The MSc Financial Analytics is the programme for you if you have an interest in financial markets or financial technology (FinTech) and enjoy working with data. It shows you how data science, analytics, statistics and programming tools are used in the real world for the analysis and modelling of financial and economic data.
The programme will equip students with cutting-edge quantitative and computational techniques utilised by industry leading firms. The course aims to bridge the gap between complex quantitative models and financial decision making and does so by equipping students with dual skillsets in both finance and data analysis.
It can lead to exciting careers in areas such as financial data science, trading, software development, portfolio management, consulting, data analytics, risk management, business analytics and academia.
Queen’s University is ranked third in the UK for Graduate Prospects in Accounting and Finance.
(Times and Sunday Times Good University Guide 2024)
Financial Analytics highlights
Professional Accreditations
The programme has been accepted into the CFA Institute University Recognition Program. It aligns with the Candidate Body of Knowledge (CBOK) - the core knowledge, skills, and abilities that are generally accepted and applied by investment professionals throughout the world.
We are also a proud academic partner of the Certificate in Quantitative Finance (CQF) Institute. The CQF is the world’s most prominent professional qualification in quantitative finance and our partnership highlights our dedication to supporting the professional development of students studying in the field of finance and quantitative finance. Through our partnership MSc Financial Analytics students benefit from access to the latest CQF institute resources including events such as industry talks and workshops, research, networking opportunities and career tools.
World Class Facilities
Queen’s Business School (QBS) has recently undergone an innovative expansion that establishes a benchmark of global excellence for one of the top business schools in the UK and Ireland. A stunning new 6,000 square metre building, adjacent to the listed red-brick Riddel Hall has been designed with the latest digital infrastructure for media lecture capture, TED Talk provision and collaborative breakout sessions.
Fostering an enhanced social and educational experience the new state-of-the-art QBS venue boasts a 250-seat tiered educational space; 120-seat Harvard style lecture theatre; 150-seat computer laboratory; breakout study spaces; FinTrU Trading Room; a café, and a Business Engagement and Employability Hub.
Many classes are held in our state-of-the-art FinTrU Trading Room. This facility provides students with access to Bloomberg software, a market leader in financial news, data, and analytics as used by many leading financial institutions worldwide.
The trading room facilitates an interactive and exciting learning environment which brings textbook theory to life. Students will also have exposure to S&P Capital IQ and will learn in-demand programming and data science skills such as Python, R, SQL, Hadoop, Spark, Google Cloud, and Posit Workbench (posit.co), an Azure-based enterprise grade data science platform.
Student Experience
Students are strongly encouraged to join the Student Managed Fund where they will have a unique opportunity to manage real money. Queen’s Business School is one of only a handful of universities in the UK and Ireland to offer this experience which is a game changer when it comes to graduate employability.
Student Testimonials
The Masters provided me with unique access to the Trading Room, an excellent facility that was essential to my learning experience. Between learning how to use industry leading software like Bloomberg to being introduced to coding software, this room bridged the gap between academic learning and practical application, an experience that I will take with me into the world of employment.
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 quantitative finance, trading, portfolio management, data analytics or risk management. You’ll learn how data science, business analytics, 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.
Financial Market Structure (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.
Semester 2
Advanced Analytics and Machine Learning
This module provides statistical and conceptual understanding for application of machine learning algorithms.
Financial Modelling in Python
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.
Learning opportunities available with this course are outlined below:
Teaching Methods
The programme will equip you with cutting-edge quantitative and computational analytical skills demanded by leading firms worldwide. It will prepare you for future careers in quantitative finance, trading or more general finance, FinTech and business consulting 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.
The information below is intended as an example only, featuring module details for the current year of study (2024/25). Modules are reviewed on an annual basis and may be subject to future changes – revised details will be published through Programme Specifications ahead of each academic year.
