Our quantitative ETF model portfolios are designed to be easy to follow and replicate.
We designed our dynamic asset allocation strategies taking into consideration that any strategy that requires daily monitoring and readiness to act is probably not feasible for most individual investors because it might conflict with their regular jobs and other responsibilities.
Another important point is that frequent allocation adjustments are not advisable because the transaction costs would overwhelm the benefits.
Thus, we allow our algorithms to make allocation changes only once a month (on the first Sunday of every month to make it even more convenient).
Given the long-term nature of our strategies, the allocations might not change every month. If there are changes, the models' adjustments will be made on the following trading day.
As a "look inside" feature, we publish weekly provisory updates (every Sunday but the first of the month) containing the ETF model portfolios' provisory calculations at that point in time.
As the name implies, the trend strategy uses our trend filter on top of a static allocation strategy. A model using this strategy aims to be fully allocated when the trend is up, and partially allocated otherwise.
The ready-made ETF models that use our dynamic strategy are designed for investors that are seeking an adaptive model. As market conditions evolve, these models' allocations are adjusted aiming to produce better risk-adjusted returns.
Let's illustrate the strategies with three simulated example portfolios.
The objective of these examples is to show how our strategies would have behaved during recent crises. The key points we want to highlight are the relative performance and the smoothness of the returns.
This example strategic portfolio is our benchmark. It keeps a fixed allocation of 40% bonds and 60% stocks (a traditional allocation) and rebalances the portfolio every quarter.
The example tactical portfolio uses a base allocation of 40% bonds and 60% stocks (like the benchmark). Then we apply our trend filter. Therefore, each asset is either fully allocated (when in an up-trend) or half allocated (when in a down-trend), creating a tactical trend-following model.
The example dynamic portfolio's composition (which changes over time) is calculated by our proprietary dynamic allocation algorithm. The portfolio is rebalanced when the allocation changes by at least 10%.
These example portfolios are only allowed to "invest" in:
Data from January 1990 until June 2016; source: Quandl.
Note that the shown performance was simulated using index data, which do not include ETF management fees. We do so because most ETFs have a short history. Therefore, had we used ETFs, the returns would have been smaller by the amount of the ETF fees. On the other hand, it would not materially impact the relative performance or the smoothness of the returns, which are the key points we want to show.
At the tables, notice how both Trend and Dynamic strategies experienced much smaller maximum drawdowns.
The Trend strategy protects the portfolio's capital by cutting exposure (holding cash) when a constituent is trending down. It may generate smaller returns (because of the cash), but it tends to endure smaller drawdowns than the portfolio without the trend filter.
The Dynamic strategy protects the portfolio's capital by moving the exposure "around" between its constituents. It dynamically reduces or increases some constituents' exposure according with market conditions. The strategy may also hold cash if the conditions dictate. It is specifically designed to generate better risk-adjusted returns.
|Data from Jan-1990 until Jun-2016.|
AARAverage Annual Return%
|Simulated results: from Jan-1990 until Jun-2016, including transaction costs.Past performance does not guarantee future results.|
AARAverage Annual Return%
In the chart, you can see both Trend and Dynamic strategies in action.
Notice how the Dynamic strategy is able to move its exposure between bonds and stocks, positioning itself to generate better risk-adjusted returns and avoiding bigger drawdowns in the process.
Specifically notice the performances during the dot-com (2000-02) and credit-crunch (2008-09) crises.
Past performance does not guarantee future results.
Evaluate the ETF model portfolios' simulated performance and subscribe to our services.
Our models are in a walk-forward mode. This means that, on an ongoing basis, the Upbias Strategist downloads the market data, calculates the ideal allocations and, when the algorithm dictates, simulates the model portfolio adjustments (including transaction costs) - like someone would manage an actual portfolio.
For a small monthly subscription fee, you get online access to the dynamic model portfolios' exact composition in addition to the Upbias Strategist's online publications (monthly newsletter and weekly updates).
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Start using our model portfolios as practical examples to help you manage your actual ETF portfolio and gradually grow it over the long term.
You always have full control. Our model portfolios give you asset allocation ideas. You make the decisions and manage your actual ETF portfolio, not us.
Note that we are not a financial advisor, and all site content is presented "as is" and on an educational and informational basis only. Any person, institution or other entity is advised to first consult their own financial advisor before using the information presented here in any way whatsoever. Please read the full disclaimer.