Investing Amid Low Expected Returns

How can you form realistic expectations about future investment returns? Antti Ilmanen’s Expected Returns: An Investor’s Guide to Harvesting Market Rewards argues that forecasting and interpreting expected returns require a multi‑lens approach—one that combines historical data, theory, forward indicators, and behavioral understanding. Ilmanen’s central message is that there is no single formula for expected returns. Instead, you need disciplined triangulation between what history has shown, what financial theory predicts, and what current market prices imply.

The Elephant and the Cube: Organizing Frameworks

Ilmanen uses two metaphors—the Elephant and the Cube—to capture this complexity. The Elephant reminds you to consider four inputs together: historical averages, theoretical reasons for differences in returns, forward-looking indicators like yields or valuation ratios, and discretionary views. Each illuminates different pieces of the puzzle; neglecting one produces distorted forecasts. The Cube asks you to analyze expected returns across three dimensions: asset classes (stocks, bonds, real estate), strategy styles (value, carry, momentum), and fundamental risk factors (growth, inflation, liquidity). Together they organize all the examples in the book and teach you to refocus from asset labels to underlying return sources.

Humility and Risk: Why Context Matters

Ilmanen argues that humility must anchor all expected-return judgments. Historical averages may mislead when structural changes or valuation extremes skew samples. Theories explain why premia exist—often linked to covariance with bad times—but cannot give precise numbers. Forward indicators reflect current pricing but can be distorted by sentiment and regime shifts. Discretionary views capture investor-specific constraints, but they’re subjective and prone to bias. By using all four inputs, you prevent false certainty and avoid relying on any single flawed lens.

Time Variation and Behavioral Forces

Expected returns vary over time. They rise in bad times when risk aversion spikes and fall when optimism prevails. This variation reflects both rational risk-pricing dynamics—covariance with recessionary states—and behavioral biases like extrapolation, conservatism, and overconfidence. Limits to arbitrage mean that mispricing can persist even when investors recognize it, because real-world traders face funding restrictions and career risks. Understanding both rational and behavioral drivers helps you avoid chasing crowded trades and misinterpreting anomalies as permanent free lunches.

From Theory to Application

Ilmanen builds bridges between theory and practice. He translates consumption‑based asset‑pricing ideas—covariance with bad times—into intuitive conclusions about when different assets perform poorly (equities and carry strategies in crises) or act as hedges (Treasuries in deflationary shocks). He distinguishes carry and value as complementary signals: carry measures current income if conditions stay the same, while value gauges how prices compare to long-term anchors. Historical evidence shows that forward-looking valuation and carry indicators often predict returns better than historical averages, but timing them effectively requires caution and patience.

The Book’s Broader Ambition

Ultimately, Ilmanen’s book is a synthesis of classical finance, behavioral research, and institutional practicality. It blends CAPM and habit formation theory with evidence from risk factors—momentum, carry, volatility selling—and discusses structural topics from bond term premia to credit downgrading bias and alternative asset traps. You learn that most observed return differentials are partly rewards for bearing bad‑time risk, partly effects of crowding and constraints, and partly transient behavioral mispricing.

Core Insight

Expected returns are not constants baked into the market; they are dynamic reflections of risk appetite, liquidity, and investor behavior. To estimate them responsibly, you must think in multiple dimensions, combine data and theory, respect uncertainty, and recognize how human and institutional forces distort prices. Ilmanen’s multi‑lens framework helps you see those forces clearly and act prudently.

(Parenthetical note: Unlike simple forecasting manuals, this work—written while Ilmanen was at AQR—aims to create intellectual discipline for professional investors. It’s closer in spirit to Irrational Exuberance by Robert Shiller and Adaptive Markets by Andrew Lo, but with portfolio applications.)

Ilmanen cautions that historical average returns, while intuitive, are unreliable guides to the future. They reflect what investors earned in specific eras, not what they should expect next. Sample choice, structural changes, and one‑off repricing booms distort averages. For example, falling bond yields from 1980 to 2009 created huge capital gains, exaggerating long-term bond returns. Similarly, equity returns reflect regimes of payout and valuations that may never repeat.

Biases and Structural Shifts

Historical samples suffer from survivorship bias, omitted disasters (“peso problems”), and shifts in rules or market structures such as buybacks replacing dividends. Fund data often overstate results because failing managers vanish from samples. These distortions can easily produce false confidence about a premium’s size or even its sign.

How to Correct Historical Views

• Use multiple windows—compare shorter and longer periods and inspect sub‑regime behavior.
• Adjust for windfalls—remove gains from yield compression or bubble repricing to approximate ex ante investor expectations.
• Combine history with theory and forward indicators—ratio signals, yield curves, and surveys usually forecast better multi‑decade returns than naïve extrapolation.

Core Insight

Historical means are stories about what happened, not forecasts. Treat them as one light among several and anchor expectations using structure and valuation, not just averages.

