DOWNLOAD ARTICLEThere's a statistic that should stop every investor in their tracks: according to S&P Dow Jones Indices' SPIVA U.S. Scorecard for the period ending December 2025, not a single one of the 22 U.S. equity categories tracked saw a majority of active managers outperform their benchmarks over the 15-year horizon. Not in large-cap growth. Not in small-cap value. Not anywhere.
And 2025 itself was brutal. It ranked as the fourth-worst year for active large-cap managers in SPIVA's 25-year history, with 79% of funds underperforming the S&P 500, a sharp deterioration from the 65% who lagged in 2024.
But underperformance is only half the story. The persistence data is equally damning. Looking at funds that were in the top quartile in 2021, none remained in the top quartile through 2025, except in small-cap.
Track record is not skill. There is no persistence. No repeatability. Just random outcomes dressed in expensive suits.
This reflects something deeper about how markets work, and how the investment industry often mistakes skill for luck.
The persistence problem
When you hire an active manager, you're paying for skill. That's the implicit contract. The manager's job is to analyze companies, interpret market dynamics, and make decisions that outperform a simple index fund. In exchange, investors pay higher fees.
But how do we know it's skill?
Increasingly, the answer is: we don't.
Study after study has shown that active manager performance lacks persistence. Yesterday's winners are statistically just as likely to become tomorrow's losers. When researchers rank mutual funds by performance over one period and track those same funds in the following period, they find no meaningful pattern.
Daniel Kahneman, the Nobel laureate whose work transformed behavioral economics, examined the year-to-year correlation of returns among mutual fund managers in Thinking, Fast and Slow. His analysis found that performance correlation was essentially zero, statistically indistinguishable from random chance (Kahneman, 2011). This led him to a striking conclusion:
"A major industry appears to be built on an illusion of skill."
The evidence is consistent across decades of data. Over 15-year periods, more than 90% of active large-cap managers underperform their benchmarks (S&P Dow Jones Indices, SPIVA U.S. Scorecard, Year-End 2025).
The numbers are uncomfortable, but they are remarkably consistent. And as our research team has noted, this is precisely why advisors have been migrating away from discretionary stock pickers - "based on relative performance studies like SPIVA," active management has simply not justified its cost.
What changed in 2000
Understanding why this pattern persists requires looking back to a regulatory shift that fundamentally changed how information flows in markets.
Before 2000, active managers had access to something valuable: selective disclosure. Companies often shared material information with favored analysts and fund managers before releasing it publicly. This informational advantage was real.
Then came Regulation Fair Disclosure, or Reg FD. Implemented by the SEC in October 2000, the rule prohibited selective disclosure and required companies to share material information with all investors simultaneously.
The playing field changed overnight.
And a lot of "skill" suddenly vanished.
This doesn't mean every active manager before 2000 succeeded because of privileged information. But it does mean that an entire category of edge disappeared. What remained was largely prediction based on publicly available information.
And prediction, particularly in complex systems like markets, is where human behavior becomes dangerous. We see patterns where none exist. We overweight recent events. We confuse confidence with competence.
Not all "active" is created equal
Our critique targets a specific form of active management: discretionary, prediction-based stock picking.
This is the world of portfolio managers making conviction bets on "undervalued" companies, analysts attempting to forecast future winners, and investment committees debating which sectors will outperform next quarter. Human judgment sits at the center of every decision.
This is not the only form of active investing. And the distinction matters.
One bright spot in the 2025 data: 59% of small-cap active managers outperformed the S&P SmallCap 600. Read in context, though, the S&P 500 outperformed the S&P MidCap 400 by roughly 10 percentage points and the S&P SmallCap 600 by roughly 12 percentage points in 2025. When a benchmark is itself depressed by mega-cap concentration, "beating" it is a lower bar, and the 15-year picture still shows small-cap active lagging the majority of the time.
The three categories: Active, passive, and New Age Alpha
Discretionary active management relies on human prediction. Success requires consistently being right about uncertain future outcomes: which companies will grow faster, which industries will outperform, which management teams will execute better. The SPIVA data measures this world. Kahneman studied this world. Both point to the same conclusion: persistent predictive skill is extraordinarily difficult to demonstrate.
Systematic, rules-based strategies, including factor investing and smart beta, operate differently. Rather than predicting winners, these approaches identify measurable characteristics - value, momentum, quality, low volatility - that have historically been associated with different return and risk patterns. The process is quantitative, transparent, and consistently applied.
New Age Alpha occupies a third category entirely.
We are not attempting to forecast which stocks will outperform. We are not tilting toward factors that historically generated premiums. Instead, we systematically identify companies where behavioral risk appears highest, where investor expectations embedded in stock prices are statistically difficult to achieve.
