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“With our approach, we hope to find these funds before they appear on the radar of database-oriented investors” (Interview – Jakob von Ganske)

Decision-making structures in the area of fund selection and asset allocation in the family office sector are viewed with great interest by product providers as well as investors in the fund industry. The know-how in the family office sector is also being continuously expanded. The importance of scientific methods in the investment process has become increasingly widespread, and the classic conflict between active and passive products is often viewed with greater composure by investors. Markus Hill spoke on behalf of FONDSBOUTIQUEN.DE with Jakob von Ganske, Head of Investment Consulting and Risk Management and member of the extended management board, 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 (long-only managers, emerging markets, absolute return, ETFs, etc.).

Hill: How does the fund selection process look like in your company?

von Ganske: The strategic asset allocation is also the first and most important step in our fund selection process. It defines the benchmark against which the added value of our active management will be measured – without SAA no objective measurement of active added value can be made. We then proceed with the actual fund selection in four steps: the first step is a very detailed analysis of the respective peer group and a compilation of the shortlist. The second step involves the analysis and selection of individual funds from this shortlist, whereby several active funds are always selected simultaneously for each asset class. The qualitative analysis as a third step serves to find “deal breakers”, i.e. problems in the investment process of the respective candidates which, despite good quantitative results, lead to the fact that we do not invest. The fourth step is then portfolio construction, in which we create an optimally diversified portfolio based on a risk weighting of the respective alpha sources.

Hill: So qualitative analysis in your company is more like searching for problems?

von Ganske: That is correct. 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 much 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.

Jakob von Ganske, Head of Investment Consulting and Risk Management and member of the extended management board, Deutsche Oppenheim Family Office AG
Jakob von Ganske, Head of Investment Consulting and Risk Management and member of the extended management board, Deutsche Oppenheim Family Office AG

Hill: In our last interview in February 2016, you mentioned that quantitative analysis is a very important part of your fund selection process. Has anything changed since then?

von Ganske: Quantitative analysis has become even more important. Specifically, we have invested a great deal of development work in assessing the respective peer groups, i.e. step 1 in our investment process. The goal was to achieve maximum diversification already when the shortlist was created. Risk management is already the main focus during the preparation of the shortlist. What is meant by this? Well, the basic academic assumption regarding active management is that all active funds have more or less uncorrelated alpha sources. All managers invest independently and all investment processes are significantly different from each other. This would mean that a fund selector only needs to buy enough active funds to get a diversified fund portfolio. Unfortunately, however, this assumption can be empirically refuted – many fund managers have similar investment philosophies and will, therefore, show very similar performance against the benchmark in certain market phases, both in a positive and negative sense. We eliminate most of these cluster risks through our investment approach. A wonderful example was 2016 in Emerging Markets equities: several active funds, all of which had generated outstanding outperformance in previous years, also all produced a high level of underperformance in 2016. For the most part, the funds had the same source of alpha, i.e. the same yield-driving factor – in this case, a factor that can be interpreted as growth.

Hill: How do you know which driver will be the profit driver in the future?

von Ganske: We simply do not know. It could be the US dollar – or momentum, value, size, sustainability, commodities, a combination of these, or some other factor not yet observed. Besides, many of the factors mentioned above are also correlated with each other, so it is not possible to make a clear distinction between the factors. Unfortunately, the quantitative fact is that most return drivers are not observable, i.e. cannot be interpreted reliably. A risk management approach must therefore explicitly take into account that neither the factor nor its influence is known. An analysis of the equities in an active fund is therefore not sufficient to say which drivers are the source of alpha and therefore, cluster risks lie dormant. Purely qualitative analyses or simple quantitative analyses are rather useless here, as in many other cases, especially as the fund universe contains a lot of funds in individual segments, i.e. there are hundreds of thousands of possible portfolio combinations. Thus, a fund selector has the additional problem that he has to apply some kind of filter to narrow down the universe – here, mostly “by eye” or using databases that provide star-based preliminary rankings. Unfortunately, today’s 5-star funds are usually tomorrow’s 3-star funds.

