A computational view of market efficiency

Authored by Jasmina Hasanhodzic, Andrew W. Lo, Emanuele Viola

Date Published: 2011

DOI: 10.1080/14697688.2010.541487

Sponsors: United States National Science Foundation (NSF)

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

We study market efficiency from a computational viewpoint. Borrowing from theoretical computer science, we define a market to be 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 spikes 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.

Tags

Agent based modelling behavioral finance Complexity in finance Bound rationality