Decision-making structures in the field of fund selection and asset allocation in family offices are being viewed with increasing interest in the fund industry. In recent years, many institutions have increasingly expanded their know-how in this area. The importance of scientific methods in the investment process is growing, and the classic conflict between active and passive products is also being intensively discussed. Markus Hill spoke on behalf of IPE Institutional Investment with Jakob von Ganske, Director Investment Consulting and Risk Management, Deutsche Oppenheim Family Office AG, about the in-house fund selection process, strategic asset allocation (SAA) and optimization potential in the area of due diligence.
Hill: What does the asset allocation process in your company look like?
von Ganske: Strategic Asset Allocation (SAA) is the first and most important step in our investment process because this is where the big mistakes are made or avoided. Academic studies show that on average more than 90% of ex-post performance depends on the SAA. The SAA also defines a long-term benchmark against which added value through active management can be measured. Without SAA, no objective measurement of the added value of asset managers or funds can take place. Experience is required to provide SAA advice. In several successive meetings, the client’s individual earnings target and risk tolerance are determined together with the client. Higher profit targets are only compatible with a higher risk, that is the principle. Once a long-term risk/return profile has been found that optimally matches the investor’s risk/return preferences, the second step is to select the individual investment components that best reflect this profile. Only from this point does the actual product selection in the form of funds, asset managers and ETFs begin. This process guarantees a consistent investment decision that is transparent for the client. To determine the risk/return profile, we use a modern and academically sound model that realistically depicts the short and long-term opportunities and risks of a portfolio using Monte Carlo simulations. The distributions of the simulated returns of our asset classes show empirically proven “fat tails” and thus – but also in various other aspects – go far beyond the familiar but outdated Markowitz model.
Hill: What does the fund selection process look like for you?
von Ganske: It depends on the right balance between quantitative and qualitative analysis. Quantitative analysis is complex and involves a lot of statistical craft. It is about more than just taking a look at the performance figures. First of all, it has to be determined whether the funds under review are providing the “right” benchmark, i.e. a benchmark that also corresponds to the investment universe. Often this is not the case; this is known as a “benchmark mismatch”. It must also be investigated whether a fund has generated outperformance because it is strategically defensive (beta less than 1) or offensive (beta greater than 1). Other risk premiums in the area of investment styles – such as value or small-cap for equities – must also be extracted. In a third step, it must then be statistically determined whether luck or skill is present relative to the “true” benchmark created above. Ultimately, the question is: Is the fund performance really due to the manager’s skills? Or was it perhaps luck, a “benchmark mismatch” or even a systematic skimming off of risk premiums. In this case, the same fund performance could perhaps have been bought by the investor at a much lower price using exchange-traded funds (ETFs).
Hill: Then what happens next?
von Ganske: Qualitative analysis is the fourth and last step in the process and is extremely important for identifying weaknesses in the investment process. We specifically look for “deal breakers”, because in our opinion a qualitative analysis can only be neutral or negative. It should never be the basis for positive decisions because a qualitative analysis is far too dependent on the subjective impressions of the analyst. With this approach, we differ strongly from the approach of other fund selectors in the market. To institutionalize the process described here, we have established certain basic rules, i.e. “axioms”, which define the framework of our analysis. First, every active fund needs a benchmark. For us, not naming a benchmark is equivalent to assuming that the fund cannot produce relative added value. The benchmark must reflect the investment universe. For example, a US fund that has 20% European equities and only measures itself against the S&P 500 is considered by us to be non-investable. A fund that measures itself against the MSCI world and holds gold, convertible bonds, EMBI bonds, etc. in its portfolio is not rateable and therefore not investable either. The exception is absolute return funds that measure themselves against an absolute benchmark (e.g. money market + 2% p.a.) and we have strict criteria when a fund is “absolute return” and when it only pretends to be one – keyword “market neutrality”. Secondly, the fund must, as mentioned at the beginning, fit the SAA. Thirdly, the spread of bets is fundamental. A fund that only takes “stock market up or down” bets has no diversification effects and is not investable for us. Fourth, the active added value of the fund manager must be consistent over time. In this way, we avoid funds that have only once or twice in their history made a large successful bet and have been “dumbed down” around the benchmark before and after. The financial market crisis in 2008 serves as a good example because funds and managers were washed to the surface of all rankings that allegedly “foresaw” the financial market crisis and have not generated any active added value since then.
Hill: Why is it important to see asset allocation and fund selection as a process that builds on each other?
von Ganske: Because this is the only way to make risks and potential returns transparent. Knowing the risks of his portfolio is elementary for an investor if only to avoid panic reactions – in the worst case, the investor will otherwise sell at the lowest point and miss the rebound, as was often observed during the financial crisis in 2008. But not only liquid assets are affected. A good SAA determines an optimal asset structure over the client’s total assets and not just over individual small liquid sub-portfolios, which, when aggregated, will usually not match the investor’s preferences. And last but not least, because only in this way can the performance of active managers be measured. This is the only way to identify and disinvest bad managers and allocate to better active managers or even ETFs in their place.
Hill: If you look at alternative approaches to asset allocation and fund selection in the industry – where do you see the potential for optimization?
von Ganske: First, SAA is mostly either ignored by asset managers and banks or only operated in an ad hoc and simplified manner. Very often the “SAA” includes short-term forecasts, which in turn leads the results ad absurdum because the inclusion of short-term forecasts leads to the SAA losing its raison d’être in terms of an objective basis for decision-making and assessment. Apart from our company and a few major players, for example, investors from the insurance sector, hardly anyone takes the trouble to put a modern, academically sound SAA environment free of conflicts of interest on its feet. Banks in particular often work with optimizers based on one-period models à la “Markowitz”, which assume a normal distribution of returns, cannot represent any time-structure effects, and use optimizers that react very sensitively to the estimated parameters. These problems have long been known in academic research. However, without a reasonable and up-to-date SAA consulting, it is impossible to assess which products are advantageous for the client or suit him/her.
Hill: …and further, please continue?
von Ganske: Secondly, that in the asset management world, the performance of the respective funds is often (though with many glowing exceptions) not transparently presented, be it by the concealment of costs, the presentation of composite time series that do not allow for any meaningful statement about the actual fund performance, or even the repeated “adjustment” of the benchmark, depending on which of the funds looks best against which. This is something we struggle with almost every day in fund analysis here. This includes the fact that funds are often recommended – even by fund analysis houses – that do not perform well and could have been better invested in an ETF instead. There are segments in which no reliable active added value is generated and, statistically speaking, cannot be generated at all, because how do you intend to generate any added value at all in an equity universe of a few, sometimes highly correlated, stocks? And yet these active and expensive funds are sold and bought. Here, as a fund analysis house, you should also have the courage to say: “There are no good active products in this segment, take an ETF”.
Hill: Thank you very much for the interview.