Building the TAA Model – Step 2
There are three steps to determining each month’s asset allocation. Our previous post covered the first, identifying which asset classes are in a bullish uptrend. Here in step #2 we’ll rank those uptrending asset classes and narrow the list to a core set to be traded.
…or put another way, why not just trade all of the asset classes that are uptrending?
First, I want to maintain a consistent number of positions in the portfolio (rather than jumping from say a couple one month to all of them the next, and back again). This will help minimize transaction costs (and hassle) and make the analysis in step #3 easier. At this moment (subject to change) the max positions in my model is four.
Second, I want to prevent the portfolio from being too heavily weighted in highly correlated assets. At this moment, the model considers eight asset classes: real estate, gold, commodities, US 10-year Treasuries, the US dollar index (long), and stock indexes for the world’s top 3 economies.
In today’s market, all the stock indexes PLUS real estate PLUS commodities are highly correlated to one another. If next month only this subset happens to make it into our portfolio, we lose all of the benefits of asset class diversification that should be a part of TAA.
When faced with either issue in a given month (too many asset classes or too many highly correlated asset classes), ranking helps us decide which asset classes to keep and which to cut.
Criteria for Ranking
So we’ve established that ranking is important, but how do we determine our ranks?
Faber doesn’t cover this specific topic, but he frequently talks about a “rotation system” where assets are ranked based on momentum by taking the average % change of each asset over the last 3, 6, and 12 months (highest average wins).
I’m using a similar momentum-based criteria for ranking with one big difference.
Returns are (as you’ve heard me repeat ad infinitum) an illusion…they’re just a function of risk. Much more important than returns are returns relative to volatility.
So in my momentum-based rank, I’m looking at which asset has exhibited the highest volatility-adjusted return over various periods (highest average wins). This prevents the portfolio from always drifting towards high-volatility asset classes.
So, to review…
In this second step, we take our list of uptrending asset classes from step #1 and rank them by volatility-adjusted return (momentum). If there are either (a) too many asset classes or (b) too many highly correlated asset classes, we use those ranks to know what to keep and what to cut.
I’m past my self-imposed word limit. Stayed tuned for part 3.
[Edit: click for a summary of all posts in this series on TAA]
Geek note re: “highly correlated assets”: it’s important to determine which asset classes are highly correlated using only the data available at that moment in history because many have changed their stripes over time (read more). For example, in today’s market, equities and commodities are fairly well correlated, but for most of the market’s history they were very much independent. I thought I’d mention it because this is a potential source of accidental curve-fitting.
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Filed under: Tactical Asset Allocation, Trading Strategies | 8 Comments