Nearly 50 doctoral students from 25 universities gathered at Yale School of Management from June 8 to 12 for the ninth Yale Summer School in Behavioral Finance. The biennial intensive course, which this year added sessions on artificial intelligence, draws PhD candidates in finance and economics who want to understand how human psychology shapes markets.
Founded and organized by Nicholas Barberis, the Stephen and Camille Schramm Professor of Finance, the program reflects Yale SOM's longstanding strength in the field. It builds on the pioneering research of Robert Shiller and the annual Behavioral Economics meeting that Barberis co-founded. Barberis teaches the core lectures, with deeper topic sessions led this year by colleagues James Choi and Kelly Shue, as well as five faculty from other universities. Karen Spitzer, assistant director of SOM's International Center for Finance, handled the logistics.
"Behavioral finance tries to make sense of financial markets and investor behavior by way of a deeper understanding of human psychology and, in particular, by acknowledging that humans sometimes think and behave in irrational ways," Barberis said. "It is a vibrant field, one that is increasingly seen as central for understanding important facts about the financial world."
New AI sessions bridge finance and machine learning
The program introduced new material on artificial intelligence this year, a move that signals how AI for Finance is becoming a standard part of advanced research training. "It is inevitable that AI will become a significant part of behavioral finance research, and these sessions gave students a sense of the kinds of research that might be possible," Barberis said.
Kelly Shue, the Amman Mineral Professor of Finance, discussed her work using AI photo analysis to study how personality traits predict career outcomes. Suproteem Sarkar, an assistant professor at the University of Chicago's Booth School of Business, showed students how to translate news articles into mathematical representations-a method that helps researchers measure how companies are perceived. This type of AI Research is giving behavioral economists practical new tools for analyzing data at scale.
Building a research community through deep dives and dinners
Attendee Bam Charoenwong, a finance PhD student at the Wharton School, said the summer school helped her think beyond the rational frameworks she typically uses. "My own work is in financial intermediation, where traditional rational frameworks are very useful," she said. "But the summer school helped me think more seriously about settings where behavioral forces such as different preferences and beliefs may help explain what we see in the data."
Clara Torslov, a PhD student at Copenhagen Business School visiting Harvard University, called the interdisciplinary approach eye-opening. "You get to challenge your current knowledge or get inspired to apply the insights in a new way in your own field," said Torslov, whose research focuses on household finance. "At the end of the day, we are all just humans making decisions."
Both students pointed to the program's structure-which included pre-arranged small-group dinners-as a key component. "This was one of my favorite parts of the program," Charoenwong said. "I left with a much better sense of the broader community, and with several people I hope to stay in touch with."
The Yale Summer School in Behavioral Finance has been supported for much of its history by the Lynne and Andrew Redleaf Foundation.
Why this matters for finance professionals
The integration of AI into a specialized doctoral program like this one underscores the shift already underway in industry. Techniques used by Shue and Sarkar-analyzing images to infer traits or converting text to numeric signals-are not academic curiosities. They mirror the kinds of alternative data analysis that hedge funds, asset managers, and corporate finance teams are adopting to improve decision-making. Professionals who understand behavioral biases and can interpret AI-driven signals will be equipped to spot market inefficiencies that purely rational models miss.
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