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Tactical Asset Allocation

portfolio-constructionLevel 3 — Advanced

Prerequisites

Regime DetectionRegime IdentificationEdge Identification

What It Is

Tactical asset allocation (TAA) is the systematic adjustment of portfolio exposures based on forward-looking signals such as valuations, momentum, and macro regime indicators — as opposed to a fixed strategic benchmark.

Correct Execution

  • Use valuations (CAPE/earnings yield) as the primary long-term expected return signal, not as short-term timing
  • Use momentum/trend as a secondary regime signal to adjust tactical tilts
  • Build the portfolio around relative differences in expected returns, not absolute level predictions — the optimizer cares about the spread, not the number
  • Quantify risk aversion explicitly and integrate it with return forecasts; do not separate the two
  • Accept that TAA is a low-frequency, low-breadth process — most of the value comes from getting the asset allocation right over multi-year horizons, not from frequent rebalancing

Progression Levels

Diagnostic Tree

Coaching Cues

  • "A static 60/40 is a dynamic portfolio — it just has a constant negative edge embedded in it." — Victor Haghani, FWM S7E13
  • "Consistency matters more than cleverness. Use the same model for all assets, all regions — the relative differences are what the optimizer cares about." — Jim Masturzo, FWM S3E4
  • "You're not going to get the expected return — you're always going to get something different. But that's fine if you had a good process." — Jim Masturzo, FWM S3E4

Common Errors

  1. Using CAPE as a short-term timing tool: CAPE has a 10-year forecasting horizon, not a 10-month one → Use it to set strategic allocation ranges, not to flip in and out of equities quarterly
  2. Abandoning TAA during underperformance: All systematic strategies go through long periods of underperformance relative to a simple benchmark → Define in advance how long you'll tolerate underperformance before reconsidering the strategy
  3. Not adjusting for structural changes: CAPE anchored to 150 years of history ignores secular shifts in return on equity, buybacks, and sector composition → Adjust earnings yield for retained earnings and industry composition before comparing across time

Edges

⚡ Conventional Wisdom Is Wrong

A Static 60/40 Is Not "No View" — It's a Permanent Negative-Edge Bet

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Most investors treat a static strategic allocation as the neutral, unbiased option. It is not. A static 60/40 is constantly making a bet that the current expected return of equities relative to bonds justifies a fixed 60% weight — a bet that is almost certainly wrong most of the time. When equity earnings yield is 3% and bond yield is 5%, a static 60% equity allocation is a bet at negative edge. TAA is not about timing the market; it is about refusing to make the implicit bet at the wrong price.

What most people do
Frame TAA as aggressive market timing and default to a static allocation as the conservative, responsible choice.
What the best do
Recognize that all portfolios embed a view — the only question is whether it's explicit and sensible or implicit and arbitrary. Build a TAA framework that adjusts the implicit bet to reflect current relative valuations.
Why it's an edge: Investors who understand the embedded view in a static allocation can compete with those who don't by simply correcting the most obvious mis-pricings over long time horizons — this is a low-frequency, high-conviction strategy with minimal transaction costs.
How to exploit: Compute today's earnings yield for equities in every major market. Compute current real bond yields. When the equity-bond premium (earnings yield minus real bond yield) is below 1%, reduce equity allocation. When above 4%, increase. Run this check annually, not monthly.
Cross-domain parallel
In sports betting, not betting is always an option — but placing a bet at negative implied value is equivalent to the static 60/40 "no view" fallacy. Refusing to bet at wrong prices IS the edge.
Victor Haghani, "The Last of the Tactical Allocators," Flirting with Models S7E13, 2024-12-09
🔑 Hidden Causal Lever

CAPE's Failure Was a Reference Point Problem, Not a Model Failure

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CAPE's persistent bearish signal on US equities from 2010-2020 wasn't a flaw in the concept — it was a calibration error. The model was anchoring to a 150-year average CAPE of ~15 that included pre-1990 conditions: lower ROE, higher payout ratios, different sector composition. Post-1990, structural changes (technology-heavy economy, buybacks, higher capital-light ROE) permanently shifted the equilibrium CAPE higher. The model was right about the mechanism, wrong about the reference point.

What most people do
Either abandon CAPE-based valuation entirely ("it hasn't worked in 15 years") or defend it uncritically ("markets are just overvalued").
What the best do
Adjust the reference frame — use cross-sectional comparison (US vs global peers) instead of time-series comparison to historical average; or adjust earnings yield for retained earnings and sector composition before comparing.
Why it's an edge: Understanding the mechanism of why CAPE "failed" tells you when valuation signals ARE reliable (extreme dislocations, cross-sectional comparisons) vs when they're contaminated by reference-point error.
How to exploit: Replace historical average CAPE anchor with industry-composition-adjusted earnings yield. Compare US earnings yield to international equivalents after standardizing industry weights. The US premium that survives this adjustment is real valuation; the part that disappears was sector composition.
Cross-domain parallel
In algorithmic trading, a factor model that worked historically stops working when market structure changes (decimalization, HFT, changes in liquidity provision). The mechanism is unchanged but the calibration has to adjust to the new regime.
Antti Ilmanen, "Understanding Return Expectations," FWM S7E21, 2025-09-15; Victor Haghani, FWM S7E13, 2024-12-09
💎 Elite-Only Behavior

Simplicity in TAA Models Is a Performance Feature, Not a Limitation

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TAA models with more parameters look more sophisticated and explain historical data better in-sample. They consistently underperform simpler models out-of-sample. The 1940s jeep analogy is apt: a system with fewer moving parts has fewer ways to fail in new conditions. Models that survive stress tests across many parameter perturbations are more likely to survive future regimes. The complexity that made the model look good in the backtest is the exact complexity that makes it fail live.

What most people do
Add parameters to explain recent underperformance, fix apparent "bugs" in the model's behavior, and introduce new signals after they would have been predictively useful.
What the best do
Start with the simplest possible specification (earnings yield vs bond yield); resist adding complexity until the simple model shows a structural reason for improvement; measure robustness by how much performance degrades when all parameters are shifted ±30%.
Why it's an edge: Model complexity is correlated with institutional prestige and marketing effectiveness but inversely correlated with out-of-sample performance in low-frequency signals. Embracing simplicity despite pressure to look sophisticated is rare.
How to exploit: Build the single-signal version of the TAA model first (earnings yield spread only). Run it through parameter sensitivity. Only add a second signal (momentum) if it provides measurable independent information that survives stress testing. Stop at two signals unless evidence is overwhelming.
Cross-domain parallel
In sports betting models, adding more input variables to a point spread model almost always improves in-sample fit and degrades out-of-sample performance. The closing line is usually more accurate than any complex model built by an individual analyst.
Eric Crittenden, "All-Weather Portfolios with Trend Following," FWM S3E7, 2021-04-10; Jim Masturzo, FWM S3E4, 2021-04-10

Sources

  • Victor Haghani, "The Last of the Tactical Allocators," Flirting with Models S7E13, 2024-12-09 — CAPE/earnings yield framework, momentum overlay, practical TAA implementation
  • Jim Masturzo, "Tactical Asset Allocation," Flirting with Models S3E4, 2021-04-10 — capital market assumptions construction, model blindness concept, qualitative overlays
  • Antti Ilmanen, "Understanding Return Expectations," Flirting with Models S7E21, 2025-09-15 — CAPE critiques, structural shifts in earnings yield, equity bond premium
  • Eric Crittenden, "All-Weather Portfolios with Trend Following," Flirting with Models S3E7, 2021-04-10 — simplicity as robustness; stress testing frameworks