Investors Systematically Misjudge Correlated Signals, New Wharton Research Shows

Investors Systematically Misjudge Correlated Signals, New Wharton Research Shows

Investors routinely treat related pieces of information as independent signals, leading to predictable patterns in stock returns that persist across decades of market data, according to new research from Wharton finance professor Jessica Wachter.

The phenomenon, which Wachter and co-author Hongye Guo term "correlation neglect," helps explain why markets sometimes overreact to clusters of seemingly different news items that actually stem from the same underlying factors. For CFOs managing investor relations and treasury functions, the findings suggest that market pricing may systematically misinterpret the significance of correlated corporate announcements.

In their paper "Correlation Neglect in Asset Prices," published this month, the researchers document striking patterns in U.S. stock market returns tied to how investors process multiple signals. The core issue: when investors receive several pieces of information that appear distinct but actually share common drivers, they tend to treat each signal as if it were independent, amplifying their response beyond what the underlying fundamentals warrant.

The behavioral bias operates even among sophisticated market participants. When a company reports strong earnings, announces a strategic partnership, and receives an analyst upgrade in quick succession, investors may view these as three separate positive signals rather than recognizing they likely stem from the same improved business conditions. The result is excessive optimism that eventually corrects, creating the predictable return patterns Wachter's research identifies.

For finance chiefs, the implications cut both ways. Companies may benefit from strategic timing of announcements to maximize perceived momentum, but they also face the risk that markets will eventually recognize the correlation and adjust prices downward. The research challenges the efficient market hypothesis's assumption that investors process information optimally, suggesting instead that systematic behavioral patterns create exploitable mispricings.

Wachter's work builds on decades of behavioral finance research but provides new evidence that correlation neglect operates at the market level, not just in laboratory settings. The patterns appear robust across different time periods and market conditions, suggesting the bias is deeply embedded in how investors interpret streams of corporate information.

The findings arrive as finance leaders grapple with increasingly complex disclosure requirements and faster information flows. When multiple data points about a company's AI initiatives, cost-cutting programs, and revenue guidance hit the market simultaneously, correlation neglect suggests investors may systematically overweight the combined signal's importance.

The research also raises questions about how CFOs should think about guidance and disclosure strategy. If markets predictably overreact to correlated positive signals, should companies space out announcements? Conversely, during challenging periods, does bundling related bad news minimize the damage by preventing investors from treating each item as an independent negative signal?

What remains unclear is whether sophisticated investors can exploit this bias or whether it persists even when market participants understand the phenomenon. The answer matters for how finance leaders should think about market efficiency and the reliability of stock prices as signals of fundamental value.