Better than Alpha

Why do investors keep chasing “alpha” even when it’s vanishing? In Alpha Masters Revisited, Schelling argues that what most people call “alpha”—excess return unattached to market exposure—has been steadily eroded by technology, scale, and transparency. He reframes the idea: true alpha is a rare residual, not a repeatable product. If you misuse benchmarks or confuse factor exposures with skill, you’re paying for beta at alpha prices.

Across the book, Schelling traces the history of alpha—from its early definition in Jensen’s model, through the factor revolution (Fama–French, Carhart), the rise and fall of hedge funds, and the evolution of private equity—and builds a practical framework for modern investors: alpha survives in places data and scale have not yet crushed. You also discover new sources of “behavioral,” “process,” and “organizational” alpha—forms of excess return arising from better human decision architecture, not market timing.

From Residual Return to Factor Awareness

At its foundation, alpha is what remains after you adjust for beta. Schelling walks through examples: a manager who earns 10% with beta 1.0 produces no alpha; one with beta 0.6 earns 4% alpha. Yet this definition depends critically on benchmark choice—if you pick the wrong index or period, you mismeasure skill. Modern finance discovered that much of what appeared as alpha was actually exposure to persistent factors such as size, value, and momentum. Once these became investable via index products and ETFs, competing for alpha within liquid public markets turned into a zero-sum game.

The Price of Crowding and Scale

Throughout Schelling’s narrative, scale undermines edge. Hedge funds illustrate how informational advantages erode as capital floods in: early players like Jones, Soros, and Steinhardt exploited sparse data and flexible mandates, but trillions later, average returns collapsed. The same dynamic hit private equity—larger funds paid higher entry multiples and relied more on leverage than operational improvement, shrinking IRRs and MOICs over time. Size converts scarce skill into commoditized beta.

Technology and Alpha Decay

Moore’s law accelerated data availability and computing power, transforming markets into hyper-efficient engines. Machines now dominate structured tasks—pattern recognition, arbitrage, factor mining—so human edges thrive only where judgment, complexity, or illiquidity resist automation. Schelling’s warning is clear: if the game is data ranking, computers win. Durable alpha must come from unstructured arenas such as private credit, litigation finance, and operational improvements in private assets.

The Human Side of Alpha

Despite technological efficiency, Schelling shows behavioral and organizational forces still matter. Investors fall victim to System 1 biases—confirmation, loss aversion, overconfidence—that distort decisions. The antidote is structured System 2 discipline: pre-specified benchmarks, falsifying evidence hunts, and process design that channels attention to high-impact choices. Organizations also breed alpha or destroy it depending on governance—trustees with expertise outperform political appointees, and clear accountability drives better long-term results.

Rethinking Success

Ultimately, Schelling reframes success: investors don’t eat alpha; they eat total return. Beating a benchmark means little if you miss your real goal—funding liabilities, endowments, or spending. Define risk as the probability of failure, not volatility. “Behavioral alpha” emerges when you set realistic expectations, implement robust—not optimized—portfolios, and govern decisions through skilled, aligned teams. The result is a cohesive philosophy: alpha rarely lives in quick trades—it grows in how you design systems, think probabilistically, and manage the inevitable biases that lead others astray.

Core message

Alpha is not just excess return—it’s evidence of scarce insight or superior decision architecture. In liquid markets, assume it’s gone; in private and behavioral dimensions, assume it’s possible if you do the work.

The book thus evolves from a definition of alpha into a multidisciplinary manual for how to create, measure, and sustain edge when traditional security selection no longer suffices. Schelling unites hard finance, behavioral science, and organizational psychology into one story: enduring advantage belongs not to the fastest, but to the most deliberate thinkers.

Alpha is often mystified, but Schelling starts simple: it’s the unexplained return after accounting for all systematic exposures. Through Jensen’s alpha, the manager’s performance equals the risk-free rate plus beta times market return plus alpha—the leftover intercept term. The catch is that wrong benchmarks or periods distort this measure, creating “false alpha.”