Hill: What is your proposal to solve this risk management problem?

von Ganske: There are concepts in the field of statistics that explicitly assume that the factors driving returns are neither known nor observable. Thus the first problem can be addressed. Some of these concepts come from the field of Big Data algorithms and are therefore also excellently suited to deal with a large number of active funds. We use one of these methods.

Hill: What does that look like in concrete terms?

von Ganske: We use the entire fund universe for one asset class, let’s say European equities, and only sort out funds that do not achieve too little volume, a too-short data history or other relatively unrestrictive parameters. Depending on the segment, we then receive 20 to 200 active funds of different types and performance. We then use Big Data algorithms to extract alpha drivers from the alphas of these active funds for the benchmark (in this case the MSCI Europe). These alpha drivers need not be known or observable – let’s call them alpha factors for the sake of simplicity. These alpha factors are uncorrelated by construction. Once we have extracted these alpha factors, we divide the fund universe into those funds whose alphas correlate most closely with alpha-factor 1, those whose alphas correlate most closely with alpha-factor 2, and so on. This gives us a certain number of subgroups to which active funds are assigned. The fund alphas within these subgroups do correlate with each other. However, the fund alphas in one group mustn’t correlate with the fund alphas of the other subgroups. This means that maximum diversification is already the goal when the shortlist is created.

Hill: Then what happens next?

von Ganske: The funds in each subgroup are then analyzed individually. We use our established quantitative analysis process to find the best fund from each subgroup in our opinion. We already discussed our analysis process in our last interview, it is largely unchanged. In summary, the common thread is as follows: if, for example, we have determined 4 alpha factors, we end up with 4 active funds in which we invest simultaneously, weighted according to risk contribution. The alpha sources of these 4 funds are uncorrelated to each other by the construction of our approach. This ensures a maximum of diversification. At the same time, we select active funds that have shown historically positive and significant alpha potential – but based on different investment approaches, that is the crux of the matter.

Hill: What is the advantage of this approach?

von Ganske: Maximum diversification of alpha sources and management skills. With this approach, we also implicitly avoid that funds are invested only because they have performed well in recent years and are therefore in the top quartile of relevant databases. What most of these top-quartile funds have in common is that they were so good because they all had a similar investment style. With our approach, we also invest explicitly in funds that have outperformed the benchmark, but which have tended to underperform in relevant databases, e.g. were in the second quantile of their peer group. It is easy to observe that funds that have received a high rating due to outstanding performance tend to generate a poor performance in the following years. At the same time, historically there are a lot of funds that only had three or four stars at any given time, but which had such a good performance in the following years that they were “upgraded”. With our approach, we hope to find these funds before they appear on the radar of database-oriented investors.

Hill: How do you deal with the topic of selecting absolute return managers in your company?

von Ganske: In the absolute return segment, an analysis of cluster risks is even more crucial than for long-only funds. This is because we believe that absolute return must be market-independent if it is to have a right to exist. There must, therefore, be no sensitivity to risk premiums, and we attach great importance to this. Such market independence can be verified excellently using principal component analysis, in addition to the use of traditional regression analyses.

Hill: Do you still use passive funds and ETFs?

von Ganske: A clear yes from our side. Meanwhile, depending on the mandate, at least 25% is invested in passive instruments, for example, but not only in the segment US equities, Euro government bonds, or also emerging market bonds. We only look for active managers in asset classes that have a proven alpha potential, for example, European equities, emerging market equities, or European corporate bonds. We also have investors who want an almost entirely passive representation at reasonable prices. We have developed a systematic approach to search and find best-in-class ETFs for a variety of investment segments to be able to meet such client requirements. I have reason to believe that we are among the pioneers in Germany in this respect. Many asset managers and fund of funds providers fail to face the reality that passive management will increasingly displace active managers. Only in the area of less efficient markets, such as in developing countries, as well as in the mixed fund sector, will it be possible to generate added value against passive concepts in the future. And it is crucial to select the right active funds.

Hill: Thank you very much for the interview.


*) Markus Hill is an independent asset management consultant in Frankfurt am Main. Contact: info@markus-hill.com; website: www.markus-hill.com

Link: You can find out more about Deutsche Oppenheim Family Office AG on the web at www.deutsche-oppenheim.de

Source: www.institutional-investment.de
Photo: www.pixabay.com

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