Course Structure

This degree will equip you with the cutting-edge quantitative and computational techniques utilised by leading finance and FinTech firms.

It will prepare you for a career in a quantitative finance, trading, portfolio management, data analytics or risk management environment. You’ll learn how data science, programming and statistical tools are used in the real world for the analysis and modelling of complex financial data.

Semester 1

Asset Pricing
Pricing assets via a modern investor perspective where markets are dynamic and biases are prevalent.

Corporate Finance (optional)
Insights into Investment Appraisal, Corporate Governance; Capital Structure; Dividend Policy; IPOs; Mergers and Acquisitions.

Market Microstructure (optional)
Provides an in-depth understanding of key participants, structures and trading processes that underpin capital markets.

Data Management
An exploration of the theory and practice of managing data, including identifying and extracting data, data pre-processing, data quality, data warehousing, relational databases, and big data solutions.

Financial Data Analytics
Introduction to applied econometric and data science techniques for contemporary financial data problems.
Statistical application will be taught via the R programming language.

Semester 2

Advanced Analytics and Machine Learning
This module provides statistical and conceptual understanding for application of machine learning algorithms.

Computational Methods in Finance
This module combines Maths, Finance and Computing to tackle key quantitative finance problems using Python.

Advanced Financial Data Analytics
This module outlines the application and conceptual understanding of statistical modelling of financial data dynamics using R programming, GitHub and cloud computing tools.

AI & Trading
Introduction to the artificial intelligence techniques and algorithms to enable financial machine learning.

Semester 3

Academic Dissertation
A thesis-based research project motivated by contemporary quantitative academic research. Supervision is provided by an academic with expertise in the chosen area.
or
Applied Research Project
An industry-focused module which has a taught component at the start of the summer. The final report is an in-depth equity analyst report of a CFA standard.

Modules are subject to change.

Learning and Teaching

Learning opportunities available with this course are outlined below:

• Teaching Methods

  The programme will equip you with cutting-edge quantitative and computational techniques and strategies used by leading firms. It will prepare you for future careers in a quantitative finance, trading or more general finance/FinTech environments. The course bridges the gap between quantitative models and financial decision-making with many modules focusing on learning through simulation.

Assessment

Assessments associated with the course are outlined below:

• Class tests, individual and group projects, oral presentations and case study investigations.

Modules

• Year 1

  Core Modules

  Dissertation- MSc Financial Analytics (60 credits)

  ×

  Dissertation- MSc Financial Analytics

  Overview

  The aim of the dissertation is to provide students with the skills needed for the advanced analysis of relevant datasets, to allow them to demonstrate an understanding of the relevant literature and to derive and test hypotheses and to draw appropriate conclusions.

  Learning Outcomes

  On completion of the dissertation students will have an understanding of:-
  
  • how to conduct a review of the current and relevant literature of the subject area chosen for the research study;
  • how to derive hypotheses or formulate research questions;
  • how to use data extracted from datasets or interviews to test hypotheses or answer research questions;
  • how to draw conclusions and identify the limitations of the study and scope for further research.

  Skills

  This module provides opportunities for the student to acquire or enhance the following skills:-
  
  • Communication
  • Effective and independent learning
  • Specific research skills relevant to the chosen research topic
  • Data analysis skills relevant to the chosen research topic
  • Quantitative Finance and econometric skills

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  60

  Module Code

  FIN9099

  Teaching Period

  Summer

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Applied Research Project (60 credits)

  ×

  Applied Research Project

  Overview

  The applied research project provides students with the opportunity to utilise the knowledge and skills acquired over the previous two semesters to plan, develop and produce a substantial piece of original, independent applied research.
  Lectures and computer-based workshops will cover the following areas:
  1. Research Methodology
  2. Fundamental analysis and strategy analysis
  3. Data Management, Analysis, Visualisation and Inference
  4. Financial analysis [ratios/cash flows], forecasting profit & EPS.
  5. Valuation 1: DDM and DCF approach
  6. Valuation 2: EVA and Price- multiples
  7. Critical assessment of model adequacy
  8. Presenting Information and Data

  Learning Outcomes

  Upon successful completion of this project, students will:
  1. Demonstrate an ability to design and manage a piece of individual research.
  2. Apply knowledge and skills developed in previous modules to contemporary issues in financial markets.
  3. Establish links between financial theory and financial practice.
  4. Exhibit intellectual discipline in identifying and critique the appropriate information.
  5. Identify appropriate econometric methods for critically analysing a contemporary issue in finance.
  6. Critically evaluate the appropriateness of modelling assumptions.
  7. Present their thinking in a professional industry-style research paper.

