Smart Portfolios 7 – Predicting Returns
Today’s post is our seventh visit to Rob Carver’s book Smart Portfolios.
Prediction
In Part Three of his book, Rob abandons the idea that risk-adjusted returns can’t be forecasted.
- His experience working in hedge funds leads him to believe that simple trading models can have some success in predicting future returns.
Which is not the same as believing in special individuals with the ability to see into the future without models.
Rob notes that markets can always move still higher (or lower) and that even if you know that something is likely, working out exactly when it will happen is hard.
- You also need to be better at predicting the future than other people are.
Models
Rob sticks to two simple models –
The chart shows the distribution of SRs for high and low asset weights in his main (“passive”) portfolio.
A more extreme version of this approach is to exclude certain assets (with low expected returns) entirely.
- Rob also likes to use selection at the last stage of the allocation process – to select the shares in each sector that are most likely to outperform, for example.
I would prefer to use these techniques in a separate, actively-managed portfolio.
- This would make it easier to track its returns, and in turn, help me to decide each year how much capital should be allocated to this approach.
One of my goals for 2019 is to move closer to a systematic approach to investing.
- Doing this would make it easier to adopt Rob’s approach, and I suspect he is a long way down that track in his personal portfolio.
Momentum
Rob uses the returns from the last 12 months (ideally including dividends) to work out a trailing SR using estimated/fixed volatilities.
- From this, he derives a weighting adjustment (to his existing position).
Since the adjusted weights won’t add up to 100%, you then have to normalise the weights within the portfolio.
You can use an absolute or relative approach.
- In relative asset which has been doing well.
In absolute Yield
Yields are a nice, easy to measure proxy for value (cheapness). The process is the same as for Adjusting a portfolio
Rob provides a long and detailed section on how to adjust a hand-crafted multi-level portfolio using these processes. Rob notes that yield (and value in general) work best when assets are similar, and therefore their prices rarely drift apart for long.
If you want to use both models (or any two models) together, you use the geometric means of the individual adjustment factors (eg. equity Active management
There are two problems with active management: To work out whether an active fund is worth investing in, you need to compare it to a suitable passive tracker. Rob imagines two funds with 10-year track records: Using a correlation between managers of 0.85 (typical if the funds have similar styles), Rob works out that there is a 78% chance that future outperformance will be enough to cancel out the 1% extra in fees. To get to 90%, you would need a 4.3% pa gross return advantage over 10 years. You also need to account for volatility, or a riskier fund may seem to do better. Rob provides a table of hurdles for the active fund to clear, based on correlation and length of track record. Rob then uses Fundsmith as an example, comparing it to IGWD. The outperformance is barely a quarter of the 6.4% pa required. Rob also mentions the “infinite number of monkeys” problem, formally known as the multiple testing problem. Since you need several decades of data to demonstrate a manager’s outperformance, and their career will be almost over by then, Rob prefers to look at how a manager makes investment decisions and manages risk, rather than analyse their track record. I wouldn’t go that far, but I tend to use active funds (ITs, actually) to access difficult and/or illiquid assets. Once again, I’m looking for lack of correlation rather than guaranteed outperformance. We know from our handcrafted portfolio design that Rob doesn’t like market cap weighting (dumb beta). Factor funds (smart beta – Robo advisors
7 Circles have a long series of articles on robo advisors. Fees typically work out at a minimum of 0.3% to 0.5% pa on top of the ETF charges Rob notes (as I have) that robos market themselves using comparisons to the most expensive platforms and advisors. Since I plan to run my active strategies in a standalone portfolio, rather than by adjusting the allocations to my passive funds, there has been less of a takeaway for me personally today. I also share Rob’s scepticism about Robos as they currently exist. Until next time. Share this with Twitter, Google+, Pinterest, LinkedIn, Tumblr, Reddit and StumbleUpon. Article credit to: https://the7circles.uk/smart-portfolios-7-predicting-returns/
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Conclusions