Tim Olson
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description, tags
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| Builds a long-only trend-following portfolio across multiple asset classes using ETFs, allocating weights proportional to cumulative momentum and optionally risk-adjusted by historical volatility, with an optional MA filter. |
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Multi-Asset Trend Following
Section: 4.6 | Asset Class: ETFs | Type: Trend-Following / Multi-Asset
Overview
ETFs allow efficient diversification across sectors, countries, asset classes, and factors in a relatively small number of instruments. This strategy constructs a long-only trend-following portfolio across multiple ETFs (and thus multiple asset classes) by allocating weights based on cumulative momentum, optionally filtered by a moving average, and weighted by historical volatility to manage risk.
Construction / Signal
Step 1 — Compute cumulative returns over a T-month formation period (T = 6–12 months):
R_i^cum = P_i(t) / P_i(t+T) - 1
Step 2 — Filter: Keep only ETFs with positive R_i^cum (positive momentum required for long-only).
Step 3 — Optional MA filter: Additionally keep only ETFs whose last closing price P_i exceeds their moving average MA_i(T') (typically T' = 100–200 days):
P_i > MA_i(T')
Step 4 — Assign weights to all surviving ETFs (not just top decile, since the universe is small):
Option A — proportional to cumulative return:
w_i = gamma_1 * R_i^cum (371)
Option B — momentum divided by volatility (Sharpe-like weighting):
w_i = gamma_2 * R_i^cum / sigma_i (372)
Option C — momentum divided by variance (Sharpe ratio optimization for diagonal covariance):
w_i = gamma_3 * R_i^cum / sigma_i^2 (373)
where sigma_i is historical ETF volatility and normalization coefficients gamma_1, gamma_2, gamma_3 are computed to satisfy sum_{i=1}^{N} w_i = 1 (N = number of ETFs with nonzero weights after filtering).
Option C (Eq. 373) optimizes the Sharpe ratio of the ETF portfolio assuming a diagonal covariance matrix C_ij = diag(sigma_i^2) (ignoring cross-ETF correlations).
Entry / Exit Rules
- Entry: At each rebalance, apply momentum and MA filters, compute weights, and enter long positions in all surviving ETFs.
- Exit: Rebalance monthly (or per the formation period schedule); ETFs with negative cumulative momentum or below their MA are dropped (weight set to zero).
- Position cap: Bounds
w_i <= w_i^maxcan be imposed to prevent overweighting of any single volatile ETF.
Key Parameters
- Formation period T: 6–12 months
- MA filter length T': 100–200 days (optional; aligns with sector momentum rotation MA filter)
- Weighting scheme: Equal (Eq. 371), volatility-adjusted (Eq. 372), or variance-adjusted/Sharpe-optimal (Eq. 373)
- Position cap: Maximum weight per ETF (optional; mitigates concentration risk)
- Holding period: Monthly rebalancing typical
Variations
- No MA filter: Use only positive cumulative return filter
- With position caps: Add
w_i <= w_i^maxto prevent overweighting high-momentum volatile ETFs - Sector rotation overlay: Combine with sector momentum rotation (Section 4.1) by restricting the universe to top-ranked sectors
Notes
- Eq. (371) is the simplest weighting; it overweights volatile ETFs since on average
R_i^cum ∝ sigma_i. - Eq. (372) mitigates volatility overweighting by dividing by sigma_i.
- Eq. (373) is the optimal Sharpe ratio solution under the assumption of uncorrelated (diagonal covariance) ETF returns.
- The key advantage of ETFs for multi-asset trend following: a small number of instruments (tens of ETFs) can provide exposure to many asset classes, sectors, geographies, and factors simultaneously.
- Long-only construction avoids shorting complexity; the MA filter prevents buying ETFs in absolute downtrends even if they have relative momentum.
- For some literature on multi-asset portfolios, dynamic asset allocation, and related topics: Bekkers, Doeswijk and Lam (2009), Black and Litterman (1992), Faber (2015, 2016), Mladina (2014).