(Note: This logic parallels Robert Shiller’s emphasis that price–earnings ratios, not historical returns, better predict future equity performance.)

Risk premia exist because some assets lose money during bad times. Ilmanen reframes classical CAPM logic using the stochastic discount factor (SDF): what matters is covariance with bad times, not mere volatility. Assets that perform poorly in recessions or crises must offer higher expected returns. Conversely, assets that hedge those states can have low or even negative premia.

Defining “Bad Times”

Weak consumption growth, tight credit, liquidity droughts, and spiking correlations define bad times. Ilmanen’s evidence shows that equities, carry trades, and volatility-selling strategies all lose when these conditions hit. Treasuries sometimes behave as safe havens—earning low returns precisely because they pay off in stress events—but this empirical relationship shifts over decades (safe in 2008, not in 1970s stagflation).

Habit Formation and Time Variation

Campbell and Cochrane’s habit‑formation model helps explain why the price of risk rises in bad times. As wealth approaches consumption habits, investors become more risk‑averse and demand higher premia. When wealth is ample, risk aversion falls and premia compress. This changing appetite creates cyclical predictability and explains why expected returns are higher precisely when you are least willing to act on them.

Practical Relevance

• Judge risk by timing of losses, not volatility alone.
• If a strategy suffers most when liquidity vanishes, its high Sharpe ratio may just compensate tail exposure.
• Long-horizon investors can profit from buying risk premia during stress—if they truly can endure drawdowns without forced liquidation.

(Parenthetical note: This reorientation echoes Raghuram Rajan’s and Andrew Lo’s warnings that risk shifts from volatility to fragility under leverage.)

Ilmanen demonstrates that forward-looking indicators—valuations, yields, payout ratios, and curve steepness—outperform historical averages in predicting multi-year returns. Long-term earnings yield (E/P), total payout yield, and bond yields provide immediate clues to future premia.

Understanding Carry and Value

Carry represents current yield if nothing changes: coupon, dividend, or FX rate differential. Value measures price relative to fundamentals (E/P, P/B, Tobin’s q). Cheap valuations predict higher long-term returns while expensive valuations warn of low future performance. Combined, they forecast both income and revaluation trends.

Time-Varying Premia

Expected returns vary through cycles—rising in fear, falling in euphoria. Timing on valuation signals can work, but must be patient and risk-aware. Ilmanen suggests valuation timing, risk-factor timing via macro indicators, and disciplined rebalancing as best practices. Cheap assets, steep yield curves, and high volatility regimes usually precede higher ex post returns—but the exact turn requires humility and diversification.

Avoiding Timing Traps

Ilmanen stresses that many predictive relationships are fragile or in-sample. Implement timing slowly, test out-of-sample, and include transaction costs and capacity limits. Robust signals rely on economic logic—carry, valuation, macro fundamentals—rather than data-mined correlations. Rebalancing toward strategic targets often extracts as much risk-premium reward as bold market timing but with lower path risk.

(Parenthetical note: His practical emphasis mirrors Clifford Asness’s caution—use signals but avoid hubris.)

Fixed income appears simple but hides multiple expected-return components. Ilmanen separates nominal yields into expectations of future short rates, inflation, and the bond risk premium (BRP). The BRP compensates for duration risk—the tendency for long bonds to lose when rates rise—and varies with inflation uncertainty, safe-haven behavior, and central-bank policy.

Estimating Term Premiums

Curve steepness is a noisy proxy for BRP. A steep curve might reflect expected policy normalization rather than attractive premia. Better measures blend survey forecasts and model-based decompositions (Cochrane–Piazzesi or Kim–Wright). These reveal that BRP swings with inflation risk, scarcity, and global demand for safe assets. Post-2009 quantitative easing compressed term premia sharply, even when valuations appeared rich.

Credit Spreads and Downgrading Bias

Corporate spreads mislead too. A nominal 120‑bp spread historically yielded only 30‑40 bp of realized excess return once default, downgrade, and index selling costs were accounted for. Downgrades have asymmetric effects: spread widening on downgrades hurts more than tightening on upgrades helps. Index rules amplify losses as funds sell fallen‑angel bonds at poor prices.

Practical Advice

• Use survey-based term-premium estimates and inflation indicators, not raw steepness.
• Quantify downgrading probabilities and avoid forced sales where mandates permit.
• Short-dated high-grade credit offers strong spread-per-volatility ratios and smoother rewards when funding costs are manageable.

(Note: His credit analysis resembles Asness’s view that many credit anomalies vanish after accounting for funding and illiquidity.)

The book broadens the lens to real estate, commodities, hedge funds, private equity, and other alternatives. Ilmanen warns that investors often misjudge alternative returns due to smoothing, illiquidity, and index biases. Private real estate indexes understate volatility; hedge-fund composites suffer from survivorship and backfill bias; private equity performance largely accrues to general partners through fees.