  Skills

  This applied research project provides opportunities for the student to acquire or enhance the following skills:-
  · Subject-specific skills
  -Use of computer-based packages to analyse and evaluate relevant data
  -Ability to critically read and evaluate finance and risk-related academic literature
  -Appreciation, construction and analysis of financial and economic models of practical risk situations
  · Cognitive Skills
  -Problem solving
  -Logical reasoning
  -Independent enquiry
  -Critical evaluation and interpretation
  -Self-assessment and reflection
  -Intellectual humility
  -Intellectual discipline
  · Transferable Skills
  -The ability to synthesis information/data from a variety of sources
  -Preparation and communication of ideas in both written and presentational forms
  -Ability to work both independently
  -Organisation and Time Management
  -Use of IT

  Coursework

  70%

  Examination

  0%

  Practical

  30%

  Credits

  60

  Module Code

  FIN9100

  Teaching Period

  Summer

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Data Management (15 credits)

  ×

  Data Management

  Overview

  The effective management of small and big data is a crucial component of all business analytics projects.
  This module explores the theory and practice of managing data, including identifying and extracting data, data pre processing, data quality, data warehousing, relational databases, and big data solutions.
  
  Course content may include, but is not limited to:
  Structured and unstructured data
  Data acquisition
  Data extraction using SQL
  Data storage (relational database management systems)
  Big data solutions
  Data preparation
  Data quality
  Security, legislation and ethical considerations

  Learning Outcomes

  Upon successful completion of the module students should be able to:
  • Evaluate the usefulness of a range of data sources and types in business decision making
  • Design a data management solution
  • Critically evaluate the main security, legal, and ethical considerations in the management of information

  Skills

  This course provides opportunities for the students to enhance the following skills:
  Database design
  Data extraction and wrangling
  Data storage
  Data management, including SQL and other big data technologies

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  MGT7178

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Advanced Analytics & Machine Learning (15 credits)

  ×

  Advanced Analytics & Machine Learning

  Overview

  Machine learning is the core technology underpinning predictive analytics and artificial intelligence, as well as many other analytical tasks.
  This module will build on the skills developed in the statistics module in terms of both programming and more advanced statistical techniques, namely the application of machine learning algorithms.
  
  Topics may include but are not limited to:
  • The analytics process
  • Analytics tools
  • Feature selection
  • Supervised learning
  • Unsupervised learning
  • Evaluating model performance
  • Programming machine learning models
  • Evaluation of the ethical implications of the use of algorithms e.g. the potential for reinforcing bias, security and privacy.

  Learning Outcomes

  Upon successful completion of the module students should be able to:
  
  • Critically evaluate a range of analytics tools and algorithms
  • Understand and apply key programming concepts as they pertain to machine learning
  • Design a predictive analytics solution

  Skills

  This course provides opportunities for the students to enhance the following skills:
  
  Application of advanced algorithms for business decision making
  Programming skills
  Problem solving

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  MGT7179

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  AI & Trading (15 credits)

  ×

  AI & Trading

  Overview

  Part 1
  These three simulation topics will focus on three distinct issues that form the foundation of modern finance:
  Law of one price
  Market efficiency
  Price formation
  Part 2
  Automation in trading

  Learning Outcomes

  Aims and objectives
  Part 1
  Students will derive core finance concepts by making their own financial decisions under real world conditions, and study several strategies that form the basis of many investment management practices. Students experience a range of environments that require actual valuation, investment, and risk management decisions, that will promote a deep understanding of the functioning of capital markets. The goal is to teach students how to learn finance through a unified framework for understanding relative valuation. This will help them stay current throughout their career.
  Part 2
  Students will understand the automating of trading and have a broad understanding of the prevalence of algorithms in the financial markets. Students will have hands on experience of algorithmic trading using a python based platform.

