The principles behind our purpose-led portfolios
Our investment philosophy is grounded in purpose, coherence and rigorous forecasting. Portfolios are built around clearly defined long-term objectives, tested across a range of scenarios and constructed through disciplined asset allocation. We focus on robust ex-ante decision making, recognising uncertainty while relying on evidence, data and repeatable principles.
A portfolio should be constructed around a clearly defined, long-term objective, ideally quantified by future cashflow targets. This allows for meaningful investment decision making, guided by a long-term plan.
A portfolio is a systematic whole and bigger than the sum of its parts, not merely a collection of assets. Portfolios should be explainable, both in terms of its ‘parts’ and as a ‘whole’. As well as asking “why is ‘this’ in the portfolio and what role does it play?” , we should also be able to ask “why is this portfolio right as a whole?”.
Forecasts should be treated with caution, but forecasting is ultimately unavoidable – so it should be done as well as possible. Good forecasting provides the coherent link between the long-term plan and the portfolio, and allows for well-informed return vs risk payoffs. It is not sufficient to simply forecast a central expectation, but also the whole spectrum of outcomes on a probabilistic basis.
An excellent portfolio is one that behaves in-line with ex-ante (before the fact) expectations, not the one that happens to have the highest return in hindsight. Behavioural biases can make high past returns appear attractive, but outcomes are heavily influenced by randomness and luck is not a repeatable strategy. Portfolios should be constructed to meet planned objectives, not to chase apparent past winners.
Asset prices incorporate expectations which reflect all available information, making short-term returns unpredictable. Over longer horizons, limits to arbitrage allow mispricing to persist and gradually correct. We use this principle to identify where long-term returns can be systematically anticipated.
Future returns cannot be assumed to replicate historical averages. We model conditional expected returns, separating the return ‘to equilibrium’ from the return ‘in equilibrium’, using high quality data, established theory, and statistically sound methodologies.
Portfolio risk is most effectively managed through broad exposure across all investable asset classes. In best practice, the asset classes included in a portfolio should meet a practical definition of “investable”: for us this means that they must be able to generate future cashflows and be readily liquidated or transferred at cash value.
Long-term outcomes are determined primarily by asset allocation, not stock selection. Evidence shows most companies underperform their index, and persistent stock-picking success is rare and often indistinguishable from luck. Selecting individual stocks adds uncompensated risk. Portfolios should instead be built through disciplined exposure to broad asset classes and established return drivers, where risk and return can be coherently assessed and managed.
Portfolios should have purpose
A portfolio should be constructed around a clearly defined, long-term objective, ideally quantified by future cashflow targets. This allows for meaningful investment decision making, guided by a long-term plan.
Portfolios should be coherent and explainable
A portfolio is a systematic whole and bigger than the sum of its parts, not merely a collection of assets. Portfolios should be explainable, both in terms of its ‘parts’ and as a ‘whole’. As well as asking “why is ‘this’ in the portfolio and what role does it play?” , we should also be able to ask “why is this portfolio right as a whole?”.
Forecasting is unavoidable, so should be embraced and rigorous
Forecasts should be treated with caution, but forecasting is ultimately unavoidable – so it should be done as well as possible. Good forecasting provides the coherent link between the long-term plan and the portfolio, and allows for well-informed return vs risk payoffs. It is not sufficient to simply forecast a central expectation, but also the whole spectrum of outcomes on a probabilistic basis.
Focus on ex-ante performance, not ex-post outcomes
An excellent portfolio is one that behaves in-line with ex-ante (before the fact) expectations, not the one that happens to have the highest return in hindsight. Behavioural biases can make high past returns appear attractive, but outcomes are heavily influenced by randomness and luck is not a repeatable strategy. Portfolios should be constructed to meet planned objectives, not to chase apparent past winners.
Long-term returns can be modelled, short-term returns are unpredictable
Asset prices incorporate expectations which reflect all available information, making short-term returns unpredictable. Over longer horizons, limits to arbitrage allow mispricing to persist and gradually correct. We use this principle to identify where long-term returns can be systematically anticipated.
Decisions should be evidence based and data-driven
Future returns cannot be assumed to replicate historical averages. We model conditional expected returns, separating the return ‘to equilibrium’ from the return ‘in equilibrium’, using high quality data, established theory, and statistically sound methodologies.
Diversification across asset classes reduces risk
Portfolio risk is most effectively managed through broad exposure across all investable asset classes. In best practice, the asset classes included in a portfolio should meet a practical definition of “investable”: for us this means that they must be able to generate future cashflows and be readily liquidated or transferred at cash value.
Asset class allocation drives outcomes; stock selection rarely adds value
Long-term outcomes are determined primarily by asset allocation, not stock selection. Evidence shows most companies underperform their index, and persistent stock-picking success is rare and often indistinguishable from luck. Selecting individual stocks adds uncompensated risk. Portfolios should instead be built through disciplined exposure to broad asset classes and established return drivers, where risk and return can be coherently assessed and managed.
