We designed our public dual momentum model, GEM, to be simple and easy to use for do-it-yourself investors. GEM exists to help protect smaller investors from significant drawdowns while allowing them to earn returns that exceed the market over the long run.
But momentum works best when it incorporates multiple trend determinants. There is a synergistic effect when doing that.
Most investors do not give enough importance to price trends. Greyserman & Kaminsky show that simple trend following has outperformed buy-and-hold and reduced downside excursions back to the beginnings of many markets. No other investment factor has shown that.
Our models are the culmination of a lifetime of investment research and experience. Three of our four proprietary models use a channel breakout approach validated on 100 years of data, as seen in our award-winning research study. We also insist on real-world superior performance.
Richard Dennis taught something similar to this to his “turtle traders.” Jack Dreyfus became a billionaire using channel breakouts of stocks, making new highs.
Most of our models incorporate mean reversion from overbought and oversold conditions as a complement to trend following. Our proprietary models use daily data and are highly adaptive to market conditions. They are based on rigorous, academic-quality research, validated against at least 100 years of data. This is the only way to be confident you haven’t overfit your data.
We spend considerable time on portfolio construction and on due diligence to find the best ETFs for our models. Thoughtful portfolio structuring is an essential part of our investing process. Doing so provides consistency, stability, and profit rebalancing. Risk parity and the construction of barbell-type portfolios, as per Nassim Taleb, are important contributors to this success.
Our models work in many different markets. We chose those whose combinations create optimal, balanced portfolios that are responsive to different market conditions. Multiple models with low-to-moderate correlations are the best way to reduce model estimation error and uncertainty. Optimal combinations of models can enhance expected returns and reduce downside exposure.
We license our proprietary model signals to substantial private and institutional investors, as well as to select investment advisors who understand and appreciate what we do.
Most trading models exit to a safe-haven asset when not in risk-on positions. Our models are unique in that they switch to other assets or models with positive trends before seeking a safe harbor. This layering captures additional profits and helps reduce whipsaw losses. Our models also all use the principle of confirming model signals with other closely aligned assets. Here are our current proprietary models.
SMURF focuses on U.S. large-cap growth stock ETFs and may take modest positions in other stock-market or non-stock ETFs when their trends are positive. SMURF exits to alternative assets when trends are no longer positive.
BADASS applies our trend model to ETFs representing blockchain and digital technology stocks. It also allocates a moderate amount to spot Ethereum and Bitcoin ETFs when their trends are positive. BADASS has been our most profitable model.
GLTR applies trend following to gold ETFs. Gold is often mean-reverting and challenging to trade, but our trend strategy handles it well. GLTR also incorporates mean reversion trading. Even without trend following and mean reversion, gold has outperformed the S&P 500 over the past 25 years. GLTR often has a low correlation to our other models and is a good portfolio diversifier.
FORT is our latest proprietary model. It is unique in that it trades the equity curves of inflation-oriented ETFs by buying dips and selling rallies. FORT is an ideal complement to our more traditional trend-following models and an important component of our barbell-based portfolios.
FIRM is anchored by short-term fixed-income ETFs. It can also hold short-term positions in other ETFs to exploit mean reversion and create a “barbell effect” within the model, and when combined with other models. FIRM has the lowest volatility and correlation among all our models. It provides portfolio stability and profits from rebalancing. FIRM also provides tax deferral on much of its investment income.
Here are the results of our SMURF, GLTR, and FIRM models since 2008.
GLTR spends about half its time in gold-stock ETFs and the rest in the SMURF and FIRM positions. SMURF spends around 60% of its time in the stock market and 40% in the FIRM positions. 60/20/20 is a balanced allocation of 60% SMURF, 20% GLTR, and 20% FIRM.
