Many portfolios start as random picks, but patterns soon emerge, offering opportunities to balance risk, enhance returns, and adopt a more intentional approach.
The moment an investor stops thinking in terms of individual stock picks and starts thinking about how each investment behaves within the entire portfolio, the whole game changes. This shift is what leads directly to the idea of portfolio optimisation.
Portfolio optimisation is a method of selecting the best mix of financial assets and securities such as stocks, bonds, cash, real estate, and commodities, for an investment portfolio, with an aim to maximise returns and reduce risks.
Understanding how to optimise a portfolio helps investors to handle uncertainty, control volatility, and build a structure that can perform through different market cycles rather than relying on guesswork or luck.
Read this blog to learn about portfolio optimisation, how to do it, tools available for portfolio optimisation, and much more.
What is Portfolio Optimisation?
Portfolio optimisation is a method of selecting the best mix of financial assets and securities such as stocks, bonds, cash, real estate, and commodities, for an investment portfolio, to achieve the best possible outcome, which can be either maximising expected returns for a given level of risk or minimising risk for a given level of return.
It uses mathematical and computational techniques to balance the trade-off between risk and return, and creates a diversified and efficient portfolio tailored to an investor’s goals and risk acceptance level.
For example, an investor wants to invest ₹10 lakh with a focus on long-term growth with minimal risks. So instead of investing in one single stock or sector, he chose to invest 40% in blue-chip stocks, 20% in mid-cap stocks, 20% in gold ETFs, and 10% in global equities.
This mix aims to keep returns stable even if one part of the market struggles, let’s say, equities fall, so here the bonds and gold may help offset the loss, creating a growth path with respect to the investor’s comfort with risk.
Why Portfolio Optimisation Matters for Investors & Traders
- Risk Management and Diversification: Portfolio optimisation involves distributing the investment value across assets and sectors, which balances risk and returns, and reduces idiosyncratic risk, that is, the risk from a single investment.
- Maximise Returns: It aims to allocate the assets and securities, using models that identify efficient asset mix, in such a way that they effectively contribute to the growth of the portfolio, while managing risks.
- Goal Alignment & Personalisation: The portfolio optimisation process allows the investors to align and personalise their portfolio according to their goals, whether it is long-term wealth creation or consistent income flow, and risk acceptance level.
Key Concepts Behind Optimisation (Risk, Return, Diversification, Correlation)
The portfolio optimisation process involves balancing risk and return by leveraging diversification across assets or sectors to manage correlation between the assets.
- Return: This is the profit or gain that may or will be generated by an investment over a period of time, and the investor’s aim is to maximise it under a given level of risk.
- Risk: It is measured by the volatility or standard deviation of the returns of an asset and represents the uncertainty or potential for financial loss.
- Diversification:.The principle here is ‘don’t put all your eggs in one basket’. It effectively distributes the investment value to lower the overall portfolio risk without sacrificing returns.
- Correlation: It measures how two different assets move in relation to each other. The assets with positive correlation move together, and assets with negative correlation move opposite. Portfolio optimisation combines assets with negative correlations, so that the losses in one area can be offset by gains in another.
Step-by-Step: How to Do Portfolio Optimisation
Let’s learn how to optimise a portfolio with this step-by-step guide:
Step 1: Define Your Investment Objective & Risk Profile
The investors must determine the investment horizon, desired rate of return, and understand their capacity and willingness to endure market volatility. They should also be clear about any constraints, such as the need for liquidity, tax, or other considerations. Investors can even use a stock-market AI to understand their risk level and estimate how much return they need before setting clear goals.
For example, an investor, at the age of 30, wants to build ₹25 lakh in 8 years for a business plan. He wants moderate risk but doesn’t want sharp losses. Also, he avoids illiquid assets because he might need money on short notice. These are the factors that would shape how his portfolio will be optimised.
Step 2: Select Assets & Asset Classes
The next step involves deciding on the asset classes the investors want to use, such as stocks, global stocks, bonds, real estate, and cash equivalents. The investors usually select a mix of different asset classes to minimise risk from one asset class.
