The Application of SAS® Hash Object to Ultra-High Frequency Financial Data: A Case Study in Limit Order Book Reconstruction

Abstract

Over the last decade, the increased availability of ultra-high frequency (UHF) financial data on trades, quotes and order flows in electronic order-driven markets has led to significant innovations in the fields of the empirical microstructure and quantitative analysis of financial markets. Unfortunately, the enormous size of these UHF datasets, usually several million observations per stock per day, has brought up new data processing and handling, in addition to computational challenges. In today’s trading environment, the key challenge posed by UHF data is how one can efficiently and more importantly, quickly exploit a massive dataset to develop consistent and profitable (alpha generating) algorithmic trading strategies. Central to this exercise is the complete and accurate reconstruction of the electronic limit order book (LOB) of order-driven markets. This paper attempts to address the research challenge presented by UHF data by demonstrating the application of SAS® Hash Object to the reconstruction of SETS (Stock Exchange Electronic Trading Service) electronic LOB of the London Stock Exchange (LSE).