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:
- Robo Advisor or computer managed and this is the Schrodinger portfolio.
- Dual Momentum™ portfolios of which there are four.
- Income oriented portfolios and there are three.
- Huygens is a “pure” income portfolio.
- Bethe is a combination of relative strength and income.
- Bohr is also a combination of relative strength and income.
- Relative Strength or Relative Momentum and there are several.
- Carson BHS where a specific model is used in this three portfolio experiment.
- Carson HA – again a specific model is being tested.
- 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.
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
John Shelton says
Good morning Lowell. Those of us trying to understand Kipling found this piece extremely valuable. Thanks. John
Lowell Herr says
Thank you. If you need more assistance, just let me know. It might be time for a new “Camtasia” on the Kipling.
etienne belotti says
Excellent thanks !!!
Lowell Herr says
Thank you. If anything I wrote is unclear, just ask again and I’ll try to clarify. This is where I miss the classroom where interactions between teacher and students was so rewarding.
Excellent review/summary of the Kipling Workbook and how it can be used.
The only thing that I can add is that all models work well – until they don’t 🙂 . I don’t think it is wise to try to figure out which system is “best” – but rather to try to layer systems on top of one another – the Carson Trio is a good example of a single portfolio (using the same set of assets) that is diversified through the use of different lookback periods. In 2021, 2 of these “variations” showed great performance and the third disappointed. However, in combination, the overall performance of the “Carson” portfolio was very good – would could not have predicted, at the beginning of the year, which lookback periods might generate the highest returns – and we don’t know whether the same systems will perform in the same way going forward – probably not.. We spread risk between models and (generally) reduce overall risk on our total holdings. Tranching is another way to “smooth” our risk (and luck) through the use of overlapping review dates.
Sorry I’ve got your “experiments” mixed up – the Carson Trio is using different models – the McClintock, Pauling, Frankin are the “trio” using the different lookbacks. But the message is still the same – layer the models/systems.
Ken Dorge says
thank you for your kind efforts to educate me
Lowell Herr says
You are most welcome. Feedback helps me know where readers (investors) need additional explanations.
Ken Dorge says
rereading all recent posts to understand your models inductively. “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. I don’t know, if there is a post I can look up.
Lowell Herr says
When the Maximum or Strategic Asset Allocation percentages are adjusted to something other than 100%, here is how it works. The percentage listed under the Strategic or Max AA pegs or limits the the number of shares to be invested in that particular asset class or ETF. Keep in mind, this is connected or related to the number of ETFs one wishes to include in the portfolio. For Dual Momentum portfolios, this is one ETF at a time. For other portfolios it might be five or 10.
Keep in mind these are only recommendations. The investor can override the recommendations and I do this frequently.
Lowell Herr says
I need to add that the Maximum percentages in the column will not add to 100%. The percentage I assign to each asset class is the maximum I want in that particular asset class.
In the above example, assume VOE (Mid-Cap Value) is the #1 performer and VTI (Total U.S. Equities) is ranked #2.
If I selected there to be only two securities in the portfolio, the software would recommend a specific number of shares of VOE to be purchased until it is 30% of the total portfolio. Then the Kipling SS would recommend a number of shares of VTI until it fills the final 70%. Nothing is then recommended beyond that point as the total reached 100% or all of the portfolio. If I select five ETFs to be in the portfolio the recommendations will differ.
The percentages in the Strategic or Max AA column is a way for the user of the Kipling to cap the percentage of the total investment to be allocated to a particular asset class or factor of the market. If all the percentages are set to 100% the software will divide the recommendations among all the ETFs that show up as a Buy.
I know this is a tad confusing and I hope I’ve cleared up a bit of the confusion.