A Computational View of Market Efficiency

TL;DR

This paper introduces a computational framework to analyze market efficiency, showing how strategies with limited memory can influence market dynamics, leading to phenomena like bubbles and increased profit opportunities.

Contribution

It presents a novel computational approach to market efficiency, modeling how memory-based strategies impact market evolution and create complex phenomena.

Findings

Memory-$m$ strategies can induce market bubbles.

Strategies with larger memory can exploit new profit opportunities.

Market conditions evolve due to strategic interactions.

Abstract

We propose to study market efficiency from a computational viewpoint. Borrowing from theoretical computer science, we define a market to be \emph{efficient with respect to resources $S$} (e.g., time, memory) if no strategy using resources $S$ can make a profit. As a first step, we consider memory-$m$ strategies whose action at time $t$ depends only on the $m$ previous observations at times $t-m,...,t-1$. We introduce and study a simple model of market evolution, where strategies impact the market by their decision to buy or sell. We show that the effect of optimal strategies using memory $m$ can lead to "market conditions" that were not present initially, such as (1) market bubbles and (2) the possibility for a strategy using memory $m' > m$ to make a bigger profit than was initially possible. We suggest ours as a framework to rationalize the technological arms race of quantitative trading firms.