S&P500 | SMURF | GLTR | FIRM | 60/20/20 | |
CAGR | 10.3 | 18.4 | 20.7 | 4.0 | 16.2 |
STD DEV | 16.5 | 12.7 | 14.3 | 2.7 | 9.1 |
SHARPE | 0.60 | 1.30 | 1.31 | 0.97 | 1.56 |
UPI | 0.99 | 6.63 | 8.39 | 19.83 | 9.62 |
MAX DD | -52.9 | -11.0 | -10.5 | -1.2 | -7.3 |
Results do not guarantee future success nor represent returns that any investor attained. All trading involves risks that may not be foreseen. CAGR is the compound annual growth rate. Charts use monthly data. Drawdowns are on a month-end basis. UPI is the Ulcer Performance Index, which divides return by the Ulcer Index. The Ulcer Index measures the depth and duration of drawdowns from earlier highs.
Because of our models’ risk controls, we can use assets more aggressively than buy-and-hold investors. We can also reduce portfolio volatility by combining models with modest correlations and by incorporating low-volatility assets. Combining low- and high-volatility assets with modest correlations creates the desirable “barbell effect.” This can lead to additional rebalancing profits and greater portfolio stability.
Here are the BADASS results with allocations to SMURF, BADASS, FIRM, and GLTR in that order. FIRM also serves as the final backstop for all models when their trends are negative. S&P 500 and BLOK ETFs are shown as benchmarks. Real-time performance since January 2018, when we went public with GLTR and BADASS, has been similar to these portfolio numbers.
SMURF | BADASS | FIRM | GLTR | S&P500 | BLOK | 15/20/50/15 | 10/30/50/10 | |
CAGR | 19.2 | 70.7 | 5.9 | 23.1 | 12.3 | 14.6 | 22.8 | 27.5 |
STDDEV | 12.5 | 37.8 | 3.8 | 14.8 | 17.2 | 40.6 | 9.7 | 12.6 |
SHARPE | 1.29 | 1.55 | 0.94 | 1.34 | 0.63 | 0.48 | 1.95 | 1.83 |
UPI | 7.07 | 11.24 | 67.61 | 9.91 | 1.08 | 0.78 | 20.07 | 18.76 |
MAX DD | -9.3 | -21.7 | -0.7 | -10.5 | -23.8 | -68.8 | -4.5 | -5.5 |
AVG DD | -1.7 | -4.6 | -0.0 | -1.4 | -4.3 | -22.4 | -0.6 | -0.8 |
W%MOS | 70 | 68 | 91 | 70 | 63 | 60 | 75 | 75 |
FORT and BADASS have the highest expected returns among our models, while FIRM is the most stable. FORT, BADASS, and FIRM therefore create near-ideal barbell-type portfolios when you incorporate risk parity. SMURF and GLTR are used as much in these portfolios through model layering.
Here is a range of barbell allocations for more-aggressive, moderately aggressive, and less-aggressive portfolios that combine BADASS, FORT, and FIRM in that order. These have some of the best reward-to-risk characteristics among the possible allocations of our models. The S&P 500 is shown as a benchmark.
S&P500 | BADASS | FORT | FIRM | 25/35/40 | 20/30/50 | 15/25/60 | |
CAGR | 12.3 | 70.7 | 25.4 | 5.9 | 28.2 | 24.0 | 19.8 |
STD DEV | 17.2 | 37.8 | 11.3 | 3.8 | 11.7 | 9.6 | 7.6 |
SHARPE | 0.63 | 1.55 | 1.87 | 0.94 | 2.01 | 2.07 | 2.15 |
SORTINO | 1.08 | 5.40 | 4.41 | 19.75 | 6.96 | 7.42 | 8.17 |
UPI | 1.94 | 11.24 | 13.20 | 67.61 | 21.24 | 23.42 | 27.04 |
MAX DD | -23.8 | -21.7 | -7.9 | -0.7 | -5.6 | -4.5 | -3.3 |
AVG DD | -4.3 | -4.6 | -1.0 | -0.0 | -0.7 | -0.5 | -0.3 |
W% MOS | 63 | 68 | 78 | 91 | 78 | 77 | 78 |
Results do not guarantee future success nor represent returns that any investor attained. All trading involves risks that may not be foreseen. CAGR is the compound annual growth rate. Charts use monthly data. Drawdowns are on a month-end basis. UPI is the Ulcer Performance Index, which divides return by the Ulcer Index. The Ulcer Index measures the depth and duration of drawdowns from earlier highs.
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