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  5. Asset Pricing: Big Data and AI
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Asset Pricing: Big Data and AI

Paper Session

Saturday, Jan. 4, 2025 8:00 AM - 10:00 AM (PST)

San Francisco Marriott Marquis, Yerba Buena Salon 8
Hosted By: American Finance Association
  • Chair: Michaela Pagel, Washington University-St. Louis

(Almost) 200 Years of News-Based Economic Sentiment

Jules Van Binsbergen
,
University of Pennsylvania
Svetlana Bryzgalova
,
London Business School
Mayukh Mukhopadhyay
,
London Business School
Varun Sharma
,
Indiana University

Abstract

Using text from 200 million pages of 13,000 US local newspapers and machine learning methods, we construct a 170-year-long measure of economic sentiment at the country and state levels, that expands existing measures in both the time series (by more than a century) and the cross-section. Our measure predicts GDP (both nationally and locally), consumption, and employment growth, even after controlling for commonly-used predictors, as well as monetary policy decisions. Our measure is distinct from the information in expert forecasts and leads its consensus value. Interestingly, news coverage has become increasingly negative across all states in the past half-century.

Is Artificial Intelligence (AI) Risk-Averse?

Yuree Lim
,
Texas Woman's University

Abstract

This study evaluates the efficacy of ChatGPT-generated data in replicating human survey responses, focusing on risk aversion (RA) metrics. Our analysis finds that while ChatGPT can replicate overall variations and capture marginal associations, it introduces biases and is sensitive to specific personas. Secondly, we document significant behavioral biases in Generative AI agents, particularly bias toward risk-seeking behavior. Finally, we reveal that ChatGPT exhibits heightened risk aversion for specific demographic groups, including females, older individuals, those with lower income, less education, and unemployment. Our results highlight the usage of AI-generated data in research, reinforcing the potential behavioral bias of AI in simulating human-like decision-making patterns.

Resolving Estimation Ambiguity

Paul Decaire
,
Arizona State University
Denis Sosyura
,
Arizona State University
Michael Wittry
,
Ohio State University

Abstract

Economic models develop conceptual frameworks for fundamental decisions but rarely prescribe a specific estimation approach. Using novel data on the inputs and assumptions in professional stock valuations, we study how financial analysts address estimation ambiguity when calculating a firm’s cost of capital. Analysts use the same return-generating model (CAPM) but diverge in their estimation choices for key inputs, such as equity betas. Such estimation choices are driven by idiosyncratic analyst-specific criteria, persist throughout their career and across brokerages, and generate large cross-analyst variation in discount rates for the same stock. The dispersion in discount rates is associated with higher market measures of investor disagreement, such as trading volume. Overall, we provide micro evidence on how financial experts resolve estimation uncertainty.

Peering into the Black Box: Trader Strategies in the Iowa Electronic Markets

Thomas Gruca
,
University of Iowa
Thomas Rietz
,
University of Iowa

Abstract

We explore strategies employed by traders in the Iowa Electronic Markets’ 2020 Presidential Election Winner-Takes-All Market. We replicate previous research on trader mistakes while documenting behavior consistent with two new biases: a disposition effect and an endowment effect. We explore how markets populated by mistake-prone and biased traders can result in efficient pricing. Efficiency arises from interactions between many biased and mistake prone traders, the market structure, and a smaller number of significantly more rational price-determining traders. The dynamics are not explained fully by current theories on efficient markets, market microstructure, or behavioral finance.

Discussant(s)
Yinan Su
,
Johns Hopkins University
Leland Bybee
,
University of Chicago
Steffen Meyer
,
University of Southern Denmark and Danish Finance Institute
Will Cassidy
,
Washington University-St. Louis
JEL Classifications
  • G1 - General Financial Markets