Slope-takers in anonymous markets

Marek Weretka and Daniel Quint
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Daniel Quint: University of Wisconsin-Madison

No 64, GRAPE Working Papers from GRAPE Group for Research in Applied Economics

Abstract: We present a learning-based selection argument for Linear Bayesian Nash equilibrium in a Walrasian auction. Endowments vary stochastically; traders model residual supply as linear, estimate its slope from past trade data, and periodically update these estimates. With quadratic preferences, this learning process converges to the unique LBN. In an example with non-quadratic preferences, it converges to a steady state close to a particular equilibrium of the corresponding deterministic setting; strategies played are not an equilibrium, but utility sacrificed is negligible. Anonymity and statistical learning therefore support use of LBN under quadratic utility, and motivate a related concept under non-quadratic utility.

Keywords: Walrasian auction; anonymous thin markets; price impacts (search for similar items in EconPapers)
JEL-codes: D43 D52 L13 L14 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2022
New Economics Papers: this item is included in nep-gth and nep-upt
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