A "Bench Strategy" by the Numbers

This post is roughly the same as the "40 months" post I just did but I realized that to some people a 7.4 percent return might sound meager so I thought I'd give some context by explaining what I originally set out to do and how I think about it and why I try to stay in this particular game.

10 years ago, before an early retirement, I wondered if I could get equity-like returns for bond like volatility.  My goal was not day trading it was portfolio "efficiency." In a retirement context this is especially important since spending against a volatile portfolio can have negative consequences.  If I could get, on the margin, the same return with less volatility or something more mean-variance efficient than what I do already then that is a game I want to play.  The purpose of the 40 month post was to tell myself that I had gotten to where I wanted to go after all those years but I had provided no context.  Here is the basic idea by the numbers.



This, as in the previous post, is the strategy over the last 40 months:

> Return.annualized(z, scale = 12, geometric = T)[1]
[1] 0.0735794
> StdDev.annualized(z, scale = 12)[1]
[1] 0.03936469
> SharpeRatio.annualized(z,Rf=Rf_m)[1]
[1] 1.336638
> SortinoRatio(z,MAR=0)[1]
[1] 1.235226
> Omega(z,L=0)[1]
[1] 3.940805
>

Here is 100% US equity, which is not really a fair comparison because it is not the asset allocation I use, over the same period. This is put here for comparison to equity because the theory of my trading is "can I get equity returns for bond-like volatility"

> Return.annualized(y, scale = 12, geometric = T)[1]

[1] 0.08028084

> StdDev.annualized(y, scale = 12)[1]

[1] 0.10426

> SharpeRatio.annualized(y,Rf=Rf_m)[1]

[1] 0.5677147

> SortinoRatio(y,MAR=0)[1]

[1] 0.3940518

> Omega(y,L=0)[1]

[1] 1.799118

Here is 100% US corp bond

> Return.annualized(y, scale = 12, geometric = T)[1]

[1] 0.0001163956

> StdDev.annualized(y, scale = 12)[1]

[1] 0.03191413

> SharpeRatio.annualized(y,Rf=Rf_m)[1]

[1] -0.6118432

> SortinoRatio(y,MAR=0)[1]

[1] 0.007914214

> Omega(y,L=0)[1]

[1] 1.01429

Here is AOM (same period) which is a 40/60 allocation that might be used as a passive allocation benchmark though I've found that it has some flaws for comparison making:

> Return.annualized(y, scale = 12, geometric = T)[1]

[1] 0.01614237

> StdDev.annualized(y, scale = 12)[1]

[1] 0.04917069

> SharpeRatio.annualized(y,Rf=Rf_m)[1]

[1] -0.07708181

> SortinoRatio(y,MAR=0)[1]

[1] 0.154545

> Omega(y,L=0)[1]

[1] 1.302336

> 

So the answer, over that cherry picked period, using those particular metrics, is that, yes, one can get equity like returns for bond like vol. It's not perfectly lined up but you get the basic idea. I call it my "bench" strategy because it is named in a way after the Rutherford Bench. In wine country, bench-land is the alluvial fan made of gravel, sand, silt, and clay that comes from the watershed drainage from the mountains nearby. The fan sits as a pile above the valley floor and below the mountain (in profile it looks like a bench hence the name -- so bonds are valley, strategy is "bench," and stocks are...) and is well drained soil which allows for deep roots. It is usually associated with high quality wine or in my case a strategy that bears the precise kind of fruit that I want[1].  While the strategy is only one among several rather than my whole pie it is accretive on the margin and as long it is accretive it stays and I either keep doing it or maybe I even expand it to 100% (or more if levered) of what I do.  If it ever gets to be a subtractive thing I will scale down to zero as fast as I can.  This is why the numbers and the evaluation of mean-variance efficiency on a sub-scale matter.  While the covariance characteristics are "just ok" for the portfolio as a whole (correlation coeff w equities <= .5), for now it seems like an easy call: "keep doing."

(don't ask about tax efficiency, that's a different story altogether...)


-------------------------------------------------------

[1] Supposedly, by reputation, preferred stocks combine the worst of credit and equity.  Here I am trying to get the best of both.  We'll see what happens when this is tested by a 1987 or 2008 type event if I'm still playing the game.