Real Estate and MBS Lessons

For real estate, appraised data inflate Sharpe ratios. Listed REITs show true volatility and behave mostly like small-cap equities. Mortgage-backed securities carry hidden short-convexity risk—refinancing options limit upside when rates fall, causing realized excess returns far below modeled OAS levels.

Commodities and Futures Mechanics

Commodity investors earn returns primarily from roll yield in backwardated markets, not from spot appreciation. When inventories are low, backwardation produces gains; when contango dominates, roll becomes negative. Trend and momentum strategies across diversified baskets have shown positive risk-adjusted results.

Hedge Funds and Private Equity

Adjusted data show hedge-fund alpha smaller than advertised, with gross returns mostly attributable to systematic factors. Private equity requires persistent access to top quartile GPs to justify illiquidity; median LPs rarely beat public equities after fees. Illusionary diversification arises because correlations spike in crises and liquidity evaporates when it’s most needed.

Core Insight

Alternative assets can enhance risk-adjusted returns only if you account for smoothing, fees, crowding, and crisis correlation. Their reported histories often exaggerate reward relative to true risk.

(Parenthetical note: This echoes David Swensen’s critique of illiquidity optimism in institutional portfolios.)

Investor psychology interacts with institutional constraints to create anomalies that persist. Representativeness, anchoring, overconfidence, and loss aversion shape buying and selling behavior. At the same time, arbitrage is risky and capital-limited—lending longevity to mispricing. Ilmanen blends behavioral finance (Kahneman, Shleifer) with market structure to explain why inefficiencies such as value and momentum coexist.

Crowding and Feedback Loops

Successful strategies attract flows until they become crowded. The August 2007 quant crisis showed how common positions can implode simultaneously. Feedback loops from leverage, mark-to-market rules, and investor herding amplify cycles. When everyone buys the same high-Sharpe signal, expected returns shrink and systemic risk grows.

Practical Implications

• Before implementing a strategy, ask if you can endure pain before gain; if not, it may not be exploitable.
• Monitor crowding indicators like AUM, position overlap, and funding costs.
• Diversify across style factors and maintain liquidity buffers for stress episodes.

(Note: Ilmanen’s depiction of style lifecycle—innovation to crowding—echoes Andrew Lo’s adaptive-market perspective.)

Markets price not only standard deviation but also the shape of distribution—skewness and kurtosis. Ilmanen explains that investors love positively skewed assets (lotteries) and require premia for negatively skewed exposures (insurance selling). Many high-Sharpe strategies hide asymmetric risk: they win steadily but crash episodically.

Lottery Preferences and Overpricing

Behavioral data show investors chase lottery-like payoffs in volatile small-cap stocks or deep OTM calls. These assets underperform later because high demand inflates prices. Bali and colleagues demonstrate that stocks with the largest single-month return spikes underperform by roughly 1% monthly—evidence of overpriced skewness.

Negative-Skew Strategies

Selling volatility, carry, or illiquidity provides negative skew—steady income punctuated by large losses. These exposures earn high long-run returns because they lose in crises. Proper diversification and sizing can harvest these premia without fatal tail concentrations. Dispersion trades and correlation swaps highlight how short-correlation bets yield compensation but implode when correlations spike systemically, as in 2008.

Acting on It

Allocate small deliberate exposure to lottery assets if you seek upside; size insurance-like positions conservatively and hedge catastrophe risk. Understand that skewness premia are crisis-sensitive and interlinked with bad-time covariance. Having liquidity and patience is the only steady edge.

Core Insight

Return asymmetry explains why high-return strategies are risky precisely when you most need capital. Recognizing skewness helps you price, size, and diversify correctly.

(Parenthetical note: Ilmanen’s treatment complements Nicholas Taleb’s focus on tail exposure but frames it quantitatively.)

The final synthesis of Ilmanen’s framework is operational: combine value, carry, and momentum to forecast and allocate systematically. Each provides distinct predictive information—carry reflects local yield, value signals long-term mean reversion, and momentum captures short-term trends. Their combination balances contrarian and trend-following forces, helping diversify predictive errors.

Building Forward-Looking Models

Practical models decompose nominal returns into carry, growth expectations, and valuation changes. Normalizing signals, averaging binary indicators, and combining them through Black‑Litterman or robust optimizers helps prevent overfitting. Out-of-sample testing and parameter uncertainty awareness are essential.

Implementation Discipline

• Match signal horizons—slow value vs rapid momentum—to turnover capacity.
• Track liquidity, funding spreads, and crowding.
• Use normalization to prevent double-counting correlated predictors.

Through integrated forward tools, you can compare expected returns across equities, bonds, credit, and commodities on consistent logic. As Ilmanen stresses, the objective is not precision but disciplined expectation formation—seeing the market with all lights on.

(Parenthetical note: The integration resembles the multi-factor approach used in AQR’s style premia funds.)