In a recent article, I analyzed a model portfolio designed by Money magazine, in conjunction with analysts at Morningstar. The focus of my piece was whether I could reconcile the projections of risk and return for this portfolio with my own calculations. I was pleasantly surprised that the results seemed very consistent.
As a follow-up to that piece, I wanted to see whether I could improve this portfolio in terms of the projected performance. The original portfolio has 40% of its assets in large domestic stocks. The second-largest allocation is to U.S. bonds. The third-largest allocation is to international developed economy stocks. Only 5% of the portfolio is allocated to the emerging economies. In the original portfolio published in Money, the authors specified asset classes but not actual funds, so I selected ETFs that closely matched each asset class selection:
Money Magazine Moderate Strategy for 45-54 Year-Olds.
When I first saw this portfolio, what struck me was that the asset allocation approach was very traditional, with a heavy emphasis on domestic stocks, very little allocation to emerging markets and only nominal exposure to asset classes other than major market indexes.
As an experiment, similar to others that I have conducted in the past, I used a portfolio optimizer connected to the Monte Carlo Simulation tool that I developed (Quantext Portfolio Planner) to seek out the asset allocation with the highest projected average return that would have the same risk level as the Money magazine portfolio.
Here’s how the process works. The Monte Carlo simulation (a.k.a. MCS) generates asset class projections for risk and return. These are based on the assumption of a positive risk premium (e.g. that you will get more return for riskier asset classes). The MCS then searches for the portfolio with the highest expected return at each risk level.
I included all of the asset classes in the original Money portfolio, as well as a few others. In particular, I added infrastructure stocks such as utilities and energy companies. I also included an index fund that focuses on consistent dividend paying stocks (SDY) and I included a number of bond funds representing different classes of bonds (corporate, short-term Treasury bonds, intermediate Treasury bonds, etc). I then ran the optimizer, which knows nothing about fundamentals, country of origin, or which asset class is which. This is purely an automated asset allocation, although I did add a constraint that no single fund could comprise more than 20% of the portfolio. The asset allocation for the final optimized portfolio is quite different than the original, as shown in the table below:
The optimized model portfolio has no allocation to the S&P 500 Index and twice the allocation to small-cap domestic stocks as the Money portfolio. There is a much higher allocation to emerging market stocks in the optimized portfolio, as well. The optimized portfolio also has a substantial 15% allocation to IGE, an ETF that invests in companies that produce and refine energy and basic materials. Bear in mind that these portfolios are purely the result of a mathematical optimization and this is not being held out as an ideal portfolio.
The table below shows that the projected average annual return for the optimized portfolio is 1.5% per year higher than the projected average annual return for the original Money portfolio, and both portfolios have the same total risk level (as measured by volatility).
What is perhaps most striking to me about the optimized portfolio, is that it more closely resembles the kinds of asset allocations used by many institutional investors. A 2011 survey of institutional investors found that the average total allocation to domestic equities and domestic fixed income in their portfolios totals 51.5% of the total portfolio. A global survey of pension plans by Towers Watson found that the average allocation to equities in U.S. pension plans had declined from 64% in 2000 to 49% in 2010.
The largest university endowments have moved away from more traditional allocations that are dominated by domestic equity and bond indexes and have invested more heavily overseas and, particularly, in emerging markets. In addition, there is an emphasis on ‘real assets’—those that produce earnings that should increase with inflation. These firms include energy firms and other natural resource firms, as represented in the optimized portfolio by IGE. Mohamed El-Erian’s excellent book, When Markets Collide, lays out the narrative for this new approach to asset allocation. It is fairly easy to explain why we might expect emerging economy stocks to provide considerably higher long-term returns than the developed economies and El-Erian explains the fundamental evidence. It is interesting, however, that a portfolio optimizer which has no information about fundamentals would also point towards a portfolio with substantially higher allocations to emerging markets.
So, what is the take-away from all this?
Using standard mathematical techniques one can optimize a portfolio (such as the Money magazine portfolio), and produce an arguably “better” (will achieve greater return for the same risk) investment result. Such a new portfolio has the following differences from the original:
- Much lower exposure to market-cap weighted domestic equity indexes
- Zero allocation to the S&P 500 Index
- Lower allocation to international developed economies
- Much higher exposure to emerging market stocks
- Targeted exposure to companies that focus on natural resources
These projections, as I have said, are purely based on a quantitative model that projects expected returns and risks and ‘knows’ nothing about geopolitical or economic trends. As always, model results must be conditioned on the basis of common sense. In addition, I have just performed the portfolio optimization on a small number of asset classes. The point of showing this analysis, however, is that once we start down the road of using tools to estimate portfolio risk and expected return, it is of considerable interest to then ask whether it is possible to use these tools to design better portfolios. If the tools suggest new and different asset allocations, we are then faced with the challenge of deciding whether we believe that the model projections have merit.
Finally, I will note that this portfolio is not being held out as optimal in a global sense. In addition, this is a risky portfolio in light of current and expected market volatility levels.
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