Wharton Research Uncovers 'Correlation Neglect' Bias Distorting Stock Valuations
Investors systematically misread market signals by treating related information as independent, according to new research from the Wharton School that could explain persistent anomalies in how stocks are priced.
In a paper published February 10, 2026, Wharton finance professor Jessica Wachter and co-author Hongye Guo identify what they call "correlation neglect"—a behavioral phenomenon where investors fail to recognize when multiple pieces of information are actually connected. The finding challenges a core assumption in financial theory: that market participants process information efficiently.
"What if investors aren't processing information as efficiently as we've assumed?" the researchers ask in their paper, "Correlation Neglect in Asset Prices." The question strikes at the heart of modern portfolio theory, which underpins everything from risk models to capital allocation decisions in corporate finance departments.
The research reveals a "striking pattern" in U.S. stock market returns tied to how investors interpret correlated data. When multiple signals about a company or sector arrive simultaneously, investors often treat them as independent confirmations rather than recognizing they may stem from the same underlying cause. This creates systematic mispricings that persist across market cycles.
For CFOs and finance leaders, the implications extend beyond portfolio management. The same cognitive bias that distorts stock valuations can infiltrate internal forecasting, capital budgeting, and risk assessment. When finance teams receive multiple data points supporting a strategic decision—say, positive customer feedback, strong sales pipeline reports, and optimistic analyst coverage—correlation neglect suggests they may overweight that evidence if the signals actually share common sources.
The Wharton research adds to a growing body of work on behavioral biases in financial markets, but focuses specifically on how investors handle information relationships rather than information quality. Unlike overconfidence or anchoring biases that have been studied for decades, correlation neglect operates at a more subtle level: investors may correctly assess individual data points while still fundamentally misunderstanding how those points relate to each other.
The phenomenon appears particularly relevant in today's data-rich environment, where finance professionals face an unprecedented volume of potentially correlated signals—from real-time market data to AI-generated forecasts to social media sentiment. As information sources multiply, the cognitive challenge of tracking which signals are truly independent becomes more acute.
Wachter, whose research focuses on behavioral finance and asset pricing, has previously explored how psychological factors influence investment decisions. This latest work suggests that even sophisticated institutional investors remain vulnerable to systematic errors in information processing—a finding that may require rethinking how risk management frameworks account for behavioral biases.
The research arrives as finance departments increasingly rely on algorithmic models and AI tools that aggregate multiple data streams. If human investors consistently neglect correlations, the question becomes whether automated systems inherit the same blind spots or can be designed to correct for them.
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