Wharton Research Reveals 'Correlation Neglect' Driving Systematic Mispricing in U.S. Stock Markets
Investors systematically misread market signals by treating correlated information as independent data points, leading to predictable patterns in stock returns that persist despite decades of market efficiency theory, according to new research from the Wharton School.
The phenomenon, termed "correlation neglect" by Wharton finance professor Jessica Wachter and co-author Hongye Guo, represents a behavioral blind spot that affects institutional and retail investors alike. Their paper, "Correlation Neglect in Asset Prices," documents how this cognitive error creates observable distortions in U.S. equity markets—distortions that CFOs and finance leaders should understand when timing capital raises, buybacks, or M&A activity.
Here's the thing everyone's missing: This isn't about investors being stupid. It's about how human brains process multiple signals simultaneously. When two pieces of information are actually related—say, strong earnings from a company and strong earnings from its largest supplier—investors often treat them as independent confirmations rather than correlated data points. The result? Overreaction in the short term, followed by predictable reversals.
Wachter and Guo's research identifies "a striking pattern in U.S. stock market returns" tied to quarterly reporting cycles, the periods when correlation neglect appears most pronounced. (Translation: When everyone's getting hit with earnings releases simultaneously, the brain starts treating each data point like it's telling you something new, even when it's really just echoing the same underlying trend.)
The implications for corporate finance leaders are immediate. If markets systematically overreact to clusters of correlated information, the pricing of your equity—and your competitors'—may be predictably wrong at specific intervals. That's not a market inefficiency in the classic sense; it's a behavioral regularity.
Let me put it this way: Imagine you're evaluating whether to launch a secondary offering. Traditional efficient market theory says your stock price reflects all available information. But if Wachter's research holds, your price might be temporarily inflated because investors just processed three pieces of good news (strong GDP, strong sector data, strong peer earnings) as if they were independent signals, when they're really all saying "the economy is good." The correlation neglect framework suggests waiting for the inevitable correction might save your shareholders meaningful dilution.
The research challenges a foundational assumption in modern portfolio theory: that investors properly weight the correlation structure of information. Turns out, they don't. And this isn't a niche finding about retail day traders—the patterns Wachter documents appear in broad market indices, suggesting even sophisticated institutional investors fall prey to the bias.
For CFOs managing investor relations, this creates an interesting tension. Your job is to provide clear, timely information. But if your earnings release drops in a cluster with correlated macro data or peer results, the market's initial reaction may be systematically biased. (Do you guide investors toward the correlation explicitly? Do you time releases differently? These are questions that didn't exist in the efficient market playbook.)
The phenomenon also explains some head-scratching market behavior that finance teams have observed but couldn't quite articulate. Why do stocks sometimes surge on "good" news that's really just confirmation of already-known trends? Why do sector rotations sometimes overshoot before reversing? Correlation neglect offers a behavioral mechanism: investors are double-counting correlated signals.
What's particularly notable is that this isn't a new discovery about a new market structure or technology. This is a fundamental insight about how humans process information—which means it's been affecting markets for as long as markets have existed. We just didn't have the framework to see it clearly.
The practical question for finance leaders: If correlation neglect is real and persistent, how should it change your approach to capital allocation timing, investor communication strategy, or even M&A windows? The research doesn't provide a playbook, but it does suggest that the "market knows best" assumption may need an asterisk: *except when processing clusters of correlated information.
Wachter's work arrives as AI-driven trading systems increasingly dominate market microstructure. The open question: Do algorithmic traders exhibit correlation neglect, or do they correct for it? If the latter, the behavioral edge may be shrinking. If the former, we've just automated the bias at higher frequency.
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