Prospecting Dual Momentum With GEM

• Gary Antonacci popularized dual momentum with an effective and simple approach for dynamic asset allocation: Global Equities Momentum (GEM).
• Using simulated ETF data series, GEM’s performance over past market conditions can be approximated.
• For longer investment horizons GEM’s implementation with ETFs obtained positive returns with high consistency.

After winning first place in 2012 in the NAAIM Wagner competition, Gary Antonacci popularized his momentum investing approach in the award winning book “Dual Momentum Investing”.

In his book Antonacci makes a strong case for combining relative strength price momentum with trend following absolute momentum. The first 90 pages are a comprehensive overview, introducing the “premier market anomaly”,  describing the history of momentum research and its early practitioners, behavioristics and lots of other interesting themes. Frankly, these pages alone make the book a must read, not least due to the conversational, at times even playful tone of Antonacci’s light pen.

At the center of the book lies the chapter covering Global Equities Momentum (GEM), where Antonacci explains the mechanics of the dual momentum approach for dynamic asset allocation. GEM is quite brilliant in its simplicity: a 12-month lookback for both absolute and relative momentum combined with just three asset classes, are all of GEM’s components.

Both in his book and on his website, Antonacci presents the Global Equities Momentum (GEM) approach with non-tradable total return index data. Going back as far as the seventies has the benefit of incorporating a rising yields decade too. Therefore, to get insight into GEM’s long-term performance with today’s ETFs, index based simulated total return proxies are required. By applying GEM’s dynamic asset allocation to such simulated ETFs, the practitioner may get a good impression (nothing more) of GEM’s “real” performance during past market conditions. Before doing so, first GEM’s performance with index data will be replicated to validate the accuracy of the presentation in this contribution.

Noteworthy, the rules often shared for GEM, derived from the flow chart on page 101, are not the official GEM rules. Actually the flow chart along with the corresponding instructions on page 112 is only a simplified way to determine GEM’s allocations for those using a website like PerfCharts to get their signals. However, when doing calculations with a charting program like AmiBroker, the instructions on page 98 are to be adhered instead.

Holiday Reading

Sampling Universes with EAA

In this post several universes will be sampled using the Elastic Asset Allocation model. The universes under review are:
- CXO Advisory's 8 assets simple momentum universe
- Stefan Solomons 12 assets tactical allocation universe
- ETFdb.com's most popular ETFs
- CXO's on steroids: a 300% leveraged universe

The backtests are performed using monthly Yahoo! Finance total return data with EAA in Equal Weigted Hedged mode with monthly reforms. So each month assets are (re-)alloced according to the below simplified formula:
$\mathit{wi}\sim \mathit{zi}={((1-\mathit{ci})\cdot \mathit{ri})}^{\mathit{eps}},$ if ri > 0 else wi = zi = 0 ETFs are extended using mutual fund data to attain a backtest period of 20 years (1995 - 2014)*.

CXO Advisory's 8 assets simple momentum universe

The line-up for CXO's is DBC, EEM, EFA, GLD, IWM, IYR, SPY and TLT. Since the liquidity of CXO's original IWB is way lower than that of its bigger sibling SPY, the latter was used. IEF is deployed as c(r)ash protection fund (CPF), but is kept outside the regular allocation basket. The maximum number of assets for capital allocation is limited to 3+1.

CXO: equity curve with key performance indicators

Engineering Returns With Multi Asset Universes

Before the engineering starts: Only four months ago, the pageview counter passed the 100,000 milestone. In all likelihood the 200,000 mark will be hit today. Thank you very much for your interest.

The series about Harvesting Momentum (see part I, part II and part III) largely covered pairwise strategies. Now it is time to investigate the performance of more diversified universes to generate returns.

A recent post on CSSA presented Momentum Score Matrices as a tool for predicting the profitability of a given asset universe. Assuming a random walk, the momentum profitability depends on the degree in dispersion of mean returns between the assets in a portfolio. David Varadi states: "A heterogeneous universe of assets such as one containing diverse asset classes will have different sources of returns- and hence greater dispersion- than a homogeneous universe such as sectors within a stock index."

