2023/18 | LEM Working Paper Series | ||||||||||||||||
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Market selection and learning under model misspecification |
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Giulio Bottazzi, Daniele Giachini and Matteo Ottaviani |
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Keywords | |||||||||||||||||
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Learning; Market Selection; Model Misspecification; Financial Markets.
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JEL Classifications | |||||||||||||||||
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C60, D53, D81, D83, G11, G12
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Abstract | |||||||||||||||||
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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.
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