Using our quantitative forecasting model for the commodities asset class, during Spring / Summer 2010, tactical asset allocators should continue to be overweight in Commodities Indexes, such as IGE, which is a proxy for the Goldman Sachs Commodities Index (GSCI), and be underweight the equity index S&P 500.
Another commodities ETF alternative you may invest with is DBC.
As a matter of fact, just pulling up the relative price returns (in Yahoo Finance) of these two in the last six months shows that our model has predicted these relative excess returns correctly. The figure below shows that while the returns on IGE and S&P500 have stayed close together, IGE has beaten the S&P500 consistently by a few percentage points over the three months. We expect this situation to continue through Summer 2010.
click to enlarge
Click to enlarge
There are macroeconomic reasons that affect the recurring pattern of recession and recovery called the business cycle. Since the length and depth of these cycles can be irregular, we have built a multi-factor commodities forecasting model to allocate funds towards or away from a commodity index such as the GSCI, proxied by the ETF with the ticker symbol IGE
Our commodities forecasting model accurately predicts excess returns to commodities as an aggregate asset class (without diving deeply into whether one should be invested in Oil or Gold or Nickel etc.) by using observable macro-economic factor values.
These macroeconomic factors in our model explain and forecast GSCI returns well because they are:
- either part of government’s policy tools (e.g. federal funds rate, term spread),
- or they indicate the amount of economic activity requiring use of commodities (oil price, industrial production)
- or otherwise (CPI inflation, non-farm payroll numbers, showing how many people are productively employed).
Backtesting analysis for this modeling over a 40 year period has shown that these aforementioned factor indicators which are in the model are somewhat more effective than some other indicators we tried from the list of Leading Economic Indicators published by The Conference Board.
Our predictive models based on clear macroeconomic or fundamental data are superior because investing should not be based simply on trend-following, which is using hindsight and driving by looking in the rear-view mirror.
It is not easy to determine when the economy is passing through a peak or a trough. It is not usually apparent that a recession or expansion has started or ended until several months after the fact. Hence, our multi-factor commodities forecasting model has proven to be more useful in judging the relative attractiveness of investing in commodities as an asset class without needing to know what phase of the business cycle the investor is in.