There is a clear signal: alpha is slipping—and not just a little. In an environment shaped by quantitative tightening, heightened correlation, and AI-driven efficiency, the very definition of outperformance is being rewritten.
And it’s not exactly surprising. Financial theory has long warned that generating consistent alpha (defined as excess return above the market benchmark) is an insurmountable task. According to the Efficient Market Hypothesis, markets are already efficient, meaning that all publicly available information is reflected in asset prices. So, unless you have a unique informational or forecasting advantage, true outperformance is nearly impossible.
The theory says investors act rationally. But that is not always the case. Herd behaviour, and emotional market swings often contradict these assumptions, especially during volatile conditions. And this is where things get interesting.
This article will dive deep into the reality of alpha. By unpacking structural shifts in markets, examine the rise of passive and systematic strategies, and explore how firms can rethink their edge in a hyper-efficient landscape. Because in a world where “information is commoditised and has given a level playing field across managers, be it large or small,” the rules for finding alpha have changed.
Still, leaders can’t talk about alpha without talking about uncertainty. After all, investing requires a constant tolerance for risk of unpredictable events, shifting cycles, and failed forecasts. And relying on those forecasts can be… risky. As one economist once quipped:
“There are two types of forecasters—those that don’t know and those that don’t know they don’t know. The [latter] are the dangerous ones, the people who think they know what the future will behold.”
So the real question becomes this: can active managers still deliver alpha in today’s complex market environment? Can alpha persist, net of fees, in an increasingly competitive and efficient landscape? Let’s find out.
The diminishing alpha premium
Alpha has traditionally been the north star of active investment. But across equity, credit, and macro strategies, it’s becoming elusive. According to a 2024 report from Morningstar, only 43% of active U.S. equity funds outperformed their passive benchmarks over 10 years—a steep drop from the mid-2000s when the figure consistently hovered around 60%.
Part of the issue is market structure. Liquidity fragmentation, shorter holding periods, and broader data access mean that there are fewer inefficiencies to exploit. The democratization of information, powered by low-latency feeds and real-time disclosures, has narrowed the window for arbitrage. In this reality, speed and scale often trump insight.
Which zooms in on the next challenge: the more investors seek returns in the same places, the less room there is for any single one to stand out.
The crowding effect
Capital chases performance. As more firms adopt similar risk models and factor strategies, crowding intensifies. Bank of America’s 2024 Global Fund Manager Survey found that a majority of institutional investors believe the market is currently “overcrowded” in key trades, particularly in growth tech and long-duration fixed income.
This crowding doesn’t just dilute returns—it increases systemic risk. When volatility spikes, crowded positions can unwind violently, eroding what little alpha remains. The March 2020 COVID drawdown is a case in point, where even historically uncorrelated hedge fund strategies exhibited high beta to broad equity markets.
And when everyone’s playing the same game with the same rules, the advantage doesn’t go to the smartest but to the fastest or the first. That’s where the machines come in.
Rise of the machines—and the plateau of quant
Quantitative strategies were once the edge. Today, they are the baseline. The growth of factor investing, machine learning, and alternative data has made markets more efficient, but it has also commoditized alpha. Even discretionary managers now lean on quant tools for signal generation and portfolio construction.
Yet there’s a limit. Research suggests that the marginal return on new alternative datasets has fallen over the past five years. It’s not that Quant is broken—it’s just that everyone is doing it. Alpha signals decay faster, and modeling advantages rarely last. And now, AI is accelerating that shift, transforming what can be known, predicted, and reacted to. But as it turns out, prediction is a double-edged sword.
AI’s double-edged sword
Artificial intelligence is transforming everything from trade execution to risk modeling. JPMorgan Chase reported that its internal AI models now run millions of market scenarios daily to stress test portfolios. That level of precision reduces risk and removes inefficiencies.
Ironically, the more predictive AI becomes, the harder it is to outperform. When all participants have access to high-fidelity forecasts, the only edge left is acting on it faster or in a more creative direction. That forces firms to rethink what true differentiation looks like.
But even the sharpest forecasts are only as useful as the environment they’re built for. And lately, that environment has become less fertile.
Policy shifts and macro compression
For over a decade, low interest rates and abundant liquidity created a fertile ground for risk-taking. That era is over. Central banks have pulled back sharply—led by the Fed’s multi-year tightening cycle—an environment marked by tighter spreads and lower volatility.
That’s great for risk management but tough for alpha generation. Macro managers, in particular, struggle to find dislocations that last long enough to profit from.
So if alpha is being squeezed from every angle—structure, tech, policy, and culture—what’s left? Where do investors go to find a new edge? Let’s explore what the most adaptive firms are doing.
What’s left?
If traditional alpha is fading, what replaces it? Leading firms are shifting focus in three ways:
Behavioral alpha. Understanding and exploiting investor psychology is harder to automate. So, firms are investing in behavioral analytics to anticipate flow-driven moves, panic cycles, or irrational pricing.
Geographic frontier markets. While U.S. and European markets grow more efficient, frontier markets still offer pricing gaps. Emerging market funds targeting regions like Vietnam, Nigeria, or Kazakhstan have outperformed broader indices by 8–12% annually, according to IMF data from 2024.
Private markets. With public market inefficiencies thinning out, capital is migrating to private equity, credit, and real assets. These markets offer less transparency, but more room for differentiated skills.
Culture as an alpha driver
Finally, culture matters. In firms where teams collaborate across quant, discretionary, and alternative disciplines, alpha generation is more resilient. BlackRock’s 2024 annual report emphasized cross-functional R&D and the embedding of data science teams within every investment pod. In contrast, siloed alpha teams reported stagnation or declines in performance.
So while the old playbook for alpha may no longer apply, the new one is already being written by teams who think beyond structure and invest in synergy.
Alpha isn’t dead—but it’s different now
Here’s the statement again: Alpha is getting harder to find. And the reasons are structural: compressed volatility, crowded trades, smarter tools, and shrinking inefficiencies.
But that doesn’t mean the search is over. It just means the definition is changing; tomorrow’s alpha will come from new places.
Outperformance still matters. But it won’t be won by doing what worked a decade ago. In this market, the edge belongs to those who rebuild their playbooks from the ground up.
And that adaptation starts with a belief: that even in the most efficient markets, human creativity still finds room to move.
