What markets miss when everyone agrees.
The Federal Reserve’s January 2026 meeting concluded as nearly everyone expected: no change. After three consecutive 25-basis-point cuts in the second half of 2025, the Federal Open Market Committee held the federal funds rate steady at 3.50%–3.75%.
Markets had priced this outcome with near-certainty. The CME FedWatch Tool showed a 94%–95% probability of no action heading into the meeting. Economists from J.P. Morgan to Goldman Sachs had aligned around the same view. The consensus held, the Fed delivered, and the financial media moved on to the next story.
But here’s what concerns me: When 95% of market participants agree on an outcome, they stop thinking about what happens if they’re wrong.
That’s not investing. That’s consensus masquerading as conviction.
Certainty is not clarity
Let me be direct about what the January pause actually represents.
I believe that the Fed held rates steady not because the economic picture is clear, but because it isn’t. Core PCE inflation remains stuck at 2.7%–2.8%—above the Fed’s 2% target and showing the kind of “stickiness” in services that makes policymakers nervous. December payrolls came in at just +50,000 jobs, a sharp deceleration from earlier in 2025. Unemployment edged down slightly to 4.4%, but only because fewer people were looking for work.
This is not an economy sending consistent signals. This is an economy sending contradictory ones.
The FOMC itself reflects this tension. Recent decisions have not been unanimous. Some members want deeper cuts to support employment before weakness becomes entrenched. Others argue inflation remains the primary threat and further cuts would be premature. Chair Powell, in his recent press conferences, has acknowledged the difficulty of balancing “upward risks to both unemployment and inflation”—a statement that captures the uncertainty without resolving it.
Add to this the structural uncertainty: Powell’s term as Chair expires in May 2026. Depending on who follows, the Fed’s entire policy orientation could shift. Markets are pricing the present FOMC, but they may soon be dealing with a different one.
When I look at this environment, I don’t see clarity. I see an accumulation of vague and ambiguous information—exactly the conditions that create systematic risk in stock prices.
The difference between forecasting and measuring
Most of Wall Street approaches Fed policy as a forecasting exercise. Analysts predict what the Fed will do. Traders position for rate moves. Portfolio managers adjust duration and sector weights based on their expectations of future policy.
This approach assumes you can be right more often than you’re wrong about what twelve people in a room will decide based on incomplete data about an uncertain economy.
We take a different approach at New Age Alpha. We don’t try to forecast what the Fed will do. We measure what human behavioral responses to Fed uncertainty have already done to stock prices.
This distinction matters more than it might appear.
We believe that when markets are consumed by Fed speculation—parsing every word of Powell’s testimony, recalibrating probabilities after each economic release, rotating in and out of rate-sensitive sectors—something predictable happens. Investors incorporate vague and ambiguous information into prices. They allow narratives about future policy to shape their expectations for individual companies. They let speculation about macroeconomic outcomes influence what they’re willing to pay for a share of a specific business.
The result is that some stocks end up priced for scenarios that depend heavily on circumstances that no one can actually know. The growth implied by the stock price requires not just company-specific execution, but a particular Fed policy path, a particular inflation trajectory, a particular labor market outcome.
That’s where the h-factor comes in.
What h-factor® measures
The h-factor quantifies what we call the “probability of disappointment”—the likelihood that a company will fail to deliver the revenue growth already embedded in its stock price.
This is not a forecast. It’s an actuarial calculation based on known, quantifiable information.
Every stock price implies a set of expectations. For a company trading at 25 times earnings, the price reflects assumptions about future revenue growth, margin expansion, competitive position, and so on. The h-factor asks a specific question: Given what we actually know about this company—not what we hope or speculate—what is the probability that those expectations will not be met?
The score ranges from 0% to 100%. A higher h-factor indicates a greater probability that the stock price reflects human optimism that reality is unlikely to support.
Here’s what makes this relevant to the current Fed environment: The uncertainty, the speculation, the narrative-driven trading around monetary policy—all of it increases the gap between what stock prices imply and what fundamentals can deliver. When investors are focused on Fed tea-leaf reading instead of business analysis, they allow expectations to run ahead of reality.
