Alternative PP Models
Posted: Fri Jan 27, 2017 6:41 pm
Although the traditional Permanent Portfolio has a great track record, I have modeled a bunch of alternative approaches to see if the results can be improved.
I used VTI (Equities), TLT (Treasuries), GLD (Gold) and SHY (Cash) as asset classes, I downloaded Yahoo Finance adjusted closing price data from January, 1, 2006 to December 31, 2016 into my models. I started with January 1, 2006 because not all of the ETFs have a longer history.
To create a benchmark for the traditional Permanent Portfolio, I started my investment on January 1, 2006 equally allocated between the four asset classes. Rebalancing only occurred when one or more assets fell below 15% or above 35% of total portfolio value. Here are the results:
Year Return CAGR
2016 8.2% 7.27%
2015 -3.0%
2014 11.0%
2013 -2.0%
2012 6.1%
2011 12.7%
2010 16.0%
2009 8.3%
2008 3.7%
2007 13.9%
2006 10.7%
MaxDD -13.77%
Of the many models I created, one particular model stood out. I would characterize this as a "momentum model" which starts with calculating the 21 day moving average of three asset price along with the 63-day asset return. The three are VTI, TLT & GLD. SHY is considered the "safe haven" investment. On the measurement date (defined in a moment), if the current price exceeds its 21-day moving average and the 63-day return is greater than "zero", the asset is considered for investment. For each asset that passes this test, its 63-day return is ranked. The top two of the three are the selected investments. If only one of the three passes the test, then the balance of the "failing" asset is invested in SHY. If none of the three pass the tests, then the entire portfolio is invested in SHY.
The measurement dates are Feb 15, May 15, Aug 15 and Nov 15, or the date closest to the 15th. I prefer quarterly updates and believe off-quarter and mid-month updates avoid all the turbulence associated with money managers updating their portfolios.
If it happens that the entire portfolio is in SHY at the next measurement date, then the portfolio is equally split between the next two "winning" assets at the next measurement date.
Having said all that, here are the results:
Year Return CAGR
2016 6.0% 9.60%
2015 1.5%
2014 11.2%
2013 6.3%
2012 5.9%
2011 26.9%
2010 29.3%
2009 5.2%
2008 9.6%
2007 6.3%
2006 3.6%
MaxDD -12.53%
As you can see, the CAGR is higher than the traditional Permanent Portfolio. the MaxDD is lower, and there are no loss years. It's hard not to like this approach. FYI-no transaction costs were included.
In a third model, I modified my momentum approach to rebalance at every measurement date - equally between the two "winning" assets. The results further improved:
Year Return CAGR
2016 5.9% 10.29%
2015 1.5%
2014 11.2%
2013 6.4%
2012 6.3%
2011 26.4%
2010 21.6%
2009 4.2%
2008 14.8%
2007 6.4%
2006 13.7%
MaxDD -11.75%
For those with better modeling skills and data, I welcome feedback on how both approaches look over longer time periods.
If anyone is interested, PM me your e-mail address and I will share my Excel file.
I used VTI (Equities), TLT (Treasuries), GLD (Gold) and SHY (Cash) as asset classes, I downloaded Yahoo Finance adjusted closing price data from January, 1, 2006 to December 31, 2016 into my models. I started with January 1, 2006 because not all of the ETFs have a longer history.
To create a benchmark for the traditional Permanent Portfolio, I started my investment on January 1, 2006 equally allocated between the four asset classes. Rebalancing only occurred when one or more assets fell below 15% or above 35% of total portfolio value. Here are the results:
Year Return CAGR
2016 8.2% 7.27%
2015 -3.0%
2014 11.0%
2013 -2.0%
2012 6.1%
2011 12.7%
2010 16.0%
2009 8.3%
2008 3.7%
2007 13.9%
2006 10.7%
MaxDD -13.77%
Of the many models I created, one particular model stood out. I would characterize this as a "momentum model" which starts with calculating the 21 day moving average of three asset price along with the 63-day asset return. The three are VTI, TLT & GLD. SHY is considered the "safe haven" investment. On the measurement date (defined in a moment), if the current price exceeds its 21-day moving average and the 63-day return is greater than "zero", the asset is considered for investment. For each asset that passes this test, its 63-day return is ranked. The top two of the three are the selected investments. If only one of the three passes the test, then the balance of the "failing" asset is invested in SHY. If none of the three pass the tests, then the entire portfolio is invested in SHY.
The measurement dates are Feb 15, May 15, Aug 15 and Nov 15, or the date closest to the 15th. I prefer quarterly updates and believe off-quarter and mid-month updates avoid all the turbulence associated with money managers updating their portfolios.
If it happens that the entire portfolio is in SHY at the next measurement date, then the portfolio is equally split between the next two "winning" assets at the next measurement date.
Having said all that, here are the results:
Year Return CAGR
2016 6.0% 9.60%
2015 1.5%
2014 11.2%
2013 6.3%
2012 5.9%
2011 26.9%
2010 29.3%
2009 5.2%
2008 9.6%
2007 6.3%
2006 3.6%
MaxDD -12.53%
As you can see, the CAGR is higher than the traditional Permanent Portfolio. the MaxDD is lower, and there are no loss years. It's hard not to like this approach. FYI-no transaction costs were included.
In a third model, I modified my momentum approach to rebalance at every measurement date - equally between the two "winning" assets. The results further improved:
Year Return CAGR
2016 5.9% 10.29%
2015 1.5%
2014 11.2%
2013 6.4%
2012 6.3%
2011 26.4%
2010 21.6%
2009 4.2%
2008 14.8%
2007 6.4%
2006 13.7%
MaxDD -11.75%
For those with better modeling skills and data, I welcome feedback on how both approaches look over longer time periods.
If anyone is interested, PM me your e-mail address and I will share my Excel file.