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
This course will introduce the modern practices in finance of using algorithms to extract computer-age statistical
inference. The purpose of this course is not to introduce students to the vast array of machine learning
algorithms. Instead, the goal is to introduce the emerging field of Financial Machine Learning as a complement to
traditional financial research techniques.
This course presents machine learning as a non-linear extension of various topics in quantitative economics,
such as financial econometrics. This course will introduce best practice techniques in financial data science,
which can help illicit economically meaningful signals and answer recent financial research questions.
Learning Outcomes
On successful completion of the course, students will be able to:
1. Evaluate fundamental financial machine learning principles
2. Synthesize theory to build investment strategies
3. Formulate code to solve problems encountered in finance
Skills
This module provides opportunities for the student to acquire or enhance the following skills:
1. Problem solving – innovative ability to design and develop algorithms
2. Logical reasoning – developing code to implement solutions
3. Digital Proficiency – ability to write code
4. Practice Ready – building empirical investment strategies
5. Critical Thinking – understanding how to create robust test plans
The aims of this module are to:
Deepen participants' understanding of financial predictions and decision-making by exploring the revolutionary
impact of combining econometrics and machine learning in financial analytics.
Integrate machine learning and classical financial time series econometrics to tackle complex financial problems
characterised by uncertainty and conflicting objectives.
Explore the role of machine learning in processing large datasets and accurately modelling the complexities of
financial markets.
Advocate for adopting a growth mindset for learning advanced financial data analytics, emphasising embracing
challenges, persisting through setbacks, leveraging criticism, and finding lessons in others' success.
Equip participants with the necessary insights and tools to navigate the sophisticated realm of financial analytics,
encouraging a lifelong commitment to learning and development in the field.
Learning Outcomes
Upon successful completion of this module students will be able to:
1. Extract meaning from noisy financial data
2. Critique stylised facts of financial data for economic inference
3. Evaluate the output of statistical tests
Skills
This module provides opportunities for the student to acquire or enhance the following skills:-
1. Problem solving – innovative ability to implement statistical tests
2. Logical reasoning – analysing data
3. Digital Proficiency – ability to write code
4. Abstraction – developing generic re-usable solutions
5. Critical Thinking – applying and interpreting statistics
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.
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.
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
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
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
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
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
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.
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:-
1. The structure and strategy of key participants in financial markets
2. The trading structures of financial markets
3. Development and organisation of major exchanges
4. How market structure will be reflected in pricing of securities, trading behaviour, trading mechanisms and market design
5. 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
Normally a strong 2.2 Honours degree (with minimum of 55%) 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 (scoring below 55%) 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.
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.
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
Academic English: an intensive English language and study skills course for successful university study at degree level
Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.
This programme 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 finance and business consulting firms employ Financial Analytics professionals in their operations as do many boutique firms, such as asset managers and hedge funds. Furthermore, many IT software organisations are attracted to graduates from this programme due to their specialism at the interface between computing, data analytics and finance.
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 from this programme have secured roles with employers such as Citi, Deutsche Bank, Bank of China, Davy Group, Citco, Amazon, FD Technologies, Data Intellect and many others.
Typical roles include data scientists, financial engineers, software developers, equity analysts, consultants, portfolio analysts, data scientists, risk analysts, software developers, business analysts, and traders. https://www.qub.ac.uk/directorates/sgc/careers/
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.
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.
Terms and Conditions for Postgraduate applications:
1.1 Due to high demand, there is a deadline for applications.
1.2 You will be required to pay a deposit to secure your place on the course.
1.3 This condition of offer is in addition to any academic or English language requirements.
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 do I fund my study?
The Department for the Economy will provide a tuition fee loan of up to £6,500 per NI / EU student for postgraduate study. Tuition fee loan information.
A postgraduate loans system in the UK offers government-backed student loans of up to £11,836 for taught and research Masters courses in all subject areas (excluding Initial Teacher Education/PGCE, where undergraduate student finance is available). Criteria, eligibility, repayment and application information are available on the UK government website.
