Information Aggregation in Markets with Analysts, Experts, and Chatbots

TL;DR

This paper demonstrates that in markets with diverse traders, optimal information aggregation occurs when either no traders or all traders publish their information, with small expert groups hindering the process.

Contribution

It introduces a model showing how the number and type of traders publishing information affect market efficiency and information aggregation.

Findings

Information aggregation is optimal with no traders or all traders publishing information.

Small groups of experts hinder effective information aggregation.

Prices' informational content varies non-linearly with the number of information-publishing traders.

Abstract

The present paper shows that it can be advantageous for traders to publish their information on the true value of an asset even if they (i) cannot build a position in the asset prior to the publication of their information and (ii) cannot charge for the provision of information. The model also shows that the informational content of prices is U-shaped in the number of traders who publish their information. Put differently, information aggregation works best if either no trader, or if every trader publishes his information. Small groups of distinguished experts are, on the contrary, an obstacle to information aggregation. The model's key assumption is that the perception/interpretation of a given piece of published information differs slightly across traders.