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Investor Beliefs and Behavior

Paper Session

Saturday, Jan. 3, 2026 8:00 AM - 10:00 AM (EST)

Loews Philadelphia Hotel
Hosted By: American Finance Association
  • Clifton Green, Emory University

Mental Models and Financial Forecasts

Francesca Bastianello
,
University of Chicago
Paul Decaire
,
Arizona State University
Marius Guenzel
,
University of Pennsylvania

Abstract

We uncover financial professionals’ mental models—the narratives they use to explain their subjective beliefs. Using 82,000 equity reports, we prompt large language models (LLMs) to extract 3.5 million narratives, each combining a topic, valuation channel, sentiment, and time outlook. To validate the reliability of our output, we introduce a multi-step LLM-based approach and new diagnostic tools. We establish three sets of findings. First, narratives are centered around a limited set of topics, primarily focused on top-line items, with variation in topic focus over time and across industries. Narratives are mostly forward-looking, with three times as many arguments focusing on the future as on the past. Second, differences in topic focus, sentiment, and time outlook across forecasters strongly predict the extent of disagreement in their subjective quantitative forecasts. Lastly, time-series variation in the average narrative’s sentiment and in the average narrative’s focus on top-line items closely track Shiller’s CAPE ratio (ρ = 0.84 and ρ = 0.42), and the cross-sectional variation in narratives predicts key asset pricing patterns. Narratives associated with growth stocks are more optimistic and forward-looking than those for value stocks, consistent with forecasters (mis)perceiving growth stocks as having above-average growth potential. Overall, this paper helps bridge the gap between ‘what forecasters believe’ and ‘why they believe it.’

Economic Representations

Suproteem Sarkar
,
University of Chicago

Abstract

Valuations depend on how people categorize, perceive, or otherwise represent economic objects. This paper develops a measure of how the market represents firms, and uses this measure to study stock valuations. I train an algorithm to structure language from financial news into embeddings—vectors that quantify the economic features and themes in each firm’s news coverage. I show that a firm’s vector representation is informative of how the market perceives its business model. Representations explain cross-sectional variation in stock valuations, cash flow forecasts, and return correlations. Changes in representation help to explain changes in stock prices. Some changes in representations and prices are forecastable, and indicate that some of the explained variation in stock valuations stems from misperception. I find that misperception and misvaluation can intensify when a firm’s news coverage includes attention-drawing features—like “internet” in the late 1990s or “AI” in the early 2020s.

The Research Behavior of Individual Investors

Toomas Laarits
,
New York University
Jeff Wurgler
,
New York University

Abstract

Browser data from an approximately representative sample of individual investors offers a detailed account of their search for information, including how much time they spend on stock research, which stocks they research, what categories of information they seek, and when they gather information relative to events and trades. The median individual investor spends approximately six minutes on research per trade on traded tickers; the mean spends approximately half an hour. Overall, the median investor carries out over two hours of research per trade; the median just over half an hour. Research is focused in the hours and days in the run-up to trade, particularly so for buys. Individual investors spend the most time reviewing price charts, followed by analyst opinions, and exhibit little interest in traditional risk statistics. Aggregate research interest is highly correlated with stock size, and salient news and earnings announcements draw more attention, as do stocks with small nominal prices, high return volatility, and high number of analysts. Individual investors have different research styles, with the first principal component corresponding to intensity of research, and the second principal component corresponding to a tilt between short- and long-lived information. Those investors that focus on short-term information are more likely to trade more speculative stocks.

FOMO Economics: External Reference-Dependence in Household Portfolios

Michael Gelman
,
University of Delaware
Liron Reiter-Gavish
,
Netanya Academic College
Nikolai Roussanov
,
University of Pennsylvania

Abstract

Individual investors are sensitive to peer performance and particularly dislike “falling behind.” We use unique granular data on the transactions and holdings of retail investors to study portfolio adjustment in response to relative performance of their portfolios. We show that investor behavior is consistent with preferences over future wealth that are S-shaped around an external reference point provided by a salient market benchmark: if their portfolio lags the “market,” they tend to increase the risky share of their portfolio, as well as purchase riskier securities, as characterized by high market beta, idiosyncratic volatility, and positive skewness. As the salience of the market index increases, investors become more sensitive to relative performance. The effect is asymmetric, more pronounced in bull market periods, and does not reverse when individual portfolios are ahead of the market. Our evidence provides a novel perspective on the individual investors’ demand for risky assets.

Discussant(s)
Baozhong Yang
,
Georgia State University
Dasol Kim
,
Office of Financial Research
Xing Huang
,
Washington University in St. Louis
Russell Jame
,
University of Kentucky
JEL Classifications
  • G4 - Behavioral Finance