Program Overview

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MSc Financial Analytics

Overview

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.

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 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
• Corporate Finance (optional)
• Financial Market Structure (optional)
• Data Management
• Financial Data Analytics

Semester 2

• Advanced Analytics and Machine Learning
• Financial Modelling in Python
• Advanced Financial Data Analytics
• AI & Trading

Semester 3

• Academic Dissertation
• Applied Research Project

Modules

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

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.

Learning Outcomes

Upon successful completion of this project, students will:

• Demonstrate an ability to design and manage a piece of individual research.
• Apply knowledge and skills developed in previous modules to contemporary issues in financial markets.
• Establish links between financial theory and financial practice.
• Exhibit intellectual discipline in identifying and critique the appropriate information.
• Identify appropriate econometric methods for critically analysing a contemporary issue in finance.
• Critically evaluate the appropriateness of modelling assumptions.
• 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
• Cognitive Skills
• Transferable Skills

Data Management

Overview

The effective management of small and big data is a crucial component of all business analytics projects.

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

Advanced Analytics & Machine Learning

Overview

Machine learning is the core technology underpinning predictive analytics and artificial intelligence, as well as many other analytical tasks.

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

AI & Trading

Overview

This course will introduce the modern practices in finance of using algorithms to extract computer-age statistical inference.

Learning Outcomes

On successful completion of the course, students will be able to:

• Evaluate fundamental financial machine learning principles
• Synthesize theory to build investment strategies
• Formulate code to solve problems encountered in finance

Skills

This module provides opportunities for the student to acquire or enhance the following skills:

• Problem solving – innovative ability to design and develop algorithms
• Logical reasoning – developing code to implement solutions
• Digital Proficiency – ability to write code
• Practice Ready – building empirical investment strategies
• Critical Thinking – understanding how to create robust test plans

Financial Modelling in Python

Overview

The aims of this module are to:

• develop the students' computational skills
• introduce a range of numerical techniques of importance in finance
• familiarise students with financial models and how to implement them

Learning Outcomes

Upon successful completion of this module, students will:

• Describe and discuss the modelling frameworks used to value financial instruments.
• Understand the salient features of prominent derivatives contracts.
• Translate financial problems into mathematical models with appropriate numerical solutions
• Have experience using Python to implement financial models
• 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
• Cognitive Skills
• Transferable Skills

Financial Data Analytics

Overview

The purpose of this course is to provide an introduction to econometric techniques used in finance.

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
• The ability to appreciate, construct and analyse mathematical, statistical, financial and economic models of practical risk situations

Advanced Financial Data Analytics

Overview

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:

• Extract meaning from noisy financial data
• Critique stylised facts of financial data for economic inference
• Evaluate the output of statistical tests

Skills

This module provides opportunities for the student to acquire or enhance the following skills:

• Problem solving – innovative ability to implement statistical tests
• Logical reasoning – analysing data
• Digital Proficiency – ability to write code
• Abstraction – developing generic re-usable solutions
• Critical Thinking – applying and interpreting statistics

Asset Pricing

Overview

Course Content

The aims of this module are to:

• provide students with the necessary theoretical and analytical tools which underpin the pricing of assets;
• familiarize students with the environment of a trading room

Learning Outcomes

Upon successful completion of this module, students will:

• Be familiar with the various theories on individuals’ investment decision making
• apply techniques for formally assessing risk.
• understand the methodologies employed in investigating asset pricing behaviour in the capital market
• be able to critically evaluate the various asset pricing models in terms of both theory and empirical evidence
• be able to critically appraise the EMH, anomalies and behavioural finance.
• 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
• Cognitive Skills
• Transferable Skills

Financial Market Structure

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.

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 trading structures of financial markets
• Development and organisation of major exchanges
• How market structure will be reflected in pricing of securities, trading behaviour, trading mechanisms and market design
• 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
• Cognitive Skills
• Transferable Skills

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.

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
• Cognitive Skills
• Transferable Skills

Entry Requirements

Graduate

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.

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.

Tuition Fees

Northern Ireland (NI) | £8,800 ---|--- Republic of Ireland (ROI) | £8,800 England, Scotland or Wales (GB) | £9,250 EU Other | £25,800 (£6,000 discount, see T&Cs link below) International | £25,800 (£6,000 discount, see T&Cs link below)

Careers

Career Prospects

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.

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 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.

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.