Following a similar, but simplified approach to the cross-sectional dispersion of mean returns, the pairwise momentum score can be calculated as the population average of the squared difference between the momentum values of asset ABC against asset XYZ. Or in "R":

Dispersion (D) for $IEF - $TIP based on 3 month returns during 2001 - 2014. Average = 8

For an asset collection consisting of 14 assets, this approach results in the below matrix. The calculations can be performed using "R", Excel or AmiBroker. Examples of each are available on the Google Drive connected to this post. AmiBroker has the benefit of exporting the momentum matrix as html-file, which then can be imported into Excel for analysis (and prettifying).

Dispersion matix based on 3 month returns using synthetic $ETF's

Harvesting Momentum: Let's Kick Tires With AmiBroker [ Part III - Final ]

The previous posts in this series covered a basic Tactical Asset Allocation approach with with signals taken from the daily and the weekly time frames. This final post will be dedicated to portfolio rotation based on monthly and quarterly time frame. Only two asset class universes will be covered. Multi asset class universes are going to be the subject of a future post.

Taking the monthly time frame into consideration basically means rotation decisions are made once a month, after the close of the month. For the quarterly time frames this comes to only 4 re-balancing opportunities per year: at the end of each quarter. However, usually the re-balancing frequency is way lower.

For backtesting once again the source code provided in the opening post will be used, basically allowing the DIY retail investor to engineer his own alpha. Even if you don't have access to thinkorswim or Amibroker, the strategy at hand can be fairly easy implemented using Excel, since momentum is the only metric taken into consideration for re-balancing decisions.

Monthly Periodicity

The typical US stock market year has 252 trading days (= 12 months with 21 days on average). So for the previously used 85 days/17 weeks lookback period, momentum calculated over a 4 month period (= 4 x 21 days) is suitable. Applied to the base pair (SPY-TLT) this 4 month lookback period renders the following results.

Portfolio Equity SPY - TLT (4 months), drawdown periods in red

Flexibile Asset Allocation With C(r)ash Protection

The "Conceptual sketch" posting presented a survey for designing a portfolio that generates stable profits during every type of economic environment the investor is faced with. Stimulating as well as  challenging comments were made providing food for thought on the building blocks for such a model. During our research we came across a paper published in late 2012 by Keller and Van Putten: "Generalized Momentum and Flexible Asset Allocation (FAA), An Heuristic Approach". The interested reader is encouraged to get acquainted with the elements of FAA.

Common asset allocation strategies (like the TAA strategy) are based on the so-called "momentum anomaly", which is known for centuries. The gist of the momentum anomaly is that assets often continue their price momentum, defined as the change in price over a given lookback period. Therefore one should buy assets with the highest momentum and sell assets with the lowest momentum.

FAA incorporates new momentum factors into risk regime determination. Next to the traditional momentum factor (R) based on the Relative returns among assets, Keller and Van Putten introduced Generalized Momentum by adding these new factors: Absolute momentum (A), Volatility momentum (V) and Correlation momentum (C). In their paper Keller and Van Putten demonstrated that by expanding the traditional momentum approach, portfolio performance increases compared to the buy and hold benchmark, both in terms of return as well as risk.

Summarizing FAA, Keller and Van Putten present their strategy with an example universe of 7 index funds. Applying a 4 month lookback, from this universe at the end of each month the top 3 assets are selected through a nested ranking process of these 7 assets based on relative momentum (higher is better), volatility (lower is better) and correlations (lower is better). Last, each of the top 3 assets chosen, has to pass the absolute momentum test: if their absolute momentum is negative, just go into cash. Capital is equally allocated over the top 3 assets or if applicable into cash.

FAA was scrutinized by Empiritrage. In their full report following findings are reached:
FAA has significantly higher risk-adjusted return than an equal weight portfolio. FAA decreases maximum drawdown dramatically. FAA is robust when adjusting look-back periods. Absolute momentum can directly add value on identifying down side risk regimes and decrease maximum drawdown.

Source: Empiritrage