- Headline
- LSE QMRM Review
- Graduation Class
- 2025
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LSE MSc Quantitative Methods for Risk Management Review
Pre-sessional course (MA400):
The programme begins with a two-week pre-sessional course covering probability theory, measure theory, and stochastic processes. The aim is to bring everyone to a common mathematical baseline. Because of this, you do not need a pure maths, statistics, or finance undergraduate degree. In my cohort there were students from economics, finance, business, and accounting backgrounds. The degree becomes mathematically heavy, especially in Stochastic Processes, so MA400 is important. It is fast paced, but the assessment is not counted towards the degree, and the material reappears throughout the degree. The module content also felt relatively self-contained, so anything assessed was covered in the module itself. The only exceptions are some calculus or matrix algebra that would be assumed knowledge. In this case there are usually resources on the module page to cover the prerequisites.
Curriculum and module choice:
There is a wide range of modules and some flexibility to choose courses that are not formally listed as programme options.
Compulsory modules when I studied were:
• Stochastic Processes (ST409)
• Statistical Methods for Risk Management (ST429)
• Computational Methods in Finance (MA417)
I understand that MA417 is now being replaced by Stochastic Simulation, Training, and Calibration (ST463). Although the degree is part of the Statistics Department, students can take modules across Mathematics and Finance. This provides significant freedom if you want more coding-based modules, more probability theory, more machine learning, or more finance.
Workload and assessment:
The degree is academically intensive. Assessment style varies by module, but expectations are always made clear. Weekly problem sheets are standard. Tutorials go through selected problems, but there is not enough time to cover everything, so it helps to work through the sheets beforehand and bring specific questions.
Exam preparation is generally straightforward. Some modules are relatively new or have updated content, so problem sheets are often the most relevant preparation material.
Teaching quality:
Lecturers are experts in their fields, approachable, and willing to answer detailed questions. Office hours are easy to access and there are forums for each module to ask questions (this rarely got used, however). Tutorials have mandatory attendance and will either be taught by the lecturer or a PhD student, but they are the best place to ask for specific advice on working through problems. I never had an issue with PhD students not being equipped to teach, in fact one of my favourite tutorials was run by the PhD student.
Student experience:
The cohort is mostly international and highly motivated, so the academic environment is competitive and hard working. LSE offers many societies and extracurricular activities. At the start of the year there will be an event showcasing the various societies, including sports, academic, and hobby societies.
A useful extra is the free language course scheme, with options such as Japanese, Chinese, French, Spanish, German, and Arabic.
Career support:
LSE Careers can help with interview practice, CV reviews, and cover letter feedback. Careers support continues for many years after graduation. Being in London is a major advantage because there are frequent company presentations and networking events. Lecturers often have industry connections, and we had several guest speakers from quant and risk roles joining lectures.
Alumni network:
LSE has an extensive alumni network and a dedicated alumni centre on campus. On LinkedIn it is common to find LSE alumni at most firms you might be interested in. This makes informational outreach and networking easier.
Job outcomes:
An LSE masters is highly regarded by employers. Graduates typically pursue roles in risk management, quantitative finance, as well as data-focused roles in finance. Being based in London gives strong exposure to firms and increases access to events and hiring pipelines.
My experience:
I didn’t get involved in much extracurricular, but I did do the Jane Street Estimathon, which was group tasks for various Fermi estimates, and the LSE Statistics Practitioners’ Challenge which involved analysing insurance data and using various machine learning methods to produce a report and presentation. My modules were,
Term 1:
• Stochastic Processes (ST409)
• Statistical Methods for Risk Management (ST429)
• Computational Methods in Finance (MA417)
• Quantitative Methods for Finance and Risk Analysis (FM442)
Term 2:
• Reinforcement Learning (ST455)
• Bayesian Machine Learning (ST451)
• Stochastic Simulation, Training, and Calibration (ST463)
• Advanced Time Series Analysis (ST418)
When I took the degree, MA417 was compulsory in Term 1 and ST463 was offered as an optional module in Term 2. There is no dissertation or thesis requirement. Assessment is based entirely on exams and coursework. My two favourite modules were Stochastic Processes and Reinforcement Learning.
There are plenty of pros to this program:
• Location
• Teaching quality
• Networking opportunities
• Strong recognition in quant, risk, and finance recruitment
• Access to resources like Bloomberg Terminals
And in my experience the only major downsides are the cost of living in London and the fact that programming was only done in Python and R. For my module choices, I was not exposed to any C++, which is typically used in high frequency and algorithmic trading.
- Recommend
- Yes, I would recommend this program
- Students Quality
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4.00 star(s)
- Courses/Instructors
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5.00 star(s)
- Career Services
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4.00 star(s)
