2022 Guidelines for Relative Strength Portfolios

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This blog post is for ITA readers currently using the Kipling spreadsheet or those who plan to use it in the future.  While it is impossible to know what the economic future is for 2022, it appears as if we will not repeat the strong equities market of 2021.  With this in mind, I’ll walk readers through the critical Kipling settings I plan to use with the Relative Strength portfolios tracked on the ITA blog.

There are several portfolio management models in operation on this blog.  The four I use are:

1. Robo Advisor or computer managed and this is the Schrodinger portfolio.
2. Dual Momentum™ portfolios of which there are four.
   1. Galileo
   2. Franklin
   3. McClintock
   4. Pauling
3. Income oriented portfolios and there are three.
   1. Huygens is a “pure” income portfolio.
   2. Bethe is a combination of relative strength and income.
   3. Bohr is also a combination of relative strength and income.
4. Relative Strength or Relative Momentum and there are several.
   1. Millikan
   2. Einstein
   3. Kepler
   4. Gauss
   5. Carson BHS where a specific model is used in this three portfolio experiment.
   6. Carson HA – again a specific model is being tested.
   7. Carson LRPC or the third model in this three portfolio experiment.

Main Menu Settings

Not frequently shown in portfolio reviews is the Main Menu of the Kipling spreadsheet.  Below are the default settings.  For a few portfolios I use longer look-back combinations than the 60- and 100-trading days shown below.  There is some growing evidence that longer look-back periods are superior, but that data is likely skewed by the strong bull market over the past two years.  Should a sharp correction (S&P drops by 10% or more) happen, the shorter look-back combination will prevail.  Over the last three years the annualized volatility of the broad U.S. Equity market exceeded 13% so it is not out of line to think we will have a market correction in 2022.

In the following screenshot the deep red arrow points to the look-back combination and the green arrow identifies the weight assigned to each variable.  The 20% assigned to volatility indicates we see investments with lower volatility.  Once these are set for a portfolio I rarely change them.  In a long-range back-testing study performed a few years back, this combination worked best in all market conditions.

Sample Portfolio

Now we come to the portfolio.  I’m using the Einstein portfolio for this example.  The investment quiver (first column on the left) includes all the critical asset classes and a number of market factor ETFs.  VTI, QUAL, BND and MTUM are examples that cover market factors.  VOE and VBK are two ETFs that fit both market factors and asset allocation requirements.

The green arrow points to the maximum percentage I want to hold in any given asset class or market factor with this particular portfolio.  Some Kipling users will just set all percentages to 100%.  In past blogs I’ve explained how these percentages work when an ETF meets the maximum percentage.  If you don’t know, just ask in the Comment section provided below.

Examining Recommendations and Tranche Settings

Below is the Tranche worksheet from the Kipling spreadsheet.  Recently, I’ve been turning the Target Filter on or setting it to Yes.  What the Target Filter does is limit the Buy recommendations to equal VTI or identify those ETFs performing better than VTI.  Since VTI represents the entire U.S. Equities market, by setting the Target Filter to Yes we are looking for asset classes that are performing better than the broad market.  We seek the best of the best when turning on the filter.

In this example, I’m using the BHS model.  One can select either the HA or LRPC models.  Thus far, the LRPC model is the top performer and if I collect more data to this effect, I’ll move over to the LRPC model.

One more critical decision is to determine how many asset classes to include in the portfolio.  In this example I set the number to 10.  I generally use five (5), but with the Target Filter turned on it is highly unlikely there will be 10 ETFs outperforming VTI.

Manual Risk Adjustment Settings

While this is the Manual Risk worksheet, most of the settings take place in the Auto worksheet.  The Auto worksheet comes before the manual worksheet in the Kipling workflow and it is where decisions such as setting the SD Multiplier take place.  In this example I set the SD Multiplier to 1.63 (red arrow) so the Stop Loss for VTI adjusts to 8.0%.  I frequently set the SD Multiplier so the Stop Loss for VTI moves down to 5.0%.  Such a move is done to protect capital.

Another critical decision is setting the Maximum Trade Position Risk.  In this example I set it to 3.0% (purple arrow) so the Maximum Portfolio Risk is 6.0% (dark arrow).  When working with Dual Momentum portfolios I nearly always move that 3.0% to a value where the Total New Cash is under $100.  In other words, I want all cash invested.

As one works more and more with the Kipling you begin to see how changing certain variables impact other values.  How you set these variables is depending on how risk averse you are as an investor.  I happen to be quite concerned about risk so keep that in mind when reading my portfolio reviews.

I hope this review helps you with your Kipling settings as we open up the 2022 investment calendar year.  Once more, comments and questions are always welcome.

Preparing Investments for 2022