Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/297126 
Year of Publication: 
2023
Series/Report no.: 
LEM Working Paper Series No. 2023/18
Publisher: 
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM), Pisa
Abstract: 
This paper studies market selection in an Arrow-Debreu economy with complete markets where agents learn over misspecified models. Under model misspecification, standard Bayesian learning loses its formal justification and biased learning processes may provide a selection advantage. However, considering two cases of model misspecification and four learning processes, our analysis reveals a general difficulty in ranking learning behaviors with respect to their long-run performances and, hence, their survival in the market. For instance, prediction averaging stops being an advantageous strategy when the truth does not belongs to the same family of models agents use to learn. In general, learning rules that generically guarantee survival require an unreasonable amount of knowledge about the whole market ecology. Thus, the goal of a parsimonious long-run asset valuation model robust to model misspecification appears out of reach.
Subjects: 
Learning
Market Selection
Model Misspecification
Financial Markets
JEL: 
C60
D53
D81
D83
G11
G12
Document Type: 
Working Paper

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