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Practitioners' Challenge

There was a good variety of projects to choose from, each one allowing us to work alongside a well renowned financial institution. It was really interesting working with academic staff at the LSE that had spent some time researching multivariate time series analysis, the topic we had chosen. In this respect we had the opportunity to ask lots of questions and get guidance from people who really knew what they were talking about.

Tom Parry

This project offers me an opportunity to apply what I learned to practice. I really enjoyed the process of thinking and cooperating with my team members and making progress step by step. The time is relatively short so we worked hard from creating ideas to implementing them and finally presenting the results. Overall, I would recommend it to all master students and I hope this project opportunity goes on and gets enlarged so more students could be involved.

Yili Peng

It's a very good access for students to have a view of the finance and insurance industry. In this activity, I did some research on the insurance and reinsurance and applied my statistical knowledge to the newest and hottest topic in insurance which really helps me to decide (my) future direction.

Jiahui Chen

Overview

Each year, we organise the Department of Statistics Practitioners' Challenge for BSc and MSc students. During this event, we collaborate with leading industry partners to initiate competitive projects focusing on real issues faced by companies. Students who take on the challenge use their personal and professional skills developed through their programme at LSE. 

Target audience

All third year undergraduate students and all taught postgraduate students within the Department of Statistics.

Details

During the project, led by Dr Gelly Mitrodima, we collaborate with leading industry partners. In the past we worked with Aviva, JP Morgan, UBS, and QBE. Companies propose a problem, from insurance to data science and students form teams in order to apply their interest for their preferred challenge. 

The students are supervised by Dr Mitrodima and other academic staff. PhD students in the Department of Statistics are offering support to the teams throughout the challenge. This way students don’t only work for well-known institutions, but they also collaborate with academic staff in the Department and get some invaluable guidance. We also organise a communication and presentation skills seminar in collaboration with LSE LIFE. This aims to help students with their actual presentations at the end of the challenge. 

The challenge runs in Lent term and lasts for five weeks. In the final stage, our students present their findings to the companies and the Department and submit a technical report. The 2018 and 2019, BSc challenges were funded by the Student Experience Enhancement Fund. The funding is required for the prizes for the teams. 

Impact

Students enjoy the variety of projects to choose from and the chance to gain experience working on solving real issues. They also appreciated the working environment during the challenge, where they were able to reach out and exchange with academics and professionals. The experience gave them more insight into their future career which often coincide with the industry they work with. A key aspect is that they learn how to research alternative approaches and develop great skills in effectively working with other people.

Previous challenges:

2018

Project 1

Title: Testing various methods of nonlinear principal component analysis (PCA) on financial time series           

Business: AVIVA       

Brief description: Apply traditional and nonlinear PCA on the overnight index swap (OIS) rates provided by the Bank of England. Investigate the forecasting performance of different time series models and select ones that are more robust to different time periods and at the same time maintain a low forecast error overall. 

Project 2

Title: Large loss prediction       

Business: QBE         

Brief description: Predict rare events (large losses, both in terms of frequency and severity), define the relationship between small and large losses, and find ways to rescale model forecasts generated from imbalanced data sets.

2019

Project 1

Title: Bouquet of interest rate models

Business: Hymans Robertson

Brief description: Compare and contrast various stochastic interest rate models given their advantages/limitations, implementation steps etc. 

Project 2

Title: Calibration risk for interest rates

Business: Hymans Robertson

Brief description: Quantify the calibration risk of interest rate models and demonstrate their impact on company’s projections. 

2020

Title: Correlation models for time series

Business: AVIVA       

Brief description: Identify the optimal length and the optimal frequency for the time series to calculate stable correlations. Establish a method to annualise the correlations calculated with daily or monthly observations. Develop a robust model able to forecast reliable future correlations. 

2021

Undergraduate:

Business: Standard Chartered Bank        

Research work on China’s ESG development history and current status. How Chinese government has been setting this development as a long-term sustainable target (especially Green GBA and Green Shenzhen)? As investor sentiment on ESG is evolving rapidly, how may Standard Chartered better identify how capital is allocated and the appetite for SDGs across our markets’ presence? Conduct a main competitor analysis on market promotion and positioning (especially from competitors’ product offerings – product components, asset scale, social and business impact…etc).   Investigate how Standard Chartered can better identify the latest regulatory priorities and enable factors in supporting sustainable finance growth, especially in GBA and in Shenzhen.  

MSc:

Business: Capgemini, UK 

The Climate Change Act 2008 declares the UK’s target of net-zero carbon emissions by the year 2050, requiring significant transformation across industries to meet this objective. Focusing on motor vehicle trends in the UK, the rapid uptake of Electric Vehicle (EV) usage presents several challenges in sustaining such a growth rate, from vehicle fueling infrastructure to smoothing demand on the national grid. By contrast, while van traffic has grown by 71% over the last 20 years, uptake of alternatively fueled LCVs and HGVs is low, and reducing carbon emissions in this domain currently requires different strategies – for example, Artificial Intelligence and Analytics can be leveraged for optimising routing and distribution centre locations. Identify drivers and risk factors of the increasing market share of passenger and commercial EVs in the UK and produce a model forecasting the carbon emissions of the evolving profile of vehicles on the UK road network (considering the constraint of the Climate Change Act and other government targets). Produce three scenarios which would expedite the de-carbonisation of the UK road network and assess their respective feasibility. Supported by technical analysis, discuss how Artificial Intelligence methods could be leveraged to reduce the carbon emissions of the UK’s current fleet of LCVs and HGVs.