Expected performance

Expected performance of a financial portfolio

The expected performance of a financial portfolio represents a reasoned estimate of how the value of the investment could evolve over time. In other words, it indicates what future returns can be expected, taking into account the historical performance of the markets and the specific characteristics of the portfolio. Because financial markets are subject to uncertainty, expected performance is neither a promise nor a guarantee, but rather a probabilistic indication based on assumptions and available data.

To evaluate and understand the expected performance of a portfolio, various analysis tools are used, each with a particular focus:

• Monte Carlo Simulation: A statistical method that generates thousands of possible scenarios to estimate the range of future outcomes of a portfolio. Find out more on the Monte Carlo page.
• Ibbotson Cone: A cone-shaped graphic representation that illustrates the expected evolution of an investment, highlighting the median scenario and the best and worst outcomes. See Ibbotson Cone.
• Efficient frontier: The concept of modern portfolio theory that, for each level of risk, there is an optimal combination of assets that maximizes the expected return. See Efficient Frontier.

In the following paragraphs we will see in more detail what expected performance is and how each of these tools contributes to evaluating it in a rigorous but understandable way.

What is expected performance

The expected performance of a portfolio is the estimate of the future return that an investor could obtain over a given time horizon. In practice it is equivalent to asking yourself: “How much could my portfolio be worth in X years’ time, considering expected market conditions?”. This measure is typically based on metrics such as average expected return (e.g. expected annual percentage growth) and volatility (which indicates how much returns can fluctuate around the average).

It is important to understand that expected performance is a probabilistic average value, not a certainty. For example, let’s say we have a portfolio with an expected return of 5% per year and a volatility of 10%. On average we would expect annual growth of around +5%, but the actual results in a single year could vary significantly: in a favorable scenario the portfolio could return +20%, while in an unfavorable year it could record -10%. Over a longer horizon these differences amplify. After 10 years, the central estimate could indicate a value almost doubled. However, more optimistic scenarios would show capital well over double, while pessimistic scenarios could even end in an overall loss.

In summary, talking about expected performance means thinking in terms of probability distribution of possible returns. A single number offers only part of the story. For a complete view, the uncertainty around that value must also be considered. And this is precisely where tools such as Monte Carlo simulation, the Ibbotson cone and the efficient frontier come into play.

Monte Carlo simulation

Monte Carlo simulation is a powerful tool for evaluating expected performance because it allows you to explore thousands of possible portfolio futures. Instead of a single forecast, it generates different scenarios respecting the statistical characteristics of the portfolio (expected return, volatility and correlations between assets). In the context of portfolio management, this helps quantify the probability of achieving objectives, estimate worst case outcomes, and evaluate the robustness of a strategy in different market conditions.

Example: If your goal is EUR 200,000 in 10 years, the simulation may indicate that you reach it in 70% of the scenarios, but that in 10% of cases you could stay below EUR 150,000. This helps calibrate expectations and risk.

The results are visualized as a range of trajectories and a distribution of final outcomes, offering an immediate read on risk and uncertainty. For further information, see the Monte Carlo page.

Ibbotson cone

The Ibbotson cone is a graphical method for representing the expected evolution of an investment over time, relating expected return and risk (volatility) over different time horizons. The results area widens as time passes, intuitively showing the risk/return trade-off. In portfolio management, this helps to set realistic expectations and evaluate whether the expected trajectory is consistent with long-term goals.

A practical example: over a 10-year horizon, the cone can show a median scenario of constant growth, but also a lower band with drawdown periods and an upper band with higher returns. This helps you assess whether the width of the cone is compatible with your goals and risk tolerance.

The graph shows a median scenario and better or worse outcome bands, useful for understanding the extent of uncertainty. For a complete discussion, see Ibbotson Cone.

Modern portfolio theory and the efficient frontier

Modern Portfolio Theory (MPT) introduces the efficient frontier, a curve that represents optimal portfolios: for each level of risk, the expected return is the maximum possible. From a portfolio management perspective, the frontier serves as a decision-making reference: it helps to verify whether the allocation is consistent with the risk profile and whether more efficient combinations exist.

Example: If your portfolio has a volatility of 10% but offers a lower expected return than a portfolio on the frontier with the same risk, you could improve efficiency simply by rebalancing allocations.

Diversification plays a central role: combining assets with different correlations allows you to improve the risk/return ratio compared to individual instruments. Find out more on the Efficient frontier page.

Conclusions

Tools like Monte Carlo, Ibbotson cone and efficient frontier transform expected performance from a single number to a comprehensive view of scenarios, probabilities and optimization. Understanding both aspects helps you evaluate expectations realistically and make informed decisions to manage and optimize your portfolio. To compare these estimates with actual results, see Realized performance. For metric definitions, see the Wallible Metrics Guide.