Observational Learning with Competitive Prices

TL;DR

This paper investigates how market participants learn asset values through observable prices in competitive markets, identifying key conditions for learning with public and private signals, and analyzing how the number of states affects learning outcomes.

Contribution

It establishes the necessary and sufficient conditions for observational learning in markets with public and private signals, and explores the impact of the number of states on learning dynamics.

Findings

Pairwise Informativeness (PI) is necessary and sufficient for learning with public signals.

Avery and Zemsky Condition (AZC) is necessary and sufficient for private signals.

For more than 3 states, PI and MLRP together ensure asymptotic learning.

Abstract

Will people eventually learn the value of an asset through observable information? This paper studies observational learning in a market with competitive prices. Comparing a market with public signals and a market with private signals in a sequential trading model, we find that Pairwise Informativeness (PI) is the sufficient and necessary learning condition for a market with public signals; and Avery and Zemsky Condition (AZC) is the sufficient and necessary learning condition for a market with private signals. Moreover, when the number of states is 2 or 3, PI and AZC are equivalent. And when the number of states is greater than 3, PI and Monotonic Likelihood Ratio Property (MLRP) together imply asymptotic learning in the private signal case.