OLZ performance explained: It depends on the market regime

Year after year, quarter after quarter, investors are confronted with the question: Is the return of my active strategy above or below the benchmark? Now, it is in the nature of an active strategy that its performance deviates from the benchmark. Short-term performance alone says little about whether the strategy is suitable for achieving long-term investment objectives. What is important is that the deviation in returns can be plausibly explained, rather than simply occurring by chance.

As an active manager who invests systematically according to strict rules, OLZ can explain the out- or underperformance generated. Using the example of our "OLZ Equity World ex CH Optimized ESG" fund, we illustrate the main drivers of the relative performance of our equity strategies. In doing so, we analyze the period from December 31, 2004 to December 31, 2020.

Market performance and market risk change

It is obvious that market development is of central importance. In phases of negative markets, one is well served by a defensive portfolio, while a riskier orientation benefits when the market performs strongly. The OLZ Minimum Risk Portfolio, as its name suggests, reduces risk and is more defensive in nature. Accordingly, our fund outperforms the benchmark during negative market moves and periods of stress, while it may not fare as well during strong positive markets.

Because of our risk focus, relative performance also depends on market risk. If investor nervousness increases (rising market volatility), our approach generates significantly greater added value than if the markets calm down. The combination of these two factors results in the following four market regimes.

Regime 1 - negative markets, increasing market risk

When prices plummet, investors are usually more nervous and volatility increases as a result. Strong price fluctuations are then the order of the day. This situation occurs in just under 25% of trading days. A good example of this market constellation was the financial crisis of 2007/2008, but stress phases with less severe price declines can also be assigned to this regime. Increased risk awareness sometimes drives investors out of equities or into stocks with less risk. Accordingly, the OLZ Minimum Risk Portfolio can profit in these market phases. The monthly excess return in market regime 1 averaged +2.8%.

Regime 2 - negative markets, decreasing market risk

Price declines with decreasing volatility are much rarer compared to regime 1. Just under 10% of trading days exhibit this characteristic. Regime 2 is most likely to be observed in advanced correction phases, when the trend is still negative but the biggest shocks have already been priced in. The outperformance is lower in this case. In an easing market situation, the stocks selected by the OLZ model with low volatility are less in demand. The monthly excess return in market regime 2 averaged +1.2%. 

Regime 3 - positive markets, increasing market risk

Simultaneously rising prices and risks occur in just under 25% of cases. This can occur, for example, in a "buy the dip" phase. Although latent market risks are omnipresent and certainly cause investors concern, every market correction is also immediately used as a buying opportunity. In this market environment, our minimum-risk portfolio was virtually on par with the benchmark on average. Depending on the investment universe and analysis period, both slight out- and underperformance can occur in regime 3. 

Regime 4 - positive markets, decreasing market risk

The last market constellation is also the most unfavorable for our approach. Rising prices and declining volatility are synonymous with recklessness and euphoria. When risks become less important, stocks and sectors with a more defensive character are less in demand. This was well observed in the aftermath of the Corona crash of spring 2020, with the monthly underperformance in market regime 4 averaging -1.3%. 

No rule without exception

The latter example from 2020 illustrates not only the most unfavorable market regime for our strategy, but also that explaining performance by dissecting market phases also has limitations. While the systematic implementation of our investment concept allows for an expectation of how the minimum variance portfolio will behave relative to the benchmark, we cannot explain everything with the market regime model. Every investment universe has its peculiarities and every market situation its particular circumstances.

In the Corona crisis, risky technology stocks suddenly proved to be safe assets. They benefited from the trend toward digital business models, which was strengthened by the pandemic, lost relatively little in the crash and then enjoyed a veritable price fireworks display. Their already large share of the market capitalization-weighted benchmark increased again - and so did our underweight. The above-average performance of risky technology stocks therefore weighed on the performance of our strategy at a time when the market environment (strongly positive market performance, declining market risk) was already unfavorable.

In the end, the long-term perspective counts

We can explain simply and clearly in which market phases to expect which relative performance. The systematic approach with the market regimes and the predictability offer clients significant advantages. The risk characteristics and relative performance of the OLZ Minimum Risk Strategy are understandable and comprehensible - which puts short-term deviations in returns into perspective. 

If we detach our view from individual market phases and take a long-term perspective, an investor can achieve outperformance on a long-term average with our active strategy. This is an added value offered by the scientifically based OLZ minimum risk approach. Over the last 20 years, an outperformance of +0.6% p.a. (after costs) has been achieved across all market regimes.

Investors can profit in the long term with the OLZ minimum risk investment solution if they develop a good understanding of portfolio characteristics and return deviations.