The h-factor aims to capture this. Not by predicting what the Fed will do, but by measuring the behavioral residue that Fed speculation leaves in stock prices.
Risk you’re not paid to take
This brings me to the concept of uncompensated risk—risk that investors bear without receiving additional expected return.
Traditional financial theory tells us that taking more risk should, on average, produce more return. You accept volatility in exchange for higher expected returns. This is the foundational bargain of investing.
But the risk created by human behavioral responses to vague and ambiguous information doesn’t follow this pattern. It’s systematic—it affects broad segments of the market simultaneously. It’s non-diversifiable—you can’t eliminate it by holding more stocks, because if you own the index, you own the overpriced stocks within it. And it’s uncompensated—the market doesn’t pay you for bearing it.
Think about what happens when Fed speculation drives a sector rotation. Growth stocks sell off on expectations of higher rates. Value stocks rally on expectations of a stronger economy. Then the narrative shifts, and the rotation reverses.
Throughout this process, individual company fundamentals remain largely unchanged. The businesses are earning roughly what they were earning before. Their competitive positions are roughly what they were. But their stock prices have moved dramatically—not because of anything the companies did, but because of human behavioral responses to macroeconomic speculation.
Generally, investors who were on the wrong side of these rotations took losses. Those losses came from a source of risk that had nothing to do with company quality or long-term value creation. They came from being exposed to the behavioral responses of other market participants.
This risk cannot be diversified away. If your portfolio holds the stocks most susceptible to Fed-driven speculation, you’re carrying exposure you didn’t choose and aren’t being compensated for.
We believe that the best solution is to avoid it.
How we approach avoidance
Our methodology begins with a premise that sounds simple but has profound implications: We don't try to pick the winners. We aim to avoid the losers.
In the current environment, “losers” doesn’t mean companies with bad products or incompetent management. It means companies whose stock prices have absorbed so much speculation—about Fed policy, about economic conditions, about future scenarios no one can verify—that the probability of disappointment has become elevated.
These companies may deliver reasonable results. But if those results fall short of what their prices imply, investors may experience losses regardless of how objectively successful the business is.
We use the h-factor to identify these situations. We systematically remove the stocks with the highest probability of failing to deliver the growth their prices indicate. What remains is a universe of companies where expectations are more closely aligned with demonstrable reality.
This is a rules-based process. It doesn’t depend on our views about what the Fed will do. It doesn’t require us to predict inflation or employment or economic growth. It uses only known, quantifiable information—the kind that can be verified and measured rather than speculated about.
The discipline is in what we exclude, not what we select.
The behavioral reality of Fed uncertainty
I want to be specific about why the current Fed environment creates particular risk.
The behavioral phenomenon we rely on is WYSIATI—“What You See Is All There Is.” Developed by the recipient of the Nobel Prize in Economics, Daniel Kahneman, it describes the tendency of human decision-makers to form confident judgments based on limited, available information while underweighting what they don’t know or can’t observe.
When the Fed’s path is uncertain—when the FOMC is divided, when inflation and employment data are sending mixed signals, when leadership transitions are on the horizon—the information available to investors becomes less reliable. The narratives that emerge to explain Fed behavior become more speculative. The confidence that investors feel in their interpretations becomes more disconnected from the actual state of knowledge.
Under these conditions, WYSIATI becomes more dangerous. Investors form confident views about rate paths based on whatever information is most salient or recent. They incorporate those views into stock prices. And they underweight the substantial uncertainty that surrounds any forecast of Fed behavior.
The result is systematic mispricing. Not mispricing based on poor fundamental analysis, but mispricing based on the predictable human tendency to act as if we know more than we do.
What the 95% probability actually tells us
Let me return to that 95% probability figure—the near-certainty that markets assigned to a Fed pause in January.
On one level, this reflects legitimate information. The economic data supported patience. Fed commentary had signaled no urgency to act. The consensus was well-founded.
But markets often confuse “well-founded consensus” with “known certainty.” And when they do, they stop pricing risk appropriately.
Consider: If 95% of investors agree the Fed won’t move, what happens to the 5% scenario where they’re wrong? Standard probability theory tells us that low-probability events with high impact deserve serious attention. But behavioral reality tells us that investors systematically underweight them.