  Skills

  1. To understand how capital markets reach equilibrium.
  2. To understand when and why capital markets can be in disequilibrium.
  3. To experience a variety of real-world capital market settings.
  4. To participate from a range of industry professional perspectives.
  5. A broad understanding of computer based trading.

  Coursework

  30%

  Examination

  0%

  Practical

  70%

  Credits

  15

  Module Code

  FIN7030

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Computational Methods in Finance (15 credits)

  ×

  Computational Methods in Finance

  Overview

  The aims of this module are to:
  
  i. develop the students' computational skills
  ii. introduce a range of numerical techniques of importance in finance
  iii. familiarise students with financial models and how to implement them
  
  Areas to be covered include:
  
  A primer on financial instrument pricing
  o Bonds, forwards, options
  o Discounting
  o Probability distributions
  o Expectation theory
  
  Python
  o Arrays and data structures
  o Programming constructs
  o Functions and classes
  
  Numerical Methods
  o Root finding
  o Linear Algebra
  
  Financial Modelling
  o Stochastic processes
  o Interest rate models
  
  Option Pricing
  o Black Scholes Merton
  o The Greeks
  o Lattice Models
  o Model extensions
  
  Monte Carlo
  o Monte Carlo simulation
  o Variance reduction
  o Markov Chains
  
  Credit Risk
  o Merton Model

  Learning Outcomes

  Upon successful completion of this module, students will:
  
  1. Describe and discuss the modelling frameworks used to value financial instruments.
  2. Understand the salient features of prominent derivatives contracts.
  3. Translate financial problems into mathematical models with appropriate numerical solutions
  4. Have experience using Python to implement financial models
  5. Critically evaluate the efficacy of different approaches to derivative pricing

  Skills

  This module provides opportunities for the student to acquire or enhance the following skills:
  
  • Subject-specific skills
  o The ability to appreciate, construct and analyse mathematical, statistical, and financial models
  o Use of coding languages to implement financial models.
  
  • Cognitive Skills
  o Problem solving
  o Abstraction
  o Logical reasoning
  o Critical evaluation and interpretation
  o Self-assessment and reflection
  
  • Transferable Skills
  o Organisation and time management
  o Use computational technology

  Coursework

  40%

  Examination

  60%

  Practical

  0%

  Credits

  15

  Module Code

  FIN7029

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Financial Data Analytics (15 credits)

  ×

  Financial Data Analytics

  Overview

  The purpose of this course is to provide an introduction to econometric techniques used in finance. It contains a treatment of classical regression and an introduction to time series techniques. There will be an emphasis on applied work using econometric packages.
  The course is designed to give students both theoretical and practical experience of statistical and econometric techniques. A wide range of topics is typically covered including the basic regression model, which includes a discussion of the classical violations of this model and methods for their correction. Students will learn a computer statistical software package (R).

  Learning Outcomes

  Upon successful completion of this course students will have an understanding of:-
  • the main issues relating to the appropriate econometric modelling of financial and economic time series;
  • and have gained experience in the use of econometric software and be able to demonstrate their software skills in completing assignments;
  • and be able to discuss, applied econometric research topics in finance;
  • and have improved their data management, programming and research skills.

  Skills

  Subject-specific Skills
  • The ability to construct arguments and exercise problem solving skills in finance
  • The ability to use computer-based mathematical/statistical/econometric packages to analyse and evaluate relevant data
  • The ability to read and evaluate finance and risk-related academic literature
  Cognitive Skills
  • Problem solving
  • Logical reasoning
  • Independent enquiry
  • Critical evaluation and interpretation
  • Self-assessment and reflection
  Transferable Skills
  • The ability to synthesise information/data from a variety of sources
  • The preparation and communication of ideas in finance, information economics and risk management
  • Organisation and time management
  • Problem solving and critical analysis
  • Work-based skills; use of IT, including word-processing, email, internet and statistical/econometric/risk management packages
  • The ability to communicate quantitative and qualitative information together with analysis, argument and commentary

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  FIN9008

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Advanced Financial Data Analytics (15 credits)

  ×

  Advanced Financial Data Analytics

  Overview

  The aims of this module are to:
  (i) provide students with knowledge of the econometric methods and techniques used in the analysis of time series finance information.
  (ii) apply the empirical techniques using economic and financial data.
  
