asset in the universe under every scenario. This is what the optimiser sees, rather than historical averages.
A large set of candidate portfolios is 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 result is a portfolio that does not depend on the central case being right. It is built to be acceptable in the state that does not happen as well as the one that does.
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.
Because the portfolio is held against cash that may be recalled, liquidity is a constraint in its own right. Candidate portfolios are scored on their cost to liquidate under stress, and that cost is an input to selection rather than something checked afterwards.
A rules-based drawdown overlay sits on top of the optimised portfolio. Once the strategy falls a set distance from its peak, the portfolio is derisked into cash in steps, reaching full cash at a second threshold. The thresholds are fixed in advance and applied mechanically.
The trigger watches a shadow version of the strategy that never derisks. Because the shadow recovers with the market, it brings the portfolio back in rather than leaving it in cash, and a trend gate forces re-entry once the shadow is back above its moving average, even if the drawdown has not fully healed.
Every recommendation is signed off by the Chief Investment Officer, and any deviation from the model’s output is documented with its rationale. Day-to-day risk oversight sits with the Portfolio Manager and the Chief Operating Officer, separately from the CIO role.
Limits are monitored independently by the risk team at Capricorn Fund Managers, the firm’s principal, which also approves marketing materials and sets the compliance framework. Breaches are escalated to the CIO immediately and to Capricorn and the client within a day.