The Hanlon Financial Systems Lab
"Stevens is well positioned in its existing systems and engineering portfolio and its location next to the financial capital of the world to make the study of financial systems at Stevens a great success. The Hanlon Financial Systems Lab will not only enhance what is already a fast growing academic – and private sector – need, but with the quantitative finance and financial engineering programs at Stevens, the students will greatly benefit from this." (Sean Hanlon, Chairman, Financial Systems Center).
The heart of the Financial Systems Center is the state-of-the-art Hanlon Financial Systems Lab. This lab came into being as a result of a generous gift by Stevens trustee and alumnus, Sean Hanlon. The Lab will integrate the latest hardware and software technologies, accessing real-time data, as well as historical time series data, to support innovative research into the most common and urgent problems in contemporary finance.
The lab will also serve as a teaching and training venue for faculty, students and industry. The Hanlon Financial Systems Lab combines:
- Advanced financial research and training facilities;
- A software engineering lab for both development and validation;
- A cybersecurity testing facility based on the Stevens team-oriented cybersecurity approach;
The combination of all three functions in a single integrated financial systems laboratory will be the first of its kind. It will also support Stevens undergraduate degree programs in quantitative finance and cybersecurity as well as graduate programs in financial engineering, systems security engineering and software engineering.
The Hanlon Financial Systems Lab will enable our students – at all levels, from undergraduate to Ph.D. – to engage directly with the financial system at a level on par with the experience of real world traders, financial analysts and policy makers.
- The Lab will provide access to a vast historical data set of financial information to support retrospective financial analysis, back-testing and model validation – including non-traditional data sets such as social network records
It will also give students the ability to process market data in real time, at the most granular resolution possible, to support projects that employ partially or fully automated trading strategies