  Statistical Properties of Financial Returns
  Stylised Facts about Financial Returns; Distribution of Asset Returns; Time Dependency; Linear Dependency across Asset Returns
  Univariate Time Series and Applications to Finance
  Wold’s Decomposition Theory; Properties of AR Processes; Properties of Moving Average Processes; Autoregressive Moving Average (ARMA) Processes; The Box-Jenkins Approach; Example: A Model of Stock Returns
  Modelling Volatility – Conditional Heteroscedastic Models
  ARCH Models; GARCH Models; Estimation of GARCH Models; Forecasting with GARCH Model; Asymmetric GARCH Models; The GARCH-in-Mean Model
  Modelling Volatility and Correlations – Multivariate GARCH Models
  Multivariate GARCH Models; The VECH Model; The Diagonal VECH Model; The BEKK Model; The Constant Correlation Model; The Dynamic Correlation Model; Estimation of a Multivariate Model
  Vector Autoregressive Models
  Vector Autoregressive Models; Issues in VAR; Hypothesis Testing in VAR; Example: Money Supply, Inflation and Interest Rate

  Learning Outcomes

  Upon successful completion of this module students will be able to:
  1. critically analyse, estimate and forecast using AR, MA, and ARMA models
  2. apply the Box-Jenkins approach to time series models
  3. model and forecast volatility using autoregressive conditional heteroscedastic (ARCH) models
  4. estimate, interpret, and forecast with generalised autoregressive conditional heteroscedastic (GARCH) models
  5. test for spill-over of volatility between assets
  6. use vector autoregressive (VAR) models to analyse and interpret interaction between financial variables
  7. examine critically evaluate the impact of shocks on financial variables using impulse response analysis

  Skills

  This module provides opportunities for the student to acquire or enhance the following skills:-
  Subject-specific Skills
  • The ability to construct arguments and exercise problem solving
  skills in the context of theories of finance and risk management
  • The ability to use computer-based mathematical / statistical /
  econometric packages to analyse and evaluate relevant data
  • The ability to read and evaluate finance and risk-related
  academic literature
  
  Cognitive Skills
  • Problem solving
  • Logical reasoning
  • Independent enquiry
  • Critical evaluation and interpretation
  • Self assessment and reflection
  
  Transferable Skills
  • The ability to synthesise information/data from a variety of sources including from databases, books, journal articles and the internet
  • The preparation and communication of ideas in finance, information economics and risk management in both written and presentational forms
  • The ability to work both independently and in groups
  • Organisation and time management
  • Problem solving and critical analysis
  • Work-based skills; use of IT, including word-processing, email, internet and statistical/econometric/risk management packages
  • The ability to communicate quantitative and qualitative information together with analysis, argument and commentary

  Coursework

  50%

  Examination

  0%

  Practical

  50%

  Credits

  15

  Module Code

  FIN7028

  Teaching Period

  Spring

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Asset Pricing (15 credits)

  ×

  Asset Pricing

  Overview

  Course Content
  
  The aims of this module are to:
  (i) provide students with the necessary theoretical and analytical tools which underpin the pricing of assets;
  (ii) familiarize students with the environment of a trading room
  
  Areas to be covered include:
  
  Financial markets
  Overview of main markets; how firms and governments raise finance; financial instruments; trading securities.
  
  Valuation
  Valuing stocks.
  
  Asset returns and portfolio theory
  Measuring asset returns; theory of choice under uncertainty; mean-variance portfolio theory.
  
  Asset-pricing models
  Assessing the theoretical and empirical validity of various asset pricing models.
  
  Equity markets
  EMH; anomalies; behavioural finance

  Learning Outcomes

  Upon successful completion of this module, students will:
  1. Be familiar with the various theories on individuals’ investment decision making
  2. apply techniques for formally assessing risk.
  3. understand the methodologies employed in investigating asset pricing behaviour in the capital market
  4. be able to critically evaluate the various asset pricing models in terms of both theory and empirical evidence
  5. be able to critically appraise the EMH, anomalies and behavioural finance.
  6. be familiar with the trading-room environment and the Bloomberg database.

