Wharton Research Reveals How 'Correlation Neglect' Distorts Market Pricing and Portfolio Risk
Investors systematically misjudge risk by treating related information as independent, according to new research from Wharton professor Jessica Wachter that identifies a behavioral blind spot with direct implications for how CFOs and treasurers assess portfolio exposure and market volatility.
The paper, "Correlation Neglect in Asset Prices," co-authored with Hongye Guo, documents a striking pattern in U.S. stock market returns where investors fail to recognize when multiple pieces of information are actually correlated—leading them to overreact to news and misprice assets. The phenomenon, which Wachter terms "correlation neglect," challenges the assumption that market participants process information efficiently.
Here's the thing everyone's missing: This isn't about investors being irrational in some vague, hand-wavy sense. It's about a specific, measurable error in how people aggregate information. When two signals point in the same direction, investors treat them as independent confirmations rather than recognizing they might stem from the same underlying cause. (Think of it like this: If your CFO and your controller both tell you earnings will miss—and they're both reading the same management dashboard—that's one piece of bad news, not two. But investors keep scoring it as two.)
The research has immediate relevance for corporate finance leaders managing treasury operations and evaluating market signals. When multiple analysts downgrade a stock, when several economic indicators flash warning signs, or when correlated risks appear across a portfolio, the natural human tendency is to weight each signal independently. That creates a compounding error in risk assessment.
For CFOs, the implications cut two ways. First, understanding correlation neglect helps explain why markets sometimes overreact to clusters of related news—a pattern that creates both risk and opportunity in timing equity issuances, buybacks, or M&A announcements. Second, it's a reminder to audit internal risk models for the same bias. If your FP&A team is aggregating forecasts from business units that all depend on the same macro assumptions, you're not getting five independent views—you're getting one view counted five times.
The Wharton research arrives as finance leaders navigate an environment where AI-generated analysis and algorithmic trading have multiplied the volume of market signals without necessarily improving their quality. When correlation neglect is baked into human decision-making, automating those decisions at scale doesn't solve the problem—it amplifies it.
Wachter's work suggests the issue isn't a lack of data or computational power. It's a fundamental quirk in how humans—even sophisticated institutional investors—process probabilistic information. The paper identifies this pattern specifically in U.S. stock market returns, meaning it's not theoretical. It's measurable. It's happening.
The practical question for finance leaders: How do you build processes that account for correlation neglect? The answer likely involves more disciplined scenario analysis, stress-testing assumptions about independence, and recognizing that when everything in your risk report is flashing red simultaneously, you probably don't have ten independent problems. You have one problem with ten symptoms.
What makes this research particularly relevant now is the proliferation of real-time data feeds and AI-powered analysis tools that promise to give CFOs "more visibility" into market conditions. But if the underlying cognitive bias remains unaddressed, more signals just means more opportunities to make the same mistake faster. The challenge isn't seeing more information—it's correctly understanding how that information relates.
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