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- Live dollar testing began October 4, 2019. From its inception October 4 through October 29, the Optimizer is +65 bps versus the S&P 500 Total Return Index. The Optimizer Model return is 3.60% while the S&P 500 is 2.95%.
- We have had numerous meeting with different segments of the financial industry: P/E, VC, Wealth Management firms, and retailers. We’ve garnered a lot of great ideas. The feedback on the Optimizer is positive and supportive.
- Our Model currently provides for three “positions” that vary from Aggressive, to Defensive, to Very Defensive. We are starting to define what would constitute “Very Aggressive” and, if it would benefit performance, how it would fit into the model.
- We think a future iteration of the Optimizer will look at being able to incorporate into the model slicing the equity platform into small cap, mid cap, large cap, etc.; there are enough differences in performance that this could prove to be worthwhile.
- Another future version may include loading in certain “base” or “mainstay” equities to more aggressively pursue upside alpha during bull markets
Our next update will be at the December 2019 webinar Alan and I will be doing. In addition to an Optimizer update, the webinar will look at the financial markets/trends, how close we are to our system of leading indicators signaling we are a “go” for the recovery in 2020, a look at how the election might impact some aspects of the forecast and how it won’t impact other aspects at all. The purpose is to get you thinking ahead of the competition so you can make the key decisions to thrive while they are wondering what is going on.
We tested the efficacy of the model back to 1991 using an industry-standard drag on earnings to account for transaction costs and taxes. The testing was done outside of ITR. The results were favorable to the Optimizer Model.
We ran additional analysis using the testing results and determined that we successfully avoided negative returns over any given 5-year period (the S&P 500 experienced negative 5-year returns five times in the test period). The Optimizer model minimized weak return periods, and essentially was on par with the best periods in the specified bands.
|# 5 Year Periods, Negative Annual Return||0||5|
|# 5 Year Periods, 0% to 5% Annual Return||2||4|
|# 5 Year Periods, 5% to 10% Annual Return||8||2|
|# 5 Year Periods, 10% to 20% Annual Return||10||9|
|# 5 Year Periods, 20%+ Annual Return||3||3|
Twice the Optimizer Model yielded 5-year annual return results slightly more than 5 percentage points lower than the S&P 500. Overall, the S&P 500 provided a better return in 9 of the 23 years we tested.
We are going to investigate those periods where the buy-hold strategy outperformed our model to determine what the causal factors/relationships were in those instances, specifically looking for some consistency in the factors.
Beyond that, we are going to test an additional idea or two to further optimize the model, but we are approaching the point of diminishing returns.
We tested the data using an outside source to verify results. We also put an industry-standard drag on earnings to account for transaction costs and taxes. Since the benchmark is a buy and hold strategy, a similar drag was not placed on the benchmark. With the drag taken into consideration, the Equity Optimizer model outperformed the benchmark CAGR over the 12-year test period by 370 bps.
- We are going to conduct further back testing to encompass an even broader array of market forces to further test the ability of our model to work under varying circumstances. We all know the admonition that prior results are not a guarantee of future performance, but we can be more confident about the model’s advantages with further back testing because of the varying forces at work in the past and conceivably in the future.
- We are also going to tinker with the model to determine if we can improve upon its performance during certain periods where we think the model perhaps could have provided more alpha.
- ITR Economics and Clark Bellin of Bellwether Wealth will be investing money to begin “live” testing of the model and create additional data points.