  Skills

  This module provides opportunities for the student to acquire or enhance the following skills:-
  • Subject-specific skills
  o Use of computer-based packages to analyse and evaluate relevant data
  o Ability to criticially read and evaluate finance and risk-related academic literature
  o Appreciation, construction and analysis of financial and economic models of practical risk situations
  
  • Cognitive Skills
  o Problem solving
  o Logical reasoning
  o Independent enquiry
  o Criticial evaluation and interpretation
  o Self-assessment and reflection
  
  • Transferable Skills
  o The ability to synthesis information/data from a variety of sources
  o Preparation and communication of ideas in both written and presentational forms
  o Ability to work both independently and in groups
  o Organisation and Time Management
  o Use of IT.

  Coursework

  40%

  Examination

  0%

  Practical

  60%

  Credits

  15

  Module Code

  FIN7026

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Optional Modules

  Market Microstructure (15 credits)

  ×

  Market Microstructure

  Overview

  The aim of this module is to ensure that students understand the structure, dynamics and trading mechanisms of global financial markets, as well as appreciate the role of key institutions involved in these markets.
  
  
  Areas to be covered:
  
  1. Firstly, we analyse the role, structure and economic principles of the key players participating in financial markets.
  2. Secondly, we examine the function and characteristics of two key markets: fixed income and foreign exchange.
  3. Thirdly, we will analyse the trading mechanics of financial markets, and in doing so, we will examine the development and organisation of major exchanges.

  Learning Outcomes

  Upon successful completion of this module, students will have an understanding of:-
  
  - The structure and strategy of key participants in financial markets;
  - The function and characteristics of fixed income and foreign exchange markets;
  - How market microstructure will be reflected in pricing of securities, trading behaviour, trading mechanisms and market design.
  - The trading structures of financial markets;
  - Development and organisation of major exchanges;
  - The role of information in financial markets and how it is processed in practice.

  Skills

  This module provides opportunities for the student to acquire or enhance the following skills:-
  
  • Subject-specific skills
  o Ability to critically read and evaluate the academic microstructure literature
  o Appreciation, construction and analysis of trading strategies
  
  • Cognitive Skills
  o Problem solving
  o Logical reasoning
  o Independent enquiry
  o Critical evaluation and interpretation
  o Self-assessment and reflection
  
  • Transferable Skills
  o The ability to synthesis information/data from a variety of sources
  o The ability to present and communicate complex ideas to a non-specialist audience
  o Ability to work in groups
  o Organisation and Time Management

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  15

  Module Code

  FIN7027

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

  Corporate Finance (15 credits)

  ×

  Corporate Finance

  Overview

  Course Description:
  The purpose of this course is to analyse how corporations make major financial decisions. The theory of corporate behaviour is discussed and the relevance of each theoretical model is examined by an empirical analysis of actual corporate decision making.
  
  Course Aim:
  The aims of this module are to:
  (i) familiarize students with the issues confronting corporations when making investment and financing decisions;
  (ii) develop the ability of students to obtain corporate information from the Bloomberg database.
  
  
  
  Course Coverage:
  • Corporate Governance
  • Investment Appraisal
  • Dividend Policy
  • Capital Structure
  • Initial Public Offerings
  • Mergers and Acquisitions

  Learning Outcomes

  Upon successful completion of this module, students will be able to:
  • describe and synthesize academic theories which explain the approaches of corporations to investment and financing decisions;
  • analyse how corporations can increase shareholder value;
  • evaluate empirical evidence regarding whether corporate decision making is consistent with academic theories;
  • apply theoretical principles to hypothetical situations;
  • use the Bloomberg database in a trading-room environment.

