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Performance of robotIQ portfolios in June 2019 - a final look on our live-trial

Persephone Team, July 2019

In March 2017 our team came up with the idea to regularly contrast performance of several robotIQ portfolios with the performance results of Germany’s major commercially available robo-advice offerings. Conveniently, the renowned financial news website “Brokervergleich” was already running a monthly comparison, hence we just hooked up on their regular updates, acquired their analysis setup and added the realized performance of our robotIQ portfolios.

For credibility reasons and to create an unalterable track record, we setup all partaking robotIQ portfolios on the Wikifolio platform. Persephone staff took on the responsibility to timely mimic any real portfolio transactions on Wikifolio.

Model wise we decided to use a “dynamic target risk” asset management approach to control the portfolios. This approach uses the investors’ risk appetite as key parameter on which assets are managed: algorithms construct, monitor and optimize portfolios as to maintain the portfolio risk within a given risk corridor favored by the investor.

The choice of this asset management model has been mainly inspired by a preceding Persephone project, in which we not only developed the greenfield concept for Deutsche Bank’s robo-advice offering “ROBIN”, but additionally validated the models with the authorities and finally managed implementation and go-to-market.  Before we went life with our setup, however, the models developed for ROBIN, which implement such a sophisticated risk targeting approach, have been significantly extended and revised. E.g., our robotIQ portfolios allow for investments in bitcoin and hedging and/ or leveraging through options and futures. Additionally, we included arbitrary asset class constraints or comprehensive ESG requirements which could be required by investors. Due to either regulatory restrictions or complexity concerns neither Deutsche Bank nor any other provider of commercial robo-advisory solutions offers such features today.

After the portfolios were setup in April 2018, we experienced exiting and insightful times. We had months, in which we couldn’t await the next Brokervergleich comparison update to add and to interpret the robotIQ portfolios’ performance. In other months, we knew in advance that robotIQ probably would not keep up with many of the commercial offerings. We struggled with data quality issues, suddenly discontinued warrant pricings, uncommunicated ISIN switches and many other, quite individual incidents – not least due to the Wikifolio platform. Some of our problems were also self-made, such as erroneously bought or sold securities. However, most issues can be attributed to the added complexity through extended asset classes and new instruments.

Learnings and excitement came at a price, too. Running the portfolios, keeping up with transaction recommendations not only on the Wikifolio platform, calculating the comparisons and writing update briefings has been taking quite some efforts. After little more than one full year cycle of monthly comparisons, we therefore decided to continue with a significantly lower intensity. Instead of closing down the entire setup, we will keep on publishing selected robotIQ portfolios on Wikifolio, but at the same time we will refrain from compiling monthly comparisons. You may, however, expect an update every now and then.

Having said this, let us take a closer look at the comparisons of the current – and until further notice last – performance analysis for June 2019.

Below figure shows Brokervergleich’s current publication of the robo-advisor test results for June, in which we have incorporated the performances of the various robotIQ portfolios [1][2][3][4].

robotiq201907

Once again, robotIQ portfolios overall achieved fantastic performance results. Indeed, those robotIQ portfolios with risk levels above 16% have grown more than twice as much than the best commercial offering. The reason for this might be once again the possibility of Bitcoin investments. In this context, it is worth noting, that for all partaking robotIQ portfolios a maximum Bitcoin allocation is strictly limited depending on the chosen risk levels. While this explains the performance jump from 4.1% for the VaR 16% portfolio to 6.4% for the VaR 19% portfolio, this also shows that the remaining robotIQ portfolios without Crypto-currency allocations still significantly outperform the commercial competition.

With stable and robust robotIQ portfolios, such an outperformance has been observed for the third month in a row. This does not come as a surprise given the fact that robotIQ portfolios are managed on a highly individual basis – similar to assets of institutional investors. Such a strong dedication and close tracking of portfolios is difficult to implement for robo-advisors that serve a large number of retail clients. Most offerings do still not even have the possibility of managing assets on an individual portfolio level, but even if they do – such as Deutsche Bank’s ROBIN – the complexity and needed effort prevents the providers to really utilize this feature to its full potential.

Last but not least, measuring up robotIQ’s asset management approach, clearly rather designed for institutional investors, with retail market portfolio management services, in our eyes could be seen as an unfair comparison of apples and oranges. This further bears out our decision to end the public robotIQ trial phase.

[1] Source: Brokervergleich, Franke-Media.net (Leipzig), please see Brokervergleich Testverfahren for a detailed description of the test approach
[2] Performance of robo-advisory offerings calculated by Brokervergleich (see above), all calculations for robotIQ© portfolios based on data from Wikifolio, Ariva, Lang & Schwarz and Persephone-own calculations
[3] robotIQ© portfolios in solid color (VaR 13, 16, 19) have risk characteristics that are similar to the ones of other robo-advice portfolios Brokervergleich has chosen for comparison
[4] Persephone robotIQ© portfolios at www.wikifolio.com

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