The Department of Statistics organised the first LSE Statistics Practitioners Challenge in Feburary 2017. During this event, led by Hao Xing, the department of Statistics collaborated with leading industry partners, including Aviva, JP Morgan, and UBS, to initiate live competitive projects focusing on real issues faced by companies. MSc students were invited to take on the challenge by using their personal and professional skills developed through their program at LSE. Companies proposed a problem, from insurance to trading, and students came with the best approach they could think of. Then a panel composed of faculty members and industry representatives vote for the three best projects.
The challenge last for only four weeks. Once the industry came to LSE to present their project, groups formed of three to four student from all master program in statistic present a solution. One group per project is selected to continue the challenge. Additionally to the support provided by the industry, each selected group is followed by a PhD student and two faculty members will give office hour to help out.
During the last event, the group which tackled the challenge from UBS on index option pricing won the challenge. The project aimed to spotted out mispricing in index option using machine learning. The student decided to solve this problem by deploying various machine learning methods to fit the best prediction. They were highly praised, as for the other participating student group, for their proactivity and motivation to take on the challenges despite the short time.
Students enjoyed the variety of project to choose from and the chance to gain experience working on solving real issues. They also appreciated the working environment during the month where they were able to reach out and exchange with academics and professional. The experience gave them more insight into their future career which often coincide with the industry they worked with.
To sum up, this challenge provides students with opportunities to apply their learning and skills and the chance to work on problems that they will encounter in the ‘real world’, enhancing their learning experience and their personal and professional development.
Some feedback on the event:
``I think the group was very proactive and achieved a lot given the short time period. What I appreciate the most is the fact that they implemented four different innovative machine learning techniques, which can help improve the fit of the aggregation model to the observed index vol surface data.” UBS
``I really enjoyed the experience of applying the skills and knowledge I have acquired to the project. The challenge is a perfect way for us to gain practical skills and study areas where we are interested.” Student