The psychology behind what we do with our money

Episode Summary

Executive Summary: The episode examines how psychology, neuroscience, and finance intersect across household decisions, trading, asset pricing, and corporate management. Guests Carrie Fridman and Colin Kammerer argue that better data and common language across disciplines are revealing stable behavioral biases—like disposition, extrapolation, regret, and overconfidence—that shape market outcomes and may eventually improve regulation, hiring, and decision-making.

Main Topics: Interdisciplinary behavioral finance (Priority: 5/5): The guests explain how psychology, economics, and neuroscience have slowly converged from decades of poor coordination into a more unified field with shared language and data-driven methods. Household finance and consumer vulnerability (Priority: 5/5): They discuss how consumers face complex products like mortgages, credit cards, and retirement accounts with limited literacy, while firms and regulators increasingly study these decisions scientifically. Trading biases and neural mechanisms (Priority: 5/5): The conversation centers on the disposition effect, regret repurchase, and realization utility, including experimental evidence from fMRI studies showing neural correlates for selling winners and holding losers. Bubbles, crashes, and asset pricing (Priority: 4/5): They describe lab-created bubbles and crashes, linking price run-ups to ventral striatum activity and impending crashes to insula warnings, while also referencing theory of mind and mood effects on markets. Corporate management and CEO overconfidence (Priority: 4/5): The discussion explores how early success, misread skill, and situational luck can create overconfidence, leading CEOs to take excessive risks, use more leverage, and make costly acquisition decisions. Future applications and hiring risks (Priority: 4/5): The guests consider how firms may use behavioral and neural correlates for recruitment and training, while warning against overfitting and stressing heterogeneity and learnability.

Key Arguments: Behavioral finance has matured because researchers now have much more data from households, investors, firms, and experimental settings, allowing theories to be tested and constrained rather than merely proposed. A major historical barrier was disciplinary fragmentation: psychologists, economists, and neuroscientists used different terminology and often mistrusted each other’s assumptions. Household finance is shaped by low financial literacy, complex products, and incentives that allow firms to exploit consumer confusion; regulators such as the CFPB and the U.K. Behavioral Insights Team emerged to address this. Financial decision-making follows life-cycle patterns, with mistakes often rising among the young due to inexperience and among the old due to forgetfulness, while middle age tends to be the peak for financial judgment. Thinking about one’s future self can increase patience and improve savings behavior, because it makes future rewards feel psychologically closer. The disposition effect is one of the most robust findings in finance: investors prefer to sell winners and hold losers, even though that can worsen returns and tax outcomes. Neural evidence suggests that selling at a gain can produce a reward-like signal in the ventral striatum, supporting the realization utility hypothesis. A similar neural driver appears to underlie both the disposition effect and regret-based repurchase behavior, suggesting that multiple anomalies may stem from a smaller set of common mechanisms. Bubbles can be studied in the lab by creating assets with known fundamental value; in those settings prices often run far above fundamentals before crashing back down. During experimental bubbles, ventral striatum activity appears linked to the price run-up, while insula activity serves as an early warning of an impending crash. Theory of mind or mentalizing may help traders infer what others think an asset is worth, echoing the Keynesian beauty contest. Mood and investor sentiment can move markets, possibly by increasing optimism or lowering risk aversion, though the exact mechanism remains unsettled. In corporate settings, CEO overconfidence can emerge from early success and from confusion between stable skill and lucky circumstances, leading to leverage, acquisitions, and other risk-taking. Firms may one day use behavioral markers to improve hiring, but the guests caution that models must avoid overfitting and should incorporate both traits and trainability rather than rely on narrow personality stereotypes.

Data Points: Overdraft fee age pattern: High in ages 18, 20, 25; declines, then rises again at 70, 75, 80 - Used as an example of a U-shaped curve in financial mistakes across the life cycle Financial literacy survey result: About a third of people - Survey evidence that one stock is less risky than a diversified portfolio, showing misunderstanding of risk Experimental stock fundamental value: 14 - In the lab bubble experiment, the artificial asset had a known fundamental value of 14 Typical bubble peak price: About 45 - In the trading experiment, prices often rose far above fundamental value before crashing Trading horizon in bubble experiment: 50 periods - Participants traded the artificial asset over 50 periods Bubble crash timing: About 30 periods into trading - Prices typically began to crash after roughly 30 trading periods Early warning window before crash: 5 to 10 periods - Insula activity appeared several periods before the crash in the bubble experiment Conference paper submissions at CFPB: 40 to 50 papers initially; about 300 papers last year - Illustrates the growth of research interest in household finance and consumer behavior Research time frame for early studies: Around 1990 - Marking the point when behavioral finance began to gain traction across disciplines Early career year cited by Colin: 1981 - Used to describe how frustrating cross-disciplinary communication was in the early days of his PhD work

Pivotal Quotes: "Upskilling boards from outside perspectives I think is essential." — Sarah Eystead: Introductory promo for a separate FT podcast episode on boardroom risk "Data and analytics can strengthen your ability to manage risk by identifying your vulnerabilities early." — Sean McGovern: Introductory promo for a separate FT podcast episode on boardroom risk "There are geopolitical and regulatory pressures that healthcare providers have." — Pam Joshi: Introductory promo for a separate FT podcast episode on boardroom risk

Implications: The episode suggests finance is becoming more predictive and practical as behavioral data improves. Expect better regulation, more nuanced hiring, and stronger models of consumer and market behavior—but only if firms avoid simplistic profiling and overfitting.