Alpha Isn’t Dead. You’ve Just Been Measuring It Wrong
New research shows smarter portfolio construction—not new factors—is the real edge.
For decades, the investment world has been locked in a frenzied hunt for alpha—the elusive, risk-adjusted return that cannot be explained by known market factors. It is the holy grail of active management. Yet, the quest has become so crowded that Nobel laureate John Cochrane famously dubbed it the "factor zoo": a sprawling menagerie of hundreds of documented anomalies, each claiming to produce excess returns.
Despite the proliferation of strategies, the reality on the ground is sobering. Mutual fund alpha has declined. Hedge fund alpha has shrunk. The playbook that generated outsized returns in 1993 looks considerably less potent today. This has led to a pervasive question haunting the industry: **Is alpha dead?**
According to Andrew Berkin and Christine Wang of Bridgeway Capital Management, the answer is an emphatic no. In their groundbreaking study, *"The Incredible Structural Alpha,"* published in the Spring 2026 issue of *The Journal of Beta Investment Strategies*, the authors argue that meaningful, persistent alpha is still available. However, finding it requires a fundamentally different mindset. Investors must stop searching for exotic new factors and start paying attention to the architecture of their portfolios.
The Laboratory: Revisiting the Classics
Berkin and Wang did not attempt to discover a new anomaly. Instead, they returned to the foundation of modern factor investing: the classic 5×5 grid of portfolios sorted by size and value, made famous by Fama and French in 1993.
Using 60 years of U.S. stock data (July 1963 through June 2023), they tested four straightforward portfolio construction scenarios. Their goal was to measure how incremental design improvements affected both raw returns and risk-adjusted alpha. They pulled four specific levers:
1. **Deeper Factor Exposure:** Concentrating tightly on the extremes (true small-cap, truly cheap) rather than broad splits.
2. **Timely Data and Rebalancing:** Using current market cap data and quarterly accounting updates rather than stale, year-old information.
3. **Momentum Screening:** Removing stocks with unwanted factor exposure (e.g., filtering out the worst momentum stocks from value portfolios).
4. **Multiple Value Metrics:** Combining four measures of value (book/price, sales/price, earnings/price, and cash flow/price) rather than relying solely on book-to-market.
The Findings: Construction Is King
The results were striking. The study demonstrates that alpha is not necessarily about *what* you buy, but *how* you construct the portfolio that buys it.
1. The Power of Extremes
Starting with a baseline Fama-French approach, the authors found that the smallest, deepest value stocks returned over **16.0% annually** across the 60 years. In contrast, small-growth stocks returned just 3.65%. Even after adjusting for known factor exposures, the deepest value corner of the market generated a statistically significant alpha of nearly **2% per year**.
The implication is clear: The nonlinearities at the extremes of the size and value spectrum are real, persistent, and economically large. Broad definitions dilute the premium; concentration captures it.
2. Fresh Data and the Momentum Drag
When the authors switched to quarterly data and current market caps, something counterintuitive occurred. Returns for the deepest value portfolios fell slightly, but their measured **alpha rose sharply**, jumping from 1.97% to 4.53% for the smallest deep-value portfolio.
Why? Timely classification often catches stocks that have recently dropped in price—stocks with negative momentum. These stocks carry a momentum headwind that suppresses raw returns but isn't fully accounted for in simple alpha calculations. The alpha is real; the momentum drag is a separate, identifiable cost that better data helps isolate.
3. Avoiding "Cheap for a Reason"
Screening out the worst quintile of momentum stocks (Scenario 3) produced higher returns across nearly all portfolio squares. The smallest deep-value portfolio's return rose to **17.85%**. While alpha itself remained stable, its statistical significance increased substantially. The message is practical: Removing poor-momentum stocks raises returns through better factor exposure, preventing investors from catching falling knives that are cheap for fundamental reasons.
4. Unlocking Large-Cap Value
Perhaps the most practically important finding concerns large-cap stocks. It is well known that using only book-to-market as a value measure works poorly for large companies; the traditional value premium mostly comes from smaller firms.
However, when the authors combined four value metrics, the largest deep-value portfolio's return jumped from **9.73% to 12.65% annually**. The value premium was restored across all size segments. Metrics like earnings and cash flow yields capture profitability that book value misses, turning what looks like alpha into compensated factor exposure.
The Structural Factors: Updating the Benchmark
Having demonstrated the benefits in portfolio terms, Berkin and Wang constructed improved versions of the famous SMB (Small Minus Big) and HML (High Minus Low) factors. The results are sobering for fans of conventional factors but encouraging for those willing to invest in better construction.
* **Standard SMB:** Returned 0.17% per month (not statistically significant).
* **Structural SMB:** Returned 0.37% per month, rising to 0.44% when "cheap for a reason" stocks were excluded.
* **Standard HML:** Returned 0.29% per month.
* **Structural HML:** Returned 0.33% per month.
The gap widens dramatically in the second half of the sample (1993–2023), a period where conventional factors have notoriously struggled. While the standard SMB averaged just 0.06% per month in this modern era, the structural version earned between 0.26% and 0.33%. Similarly, standard HML earned just 0.13%, while the structural version maintained 0.28%.
**The takeaway:** When conventional factors weaken, thoughtful portfolio construction preserves most of their return premium. The factors aren't broken; the definitions need updating.
## Key Takeaways for Investors
For investors navigating a world where conventional factor premiums have compressed, Berkin and Wang's work offers a roadmap.
* **Alpha From Construction, Not New Factors:** The "factor zoo" is partly an illusion. Many apparent new anomalies simply capture structural alpha that already exists in well-known factors when they are properly constructed. Be skeptical of claims that new metrics are distinct from size and value.
* **Go Deeper, Not Broader:** Concentrating on the true extremes of size and value produces higher returns meaningfully. Most mainstream indexes and ETFs do not do this; they cannot capture this structural alpha.
* **Respect Momentum Interactions:** A stock that looks cheap may be cheap for a reason. Screening out the worst-momentum stocks from value portfolios is a disciplined way to improve returns without abandoning the value thesis.
* **Diversify Value Signals:** Book-to-market alone is a poor signal for large caps. Combining it with earnings, sales, and cash flow yields produces a robust signal across the full market-cap spectrum.
* **Implementation Is Not a Footnote:** Disciplined execution matters as much as smart factor design. Patient trading, position bounds, and securities lending are levers that can add or destroy structural alpha. A beautifully designed strategy, poorly executed, will still underperform.
* **AUM Matters:** As systematic managers grow, they face liquidity constraints. For megafirms trading less liquid small-value securities, positions may take quarters to build or unwind to avoid slippage. This creates latency and lowers exposure to the very factors generating the premium. Smaller, more agile managers may have a structural advantage in capturing these specific premiums.
* **Design Matters More Than Fees:** Structural alpha can amount to several percent annually—an order of magnitude larger than typical fee differences. Investors fixated on minimizing basis points may be optimizing the wrong variable if they ignore the quality of portfolio construction.
The search for alpha doesn't have to mean chasing increasingly exotic signals in crowded corners of financial markets. Berkin and Wang make a compelling case that the building blocks—size and value—still work, and work considerably better when handled with care.
The "incredible structural alpha" isn't magic. It comes from the disciplined application of things sophisticated investors already know they should do: Concentrate on the true extremes of your target factors, keep your data current, avoid stocks with compounding headwinds, diversify your signal, and execute with precision.
For an industry weary of the factor zoo, this is a meaningful source of hope. Alpha isn't dead. It was just poorly measured.