The Predictive Power of Zero Intelligence in Financial Markets

TL;DR

This paper demonstrates that a simple zero intelligence model can accurately predict key market statistics, highlighting the dominant role of market structure over strategic agent behavior in certain contexts.

Contribution

It introduces a minimalistic model based on random order placement that explains significant market phenomena with only one free parameter.

Findings

The model explains 96% of bid-ask spread variance.

It accounts for 76% of price diffusion variance.

Market impact data collapses onto a single curve across stocks.

Abstract

Standard models in economics stress the role of intelligent agents who maximize utility. However, there may be situations where, for some purposes, constraints imposed by market institutions dominate intelligent agent behavior. We use data from the London Stock Exchange to test a simple model in which zero intelligence agents place orders to trade at random. The model treats the statistical mechanics of order placement, price formation, and the accumulation of revealed supply and demand within the context of the continuous double auction, and yields simple laws relating order arrival rates to statistical properties of the market. We test the validity of these laws in explaining the cross-sectional variation for eleven stocks. The model explains 96% of the variance of the bid-ask spread, and 76% of the variance of the price diffusion rate, with only one free parameter. We also study the market impact function, describing the response of quoted prices to the arrival of new orders. The non-dimensional coordinates dictated by the model approximately collapse data from different stocks onto a single curve. This work is important from a practical point of view because it demonstrates the existence of simple laws relating prices to order flows, and in a broader context, because it suggests that there are circumstances where institutions are more important than strategic considerations.