Eugene Botha | Head | Research Hive | Momentum Investments | mail me |
Investment management is fundamentally about improving the odds of success in an environment defined by uncertainty. Market commentary often centres on returns. However, what truly matters to clients is whether their portfolios deliver the outcomes they need, when they need them and within tolerable levels of risk.
Achieving this consistently requires more than intuition or historical precedent. It demands evidence-based approaches such as outcome-based investing frameworks that connect probabilities, risk management, performance drivers and skill to practical decision-making.
Using an outcome-based lens
A foundational shift in thinking begins with how we define and measure risk. Rather than viewing risk as volatility, it is more meaningful to frame it in terms of the probability of achieving specific investment outcomes. This outcome-based lens challenges the conventional pursuit of higher expected returns. That pursuit can inadvertently increase the likelihood of missing client objectives.
By focusing on and measuring the odds of success, investment committees can better align portfolios with real-world goals. This approach enables structured discussions, strategy comparisons on a common scale and a disciplined way to balance growth potential with the need to stay within acceptable risk corridors.
As a result, this reframing transforms decision-making. It clarifies trade-offs, not just between return and risk, but also between the allure of higher growth and the discipline required to maintain a high probability of success.
The central question becomes clear: Are we optimising for headline returns, or for the probability of delivering the results clients truly need? To deliver on this requirement, every investment decision must be measured through a probabilistic lens. Outcome-based investing frameworks make this type of measurement both practical and consistent.
Turning statistics into tangible insights
Building on this foundation, it is important to make downside risk both visible and actionable. Traditional measures like Value-at-Risk (VaR) and Conditional VaR (CVaR) have long carried theoretical value. Yet, their practical application has often remained limited.
To address this, we developed a dedicated VaR dashboard. It integrates six decades of historical returns, Monte Carlo simulations of crisis periods and forward-looking market assumptions. This tool tracks and reports the likelihood and severity of losses across various time horizons, turning abstract statistics into tangible insights.
Importantly, the dashboard recognises the psychological reality that losses are felt more intensely than gains. This principle is rooted in Prospect Theory.
A key innovation is the Client-Experience VaR (CE-VaR). It reflects how investors perceive drawdowns relative to recent portfolio peaks. This client-centred metric bridges the gap between statistical models and investor experience. It informs pre-trade sizing, tactical asset allocation and ongoing portfolio adjustments.
It also enhances transparency and ensures consistency in reporting to oversight committees and clients. Outcome-based investing frameworks strengthen this by aligning statistical insights with client experience.
Monitoring and understanding style exposures
While VaR helps quantify how much risk a portfolio carries, it does not explain why returns behave as they do.
To understand portfolio behaviour, clarity is needed about the styles and factor exposures that drive performance. This is where Returns-Based Style Analysis (RBSA) becomes essential. RBSA uses time series of returns to dynamically quantify how much of an equity asset manager’s behaviour is attributable to systematic factors such as value, momentum and quality.
RBSA offers a scalable and transparent method for tracking whether managers deliver exposures aligned with their philosophical approach. It translates manager behaviour into a consistent factor taxonomy. This process bridges rigorous quantitative models with qualitative manager research. It enables stronger diversification analysis and ensures that style exposures are continuously monitored and understood.
Skill vs luck
Another core piece of the multi-manager investing puzzle is evaluating whether a manager’s performance reflects genuine skill or mere luck. Traditional peer and benchmark comparisons often obscure this distinction.
We address this challenge through the MIPODS framework. It builds on Ron Surz’s original Portfolio Opportunity Distributions (PODS concept).
MIPODS simulates thousands of hypothetical “no-skill” portfolios. These are tailored to each manager’s style constraints, liquidity limitations, and benchmark awareness. The simulations create a bespoke distribution of potential returns that any manager without skill could have achieved by random chance.
By comparing actual performance to this distribution, the investment team can estimate the likelihood that observed returns reflect true skill. MIPODS also models realistic constraints. It controls for turnover frequency and size to avoid stale factor environments. It incorporates transaction costs to produce robust benchmarks.
Real-world case studies illustrate how MIPODS identifies managers who consistently rank in the top quartile relative to their simulated peers. This empowers more effective capital allocation and greater accountability.
Tilting the odds in the investor’s favour
Taken together, these concepts demonstrate how advanced tools can transform investment complexity into structured and actionable insights. Each framework replaces time-consuming, ad hoc processes with disciplined workflows. These workflows accelerate analysis, improve transparency, and increase confidence in decision-making.
Robust portfolio construction is no longer just about selecting asset classes or managers. It is about designing systems that quantify risk precisely, track style exposures continuously, and assess skill objectively.
Markets remain unpredictable, and no framework can eliminate uncertainty. Yet, outcome-based investing frameworks, supported by rigorous measurement and evidence-based decision-making, can decisively tilt the odds in favour of investors.
