The vast majority of models that decompose the bid/ask spread assume the quote-driven, specialist structure of the NYSE. This paper critically evaluates these models to construct a model specific for an electronic order-driven exchange. The model not only captures adverse selection and the impact of order flows on price discovery but it includes the imbalance of supply and demand inherent in the public limit order book. With this new model we investigate the change to anonymity on the Australian Securities Exchange (ASX). Following the change to anonymity, both adverse selection and the demand/supply imbalance have an increased impact on prices while order flow has a decreased influence, suggesting the change to anonymity has improved market efficiency. The model also uncovers a change in traders’ behavior once their fear of front-running is reduced. We show that the model is stable and robust across high liquidity stocks as well as stocks with as few as 5 trades per day.

Louis is completing a PhD in Quantitative Finance at UTS and his thesis is entitled, "Market Microstructure Inefficiencies". He is currently working with a Melbourne based Hedge Fund to develop their first quantitative fund and he also worked for the algorithmic trading group at CitiGroup for 2 years. Before focusing on Quantitative Finance, Louis was a management/technology consultant for Accenture (6 years) and Deloitte Consulting (4 years) working in the US, Europe and Asia-Pac for large financial institutions like Barclays, American Express, AXA, CitiGroup, Banc One, CBA, etc.