9. The Growth Question

🧩 Before you read: a problem to solve

You read a rigorous academic study showing that value stocks — companies trading at low prices relative to their earnings, book value, or cash flows — have outperformed growth stocks by roughly 4% per year on average from 1927 to 2023. The evidence spans nearly a century, multiple countries, and survives realistic transaction costs. The mechanism is well-documented: investors systematically overpay for glamorous, fast-growing companies and undervalue boring, cheap ones. The premium is persistent, investable, and supported by both risk-based and behavioural explanations.

You are about to shift 30% of your equity allocation from a global cap-weight index fund into a low-cost value factor fund. The evidence is about as strong as evidence gets in finance. Is there any reason not to make this change?

“Cap weight is not a neutral allocation — it is the market’s collective judgment, which includes all known mispricing, speculative excess, and crowding. But all alternatives embed their own active bets.”

Consider an investor who, in 2005, read the academic literature on factor investing. They learned about the Fama-French three-factor model. They saw the evidence: value stocks — companies trading at low prices relative to earnings, book value, or cash flows — had outperformed growth stocks by a substantial margin over multiple decades and across multiple countries. The mechanism made intuitive sense: investors overpay for glamorous growth stories and undervalue boring, cheap, out-of-favour businesses. The premium was well-documented, persistent, and supported by a risk-based explanation. They tilted their portfolio toward value.

Over the next fourteen years, value underperformed growth in nine of them.

From 2017 to mid-2020, the cumulative underperformance was severe. By some measures, it was the longest and deepest value drawdown in U.S. market history. The investor watched growth stocks — the companies they had deliberately underweighted — soar. Their value fund, which owned banks trading below book value and energy companies at single-digit earnings multiples, drifted sideways or fell. Every year, the commentary supplied an explanation for why it was different this time: low interest rates favoured growth companies with distant future cash flows; technology platforms had network effects and intangible assets that old-economy value metrics could not capture; “value is dead.” By mid-2020, many value investors capitulated. They sold their value funds, bought the growth stocks that had been outperforming, and concluded that the academic evidence had been overturned by a new economic reality.

In November 2020, value began one of its strongest recoveries on record. Over the next two years, value dramatically outperformed growth. The investors who had abandoned the factor after a decade of pain locked in all of the underperformance and captured none of the recovery.

This story is not an argument against factor investing. It is an argument about what factor investing actually demands. The evidence for value, momentum, and quality is among the strongest in academic finance — but evidence of a long-term premium is not evidence of a smooth ride. Factor premia, if they exist, are harvested by investors who can survive the droughts. Most cannot. And this is the central tension of the growth question: the default is hard to accept, but every alternative is harder to live with.

The default growth answer — broad global cap-weight equity — is simple to state and hard to accept. It means holding the market’s composition, including its concentrations, its fads, and its bubbles. This chapter examines the alternatives: equal weight, factor tilts, and home bias. None is clearly superior; all have identifiable costs and failure modes.

Cap weight: what you’re actually holding

A free-float market-cap-weighted global equity index weights each company by the market value of its freely tradable shares. The consequences:

• It is concentrated. As of mid-2025, the U.S. is ~60% of global free-float market cap. The top 10 companies are a material fraction of the total. This describes the market’s composition; it does not predict a crash — but it also does not deliver equal geographic or sectoral exposure.
• It is self-adjusting. When a company or country grows relative to others, its weight increases automatically. When it shrinks, its weight decreases. No rebalancing is required beyond the index’s own reconstitution.
• It is cheap to implement. Global cap-weight index funds exist at 0.03–0.20% expense ratios. Turnover is low (index changes are infrequent and typically at the margin).
• It embeds the market’s errors. If the market overvalues a sector or country, cap weight overweights it. This is a feature if you believe markets are efficient; it is a bug if you believe valuation matters and mean-reverts.

The Japan precedent is the strongest cautionary example: ~45% weight in the late 1980s, followed by decades of negative real returns. An investor who mechanically held global cap-weight through that period would have experienced a severe and prolonged drag from a single country’s weight. The framework acknowledges this; deviation rules (below) exist for investors willing to make a defined bet against market concentration.

Equal weight: factor tilt, not free diversification

Claim (often heard): Equal weight provides “free diversification” by reducing dependence on the largest companies.