Then, they need to select the type of securities within each asset class, for example, HDFC Bank shares, Government of India Bonds, or ICICI Prudential Gold ETF. The number of assets should be manageable for the optimisation process while still providing sufficient diversification.
Step 3: Estimate Returns, Risk & Correlations
The investors will next need to do an estimation of the average return they expect each asset to generate over the investment horizon. The historical averages are a common starting point, though future returns are not guaranteed.
It is also very important to measure the standard deviation of each asset’s returns to understand potential risks. For example, a higher standard deviation will indicate high volatility.
Correlation is the measure of how the returns of different assets move in relation to one another. The assets that move in opposite directions, for example, stocks and bonds, usually have a negative correlation, help to stabilise the portfolio’s returns. If one asset class is performing in a poor way, the other may be performing well.
Step 4: Apply Optimisation Method & Select Weights
This is where the mathematical models come into play. The Modern Portfolio Theory (MPT) is a common method that is used across.
By using the data collected in the third step, the optimisation algorithm calculates all possible combinations of asset weights and plots them on a graph, where the efficient frontier curve represents the set of optimal portfolios, offering the highest possible expected return for each level of risk.
The investors may also use optimisation tools, spreadsheet add-ins such as the Solver function in Excel, or programming libraries such as PyPortfolioOpt by Python, to perform these complex calculations.
Step 5: Monitor, Rebalance & Adjust
Portfolio optimisation isn’t some set-and-forget process. The market dynamics change, and so do the investors’ circumstances.
The investors need to regularly check their portfolio’s performance against its stated goals and the market benchmarks.
Over time, the market movements will cause the asset allocations to drift away from the optimal weights. So, they need to rebalance by selling the assets that have grown and buying assets that have shrunk to return the portfolio to its target allocation.
The investors might need to revisit the first step annually or after major life events, for example, a new job, moving places, or approaching retirement, as the risk tolerance and objectives may change, requiring a new optimisation process.
Tools for Portfolio Optimisation
- Stoxo: Stoxo can help the investors with portfolio optimisation by breaking down messy parts of analysis into clear, usable insights. It can calculate risk, check correlations between assets, show how different combinations of assets change overall volatility, and forecast expected returns. The investor can compare portfolios, test scenarios, and spot imbalances that are easy to miss manually, with simple prompts.
- Trendlyne: It provides a stock analysis with detailed financial visualisations, advanced screeners, and scoring models to evaluate company performance within the Indian market. It also offers a Smart Portfolio tracking feature.
- Jarvis Invest: This is also an AI-based stock market advisor that creates customised, long-term portfolios based on an investor’s risk profile and financial objectives. It continuously monitors the portfolio and provides exit alerts for the underperforming stocks.
Methods of Portfolio Optimisation
Here are some of the widely recognised methods of portfolio optimisation, which determine the best mix of assets to maximise returns at the given level of risk.
Mean-Variance Optimisation (MVO)
Mean-Variance Optimisation (MVO) is the foundation of Modern Portfolio Theory (MPT), which aims to find the optimal asset weights to achieve the highest expected return for a given level of risk or variance, or the lowest risk for a target return, by leveraging the benefits of diversification and assets with low correlations.
Black-Litterman Model
The Black-Litterman Model was designed to overcome some practical problems of MVO, such as its high sensitivity to input estimates. It combines a market equilibrium allocation with the investor’s views on the asset performance to form a more stable and intuitive set of expected returns and portfolio weights.
Risk Parity & Hierarchical Risk Parity
The Risk Parity allocates the investment value in a way that each asset class contributes equally to the total portfolio risk , rather than equally to capital.
The Hierarchical Risk Parity (HRP) is a machine-learning-based approach that uses hierarchical clustering to group similar assets and then assigns weights, resulting in a more effective allocation that is less sensitive to estimation errors than the traditional MVO.