When nearly everyone agrees on an outcome, the market’s ability to absorb a surprise deteriorates. Positions become crowded. Hedges become expensive and get discarded. The “tail risk” that should discipline overconfidence gets pushed to the margins.
We saw versions of this pattern throughout 2025. Markets repeatedly adjusted to Fed surprises—not because the surprises were unforeseeable, but because consensus had become so dominant that alternative scenarios weren’t priced.
The h-factor captures this indirectly. When investor confidence becomes unmoored from genuine certainty, the expectations embedded in stock prices become more fragile. The probability of disappointment rises, even if the most likely outcome is benign.
A question of risk
Behind every portfolio built on Fed forecasts is someone’s actual financial future.
A retiree depending on that portfolio for income. A family saving for education. A foundation funding essential work. An institution meeting obligations to beneficiaries.
When portfolios are constructed around speculative views about monetary policy—when they carry uncompensated, non-diversifiable risk from Fed-driven narrative trading—those real people and institutions bear the consequences of being wrong.
I don’t say this to be dramatic. I say it because our industry often forgets it.
The language of finance is abstract. We talk about “positioning” and “rotation” and “rate sensitivity” as if these are technical matters divorced from human outcomes. But they’re not. Every basis point of unnecessary risk in a portfolio is a potential reduction in someone’s security, someone’s options, someone’s capacity to live the life they planned.
At New Age Alpha, we think about this constantly. Our approach isn’t marketing language. It’s a recognition that every bad investment can impact people negatively in real and tangible ways. We can’t eliminate risk. No one can. But we can avoid risks that don’t need to be taken, risks that investors often don’t even know they’re carrying.
The h-factor approach: Measuring what speculation has already done
At New Age Alpha, we don’t try to predict what the Fed will do. We don’t forecast whether Chair Powell will be reappointed, whether the doves will prevail over the hawks, or whether inflation will hit the 2% target by summer. Those are questions for speculators.
Instead, we measure something different: the probability of disappointment already embedded in stock prices.
Our h-factor methodology is designed to quantify overpricing risk—the likelihood that a company will fail to deliver the revenue growth its stock price currently implies. This risk doesn’t come from fundamentals. It comes from human behavior. It comes from investors incorporating vague and ambiguous information into their expectations.
Fed policy creates precisely this type of information in abundance.
Consider what happens when investors hear that the FOMC is divided, that inflation remains sticky at 2.7%–2.8%, that labor markets are weakening, and that the Fed Chair’s term expires in May. Some investors interpret this as bullish—the Fed will cut more. Others see it as bearish—the Fed will hold firm or even hike. Both groups incorporate their interpretations into prices.
This is the mechanism behind overpricing risk. Not fraud. Not manipulation. Simply humans responding to uncertainty with narrative, conviction, and often, overconfidence.
The h-factor aims to capture this. A stock with a high h-factor score (closer to 100%) carries a high probability of failing to deliver the growth its price indicates. A low h-factor score suggests the market’s expectations are more grounded in quantifiable reality.
This is not forecasting. It is measurement.
We don’t claim to know what the Fed will do. We don’t claim to know how markets will react. We claim to measure the probability that human behavioral responses to Fed uncertainty have already pushed certain stock prices beyond what their fundamentals can support.
Why this risk matters: Uncompensated and non-diversifiable
This brings me to the concept of uncompensated risk—risk that investors bear without receiving additional expected return.
This is the critical insight that separates our approach from conventional asset management. Most strategies assume that diversification solves risk. We recognize that certain risks—specifically, the risks created by human behavior incorporating vague and ambiguous information into prices—cannot be diversified away.
They can only be avoided.
Uncompensated means you don't get paid to take it. Traditional finance theory suggests that higher risk should come with higher expected returns. But overpricing risk doesn't work that way. When a stock is overpriced because investors have incorporated vague and ambiguous information into its price, the expected return isn't higher—it's lower. The risk is real, but the compensation is absent.
Non-diversifiable means you can't solve this problem by owning more stocks. If you own the index, you own the overpriced stocks. Period. Diversification protects against idiosyncratic risk—the risk that any single company will underperform for company-specific reasons. It does not protect against systematic behavioral risk embedded across multiple securities.