What S&P’s own factor attribution shows (S&P 500 Equal Weight Index vs. S&P 500, January 1990–May 2018):

Equal weight is not a neutral alternative to cap weight. It is a deliberate tilt: more small-cap, more value, less momentum. Whether this tilt is desirable depends on whether you want those factor exposures — and whether you can tolerate their droughts.

The rebalancing mechanism. The S&P 500 Equal Weight Index rebalances quarterly, resetting each constituent to ~0.2%. This constant-mix approach (Perold–Sharpe framework) can generate a rebalancing return when constituents are volatile and imperfectly correlated — but this return is path-dependent, model-dependent, and typically small relative to factor-driven returns. It is not a reliable source of alpha.

The costs. Equal weight has higher turnover than cap weight (quarterly rebalancing to reset weights). This creates transaction costs, tax inefficiency in taxable accounts, and capacity constraints. The factor tilts — especially the negative momentum exposure — can cause prolonged underperformance during mega-cap-led rallies.

The rule for equal weight: Equal weight is a conditional factor/rebalancing tilt, not free diversification. It requires acceptance of higher turnover, tax drag, and potential tracking error versus cap weight. It is not the default.

Explicit factor tilts: value, momentum, quality, and size

The claim: Specific factors — value (cheap beats expensive), momentum (recent winners continue), quality/profitability (strong fundamentals outperform), and size (small beats large) — offer premia beyond the market return.

The evidence, after realistic costs. Frazzini, Israel, and Moskowitz use approximately $1 trillion of live institutional trading data across 19 developed markets from 1998–2011 to measure real-world transaction costs for factor strategies. Unlike most academic studies that estimate costs from models or spreads, this dataset captures actual executed trades, commissions, and market impact at scale. After realistic costs:

• Value and momentum retain economically meaningful net returns at substantial capacity. The returns survive the friction test.
• Quality/profitability (companies with strong fundamentals — high margins, stable earnings, low leverage) has strong post-cost support.
• Size (small-cap premium) is more capacity-constrained with weaker expected return after realistic trading costs in large portfolios. The premium mostly concentrates in micro-caps that institutional investors cannot access at scale.

Post-publication decay. McLean and Pontiff examine 97 published cross-sectional return predictors (anomalies) and find that their returns decline by approximately 26% on average after publication (published in Journal of Finance, 2016). An earlier working paper version found only ~10% decay — the difference highlights sensitivity to specification and the importance of using the most current, peer-reviewed estimate. The decay suggests that some portion of published factor returns reflects data mining (researchers finding patterns that existed in the sample) or that arbitrageurs trade away the premium once it is publicly known. Either way, using pre-publication returns to estimate future factor performance overstates expected returns.

Multiple testing. Harvey, Liu, and Zhu raise the statistical significance bar for new factor discoveries. In any large dataset, if you test enough variables, some will appear significant purely by chance. The authors propose a minimum t-statistic of 3.0 (rather than the conventional 2.0) to account for the hundreds of factors that have been tested and published. Many factors that appeared significant under conventional standards would fail this higher threshold.

The net picture: value, momentum, and quality/profitability have the strongest post-cost support among equity factors. Size is weak. Many other published factors may not survive the combination of post-publication decay, multiple-testing correction, and realistic implementation costs. Factor timing — switching between factors based on valuation spreads, macroeconomic conditions, or recent performance — is unsupported by the evidence.

The failure modes:

• Factor droughts. Value can underperform for a decade or more — the period from 2006 to mid-2020 was one of the deepest and longest value drawdowns on record, and the investor in the story above lived through all of it. Momentum can crash violently at market reversals, as it did in August 2007 when a multi-strategy quant unwind produced rapid, correlated losses across momentum, value, and quality simultaneously — a reminder that factors which appear uncorrelated in long samples can become sharply correlated in stress. Factor investors must be willing to endure potentially long periods of underperformance and tracking error, during which the commentary will declare their factor permanently broken and the evidence overturned. Most investors abandon the factor before it recovers. The opening story of this chapter is not a hypothetical — it describes the central risk of every factor strategy.
• Crowding and saturation. As more capital pursues a factor, the premium can shrink or disappear.
• Turnover and tax drag. Factors that require frequent rebalancing are tax-inefficient in taxable accounts. The net-of-tax return may differ materially from the pre-tax return.
• Abandonment risk. The most common factor-investing error: invest after a factor has performed well, then abandon after it underperforms — locking in the underperformance without capturing any subsequent recovery.