Why the distinction matters
When we critique discretionary active management, we are making a specific claim: human judgment about future stock performance has not demonstrated persistent value after costs.
That claim is well supported by evidence.
But that does not mean all non-passive approaches fail equally.
Systematic approaches do not depend on forecasting the unknowable. They rely on disciplined application of transparent rules. The growth of smart beta and systematic investing reflects this distinction. Investors are increasingly favoring approaches that reduce behavioral noise, improve consistency, and remove emotional decision-making from the process.
The direction of investor adoption tells its own story: capital is steadily moving away from discretionary prediction and toward systematic discipline. As Morningstar data shows, "by early 2025, passive assets hit $16 trillion. Active sat just above $14 trillion. That's not a small change. That's a transformation."
Where we fit
New Age Alpha shares systematic investing's commitment to rules-based discipline, but our objective differs from traditional factor investing.
We are not trying to identify future winners. We are trying to identify and avoid the losers. The problem with most portfolios is that you don't have too few winners. It's you have too many losers.
Our proprietary score, the h-factor®, does not attempt to forecast upside. It seeks to identify companies where expectations appear disconnected from statistical reality, creating a risk that is both unpriced and undiversifiable.
Factor investing asks: "Which characteristics have historically delivered premium returns?"
We ask: "Which stocks are most vulnerable to disappointment because expectations have become excessive?" And we avoid them.
Those questions lead to very different portfolios, constructed for very different reasons.
The cost of randomness
The financial industry has strong incentives not to discuss these dynamics openly. Entire research departments, analyst teams, and portfolio management infrastructures depend on the belief that predictive skill exists and can be identified in advance.
But the cost to investors is real.
Fees compound. Underperformance compounds. And the psychological toll of chasing yesterday's winners creates its own damage.
The SPIVA persistence data is not just an indictment of discretionary active management. It is an invitation to ask a different question: if we cannot reliably identify winners in advance, what should we do instead?
A different framework
At New Age Alpha, our conclusion is straightforward: persistent stock-picking skill is extraordinarily difficult to distinguish from luck.
But that does not mean investors are helpless.
There is another approach, one grounded not in prediction, but in mathematics.
We apply actuarial science instead of traditional portfolio management ideas to equity selection. Instead of attempting to identify which companies will outperform, we calculate the probability that a company will fail to deliver the revenue growth implied by its stock price. When that probability exceeds our threshold, we exclude the stock from portfolios. And it's all mathematical.
This is not about forecasting the future.
It is about measuring risk already embedded in prices.
The process is rules-based, transparent, and systematic.
Beyond the binary
The traditional investment debate presents two choices: active or passive. Pay higher fees for the possibility of outperformance or accept market exposure at lower cost.
Both approaches carry risks that are often overlooked. Many active managers deliver portfolios that closely resemble their benchmarks while charging significantly higher fees. Passive indexing solves the fee problem, but it forces investors to own every stock in the index, regardless of valuation, speculation, or behavioral bias.
There is a third path: systematically excluding stocks where behavioral risk appears highest through a transparent, probability-based process. The objective is not to predict winners. It is to avoid the losers.
What the data is telling us
The active management industry will continue to market the narrative of skill. Short-term outperformance will always produce compelling stories.
The broader evidence tells a different story. When no category demonstrates persistent outperformance over long horizons, and when 2025 marks the fourth-worst year for active large-cap managers in a quarter-century, it becomes increasingly difficult to argue that discretionary stock picking is a reliable source of value.
The question is not whether active management is dead.
The question is whether the version of active management most investors are paying for - discretionary, prediction-based, expensive - has ever reliably delivered what it promises.
The data, now spanning 25 years of SPIVA history, suggests the answer is no.
The right response is not to abandon active thinking entirely. It is to apply that thinking differently, systematically, mathematically, and without the behavioral noise that undermines so many well-intentioned investment decisions.
That is what we are building at New Age Alpha.
Sources
- S&P Dow Jones Indices, SPIVA U.S. Scorecard, Year-End 2025. https://www.spglobal.com/spdji/en/documents/spiva/spiva-us-year-end-2025.pdf
- S&P Dow Jones Indices, U.S. Persistence Scorecard, Year-End 2025. https://www.spglobal.com/spdji/en/documents/spiva/persistence-scorecard-year-end-2025.pdf
- Kahneman, Daniel. Thinking, Fast and Slow. Farrar, Straus and Giroux, 2011.
- Morningstar, asset flow data cited in New Speech Draft, 28 August 2025 (New Age Alpha internal reference).
- Hortz, Bill. Interview with Julian Koski, March 26, 2025 (New Age Alpha reference document).
- SEC Regulation Fair Disclosure (Reg FD), 17 CFR §243.100, effective October 2000.