The thoughtful imperative
Behind every portfolio built on Fed forecasts is someone’s retirement. Behind every allocation constructed around assumptions about what Chair Powell will do next is a family’s financial security.
This is why we approach risk the way we do. Not because avoiding losers is intellectually elegant, though it is. Not because probability-based approaches are academically rigorous, though they are. But because the consequences of being wrong fall on real people with real aspirations.
When a fund manager increases equity exposure because they’re confident the FOMC will pivot dovish, and the FOMC doesn’t, the fund manager still collects their fees. The investor absorbs the loss.
We believe this asymmetry demands a different approach.
Our approach doesn’t require us to be right about Fed policy. It requires us to identify where other investors’ assumptions about Fed policy have pushed prices beyond sustainable levels. We don’t need to predict the future. We need to measure the present accurately.
The January 2026 context
Let us be specific about the current environment, without making predictions about what it means for markets.
The Federal Reserve has cut rates three times since September 2025, bringing the federal funds rate to 3.50%–3.75%. Core PCE inflation remains at 2.7%–2.8%—above the 2% target but slowly declining. December job growth was weak at +50,000 positions, though unemployment edged down to 4.4% due to lower labor force participation. Consumer sentiment, while improved to 54.0 in January per University of Michigan surveys, remains fragile compared to a year ago.
The FOMC itself is divided. Recent decisions have not been unanimous, with some members favoring more aggressive cuts to support employment and others prioritizing inflation control. Chair Powell’s term expires in May 2026, introducing additional uncertainty about future policy direction.
Major institutions hold varying views. J.P. Morgan projects steady rates throughout 2026. Goldman Sachs and Barclays anticipate possible mid-year adjustments contingent on economic data. Markets currently price a 94%–95% probability of no change at the January meeting, with modest expectations for one or two cuts later in the year.
These are facts, not forecasts.
What concerns us is not what the Fed will do—we don’t know, and neither does anyone else. What concerns us is how this uncertainty creates the conditions for overpricing risk. Divided Fed. Expiring Chair term. Mixed economic signals. Sticky inflation. Weak but not collapsing labor markets.
This is precisely the type of environment where investors incorporate vague and ambiguous information into their expectations. Some will bet on continued easing. Others will bet on prolonged pause or even tightening. Both groups will push prices in directions that may not be supported by quantifiable fundamentals.
The h-factor methodology is designed to identify where this has already happened.
A different standard
We don’t claim to have a crystal ball. We don’t claim to know what the Fed will decide in March, or June, or at any subsequent meeting. We don’t claim to know whether inflation will hit target, whether the labor market will strengthen or weaken, or whether a new Fed Chair will change policy direction.
What we claim is simpler: We aim to measure the probability of disappointment embedded in current stock prices.
When that probability is high, we avoid. When speculation has pushed prices beyond what fundamentals can support, we step aside. When human behavior creates risk that is uncompensated and non-diversifiable, we choose not to accept it.
This is not a prediction strategy. It is a disciplined strategy built on math and actuarial principles.
In an environment where the Federal Reserve’s path forward is genuinely uncertain—where reasonable people disagree about policy direction, where economic data sends mixed signals, where institutional forecasts vary widely—the prudent approach is not to out-predict the unpredictable.
The prudent approach is to avoid the stocks where others’ predictions have already pushed prices into dangerous territory.
That is what we do. Not because we’re smarter about Fed policy. But because we’ve built a systematic, rules-based process to identify and avoid overpricing risk—regardless of what creates it.
The Fed will do what the Fed will do. We will continue to measure the risk that human responses to Fed policy have already embedded in prices. And we will continue to seek to avoid the losers, aiming to shield our clients from the consequences of speculation they never chose to accept.
Julian Koski
Chief Investment Officer, New Age Alpha
Sources:
- CME FedWatch Tool, January 2026 probability data
- Federal Reserve FOMC meeting schedule and policy statements
- University of Michigan Consumer Sentiment Survey, January 2026
- Bureau of Labor Statistics employment data, December 2025
- J.P. Morgan, Goldman Sachs, and Barclays economic forecasts
- Investopedia, Morningstar, and The Street market analysis, January 2026
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