MSc Financial Mathematics
September
Full time
18 months
Suzhou
This programme leads to a University of Liverpool degree which is recognised by Ministry of Education, China.
Knowledge and skills
Graduates of the MSc Financial Mathematics programme will have:
- an advanced understanding of financial mathematics and quantitative analysis, spanning stochastic pricing theory, financial econometrics and fixed income modelling;
- a solid command of ML fundamentals, including regression, classification, regularisation, and ensemble methods, as well as advanced techniques such as DL, transformers, LLMs, and autoencoders applied to financial time series and text;
- practical computational skills in Python and R, gained through working with real-world high-frequency market data provided by our industry partners;
- the ability to devise, plan and undertake complex independent research projects, demonstrated through a full-semester dissertation that can support employment or PhD applications; and
- advanced skills in research, evaluation and analysis, with the quantitative and data-driven techniques needed to interpret complex data, markets and research literature.
Quantitative finance is being reshaped by AI, and our MSc Financial Mathematics programme is designed for this new era. The programme is an intensive 18-month journey, with one year of coursework taught by specialists with international teaching, research and industry experience, followed by a semester of supervised research that allows students to pursue internships or look for jobs in parallel. What sets us apart is the connection between the classroom and the trading floor. Machine learning runs throughout our curriculum, from the fundamentals to deep learning, and our students work with real market data and industry practitioners through our partnerships with ForthTech and Predictiva. Having spent six years as a professor of fintech at the University of Edinburgh and two years as a quantitative researcher on an options desk at Susquehanna International Group on Wall Street, I designed this programme to deliver exactly what the industry needs: mathematicians who can speak the language of AI, and AI practitioners who understand markets.
Dr Yi Cao
Programme Director
Modules
*Programme modules listed are illustrative only and subject to change. XJTLU students are advised to log in to the e-Bridge Portal to view the effectuated module structure.
Semester 1
Semester 2
Careers
The MSc Financial Mathematics programme provides rigorous training at the intersection of mathematics, finance and AI. Graduates are prepared for a range of quantitative roles, including quantitative analyst, quantitative researcher, risk manager, data scientist, and algorithmic trading specialist, across investment banks, hedge funds, asset management firms, insurance companies, consulting firms, government agencies, and the fintech sector. Our industry partnerships help create career pathways: top students in the Deep Learning in Finance module are offered quantitative analyst internships at ForthTech’s Shenzhen office, while industry lectures from Predictiva’s CTO provide firsthand insight into how autonomous AI trading platforms are engineered and deployed. The dissertation semester is designed to run in parallel with internships and job searches, so students graduate not only with an advanced degree, but also with demonstrable, industry-recognised skills and, for many, valuable industry experience.
Overview
Where mathematics meets machine learning, and finance meets artificial intelligence.
MSc Financial Mathematics at Xi’an Jiaotong-Liverpool University is a redesigned 18-month programme enhanced by artificial intelligence (AI), with machine learning (ML) at the heart of quantitative finance education. Its rigorous mathematical core covers stochastic calculus, derivative pricing and hedging, financial econometrics, portfolio and risk management, and fixed income modelling. The programme complements this mathematical foundation with the fundamentals of ML and its applications in finance, including statistical learning, model regularisation, deep neural networks, transformers, large language models (LLMs), and blockchain technology.
A dedicated ML pathway (Mathematics and Statistics for Data Science -> Advanced Statistical Learning Theory -> Deep Learning in Finance) progresses from the mathematical language of ML to advanced AI-driven fintech. All students, regardless of which electives they take, gain hands-on experience with ML through the compulsory Computational Methods in Finance II module.
Delivered in collaboration with two AI-driven quantitative trading firms, ForthTech Research (HK) Limited and Predictiva LTD, the programme prepares students for research and quantitative finance roles in the global financial industry.
Why choose the MSc Financial Mathematics programme at XJTLU?