Xing Huang brings expertise on financial behaviors and decision-making

Xing Huang | Associate Professor | Dyson School

Meet Xing Huang, one of our newest faculty members to join the Cornell University SC Johnson College of Business. Huang received her Ph.D. in Economics from the University of California, Berkeley, and holds both a B.A. and an M.A. in Finance from Peking University. Prior to joining Cornell, she served on the faculty at Washington University in St. Louis and Michigan State University.

Huang brings research expertise at the intersection of behavioral finance, investor behavior, and asset pricing to the Charles H. Dyson School of Applied Economics, with her particular focus on how individual and institutional investors process information and make financial decisions, and how these behaviors influence market outcomes.

Her work has been published in leading academic journals, including the Journal of Finance, Review of Financial Studies, and Journal of Financial Economics. She has received multiple best paper awards at major conferences, including the MFA Annual Meeting, Financial Research Association (FRA) Conference, Utah Winter Finance Conference, and Red Rock Finance Conference. Her recent research has been supported by the NBER-OFR Financial Frictions and Systemic Risk Research Grant. Her findings have been featured in major media outlets, including The Financial Times, The Wall Street Journal, Bloomberg, and CNBC.

What is a research paper that is important to you, your work, or the world at large?

“My research in behavioral finance focuses on how individual investors process information and make financial decisions, making retail investor trading a natural and compelling area of my study,” Huang says. “My recent series of papers explores the evolving retail trading landscape and the emergence of a new breed of retail investors. In a recent paper titled “The ‘Actual Retail Price’ of Equity Trades,” we conducted a large-scale controlled experiment involving 85,000 simultaneous market orders of identical size on the same stock across six brokerage accounts. We uncover striking disparities in price execution across brokers — findings that challenge the priors of researchers, practitioners, and policy makers alike.”

“What makes this work particularly meaningful is how it illuminates our understanding about the hidden costs of seemingly commission-free trading,” she says. “Most surprisingly, in equity trading, these cross-broker differences aren’t explained by payment for order flow (PFOF) arrangements, contradicting widely-held beliefs about how modern market structure affects retail execution. One possible explanation which connects directly to my core behavioral finance research is that these differences may stem from variations in brokers’ investor clientele, which differ in the informativeness of their trades or exhibit systematic patterns in their behavior. The regulatory impact of this work has also been significant. Our findings were heavily referenced and directly influenced major SEC proposals and helped to reshape how market structure protections evolve.

What is a current issue in business or business education that you are interested in, and why is it important to you and your work?

Huang says that an issue she’s particularly interested in is the growing disconnect between the democratization of financial markets and retail investors’ ability to navigate them effectively.

“While technology has made investing more accessible than ever, this increased access hasn’t necessarily translated into better financial outcomes for most individual investors, but to the contrary, has often amplified behavioral biases,” Huang says.”This is where prescriptive behavioral finance becomes essential. Rather than simply documenting these biases, we need to develop actionable interventions that help investors make better decisions in real-world contexts. The emergence of AI also presents both opportunities and new challenges in this domain. While AI could revolutionize personalized algorithmic decision making, we must first understand how these algorithms actually make decisions, and, more importantly, how people interact with, trust, and act upon AI-driven recommendations. We’re currently conducting research projects examining the behavioral economics of AI. We’re also developing platforms for real-world deployment that combine behavioral finance insights with AI capabilities.”