Asset Pricing: Behavioral
Paper Session
Friday, Jan. 5, 2024 10:15 AM - 12:15 PM (CST)
- Chair: Marianne Andries, University of Southern California
The Cryptocurrency Participation Puzzle
Abstract
Ongoing zero portfolio weights in cryptocurrency are surprisingly difficult to generate in a Bayesian portfolio theory framework. With ten years of prior data, equity investors would need very pessimistic priors on mean returns to never buy cryptocurrency: -10.6% per month for Bitcoin, and -19.6% for a diversified cryptocurrency portfolio. Most priors that involve never purchasing cryptocurrency imply shorting it. Optimal weights are generally small, non-trivial (1-5% magnitude), frequently positive, and smooth. The certainty equivalent gains from cryptocurrency are comparable to international diversification and exceed the size anomaly. Costs (storage, fees) would need to exceed 21-39% per year to deter trading.Consumption out of Fictitious Capital Gains and Selective Inattention
Abstract
Do retail investors’ behavioral biases in trading affect their consumption? We exploit a naturalexperiment that changed the displayed purchase prices in investors’ online portfolios. We find that
investors readily sell and consume “fictitious” capital gains: displayed capital gains based on the new
purchase prices that are truly capital losses based on the actual purchase prices. We argue that investors
are selectively inattentive: they sell more fictitious winners when fictitious gains are larger and actual
losses are smaller, they sell them even when actual purchase prices are very salient, but they notice
fictitious losers, treating them the same as actual winners.
The Cross-Section of Subjective Expectations: Understanding Prices and Anomalies
Abstract
We propose a structural model of constant gain learning about future earnings growth that incorporates preferences for the timing of cash flows. As implied by the model, a cross-sectional decomposition using survey forecasts shows that high price-earnings ratios are accounted for by both low expected returns and overly high expected earnings growth. The model quantitatively matches a number of asset pricing moments, as learning about growth interacts strongly with the preference for the timing of cash flows, and provides insights on the roles of risk premia and mispricing in the cross-section of stocks. The magnitudes and timing of the comovement between prices, earnings growth surprises, and anomaly returns are all consistent with a gradual learning process rather than expectations being highly sensitive to the most recent realization. Large earnings growth surprises do not immediately translate into large one-period returns, but instead are gradually reflected in future returns over time.Discussant(s)
Peter Maxted
,
University of California-Berkeley
Amin Shams
,
Ohio State University
Alberto Rossi
,
Georgetown University
Cameron Peng
,
London School of Economics
JEL Classifications
- G1 - General Financial Markets