apeiron protect

Our risk management technology

In order to maintain optimal portfolio conditions in a constantly changing market, we have developed our proprietary risk management algorithm apeironprotect. It ensures that your portfolio is protected from unexpected fluctuations at all times by rule-based rebalancing. If individual components of your portfolio should fluctuate too much, our algorithm restores the target allocation again.
For this purpose, securities that are relatively expensive are sold and other securities that are relatively low priced are bought. This makes your portfolio less susceptible to fluctuations and keeps you optimally diversified at all times.

Optimal diversification

Your portfolio is constantly monitored by our algorithm. So you always remain optimally diversified.

Lower risk

Thanks to our risk management algorithm apeironprotect, your portfolio has a lower long-term risk.

returns icon cash

Higher returns

Because of our anti-cyclical rebalancing, you benefit from the scientifically proven rebalancing bonus.

Superior concept

Better returns, lower risk – our approach is clearly superior to a value-at-risk approach.

apeironprotect ensures diversification

Even if a portfolio is balanced at the beginning, the weights of the individual securities change over time due to market fluctuations. Building blocks, which were originally only slightly mixed with the portfolio, can thus take on a significantly higher weight over the course of time. Anyone who simply buys his securities and then leaves them in the custody account, as in the classic buy-and-hold approach, is taking on unnecessary risk.

Simulated portfolio developments show that the weights of individual portfolio components can fluctuate significantly over time. Such misallocations can be avoided by rebalancing.

It turns out that annual rebalancing leads to significantly more stable portfolio weightings. With our risk management algorithm apeironprotect, we ensure that your portfolio is balanced and diversified at all times and that the weights remain within a specified range.

Ginmon will automatically rebalance your portfolio when needed so you never have to take care of it yourself.

Rebalancing helps you not to fall for classic investing errors such as buying high and selling low.

Lower risk due to rebalancing

A rule-based rebalancing process is important to keep the risk level of a portfolio constant.

The risk profile of buy-and-hold portfolios tends to worsen over time, while that of rebalancing portfolios is more likely to stay close to the optimum. Our simulation shows that the risk of a buy-and-hold portfolio has risen significantly after ten years compared to a portfolio with annual rebalancing.
Ginmon uses a large variety of different investment building blocks in its portfolios. A regular rebalancing with such a large number of securities would be very time-consuming for investors. Here the benefits of Ginmon’s technology-based investment approach become very clear. Because of our algorithm apeironprotect we are able to do this more efficiently than a human could.

Portfolio volatility after ten years with and without rebalancing

information icon whiteThe graph shows the median of twenty simulated ex-ante portfolio volatilities after ten years

Simulated portfolio volatilities of a buy and hold portfolio over 10 years**

information icon whiteThe graph shows the development of twenty simulated ex-ante portfolio volatilities for the hypothetical buy-and-hold portfolio

Risk here means the ex-ante volatility of a hypothetical portfolio consisting of 60% equities and 40% bonds. As an approximation, the Vanguard Total Stock Market ETF (VTI) for the equity component and the Vanguard Total Bond Market ETF (BND) for the bond component were chosen. The volatilities were calculated on the basis of simulated monthly returns over a ten-year period, assuming normally distributed annual returns. The monthly returns are calculated from the assumed annual returns. Assumptions for expected annual return, volatility and correlation between VTI and BND are based on data from Portfolio Visualizer from April 2009 to July 2018. The simulation results are for illustrative purposes only and do not reflect real portfolios.

Empirical research explains “rebalancing bonus”

Anti-cyclical rebalancing can not only reduce the risk of your portfolio, but also bring additional returns.

This additional return is known in science as a “rebalancing bonus”. Various scientific studies have empirically shown that rule-based rebalancing has better long-term returns and lower drawdowns than the classic buy-and-hold approach.

The chart refers to Ilmanen, Maloney (2015), “Portfolio Rebalancing Part 1 of 2 – Strategic Asset Allocation”. In their study, the authors consider a global portfolio consisting of stocks, bonds and commodities over the period 1972-2014. Similar results can also be found in the study Empirical Analysis of Rebalancing Strategies of the Norwegian State Fund (2012).

Further scientific papers dealing with the “Rebalancing-Bonus” can be found here:
• Nardon, Kiskiras (2013), Portfolio Rebalancing: A stable source of alpha
• Bernstein (1997), Rebalancing Bonus: Theory and Practise

Rebalancing bonus: More return with lower drawdowns

Rebalancing is superior to the value-at-risk approach

A rule-based rebalancing approach is also convincing in comparison to other investment strategies such as the value-at-risk approach (VaR).

In a study, the Munich Institute for Asset Buildings examined the differences between rebalancing-based risk management and risk management with fluctuating equity ratios (value-at-risk). The result: Rebalancing leads to higher returns and the probability of extreme losses is significantly lower.
The lower return on the VaR approach is explained by the researchers’ significantly higher transaction costs. Again it is shown that frequent trading offers no added value – on the contrary! It also shows that an interim loss of -10% for a VaR is much more likely than for a rebalancing approach. The alleged advantage of a volatility control can therefore not be proven scientifically.

Expected return

Source: Institut für Vermögensaufbau (2018)

Probability of losing -10% or more

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