When Mood Meets Markets: UTA Research Uncovers a Surprising Forecasting Advantage

Dr. Sima Jannati

Wall Street may trust numbers, but a new study from UTA suggests the real story starts with the people interpreting them.

Dr. Sima Jannati, assistant professor of finance at the University of Texas at Arlington College of Business, recently published research that sheds new light on the link between emotional states and financial decision making. Working with her co-authors, Jannati found that analysts experiencing higher levels of non-severe, or mild, depression were more likely to produce accurate earnings forecasts.

Jannati said the team’s motivation grew from earlier psychological studies that show how mood can shape judgment.

“My co-authors and I were motivated by longstanding evidence in psychology showing that mild or persistent depression can reduce overly positive biases and lead individuals to think more cautiously,” Jannati said. “Since optimism bias is a major source of forecasting errors in finance, we wanted to test whether this mechanism could help explain variation in forecast quality in a real financial setting.”

Optimism bias refers to the tendency to overestimate positive outcomes and underestimate risks. In financial forecasting, this bias can lead analysts to issue projections that are unrealistically high or incomplete, ultimately affecting decision making for businesses, investors and markets.

To explore this relationship, the researchers combined large-scale crowdsourced earnings forecasts from Estimize with nationally representative mental health data from Gallup. Estimize is a public forecasting platform where users ranging from students to professionals submit earnings predictions for publicly traded companies. Jannati said it offered a rich dataset because it includes a diverse mix of forecasters, timestamps every estimate and allows direct comparison with actual earnings results.

The team matched each earnings forecast with the national level of non-severe depression at the time it was issued, then examined whether forecast accuracy changed as depression levels rose or fell. They also tested whether reduced optimism and slower, more deliberate thinking helped explain the results.

The researchers ultimately found that higher levels of non-severe depression are associated with improved forecast accuracy, especially when forecasts are overly optimistic or when analysts take longer to issue their estimates.

“In simple terms, when people are mildly depressed, they tend to be less overly optimistic. They think more cautiously and process information more slowly and carefully,” Jannati said. “Because financial forecasts often suffer from excessive optimism, this reduction in bias can bring predictions closer to reality, improving accuracy in certain situations.”

Jannati said she was most surprised by how consistent the accuracy improvement was across many different tests, including alternative measures of depression and variations across states.

“We expected some relationship, but not one that remained robust across so many tests including instrumental-variable analysis, alternative depression measures, and state-level variation,” she said.

For business leaders and investors, the research reinforces that emotional states can meaningfully influence financial judgment. Forecasts are not only shaped by models and data, but also by the mindset and biases of the people creating them.

“Optimism, while beneficial in many contexts, can impair objective assessment of information,” Jannati said. “Conditions that temper excessive optimism, whether structural or psychological, may lead to more realistic expectations and better forecasting performance.”

Jannati said the study opens new avenues for research, including how other long-term psychological states influence market behavior, whether similar patterns appear in high-pressure professional environments and how organizations can reduce optimism bias through system design rather than mood. Exploring these questions, she said, may reveal even more about the human side of financial decision making.