Our asset allocation approach

1. There is a customer-first focus because it’s customer money that’s being looked after, and we want to give them the investment outcomes they need. As such, we’ve aligned our portfolio risk bands with those used by intermediaries.
2. Our process is data-orientated, with my Team working to harness the power of data to help deliver robust returns. For example, we use long-term economic growth forecasts, market data and historical analysis to help understand potential risks and asset correlations.
3. The process is cost-aware, aiming to implement portfolios in a cost-effective way. There’s a focus on, for example, asset-class liquidity and weighing the costs of changing portfolios against the expected benefits.
4. It’s also scalable, as we aim to ensure the process works and grows with us. We’ve developed a three-tier asset allocation framework to drive consistency throughout our fund range, and to enable it to be future-ready.
5. The process is robust, and adaptable to change as and when new ideas develop, or market conditions change. For example, we’ll look beyond traditional public-market investments for opportunities to improve returns and portfolio diversification.

Capital Market Assumptions (CMAs) are our expected return forecasts for a broad range of asset classes. These consider the risks and interactions of asset classes, based on market and economic indicators. Risks analysed include measures of volatility, as well as liquidity, inflation expectations, and country and global risks. As a Team, we’ll challenge our own forecast assumptions by analysing the differences between our assumptions and those of others in the market. For example, comparing if our forecasts are relatively bullish or bearish on certain assets, like international equities or UK bonds.

We then input our forecasts into market-leading software to derive an efficient frontier that shows what assets to hold and in what quantities for various levels of risk tolerances.

An efficient frontier is a concept that helps in portfolio construction. It represents the optimal portfolio positioning in each asset, showing the highest expected returns for each level of risk that has been defined.

We’ve developed a ‘building-block’ approach for the modelling of expected returns. For each asset class, the modelling starts with the risk-free rate for the market and then returns are added to this, considering what an investor would expect to receive for taking the risk of holding each specific asset type. This could include bond default risk for fixed income, or currency risk for international assets.

For each portfolio risk level, an allocation is selected from the efficient frontier. We look to align market exposure as closely as possible to the leading risk profilers in the market, comparing our modelling outputs and methodologies with theirs. This is because we believe that around three-quarters of asset allocation returns are as a result of market exposure levels.

We’ve produced a seven-point sliding-scale risk banding, with the growth assets allocated at broadly equal intervals across a seven-point scale.

We work with third parties to understand forward looking risks, factor exposures, and potential stresses to fine tune our selection and ensure the expected outcomes of the proposed portfolio allocations align with customer expectations at each risk level.