Benchmark Choice and Measurement

The author emphasizes five benchmark criteria: specified in advance, relevant, measurable, investable, and comprehensive. If any fail, you might be comparing apples to luck. Managers often manufacture alpha post hoc by changing comparison indices. For you, this means insisting on pre-specified benchmarks and transparent attribution—disentangle exposures to equity, credit, leverage, liquidity, and timing.

Alpha as Residual and Zero-Sum

In any given market, aggregate alpha is zero—some win only because others lose relative to the risk-adjusted average. Therefore durable alpha must be causal (rooted in skill or structure) rather than random (luck or benchmark error). Schelling illustrates this through numerical examples and reminds that sampling frequency alters perceived risk exposures—ExxonMobil’s beta differs across daily vs. monthly data, showing even measurement itself is not neutral.

Practical Takeaway

Your job is to ask, “What exposure am I actually paying for?” If a manager’s outperformance disappears once factor or sector effects are removed, you’re buying expensive beta. Quantitatively competent due diligence—proper regression windows, factor inclusion, and benchmark predefinition—is what separates noise from true skill.

Essential reminder

Most alpha is measurement illusion. True edge shows up consistently across time, teams, and markets after every exposure is adjusted away.

What once looked like manager brilliance became academic models. Schelling narrates how factor discovery—from Fama–French’s size and value, to Carhart’s momentum—transformed alpha into systematic beta. By identifying common drivers of return, researchers democratized what had seemed like intuition or wizardry.

Historical Evolution

Early investors like Lovelace and Graham practiced active stock selection; Bogle’s innovation was making market exposure cheap. As academics unpacked repeatable factors, firms like BARRA and BGI commercialized them into portfolios, eventually becoming “smart beta” products. Dimensional, AQR, and Vanguard built inexpensive ways to capture these premia.

The Factor Zoo

Hundreds of alleged factors appeared—size, value, momentum, profitability, investment, etc. Schelling insists on scientific rigor: persistence, cross-market evidence, causal logic, and practical usefulness. Value and yield survive these tests; momentum might reflect behavioral mispricing; others fail replication.

Implications for You

Understanding factors protects you from paying active fees for systematic tilts. If you can’t verify a manager’s skill beyond known exposures, buy the factors cheaply. Yet beware of crowding—once factors are widely implemented, their excess return compresses. Continually reassess relevance rather than assuming historical premia persist unchanged.

Key principle

Factors did not destroy alpha—they explained it. Recognizing what’s systematic is the first step to finding what remains scarce.

Schelling contrasts the romantic image of hedge fund geniuses and private equity titans with empirical data: scale and crowding kill performance. The early hedge fund model—Jones’s leveraged long–short balance—worked when capital was rare; decades later, trillion-dollar establishments deliver muted single digits. Private equity’s pattern repeats—larger funds chase bigger deals, pay higher multiples, and rely on financial engineering rather than genuine value creation.

Hedge Fund Cycles

From the 1990s boom of Steinhardt, Soros, and Robertson to 2005 onward, hedge fund averages fell sharply. Small, capacity-aware funds still outperform; quant and AI strategies with unique data remain competitive. If you want hedge-fund-like returns today, look for smaller managers, true quant edge, or semi-illiquid niches like direct lending or insurance-linked securities.

Private Equity Levers

Private equity returns arise from revenue growth, margin improvement, leverage, and multiple expansion. Historically strong, but diminishing: MOICs dropped from ~4.7x to ~2.0x as fund sizes ballooned. Persistence still exists—teams repeating successful strategies often outperform larger, different vintages. Manufactured alpha comes from genuine operational improvements, not leverage or timing luck.

Practical Lessons

Alpha lives where scale is limited and operational change is possible. Smaller funds, disciplined capacity, stable teams, and patient capital matter more than brand prestige. Evaluate whether returns came from skill, not cyclical multiples.

Not all alpha is equal. Schelling’s hierarchy ranges from rare true alpha to mass-produced beta, helping you decide where to pay fees and where to economize.

