Ivy Portfolio (Meb Faber)

Tactical Asset Allocation Medium

About Strategy

Meb Faber's tactical allocation model divides capital equally across five asset classes and applies a 10-month moving average trend filter to each. Assets below trend are moved to cash.

Performance Metrics (2016-2026)

CAGR (10-Year)8.8%
Max Drawdown-14.2%
Sharpe Ratio0.64
Volatility (StdDev)8.9%
Best Year+19.6%
Worst Year-8.5%
Strategy TypeTactical Asset Allocation
Risk ProfileMedium

Asset Allocation

The Ivy Portfolio allocates equally across five asset classes (20% each): US Equities (VOO), International Equities (VXUS), Real Estate/REITs (VNQ), Commodities (PDBC), and Intermediate-Term Bonds (IEF). Each asset is independently evaluated using its 10-month simple moving average.

Execution Rules

  1. Allocate 20% to each of five asset classes: US stocks, international stocks, REITs, commodities, and intermediate bonds.
  2. At month-end, compare each asset's price to its 10-month simple moving average (SMA).
  3. If an asset is above its 10-month SMA, maintain the position. If below, sell and hold cash for that allocation slice.
  4. Rebalance monthly — each asset is evaluated independently, so you may hold 0-5 assets at any given time.

ETF Proxies Used in Our Backtest

  • Vanguard S&P 500 ETF (VOO) — 20% US equities
  • Vanguard Total International Stock ETF (VXUS) — 20% international equities
  • Vanguard Real Estate ETF (VNQ) — 20% REITs
  • Invesco Optimum Yield Diversified Commodity ETF (PDBC) — 20% commodities
  • iShares 7-10 Year Treasury Bond ETF (IEF) — 20% intermediate bonds
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History & Background of the Ivy Portfolio

The Ivy Portfolio was developed by Meb Faber, co-founder and CIO of Cambria Investment Management, based on his influential 2006 research paper "A Quantitative Approach to Tactical Asset Allocation." The paper's title is now one of the most-downloaded academic papers on SSRN, with over 300,000 downloads.

The "Ivy" name references the endowment funds of Ivy League universities — particularly Harvard and Yale — which pioneered the use of diversified, multi-asset allocation strategies. Faber observed that these endowments achieved superior risk-adjusted returns not through stock picking, but through broad diversification across equities, real estate, commodities, and bonds. The Ivy Portfolio democratizes this approach for individual investors using low-cost ETFs.

The key innovation is the 10-month SMA trend filter. Rather than staying fully invested at all times, the Ivy Portfolio moves to cash when any individual asset class enters a downtrend. This tactical overlay has historically reduced portfolio drawdowns by roughly 50% compared to a buy-and-hold version of the same allocation. The 10-month SMA was chosen because it approximately equals the 200-day moving average, one of the most widely followed technical indicators.

When to Use Ivy Portfolio: Pros & Cons

Strengths

  • Broad diversification across 5 uncorrelated asset classes
  • SMA trend filter reduces drawdowns significantly (-14.2% vs buy-and-hold's ~-30%)
  • Includes real estate and commodities for inflation protection
  • Backtested with 10 years of ETF data (2016–2026); original academic research spans 100+ years
  • Can be partially or fully in cash during severe bear markets

Limitations

  • Monthly monitoring required — more work than passive strategies
  • SMA filter can generate false signals (whipsaw) in range-bound markets
  • Commodities and REITs add tracking error and higher expense ratios
  • Tax-inefficient due to potentially frequent switching
  • Requires 5 separate ETFs — more complex than GEM or 60/40

Ideal for: Investors who want endowment-style diversification with tactical downside protection, those comfortable with monthly rebalancing, and anyone who values trend-following as a risk management tool.

Related Strategies

Global Equity Momentum (GEM)

More concentrated tactical approach — 100% in one asset. CAGR: 12.3%

Adaptive Allocation

Momentum-based approach with fewer assets. CAGR: 11.5%

All Weather Portfolio

Similar diversification philosophy but passive (no trend filter). CAGR: 7.2%

MK
Marcin Kowalski Quantitative Researcher

Marcin Kowalski designs and backtests rules-based quantitative strategies. He holds an MS in Quantitative Finance and leads research for systematic asset allocation at StrategyIndex.io.

Backtest Methodology

Backtests are based on historical monthly Total Return data (dividends reinvested) of proxy index ETFs. We assume zero transaction slippage, annual/monthly rebalancing frequency, and no leverage. All calculations are executed systematically without human discretion.

Data Sources & Integrity

Historical figures are sourced from Yahoo Finance API, Tiingo Cloud API, and FRED Federal Reserve Database.

Last Data Update: June 30, 2026
Educational Purpose Only & Disclaimer

All content and calculation tools on StrategyIndex.io are intended solely for educational, research, and informational purposes. They do not constitute financial advice, tax planning, investment recommendations, or legal counsel. Hypothetical backtesting results have inherent limitations and do not represent actual trading. Past performance is never an indicator or guarantee of future returns. Asset allocation models are subject to market volatility, tracking errors, and strategy breakdown. Consult a certified financial planner before making any investment decisions.