Up about a third of a percent for the week. Volatility has cooled off and the portfolio is back to a fairly “average” position. On February 12th, the strategy rebalanced to:
56% SPY , 28% TLT , 16% GLD , 0% Cash
The test of the automatic update didn’t go well. I’ll get there, but probably still two weeks away from going automatic on a separate page.
*All investing strategies come with the risk of loss, including this one. This portfolio may not be appropriate for your investment goals and requirements, and it is not investment advice.
It should not be assumed that recommendations made in the future will be profitable or will equal the performance of the securities in this list.
Calculations are my own. Returns shown do not include trading costs. They do not include any fees. Past performance is not indicative of future performance. Dividends are re-invested.
Annually rebalance equal split (i.e. simply putting 1/3 into gold, stocks and bonds) still beating Geometric…
https://i.imgur.com/5XRPZ2H.png
That image is exact time frame, including trading costs (of which there are practically none because it’s simply rebalanced annually).
Again, I appreciate that this is a short period of time, but I still maintain you need to provide a better benchmark than simply S&P.
Oh, and in case I’m coming across as critical or harsh, I apologise!
You’ve really opened my eyes to the importance of correlation and rebalancing and your blog is fascinating to read.
Where I remain sceptical is that recent correlation / volatility predicts future correlation / volatility, which is essentially what Geometric uses to determine weights.
My default position is that the future is unknowable and that any possible pattern in stock markets would quickly be arbitraged away due to the volume of people trying to beat it and the amount of HFT / algorithms / hedge funds trying to do the same thing – i.e. predict the future, whether that be 1ms, 1 day, 1 week or 1 year.
Your just pushing to me get the post comparing to the PP finished sooner!
Strangely there isn’t a really good post or article talking about how volatility clusters. Benoit Mandelbrot has talked about it in some of his books, and this article discusses it some as well:https://faculty.fuqua.duke.edu/~charvey/Research/Published_Papers/P135_The_impact_of.pdf
I’m going to do a post at some point which I try and talk about why this is after I refine it further. Essentially I’m not sure if volatility really clusters, but that volume clusters. More trades –> more likelyhood for price movements–> more volatility. And to me it does makes sense that if trade volume is high one day, it will be high the next, and vice versa.