The Spectrum

• True Alpha: scarce, uncorrelated skill—Renaissance Medallion is the archetype.
• Manufactured Alpha: value from direct improvement (private equity, real estate).
• Transitional Alpha: temporary inefficiencies from restructuring or regulation.
• Inaccessible Risk Premium: regulated or capacity-limited opportunities.
• Alternative Beta: now‑liquid strategies once private.
• Pure Beta: commoditized exposures like broad indices.

Pricing and Scarcity

Fees track scarcity. If replication is easy, cost must drop. If replication is nearly impossible, high carry may be justified. Preqin data confirm incentive‑fee funds earn higher net results. Understanding a strategy’s position in this hierarchy clarifies whether you’re paying for real skill or marketing language.

How to Act

Identify whether a strategy lies near true or manufactured alpha. Demand alignment and transparency at the top; pursue low‑cost replication near the bottom. The insight is simple: buy scarcity, not stories.

Schelling integrates behavioral science to explain why investors overpay for fading edges. System 1—fast, emotional—dominates choices; System 2—slow, logical—is costly to engage. Alpha disappears fastest where System 1 rules and biases persist.

Common Biases

• Confirmation bias: seeking evidence that supports existing views.
• Pareidolia: seeing patterns in noise when control feels lost.
• Loss aversion and endowment effect: valuing owned positions excessively.
• Overconfidence: overrating skill; common in trading and manager selection.

Reducing Bias

Awareness is insufficient—design disciplined processes that force System 2 checks. Use pre-mortems, fixed sell rules, and structured diligence. Schelling calls this “behavioral alpha”: outperformance earned by defeating one’s own biases, not rivals.

Decision Architecture

Because willpower depletes (Baumeister), schedule major deliberations when fresh. Avoid multitasking (task switching costs accuracy) and design workflows that allocate sustained focus to strategic decisions. Train intuition only where feedback is fast and outcomes clear; elsewhere, rely on checklists and after‑action reviews.

Practical moral

Behavioral and process alpha arise when cognition meets discipline—systematized thinking is the last sustainable human edge.

Beyond investment selection, Schelling urges mastering process and governance. Process alpha comes from repeatable, high-quality diligence habits; organizational alpha arises when authority aligns with expertise and accountability.

Smart Habits and Diligence

The Five P’s—Performance, People, Philosophy, Process, Portfolio—anchor effective manager review. Evidence backs this: angel investors who spent over 40 hours on due diligence earned ~7x multiples, those under five hours lost money. In institutional portfolios, operational due diligence adds measurable returns. Smart rebalancing also counts—adjust weights using valuation and momentum signals, not rigid calendars.

Governance Makes or Breaks Results

Authority must reside with informed experts. Research shows higher proportions of political trustees correlate with lower pension returns. Ambachtsheer’s checklist—clear role delineation, board education, incentive alignment—turns governance from bureaucratic drag into alpha source. SWIB exemplifies best practice: professionalized decision rights, long-term incentives, and near fully funded results.

Lesson For You

Build structure before strategy. Codify processes, delegate to expertise, and reward results aligned with beneficiaries’ goals. System quality compounds like capital—it’s invisible until it fails.

Schelling concludes by challenging the benchmark obsession. Success is not beating an index—it’s meeting objectives with high probability. Define risk as the chance of missing goals, not as volatility.

From Benchmark to Real Goals

Investors eat total return, not alpha. A portfolio earning 6% when you require 7% fails even if it beats peers. Policy decisions—expected returns, contribution rates—often embed optimism bias, creating “negative policy alpha.” Realistic assumptions anchored to valuation metrics, like CAPE ratios, drive honest planning.

Robustness Over Optimization

Mean-variance optimization magnifies input errors; a robust design tolerates uncertainty. Schelling suggests modeling P(success)—the probability your portfolio meets needs—and preferring designs with higher odds, even if volatility is larger. The goal is sustainability, not perfection.

Practical Tools

• Define objectives probabilistically (success/failure).
• Use scenario analysis and ex-ante documentation.
• Separate policy, allocation, and implementation responsibilities.

The book’s end message is liberating: forget the scoreboard and optimize for mission success. That’s behavioral and organizational alpha combined—success measured by outcomes, not relative bragging rights.