Algorithmic Trading Engines and Liquidity Contribution: The Blurring of "Traditional" Definitions

Abstract

Being provided with a unique high-frequency dataset, we are able to show by means of an empirical analysis that computer-based traders, i.e. Algorithmic Trading (AT) engines, behave significantly different from human traders with regard to their order cancellation behaviour. Furthermore, given exactly this difference we point out that the application of well-established "traditional" liquidity measurement methods may no longer be unequivocally applicable in today's electronic markets. At least those liquidity measures that are based on committed liquidity need to be questioned. © IFIP International Federation for Information Processing 2009.