I set myself last year a goal of doing one blog post a month. I have an idea for an interesting series of posts related to forecast strength, which I'd hoped to find time to research and post about. However, I've been quite busy marking exams and pushing through the production trading code for pysystemtrade (it's now at the point where I can trade manually; auto trading is next. Expect a lot of posts once it's finished explaining how to use it for live trading).
So instead here's a post about systematic investing rather than trading. Although this blog is ostensibly for both traders and investors, I do tend to focus more on the former. To be fair this is because I try to only spend a few days a year rebalancing my long only investment portfolio, whereas I trade every day (or rather my computer does).
However the recent activity in the markets, and resulting churning of my portfolio, made me realise that I needed to seriously update my long only investment toolkit. At some points during March I was trading my investing portfolio every day. Even now, I expect to be rebalancing monthly as I gradually get back to my strategic weighting.
I though I might as well share with you the results of that updating.
So, in this post I:
- Come up with a list of curated ETF's to match the major asset classes, regions and categories.
- Use the principles in my second book, Smart Portfolios, to create a model portfolio of these ETFs.
- The model portfolio can be found in this google docs spreadsheet, and I hope to update it every month or so.
(I have to say this every time: This link is for a read only version of the spreadsheet. If you want to modify it then do not ask for edit permissions. Think about it - why would I allow anyone to do that? Instead download it or copy it inside google docs).
A curated list of ETFs
First a caveat: this is a list of UK listed ETFs. I am doing this for my own purposes, so it doesn't make sense for me to do a load more work for US or European readers. However hopefully you can use the principles here (and covered in more detail in Smart Portfolios) to choose equivalents. I used the excellent service, justetf.com, to research this list. Most of these ETFs are listed on European exchanges as well, so if you search the ticker you can find an alternative easily. In the US I would use etfdb.com.
Using my top down methodology I decided to use the following categorisation:
- US equities
- Beta (the whole market), High yield, Value
- European equities
- Beta ....
- UK equities
- Beta ....
- Asian equities
- Beta ....
- Emerging market equities
- Beta ....
- US bonds
- Government, Corporate IG, High yield
- European bonds
- UK bonds
- Asian bonds
- Emerging market bonds
There are no alternative assets here. In practice my alternative asset is effectively my trading account, which gives me exposure to a large variety of risk factors. This has a target allocation of 25% of my risk, but that isn't included here. I also have a ragtag of a few property and gold ETFs that I am excluding for simplicity.
I went for the Beta, High Yield, Value split because this is a particular bias I wanted in my equity portfolio. I did not want to go any deeper into the structure (eg down to equity sectors), as I didn't want this portfolio to end up requiring too much work on a regular basis. In practice I invest in individual UK equities and there may be other places in my real portfolio where I go into a more granular portfolio than implied here.
I also needed a global equity and global bond ETF to do my tactical asset allocation (of which more later).
Here is how I selected the ETFs:
- Select the appropriate category
- Where possible, exclude accumulating funds (since I wanted to use dividend yield as a valuation metric. In practice I might choose to invest in the accumulated fund depending on whether the ETF was in a taxable or non taxable account)
- Exclude leveraged funds
- Where possible, exclude currency hedged funds (unless a hedged fund was much much cheaper). I explain the problem with these in the book
- Choose the lowest TER (as readers of my book will know there is more to costs than just this single figure; nevertheless I didn't want to spend too much time on this exercise)
There are other selection criteria discussed in the book, but again in the interests of time I just stuck to the points above.
I was also interested in introducing a degree of diversification across providers, since historically my portfolio was very heavy in iShares and latterly Vanguard (thus creating some potential counterparty concentration risk). However I was pleasantly surprised by how much more competition there is in the market now. The 28 ETFs ended coming from 10 providers, and although iShares (7 funds) and Vanguard (5 funds) are still the most popular there is plenty of diversification here.
I was also pleasantly surprised by how cheap the market has become since I last did this for Smart Portfolios about 3 years ago. The average TER was just 0.22%, with 17 funds coming in at 0.2% or less.
Here is the list of funds. Note that there may be data errors here which I am not responsible for.
Unavailable indicates there was no suitable fund available with the given criteria. In some cases I've stretched the term 'Value' to include things like Quality if no pure value fund was available.
Note: IEFV is an accumulating fund; the yield I use will be taken from the distributing fund (IEDL) but do not buy this fund as it only has £2m AUM.
The model portfolio
Broadly speaking the portfolio will be done in my usual top down way, with the tactical weighting done using my favourite methods:
- Equity / Bond with a strategic 90/10 allocation (which equates to about 80/20 in cash terms). Tactical weighting, relative momentum (discussed in this post as well as the book)
- Regional allocation with strategic weights (equal split for bonds, less equal for equities).
- Intra-regional allocation with strategic weights (discussed below)
- 'Intra-asset allocation done as a single process using relative dividend yields'
'Intra-asset allocation done as a single process' might need some explaining. Don't worry, I will explain everything.
Let's dive in to the google docs spreadsheet
In sheet 'ETF list' I include the information from the curated list above, but also add columns for the price and 1 year dividend history. I then calculate the yield. The reason I do this is that the just ETF site only includes dividends as a premium product. So every time I update the sheet, I just need to change the price and the yield will update automatically. Every now and then I will need to change the 1 year dividend history (these normally pay quarterly, so 4 times a year should do).
The 'calculations' sheets deducts the TER from the dividend yield to get a net dividend (a crude way of handling costs), and then converts that to a Sharpe Ratio (SR).
In the sheet 'returns' I put the one year total return for my global asset class ETFs (I don't actually invest in any global ETFs). These are used in the momentum model on the next sheet 'asset' to calculate the adjusted asset class risk weights (cash weights are also shown for information, but not used).
The 'bonds' and 'equity' sheets do the allocation within each asset class as described above. I calculate the strategic risk weights inside each asset class across and then within regions. At this point I have a risk weight for every ETF inside it's asset class. I then use the relative dividend yield SR within the asset class to adjust those risk weights.
Finally in the sheet 'Total' I use the asset class risk weights (adjusted for momentum) to calculate my final risk weights for each ETF. These are then converted to cash weights.
Note: The SR scaling adjustments are different in each sheet. That's because I've recalibrated them so that they are more like my trading rules. Broadly speaking the SR differences have different meaning across different places.
In the asset allocation section, it's not unusual for equities to go up by 30% a year (about 2 SR units) with bonds flat; a SR difference of about 2 units. But within bonds you would be surprised to see one corporate bond regional ETF with a yield of 10% (about 2 SR units) and another with a yield of 0%. Similarly, you'd rarely see a developed regional equity ETF with a yield of 44% (about 2 SR units) whilst another had a zero yield. So I've changed the SR adjustments to reflect this.
Using the model portfolio
My own monthly routine will now look something like this:
- Update the ETF prices, and possibly the dividends
- Compare my own portfolio weights
- Consider selling and buying to match those weights if they are too far out of line with my current portfolio weights
The question of whether to buy or sell is complex and is covered in 80 or so pages of part four of Smart Portfolios, and I certainly won't be repeating it here!
At some point I hope to go back to annual rebalancing, but if the market moves a lot I can quickly fire up this spreadsheet and do an ad-hoc rebalance if required.
|I realise I wrote this whole post without including a picture, so here's a picture of my second book.