  Skills

  This course provides opportunities for the student to acquire or enhance the following skills:
  
  Subject-specific Skills
  • The ability to construct arguments and exercise problem solving skills in the context of theories of finance and risk management
  • The ability to use computer-based mathematical / statistical / econometric packages to analyse and evaluate relevant data
  • The ability to read and evaluate finance and risk-related academic literature
  • The ability to appreciate, construct and analyse mathematical, statistical, financial and economic models of practical risk situations
  
  Cognitive Skills
  • Problem solving
  • Logical reasoning
  • Independent enquiry
  • Critical evaluation and interpretation
  • Self assessment and reflection
  
  Transferable Skills
  • The ability to synthesise information/data from a variety of sources including from databases, books, journal articles and the internet
  • The preparation and communication of ideas in finance, information economics and risk management in both written and presentational forms
  • The ability to work both independently and in groups
  • Organisation and time management
  • Problem solving and critical analysis
  • Work-based skills; use of IT, including word-processing, email, internet and statistical/econometric/risk management packages
  • The ability to communicate quantitative and qualitative information together with analysis, argument and commentary in a form appropriate to different intended audiences.

  Coursework

  40%

  Examination

  60%

  Practical

  0%

  Credits

  15

  Module Code

  FIN9005

  Teaching Period

  Autumn

  Duration

  15 weeks

  Pre-requisite

  No

  Core/Optional

  Optional

Entrance requirements

Graduate

Normally a 2.1 Honours degree or equivalent qualification acceptable to the University in Finance, Mathematics, Economics or other relevant quantitative subject. Science and Engineering disciplines will be considered where there is a significant mathematical component. Performance in relevant modules must be of the required standard.

Applicants with a 2.2 Honours degree or equivalent qualification acceptable to the University and sufficient relevant experience will be considered on a case-by-case basis.

Applicants are advised to apply as early as possible and ideally no later than 16th August 2024 for courses which commence in late September. In the event that any programme receives a high number of applications, the University reserves the right to close the application portal. Notifications to this effect will appear on the Direct Application Portal against the programme application page.

Please note: international applicants will be required to pay a deposit to secure a place on this course.

International Students

Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.

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English Language Requirements

Evidence of an IELTS* score of 6.5, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required. *Taken within the last 2 years.

International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.

For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.

Career Prospects

Introduction

This MSc will equip students with cutting-edge quantitative and computational techniques and strategies used by leading finance and financial technology (FinTech) firms.

Today, all full-service institutional finance firms employ Financial Analytics professionals in their operations as as do other boutique firms, such as hedge funds, ranging from 20 or fewer employees to several thousand.

Many IT software organisations also specialise in the interface between computing and finance and would be very attracted to graduates from this programme. Upon completion of this MSc there is a wide variety of roles available for graduates, some of which will suit those with strong data analysis skills who wish to use cutting-edge quantitative modelling techniques and work in collaboration with traders to develop bespoke financial products.

Queen's postgraduates reap exceptional benefits. Unique initiatives, such as Degree Plus and Researcher Plus bolster our commitment to employability, while innovative leadership and executive programmes alongside sterling integration with business experts helps our students gain key leadership positions both nationally and internationally.
http://www.qub.ac.uk/directorates/sgc/careers/

Employment after the Course

Graduate prospects from the MSc Financial Analytics are excellent, culminating in Queen’s being ranked first in the UK for Graduate Prospects in Accounting and Finance (Times and Sunday Times Good University Guide 2023). Graduates have gone on to work at world-leading companies including Amazon, Citadel Securities, Citigroup, Deutsche Bank, First Derivative, FinTrU, Merrill Lynch and PwC.

Graduate Plus/Future Ready Award for extra-curricular skills

In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.

Tuition Fees

Northern Ireland (NI) 1	£8,360
Republic of Ireland (ROI) 2	£8,360
England, Scotland or Wales (GB) 1	£8,360
EU Other 3	£23,100
International	£23,100

MSc (T) Quanitative Finance 

¹EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.

² EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.

³ EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.

All tuition fees quoted relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.

More information on postgraduate tuition fees.

Additional course costs

There are no specific additional course costs associated with this programme.

All Students

Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.

Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.

Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.

If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.

Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.

There are also additional charges for graduation ceremonies, examination resits and library fines.

How to Apply

Apply using our online Postgraduate Applications Portal and follow the step-by-step instructions on how to apply.

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Terms and Conditions

The terms and conditions that apply when you accept an offer of a place at the University on a taught programme of study.
Queen's University Belfast Terms and Conditions.

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