--- title: "Portfolio Optimisation Explained: How Investors Can Balance Return and Risk" description: Learn how portfolio optimisation works, including asset allocation, diversification, constraints, risk measures and the trade-offs behind an efficient portfolio. image: https://blog.palance.co/hubfs/alexander-mils-lCPhGxs7pww-unsplash.jpg --- [Skip to main content](https://blog.palance.co/superior-investment-results-portfolio-optimisation-tools#main) [![palance\_primarylockup\_reverse](https://blog.palance.co/hs-fs/hubfs/palance_primarylockup_reverse.png?width=150&height=50&name=palance_primarylockup_reverse.png) ![palance\_primarylockup\_reverse](https://blog.palance.co/hubfs/palance_primarylockup_reverse.svg)](https://www.palance.co) Open main navigation Close main navigation - Search - [Get Started](https://palance.co/) [Blogs](https://blog.palance.co) [Get Started](https://palance.co/) [Blogs](https://blog.palance.co) Search # Portfolio Optimisation Explained: How Investors Can Balance Return and Risk # Portfolio Optimisation Explained: How Investors Can Balance Return and Risk ![Palance](https://blog.palance.co/hs-fs/hubfs/palance_icononly.png?width=50&height=50&name=palance_icononly.png) by [Palance](https://blog.palance.co/author/palance) Oct 16, 2023, 5:17:50 AM Portfolio optimisation is the process of choosing portfolio weights to balance expected return, risk and investment constraints. In theory, optimisation can identify combinations of assets that provide a more attractive risk-return trade-off than holding the assets independently. In practice, the quality of the result depends heavily on the assumptions, data and constraints used. ## What Is Portfolio Optimisation? Portfolio optimisation treats allocation as a problem of combining assets rather than selecting securities one at a time. The investor defines a goal, such as maximising expected return for a given level of risk, and then determines how much to allocate to each asset subject to chosen constraints. The inputs can include expected returns, volatility, correlations, liquidity, minimum and maximum weights and other portfolio requirements. Different inputs produce different solutions, so there is no single “optimal” portfolio without defining the objective first. ## Why Diversification Matters Optimisation works because assets do not move independently. If two investments have imperfect correlation, combining them can reduce portfolio risk relative to simply adding their individual volatilities together. This is the mathematical foundation of diversification. It also explains why the number of holdings is not the key measure. What matters is how the holdings interact, how much each contributes to total risk and whether their economic drivers are genuinely different. ## The Main Inputs | Input | Why it matters | | --- | --- | | Expected return | Sets the return assumption for each asset | | Volatility | Estimates the variability of returns | | Correlation | Measures how assets have historically moved together | | Portfolio constraints | Prevents impractical or unacceptable allocations | | Liquidity | Helps ensure positions can realistically be traded | ## Efficient Portfolios and the Efficient Frontier The efficient frontier describes portfolios that offer the highest expected return for a given level of estimated risk, or the lowest estimated risk for a given expected return. Investors can then choose a point on the frontier that matches their objectives and tolerance for losses. The concept is useful, but it should not be mistaken for a precise forecast. Expected returns and correlations are uncertain, and small changes in assumptions can produce large changes in an optimiser's recommended weights. ## Why Optimisation Can Go Wrong The biggest problem is often **input sensitivity**. If a model expects one asset to outperform another by a small amount, the optimiser may assign a surprisingly large weight because it is attempting to exploit the assumed difference mathematically. Historical data can also be unstable. Relationships that held during one market regime may weaken when inflation, interest rates or liquidity conditions change. A portfolio that looks highly efficient in a backtest can therefore behave very differently in live markets. ## Use Constraints to Make Results More Realistic Practical optimisation usually needs constraints. These can include maximum position sizes, minimum diversification, sector limits, turnover limits, liquidity requirements and restrictions on leverage or short positions. Constraints are not necessarily a weakness. They allow the model to reflect the investor's real-world requirements. An unconstrained mathematical solution may be theoretically attractive while being impossible or undesirable to implement. ## Optimisation vs Simple Asset Allocation Investors do not always need a sophisticated optimiser. A simple strategic allocation can be easier to understand, cheaper to maintain and less sensitive to noisy estimates. The value of optimisation depends on whether the additional complexity improves the decision enough to justify it. For many investors, a useful middle ground is to define target ranges, diversification rules and risk limits, then use analytics to monitor whether the portfolio remains inside them. ## Use Portfolio Analytics to Validate the Result Optimisation should be followed by independent portfolio analysis. Review the proposed allocation using volatility, drawdown, concentration, correlation, sector and geographic exposure rather than relying only on the optimiser's headline score. [Palance's portfolio analytics framework](https://blog.palance.co/understanding-portfolio-analytics) can help investors inspect how the selected assets behave together and identify hidden overlap inside funds and ETFs. ## Stress-Test Before Implementing A portfolio can look attractive under average historical assumptions and still perform poorly in a stressed environment. Test scenarios such as rising rates, equity sell-offs, widening credit spreads, currency moves or higher correlations. The purpose is not to predict the next crisis. It is to determine whether the portfolio remains acceptable when the assumptions behind the optimisation are less favourable. ## A Practical Optimisation Workflow 1. **Define the objective:** set the target return, risk tolerance or other goal. 2. **Build the investable universe:** choose assets that can realistically be held. 3. **Estimate inputs:** use reasonable assumptions for return, volatility and correlations. 4. **Add constraints:** incorporate position, liquidity, turnover and concentration limits. 5. **Optimise:** generate candidate allocations rather than treating one solution as absolute. 6. **Stress-test:** examine downside scenarios and regime changes. 7. **Monitor:** compare the live portfolio with the intended risk and allocation profile. ## Common Mistakes - Treating expected returns as facts rather than uncertain assumptions. - Using too much historical data without considering whether the regime is relevant. - Accepting extreme weights because the optimiser says they are mathematically efficient. - Ignoring transaction costs and liquidity. - Optimising for return while overlooking drawdown tolerance. - Failing to monitor the portfolio after implementation. ## FAQ ### Does portfolio optimisation guarantee better returns? No. Optimisation produces a solution based on assumptions. It cannot guarantee that those assumptions will be correct or that the resulting portfolio will outperform. ### Is optimisation only for institutional investors? No. The concept can be applied at different levels of complexity. Even a simple allocation process using target weights, correlations and risk limits is a form of portfolio construction discipline. ### What is the most important optimisation input? There is no single answer. Expected returns, volatility, correlations and constraints all matter. In practice, understanding the sensitivity of the result to uncertain inputs can be more important than finding a precise estimate. ## Conclusion Portfolio optimisation is most useful when it improves the trade-off between return and risk without creating false precision. Strong implementation begins with a clear objective, realistic constraints, sensible assumptions and stress testing. Optimisation is a decision-support tool, not an oracle. The final portfolio still needs to make economic sense to the investor who owns it. ###### Tags: [Fundamental Investing](https://blog.palance.co/tag/fundamental-investing) ![Palance](https://blog.palance.co/hs-fs/hubfs/palance_icononly.png?width=50&height=50&name=palance_icononly.png) Post by [Palance](https://blog.palance.co/author/palance) Oct 16, 2023, 5:17:50 AM Developing the world's most powerful portfolio intelligence tool. 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