Working Paper: NBER ID: w24723
Authors: Nicholas C. Barberis
Abstract: 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.
Keywords: No keywords provided
JEL Codes: G11; G12; G40
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Recent past returns (G17) | Investors' expectations of future returns (G17) |
| Investors' expectations of future returns (G17) | Increased demand for risky assets (G19) |
| Increased demand for risky assets (G19) | Price increases (E30) |
| Overconfidence (D83) | Excessive trading volume (G14) |
| Excessive trading volume (G14) | Market prices (P22) |
| Past gains/losses (G11) | Investor preferences (G11) |
| Investor preferences (G11) | Biased expectations about future performance (D84) |
| Biased expectations about future performance (D84) | Asset price fluctuations (G19) |