Meb Faber’s Momentum and Trend-Following Strategy For Gold, Stocks, And Bonds

Key Takeaways

• Strategy executed 382 trades, averaging 0.7% gain each
• Win ratio 55% and profit factor 1.6
• Annual CAGR 8.7% with 92% time‑in‑market exposure
• Maximum drawdown capped at 15% since 2010
• Applies simple momentum to gold, stocks, and bonds

Pulse Analysis

Momentum and trend‑following have long been cornerstones of quantitative investing, yet many practitioners assume they require sophisticated data pipelines and high‑frequency trading. Meb Faber’s 2015 framework strips the concept down to its essentials: rank three core asset classes—gold, equities and bonds—by six‑month price performance, then rotate into the top performers on a regular schedule. By limiting the universe to liquid, globally diversified instruments, the model sidesteps the data‑intensity of multi‑factor strategies while preserving the core premise that assets in strong trends tend to continue outperforming in the near term.

The back‑test results speak to the model’s robustness. An 8.7% CAGR over a 15‑year horizon outpaces traditional 60/40 portfolios, while the 92% time‑in‑market metric indicates the strategy remains largely invested, avoiding the cash drag that plagues many tactical approaches. A profit factor of 1.6 and a win ratio above 50% suggest the edge is not fleeting, and the 15% maximum drawdown demonstrates a relatively shallow trough compared with equity‑only momentum funds that can suffer 30%‑plus declines. When adjusted for the high exposure, the risk‑adjusted return of 9.3% underscores the efficiency of the trade‑off between return and volatility.

For investors, the appeal lies in its implementability. The rules can be codified in a spreadsheet or a low‑cost broker platform, requiring only monthly price data and a simple ranking algorithm. However, practitioners should monitor transaction costs, especially in less liquid bond ETFs, and remain vigilant for regime shifts where momentum signals weaken, such as prolonged low‑volatility environments. As markets evolve, integrating complementary filters—like volatility scaling or macro‑trend overlays—could enhance performance without sacrificing the model’s core simplicity, making it a compelling building block for diversified, systematic portfolios.