Masters in Quantitative Finance - MSc Degree

Program Overview

The MSc in Quantitative Finance from Strathclyde University combines finance theory with mathematical modeling and computer implementation, preparing graduates for careers in financial engineering and risk management. The program's focus on industry relevance and application ensures that graduates possess the skills and knowledge required by the financial sector. Students benefit from a small cohort size, modern facilities, and access to a professional network through the LinkedIn group.

Program Outline

Degree Overview:

The MSc in Quantitative Finance is designed to meet the demand for graduates who understand the mathematical models used in financial tools, products, and software. It's a cross-faculty alliance between Strathclyde Business School and the Faculty of Science, aimed at individuals with a strong aptitude for mathematics, statistics, and computing, even if they haven't studied these topics in detail during their undergraduate degree. The program equips students with the necessary skills to enter the financial industry, preparing them for careers in financial engineering and risk management, leading to roles such as hedge fund manager or financial analyst.

Outline:

The curriculum balances finance and mathematical theory, computer implementations of this theory, and practical skills and knowledge.

Semester 1:

• Foundations of Mathematical & Statistical Finance (20 credits): This class brings all students to the same level of mathematical and statistical training, preparing them for the financial theory classes in Semester 2.
• Principles of Finance (20 credits): This class introduces financial decision-making, focusing on corporate finance.
It explains how companies decide on investments to meet their objectives, emphasizing the maximization of value. It covers the capital market risk-return relationship, requiring an understanding of risk and its management through portfolio development.
• International Financial Markets & Banking (20 credits): This class provides an understanding of the financial system, the roles and functions of financial markets and institutions, with a focus on intermediaries like banks and investment firms.
It covers fixed income, equity, and foreign exchange markets, emphasizing the global nature of financial markets and their regulation.
• Big Data Technologies (20 credits): This module covers:
• Fundamentals of Python for using big data technologies
• Application of classical statistical techniques in modern data analysis
• Potential applications of data analysis tools and their limitations
• Cloud NoSQL systems, their design, implementation, efficiency, scalability, and design trade-offs
• Map-Reduce programming paradigm

Semester 2:

• Elective Classes (60 credits): Students choose 60 credits from the following lists:
• List A (20 credits):
• Behavioural Finance (10 credits):
This class explores the main ideas of behavioural finance, focusing on non-rational actions and the development of financial models incorporating these ideas.
• Security Analysis (10 credits): This class develops an appreciation of the investment characteristics of different securities, particularly bonds and shares, and how they are valued.
• Portfolio Theory & Management (10 credits): This class examines the Markowitz approach to optimal portfolio selection, exploring issues related to optimal portfolio choice and passive and active fund management.
• Derivatives & Treasury Management (20 credits): This class provides a strong grounding in derivatives used to manage financial risks faced by individuals, financial institutions, and business corporations, with an emphasis on corporate treasury management and the role of derivatives in managing treasury risk.
• List B (20 credits):
• Database & Web Systems Development (20 credits):
This module provides conceptual and practical understanding of data modelling, database design, and database technology, with practical experience in developing web-based applications that integrate database server interaction.
• Machine Learning for Data Analytics (20 credits): This class equips students with an understanding of machine learning principles, popular approaches, and how and when to apply these techniques.
• List C (20 credits):
• Financial Stochastic Processes (10 credits):
This module covers diverse topics in stochastic processes used to model real systems, with an emphasis on the valuation of financial derivatives. It includes theoretical analysis and computational algorithms using R.
• Financial Econometrics (10 credits):
This module covers diverse topics in econometrics used to model real financial data, with an emphasis on the analysis of financial time series. It introduces the statistical software R for financial modelling.

Semester 3:

• Research Project (40 credits): Students undertake a research project.

Assessment:

The form of assessment varies from class to class and normally involves both coursework and examinations.

Teaching:

Teaching is student-focused, encouraging students to take responsibility for their own learning and development. Classes are supported by web-based materials, and class sizes allow for good contact between students and teaching staff. Classes are delivered through various methods:

• Lectures (using electronic presentations and computer demonstrations)
• Tutorials
• Computer laboratories
• Coursework
• Projects

Careers:

Graduates have found careers with companies such as Deloitte, PWC, Bank of Ireland, and BlackRock, among others. Job titles include:

• Business Change Consultant
• Manager Transfer Pricing Economist
• Risk Officer
• Trainee Actuarial Analyst
• Management Trainee
The program encourages students to join a closed group on LinkedIn, providing a professional network for graduates. The group posts jobs, encourages interactions, and keeps members up-to-date with relevant industry news.

Other:

• The course has a focus on application and was formed with input from the industry, ensuring relevance to current industry demands.
• The Department of Mathematics & Statistics has teaching rooms with modern equipment and access to University computing laboratories with necessary software.
• Students have access to a common room for individual and group study work and socialising.
• The cohort size is small enough to ensure good service from staff.
• Students are happy with the facilities provided by the University.