A Guide to Portfolio Optimization: Balancing Risk and Return

Portfolio optimization is the process of establishing the best asset mix to generate the desired amount of return while minimizing risk. The goal is to create a portfolio that strikes a balance between the potential for high returns and an acceptable level of risk. In this blog article, we’ll go through the principles of portfolio optimization and provide some recommendations for controlling risk and return.

The first step in portfolio optimization is to determine your investment goals and risk tolerance. This helps you determine how much risk you are willing to tolerate in exchange for greater benefits. Following the determination of your risk tolerance, you may build a diversified portfolio that includes a mix of assets such as stocks, bonds, and cash.

Diversification is essential for risk management in a portfolio. You may limit the influence of any single investment on your whole portfolio by spreading your investments over several asset classes, industries, and geographic locations. This can assist in leveling out returns and minimize overall portfolio volatility.

One way to optimize your portfolio is to use Modern portfolio theory (MPT). MPT is a mathematical framework for constructing a portfolio that maximizes expected return for a given level of risk. The theory assumes that investors are rational and seek to maximize their returns while minimizing their risk. MPT uses statistical measures such as mean and variance to calculate the expected return and risk of a portfolio.

Our Portfolio Manager tool at the Analytical Platform gives our users the ability to quickly optimize, analyze, and backtest their portfolio based on Markowitz’s theory. It helps analysts and portfolio managers to efficiently use their time while generating more alpha for their strategies.

Another technique we use in the Analytical Platform is Factor-based investing. Selecting assets based on particular qualities or “factors” that have been proven to influence returns is what factor-based investing entails. Value, momentum, capitalization, and quality are examples of such factors. Using this approach, investors could boost their profits while limiting risk by picking assets based on these variables.

An advantage of this approach is built on the assumption that analysts aren’t able to predict market timing. But if they are able to evaluate stocks based on factor investing and machine learning, they can build robust strategies, in some cases almost “market neutral”. In the image, you can see the results of various strategies.

In real operations, since 6/2020 we have been running an AP Hedged strategy with an exposition of 60% in the most undervalued stocks and by 40% shorting most overvalued stocks. You can see that this strategy (red column) realized a 15% lower max. drawdown compared to its benchmark S&P 500. If we used strategy with exposition 50/50 and leverage 4.5 we could gain a 68% higher absolute yield with a similar risk in the same period. We could also use the same leverage for the S&P 500 and get a similar yield but a scary 78% maximal drawdown.