Psychology-based Models of Asset Prices and Trading Volume

By Nicholas Barberis. 2018

174 citations recorded by the source corpus.

Behavioral finance tries to make sense of financial data using models that are based on psychologically accurate assumptions about people's beliefs, preferences, and cognitive limits. I review behavioral finance approaches to understanding asset prices and trading volume, with particular emphasis on three types of models: extrapolation-based models, models of overconfident beliefs, and models of gain-loss utility inspired by prospect theory. The research to date shows that a few simple assumptions about investor psychology capture a wide range of facts about prices and volume and lead to concrete new predictions. I end by speculating about the form that a unified psychology-based model of investor behavior might take.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/586827fe040eae755ba8266225ffae1d5b9f5abf

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In Economics and Econometrics

In Finance

In Management Science and Operations Research

Citations in the corpus

Policy News and Stock Market Volatility (2019, background): “Another prominent line, also partly motivated by Shiller, stresses non-rational beliefs, limits to arbitrage, and fads that move equity prices in ways not fully tethered to real investment opportunities.2 See Cochrane (2017) and Barberis (2018) for recent reviews.”
Extrapolative beliefs in the cross-section: What can we learn from the crowds? (2020, unknown): “It is important to note that the results reported in Table 3 provide direct support for return extrapolation, as they do not arise from alternative models of biased beliefs such as Barberis et al. (1998) and Daniel et al. (1998) .”