Return on investments is highly correlated with portfolio risk so every effort is made to increase the Return/Risk ratio. A six-year study running from June 2007 through June 2013 examined a number of models using eighteen ETFs with the distinct purpose of reducing portfolio volatility. The time frame was selected as it included both a major bear and bull market. Vanguard's Total Market Index Fund (MUTF:VTSMX) was used as the benchmark.
The model explained below is known as the "Dynamic" plus SHY. "Dynamic" comes from the fact that an optimizer was used to guide the investor as to how many shares to hold in each ETF. The iShares 1-3 Year Treasury Bond ETF (NYSEARCA:SHY) is used as a cutoff performance point. In other words, if an ETF is not ranked higher than SHY based on performance, shares in the ETF are sold and shares are purchased in ETFs that are outperforming SHY. In the table below, seven ETFs are currently outperforming SHY so they are candidates for purchase. The optimizer information is not included in this article so the number of shares recommended for each ETF is not identified in the table.
The following Rankings table shows the ETFs that were selected for this analysis known as The Feynman Study, which was conducted by "HedgeHunter," an author at ITA. There are several possible models explained in the study of which the "Dynamic" plus SHY is one.
The eighteen ETFs were selected for their global outreach and diversity over many different asset classes. Instead of a "curve fitting" analysis, these ETFs or Vanguard equivalents are used in all the portfolios tracked at ITA Wealth Management. There is nothing particularly special about these ETFs. I use commission free ETFs, with few exceptions, in the ITA portfolios.
(click to enlarge)
During the six-year period the "Dynamic" plus SHY model returned 57.9% or an annualized 7.9%. The maximum drawdown was negative 5.5%, a remarkable number considering the bear market of 2008 and the first quarter of 2009. The goal of reducing portfolio volatility is met using this model.
In the following graph, the top set of data is the performance of the "Dynamic" plus SHY. The second best curve is just a "Dynamic" set of ETF where SHY is not used as a cutoff performance reference. The third and bottom graph is the performance of the VTSMX index fund, our benchmark. We used VTSMX as it is better performer than the S&P 500 so it is more difficult to outperform.
The process to implement such a portfolio is rather simple and mechanical. As mentioned before, no effort was made to buy and sell ETFs at opportune moments. Instead, the portfolio was rebalanced once a quarter.
Here are the guidelines for implementation.
1. Select a group of ETFs similar to the 18 above, making sure to include a number of securities that have a low correlation with U.S. Equity ETFs.
2. Rank the ETFs each time the portfolio is up for review. In the Feynman Study this occurred every quarter. Rank is determined by the performance over the most recent three months (50% weight), six months (30% weight), and volatility (20% weight). The above table shows such a ranking.
3. Sell ETFs that are performing below the performance of SHY.
4. Buy ETFs that are performing above the performance of SHY. This is definitely a momentum play.
5. The number of shares to purchase is determined by recommendations coming from the optimizer. A commercial optimizer was used in this study. If there is remaining cash, hold it either in a money market or invest in shares of SHY as was done in this study.
Even though this six-year study was not set up with back tested curve fitting as a goal, one always needs to test such a model going forward and that is what is going on with several portfolios over at ITA Wealth Management. Eleven portfolios are reviewed on a revolving basis. Each portfolio is updated and rebalanced every 33 days. One of the eleven is completely passive. This passive portfolio can be used as a reference to compare the more actively managed portfolios.
In Part II an explanation of a momentum model where an optimizer is unnecessary. The results are also spectacular.
Additional disclosure: Credit for this research belongs to "HedgeHunter," an author at ITA Wealth Management.