Monte Carlo Simulation & CVaR-based Methods
The Monte Carlo Simulation involves running thousands of random simulations of probable market scenarios to test the effectiveness of a portfolio and forecast investment outcomes.
Conditional Value-at-Risk-based (CVaR-based) methods focus on managing the tail risk, which is the probability of the investment returns deviating from the average by more than three standard deviations. While MVO uses variance as a risk measure, CVaR quantifies the expected loss in the worst-case scenarios, which allows the investors to optimise for downside protection.
Practical Example / Case Study: Optimising a Stock Portfolio (India/Global)
Millennial Banker: Stability, Long-Term Growth, and Low Risk
A 32-year-old banker, Rohit, wants his portfolio to grow slowly and steadily without any sharp fall. His original setup is heavy on names like HDFC Bank, Infosys, and a pharma-focused fund, which makes his portfolio too dependent on the Indian market and a single sector.
He reworks his strategy to make the portfolio more balanced. So, he adds a Nifty 50 ETF, while a flexi-cap fund with holdings such as ICICI Bank, L&T, and TCS brings diversified domestic strength. Also, to reduce his reliance on India alone, he adds a global stock through an S&P 500 ETF like the Motilal Oswal S&P 500 ETF, along with small allocations to sovereign gold bonds and the Bharat Bond ETF.
The result is a portfolio that behaves smoothly and supports the kind of predictable long-term growth he wants.
Gen Z Professional: Building Wealth for the 30s
Jerry, a 24-year-old just starting her corporate career, wants to grow her money a little aggressively but doesn’t want chaos in the process. Her early investments are scattered, with a couple of trendy picks like Zomato and Paytm, having no real structure.
She adopted the idea of portfolio optimisation, which helped her organise things. She added a Nifty Next 50 ETF, which involves companies that grow faster than the largest blue chips. A midcap ETF, Motilal Oswal Nifty Midcap 150 ETF, for long-term growth potential, and a global fund, ICICI Prudential US Bluechip Equity Fund, to widen her opportunities beyond India. To avoid any volatility spirals, she includes a small corporate bond allocation and commits to a monthly SIP.
By this, her portfolio becomes an engine for wealth creation, aligned with her age, ambition, and ability to take more risk.
Common Pitfalls & Limitations of Portfolio Optimisation
- Reliance on Historical Data: The portfolio optimisation models depend on historical data to estimate future returns and risks.
- Estimation Error Magnification: The small errors in input estimations can result in different and unstable portfolio allocations.
- Ignoring Real-World Constraints and Transaction Costs: The standard models usually overlook practical constraints like taxes, liquidity issues, and significant transaction costs.
Bottom line
Portfolio optimisation helps investors turn scattered choices into a structured plan that balances risk and return with clarity. By understanding correlations, picking the right mix of assets, and adjusting over time, investors can build portfolios that stay steady through the changing markets. It shifts investing from guesswork to disciplined decision-making, while supporting long-term growth and financial confidence.
FAQ‘s
Portfolio optimisation is the process of choosing the right mix of assets to maximise returns for a given level of risk or to reduce risk without sacrificing any expected returns.
In order to optimise a portfolio, investors first need to define their goals, pick asset classes, estimate returns and risks, apply an optimisation method to set weights, and rebalance as markets and personal needs change.
The common methods of portfolio optimisation include Mean-Variance Optimisation, the Black-Litterman Model, Risk Parity, Hierarchical Risk Parity, Monte Carlo simulations, and CVaR-based approaches.
Mean-variance optimisation is a method that finds the best asset mix by comparing expected returns with volatility, and helps in identifying portfolios that offer the highest return for each level of risk.
The portfolio optimisation models rely on past data, can be sensitive to small input errors, and usually ignore real-world issues like taxes, liquidity, and transaction costs.
The investors usually rebalance annually or when the market movements cause the asset weights to shift from their targets.
Yes, portfolio optimisation can work for only stocks, by mixing large-caps, mid-caps, sector funds, and international equity to improve diversification and create a stable return path.
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