This is the second part in our series on tactical asset allocation. Our initial pieces outlined the basics of tactical asset allocation and offered some foundation knowledge. In the series that follow (including this one), we will highlight all the models we present on our tactical asset allocation module of our software package (this is free–simple click here to login).
The models and basic explanation follow:
- SP500=Standard and Poors 500 Total Return Index
- EAFE=The MSCI EAFE Total Return Index
- EEM=MSCI Emerging Markets Total Return Index
- REIT=FTSE NAREIT All Equity REIT Total Return Index
- GSCI=S&P GSCI Total Return Index
- US_10Yr=Merril Lynch US Treasury 10-Year Treasury Futures Total Return Index
- ew_index=equal-weight Core 6.
- ew_index_ma=equal-weight Core 6 with 12-month moving average trading rule.
- mom=equal-weight Core 6 shifted by relative 12-month momentum.
- mom_ma=mom strategy with 12-month moving average trading rule.
- risk_parity=unlevered risk parity for Core 6.
- risk_parity_ma=unlevered risk parity for Core 6 with 12-month moving average trading rule.
- risk_parity_mom=unlevered risk parity weights for Core 6, adjusted by relative 12-month momentum.
- risk_parity_mom_ma=unlevered risk parity weights for Core 6, adjusted by relative 12-month momentum, with a 12-month moving average trading rule.
Equal-Weight Tactical Asset Allocation
The equal-weight system is the simplest model one can devise: an investor simply invests 1/n in each asset class, rebalanced monthly.
Academic research on asset allocation clings to the tenants of mean variance optimization and concludes that investors should hold the market portfolio, or the portfolio of all risky assets weighted by their respective value in the market portfolio. But how does the theoretical answer actually stack up against the very simple equal-weight strategy?
DeMiguel, Garlappi, and Uppal has a answer to this question in their Review of Financial Studies Paper:
We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1/N portfolio. Of the 14 models we evaluate across seven empirical datasets, none is consistently better than the 1/N rule in terms of Sharpe ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversiﬁcation is more than offset by estimation error. Based on parameters calibrated to the US equity market, our analytical results and simulations show that the estimation window needed for the sample-based mean-variance strategy and its extensions to outperform the 1/N benchmark is around 3000 months for a portfolio with 25 assets and about 6000 months for a portfolio with 50 assets. This suggests that there are still many “miles to go” before the gains promised by optimal portfolio choice can actually be realized out of sample.
So what is the real takeaway from this research? It is hard to do much better than 1/N in a world with so much volatility!
Here are some additional pieces on the subject written by fellow bloggers/researchers:
Use our tool to generate the latest weights, or simply type 1/5 into a calculator:
Here are the performance stats over time (January 1, 1979 to August 31, 2012):
The diversification concept is interesting, but did it really help? The simple 60/40 portfolio and the long-bond trade have outperformed!
A few takeaways:
- The equal-weight strategy reduces risk relative to a domestic-equity-only index, but the risk reduction is limited.
- Long bonds have experienced quite a run.
- 60/40 is tough to beat.
In our next series we will look at tactical asset allocation strategies that integrate momentum into the mix. Stay tuned…
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Past performance is not indicative of future results, which may vary.
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