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Course & Unit Catalogues


MA50295: Advanced mathematics and data science techniques for finance

[Page last updated: 09 August 2024]

Academic Year: 2024/25
Owning Department/School: Department of Mathematical Sciences
Credits: 6 [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Masters UG & PG (FHEQ level 7)
Period:
Semester 2
Assessment Summary: CWRI 100%
Assessment Detail:
  • Short essays (CWRI 20%)
  • Substantial essay (CWRI 80%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites:
Learning Outcomes: Critically evaluate existing mathematical or data science solutions applied to financial challenges.
Demonstrate an understanding of the need for responsible and ethical approaches to applying mathematical or data science methods to financial applications.
Explain recent examples of mathematical or data science techniques which have had impact in the financial industry.
Implement a chosen mathematical or data scientific method for solving a problem of relevance in the financial industry.


Synopsis: This is a flexible unit teaching contemporary issues in finance. Topics will be chosen to ensure you have examples of cutting edge mathematical and data science topics. Typically, you will choose 3 items each year. The content will be presented by academics, but some lectures or presentations by practitioners may also be incorporated to add relevance to the topic.

Aims: This is a flexible unit teaching contemporary issues in finance. Topics will be chosen to ensure students are provided with examples of cutting edge mathematical and data science topics. Typically 3 items will be chosen each year. The content will be presented by academics, but some lectures or presentations by practitioners may also be incorporated to add relevance to the topic.

Skills: Mathematical or Data Science research (F, A) Report writing (F,A)

Content: The unit will discuss recent examples of relevant mathematical or data science solutions of relevance in the financial industry. Example topics: Blockchain technologies. Market microstructure problems. XVA. NLP methods for predicting investor sentiment. (Ultra-)High-frequency trading. Limit-order book modelling. Fraud detection. Rough volatility models and path variation. The course will also include a discussion of ethical issues that arise in the financial industry, and the need for accurate and responsible modelling, and clear communication of assumptions used and an introduction to the universities HPC facilities.

Course availability:

MA50295 is Compulsory on the following courses:

Department of Mathematical Sciences

Notes:

  • This unit catalogue is applicable for the 2024/25 academic year only. Students continuing their studies into 2025/26 and beyond should not assume that this unit will be available in future years in the format displayed here for 2024/25.
  • Courses and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Find out more about these and other important University terms and conditions here.