Factor timing is unsupported. The evidence does not support switching between factors based on valuation, macroeconomic conditions, or recent performance. Factor premia, if they exist, are harvested by persistent exposure — which requires surviving the droughts.

The rule for factor tilts: Cap weight is the default growth reference. Explicit factor tilts are conditional tools requiring: (1) a named factor and mechanism, not just a backtest; (2) willingness to endure potentially long droughts and tracking error; (3) explicit cost, turnover, tax, and capacity estimates; and (4) a precommitment rule that does not abandon the factor after poor performance. Value, momentum, and quality have the strongest post-cost support; size is weak; factor timing is unsupported.

Home bias: the comfort trap

The claim: Investors should overweight their home country because they spend in the domestic currency, receive favourable tax treatment, or avoid currency-hedging costs.

The evidence. French and Poterba (1991) document persistent home bias across major markets — investors hold far more domestic equity than global diversification would recommend. Cooper and Kaplanis (1994) test whether observable costs (currency hedging, taxation, capital controls) can explain the magnitude of home bias. They find the costs are too small — the observed bias is far larger than rational cost-based explanations can justify, pointing to informational frictions or behavioural causes.

The commonly cited rational grounds for home bias therefore have weaker empirical support than typically assumed. The baseline should be global market weights, with deviations requiring named reasons.

But home bias can be rational when:

• The investor’s liabilities, spending, and tax regime are overwhelmingly domestic;
• The investor already has substantial domestic economic exposure through employment, property, or business — and adding domestic financial exposure creates total-wealth concentration;
• Capital controls, custody issues, or product access constraints make foreign investment impractical.

The rule for home bias: Start from global market weights. Deviate only for identified currency, tax, consumption, access, or total-wealth-concentration reasons. Recognize that 100% domestic and 100% global are both extreme positions.

Equity FX hedging: not a universal rule

The question: Should foreign-equity currency exposure be hedged back to the investor’s base currency?

The evidence. Campbell, Serfaty-de Medeiros, and Viceira (2010, 1975–2005 developed markets) find that the risk-minimizing currency strategy for a global equity investor is not a full hedge. The U.S. dollar, euro, and Swiss franc tended to appreciate when global equity markets fell — making these “safe haven” currencies attractive to retain rather than hedge away. For global bond investors, the risk-minimizing strategy was close to a full currency hedge.

The mechanism: some currencies have historically served as safe havens, appreciating during risk-off episodes. Hedging them away removes a natural equity hedge. Commodity-linked and emerging-market currencies tend to depreciate when equities fall and are better candidates for hedging.

Boundary. The sample is 1975–2005 developed markets. Which currencies serve as safe havens can change. The analysis minimizes short-term volatility, not long-horizon real purchasing power. Liability currency governs — if spending is in the base currency, foreign-currency exposure adds volatility that may be undesirable even if it diversifies equities.

The rule for equity FX hedging: Equity FX hedging is an adaptation-layer conditional tool. The default is to understand currency exposure, not to automatically hedge or leave unhedged. Report local-currency, unhedged, and hedged returns separately where material. No universal equity hedge ratio follows.

The value investor who opened this chapter learned something that the academic papers do not teach. The evidence for value was strong — and it did not make the fourteen-year wait any easier to endure. Cap weight is hard to accept. Factor tilts are hard to live with. Home bias is comfortable but unjustified by evidence. There is no path free of discomfort. The durable response is not to choose one and declare it correct. It is to name the bet you are making, cost it honestly, and build a process that can survive its inevitable droughts.

Key idea: Cap weight is not neutral, equal weight is not free, factors are not reliable, and home bias is not rational by default. Every growth-layer deviation from global cap weight is an active bet — and should be acknowledged, costed, and stress-tested as such. The most important factor is not value or momentum. It is the ability to follow a strategy through the periods when it feels like it has stopped working.

Growth deviations are active bets on how to capture equity returns. But some assets are not primarily growth or defence — they are insurance against specific, rare harms, and they come with distinct costs and failure modes. The next chapter examines the case for gold, commodities, and crypto.