The Minimum-Variance Strategy in the Swiss Stock Market – Why Risk Management Is Crucial Here, Too!

This is precisely where the minimum variance approach comes in. Instead of predicting which stocks will generate the highest returns, the approach starts with a more empirically robust question: How can a portfolio be constructed that provides exposure to Swiss equities with the lowest possible overall risk? Technically, this means that the portfolio is optimized to minimize total variance under clearly defined, practical constraints. The key inputs here are not return forecasts, but rather the volatilities of individual securities and their joint movement patterns—that is, correlations. The strategy aims to improve diversification, reduce fluctuations, and support long-term value growth.

Lower-risk stocks generate higher returns than higher-risk stocks

The success of the minimum variance strategy is closely linked to the so-called low-volatility anomaly—or, more generally, the low-risk anomaly. According to classical textbook theory, higher-risk stocks should deliver higher expected returns over the long term—as compensation for the higher risk. Empirically, however, many markets and long-term data show that lower-risk stocks often generate higher returns than higher-risk stocks—while exhibiting significantly lower volatility and losses. This observation ranks among the most important findings of financial market research in recent decades, as it is difficult to reconcile with classical models such as the CAPM and strict interpretations of the efficient market hypothesis.

Although the Swiss market appears defensive at the index level, risk profiles within the SPI nevertheless differ significantly—by sector, business model, and stock-specific behavior—so that the low-risk anomaly can manifest itself here as well. As shown in the figure above, stocks with the lowest volatility (“low-vola”), i.e., those with the historically lowest range of fluctuation in the previous year, have achieved significantly better returns over the long term than high-risk stocks (“high-vola”). 

This pattern was also very pronounced in the Swiss stock market over the shorter term—for example, over the past five years. Here, the Swiss market differs from the U.S. market, where the massive rally in technology stocks (“Magnificent 7,” AI boom) led to riskier stocks (such as Nvidia, Tesla, or Meta) outperforming defensive stocks, contrary to the long-term trend. In the domestic stock market, which includes less risky tech firms, this distortion did not occur, and defensive stocks were able to demonstrate their strength. The chart below illustrates the enormous differences in returns between the various risk classes over the past five years as well.

The minimum-variance approach aims to combine securities in such a way that the overall risk of the portfolio is minimized as much as possible. What is relevant here is the covariance structure of the entire universe—not a one-dimensional ranking based on volatility. To achieve the highest possible quality in the estimation of the covariance matrix, the OLZ minimum variance models therefore use a state-of-the-art estimator—the so-called Quadratic Inverse Shrinkage (QIS) estimator. This significantly reduces estimation errors in the risk parameters, leading to a more efficient and robust minimum-variance portfolio. In addition, liquidity filters, position restrictions, and turnover controls play a crucial role as part of regular portfolio rebalancing.

OLZ Minimum Variance Strategy with a Strong 15-Year Track Record

Although there have been cautious model updates over time to consistently incorporate new scientific findings, OLZ AG has been following this approach in the OLZ Equity Switzerland Optimized ESG Fund for over 15 years. The table below shows that since its inception, the fund has not only achieved an attractive return after costs but has also been able to reduce risk. Today, the OLZ fund is the largest publicly offered fund with a minimum variance focus for Swiss equities. 

Another market segment where a risk-optimized investment strategy can be particularly rewarding is small- and mid-caps. In the Swiss stock market, the SPI Extra represents this investment universe. Historically, companies with small and medium market capitalization have generated higher returns than large caps—an effect known as the size premium. However, academic studies have shown that this excess return can be significantly increased by filtering the riskiest small-cap stocks out of the investment universe. The OLZ Equity Switzerland Small & Mid Cap Optimized ESG Fund pursues precisely this goal through minimum variance optimization of the SPI Extra universe. As the table below shows, the OLZ fund’s performance since launch has far outperformed the benchmark index, the SPI Extra. In the current Citywire ranking, the OLZ Equity Switzerland Small & Mid Cap Optimized ESG therefore ranks first over three years in terms of return, volatility, and maximum loss[1] .

Conclusion: Minimum-variance portfolios deliver attractive equity returns with less risk

The minimum-variance approach offers a systematic way to reduce volatility and drawdowns within the SPI or SPI Extra universe without sacrificing the long-term equity market premium. While the approach cannot outperform in every phase—during strong rallies, a defensive portfolio will tend to lag behind the benchmark— However, over a full cycle, the goal of providing Swiss equities with a more efficient risk profile and a more stable return path is achievable—the OLZ funds prove this! For investors who take not only the return but also the path to it seriously, this approach is highly relevant in a world marked by uncertainty.

[1] See: https://citywire.com/ch/sector/equities-switzerland-small-and-medium-companies/i1569?periodMonths=36, website accessed on March 10, 2026