High-Frequency Trading, Computational Speed and Profitability: Insights from an Ecological Modelling

High-frequency traders (HFTs) account for a considerable component of equity trading but we know little about the source of their trading profits and how those are affected by such attributes as ultra-low latency or news processing power. Given a fairly modest amount of empirical evidence on the subject, we study the relation between the computational speed and HFTs' profits through an experimental artificial agent-based equity market. Our approach relies on an ecological modelling inspired from the r/K selection theory, and is designed to assess the relative financial performance of two classes of aggressive HFT agents endowed with dissimilar computational capabilities. We use a discrete-event news simulation system to capture the information processing disparity and order transfer delay, and simulate the dynamics of the market at a millisecond level. Through Monte Carlo simulation we obtain in our empirical setting robust estimates of the expected outcome.