NYU’s Mathematics in Finance program selects BMLL for market microstructure research
NYU quant team to use BMLL Data Lab to run computations at scale and conduct futures market research
London and New York, 20 June 2022:
BMLL, the leading, independent provider of harmonised, historical data and analytics, today announced a collaboration with New York University’s Mathematics in Finance program, making its Data Lab available to its team of quantitative researchers.
Part of NYU’s Courant Institute of Mathematical Sciences, the program and its research activities are directed by Professor Petter Kolm, a leading quantitative analyst specialising in market microstructure modelling and buy-side trading, who was named as a “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 3 years of Level-3 harmonised futures data, process it at scale, and find inferences by drilling down into every single message.
Professor Petter Kolm, NYU Courant Professor, said “through the BMLL Data Lab, we can access pristine Level 3 Data and run computation at scale to work through huge data sets in a fully managed and documented environment”.
Paul Humphrey, CEO of BMLL, said: “We are delighted to collaborate with this NYU program and support Professor Petter Kolm and his colleagues in carrying out their cutting-edge limit order book research, and further developing the research they conducted on equity markets into futures exchanges. This collaboration is an acknowledgement of the quality and depth of our Level 3 order book data, and how our Data Science as a Service solution makes it possible to rapidly achieve actionable results.”
Dr. Elliot Banks, Chief Product Officer, BMLL, said “BMLL combines easy to use APIs and analytics libraries in a secure cloud environment, allowing scalable research without the burdens of data sourcing, curation or engineering. We are proud that our tool has been selected by one of the world’s best researchers to further explore the predictive power of historic data.
ENDS
About BMLL Technologies
BMLL Technologies is the leading, independent provider of harmonised, Level 3 historical data and analytics to the world’s most sophisticated capital market participants.
BMLL offers banks, brokers, asset managers, hedge funds and global exchange groups immediate and flexible access to the most granular Level 3, T+1 order book data and advanced analytics, enabling them to accelerate research, optimise trading strategies and generate alpha at unparalleled speed and scale.
Founded in 2014 in the machine learning laboratories of the University of Cambridge, the platform enables researchers and quants across global financial services firms to apply complex statistical techniques to BMLL’s unique big-data sets with applications such as market impact, pre and post trade analytics, order book simulation and compliance. Users no longer need to buy, curate and harmonise data. With BMLL, they gain cost-effective, instant access to a cloud-native Data Science environment via a single web portal, with a long history of the most granular, full order book data across global equities, futures and ETFs for back-testing and simulation, delivered directly into their workflows.
About NYU’s Mathematics in Finance Master of Science Program
New York University’s Mathematics in Finance Master of Science Program, housed at the Courant Institute of Mathematical Sciences, offers a tightly integrated curriculum that provides an efficient and foundational introduction to the theoretical and computational skills that are valued in the financial industry.
Located in one of the world’s financial capitals and founded at the end of the 1990s, the program combines cutting-edge coursework with mentoring by professionals drawn from the field’s many areas. Typical placements for its graduates are in research, trading, asset management, algorithmic trading, regulation, or risk management groups at firms and institutions such as AQR, Bank of America, Merrill Lynch, BlackRock, BNP Paribas, Credit Suisse, Goldman Sachs, Federal Reserve, J.P. Morgan Chase, Morgan Stanley, Two Sigma, and DE Shaw.
For more, please visit its website: math.nyu.edu/financial_mathematics
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