BMLL, an independent provider of harmonised, historical data and analytics, has been selected to partner with New York University’s Mathematics in Finance programme, making its Data Lab available to NYU's team of quantitative researchers. 

Part of NYU’s Courant Institute of Mathematical Sciences, the programme and its research activities are directed by Professor Petter Kolm, a quantitative analyst specialising in market microstructure modelling and buy-side trading, who was named as “Quant of the Year” in 2021 by Portfolio Management Research (PMR) and Journal of Portfolio Management (JPM) for his contributions to the field of quantitative portfolio theory. 

An NYU team, led by Professor Kolm, has previously conducted research that showed that deep-learning models across a large number of stocks at large scale is found to be fully feasible, and that future returns of “information-rich” stocks can be predicted more accurately by deep learning.

The BMLL Data Lab is a data science platform that allows users to access over three years of Level-3 harmonised futures data, process it at scale, and find inferences by drilling down into every single message.