MSc Financial Technology (Computer Science)

Program Overview

The MSc Financial Technology (Computer Science) program at the University of Essex combines expertise from computer science, economics, and business to provide students with the knowledge and skills needed to thrive in the modern financial industry. Students will gain an understanding of financial and economic markets, advanced computing methods, and the ability to design and program solutions that leverage the intersection of finance, economics, and computation. The program features cutting-edge computational and evolutionary methods, industry expert lectures, and specialist facilities, ensuring graduates are highly competitive in the financial sector.

Program Outline

Degree Overview:

The MSc Financial Technology (Computer Science) program at the University of Essex is designed to equip students with the knowledge and skills needed to thrive in the modern financial industry. It combines expertise from the departments of Computer Science, Economics, and Business, offering an interdisciplinary approach to financial technology.

Objectives:

The program aims to provide students with:

• Understanding of financial and economic markets: This includes the specifics and intricacies of these markets, going beyond simplified assumptions.
• Advanced computing methods: Students will learn to apply these methods to analyze financial data and design solutions.
• Ability to design and program solutions: The program emphasizes the synergy between finance, economics, and computation, enabling students to develop solutions that leverage this intersection.

Description:

The program covers a range of topics, including:

• Financial markets
• Big data analytics
• Blockchain
• AI
• Python programming
It provides both theoretical and technical skills specific to the finance industry, as well as transferable skills like argument development, presentation, independent work, and teamwork.

Outline:

Year 1:

Component 01: CORE

• CCFEA MSc Dissertation (40 CREDITS): This dissertation is worth 90% and is submitted in week 48.
It explores topics like auction design, market microstructure of the stock market, liquidity provision in electronic financial markets, and capital adequacy of centralized clearing platforms. It also introduces complexity economics, network modules, and strategic proteanism, using network models to study economic interactions.

Component 03: COMPULSORY

• Introduction to Programming in Python (20 CREDITS): This module provides an introduction to computer programming for students with little or no prior experience.
It uses the Python language in the Linux environment, covering both comprehensively. The focus is on developing practical programming skills, with examples from data processing and analysis. Students learn to manipulate and analyze data, graph it, and fit models to it. Teaching takes place in workshop-style sessions in a software laboratory.

Component 04: COMPULSORY

• Big Data in Finance (20 CREDITS): This module explores the analysis of big data in financial organizations, covering areas like predictive analytics, risk modeling, corporate finance, and the application of data analytics in high-frequency finance, fraud, and personal finance.

Component 05: COMPULSORY

• Machine Learning for Finance (20 CREDITS): This module combines theory and practice with big data cases in finance.
It introduces algorithmic and data science theories, followed by data-driven algorithms for structured and unstructured data. Modern machine learning and data mining algorithms are covered with case studies on the financial industry.

Component 06: COMPULSORY WITH OPTIONS

• CF961-7-AT or CF962-7-AT (20 CREDITS): Students can choose one of these modules.
• Option(s) from list (40 CREDITS): Students can choose modules from a list of options.

Teaching:

• Lectures: Introduce cutting-edge computational and evolutionary methods for analyzing and simulating markets, designing real-time trading systems, and managing risk.
• Lab sessions: Provide hands-on experience in implementing quantitative methods in finance and economics, modeling and developing machine learning algorithms, and optimizing algorithms in financial settings.
• Industry expert lectures: Offer insights into the latest developments and challenges faced by leading practitioners in quantitative finance and risk management.

Faculty:

• Michael Kampouridis: Senior Lecturer in Computational Finance, specializing in machine learning algorithms for solving real-world problems in economics and finance.
• Panagiotis Kanellopoulos: Lecturer in Computational Finance, focusing on algorithmic game theory.
• Dr. Maria Kyropoulou: Senior Lecturer, specializing in algorithmic game theory, algorithmic mechanism design, blockchain, and algorithm design and analysis.
• Alexandros Voudouris: Lecturer, specializing in the intersection of theoretical computer science, artificial intelligence, and microeconomic theory.
• Dr. Themistoklis Melissourgos: Lecturer, specializing in Algorithmic Game Theory, computational social choice, and the intersection of theoretical computer science and economics.

Specialist Facilities:

• Labs: Equipped with Matlab for implementing quantitative methods in finance and economics, Python for modeling and developing machine learning algorithms, and optimization solvers (like Gurobi) for financial optimization problems.
• Networked computer facilities and software aids: Provide access to a wide range of test and instrumentation equipment.
• Six laboratories: Exclusively for computer science and electronic engineering students, three of which are open 24/7.
• Software: Includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project.
• Coursework: Includes assignments and projects that demonstrate practical application of knowledge.
• Dissertation: A significant piece of work that allows students to delve deeper into a specific area of financial technology.
• Presentations: Provide opportunities for students to present their work and engage in discussions.

Careers:

• High demand in the finance industry: Graduates are well-equipped for roles in the UK and beyond.
• Competitive advantage: The program's interdisciplinary approach and focus on practical skills make graduates highly competitive in the financial sector.
• Employment opportunities: Graduates can find positions in financial institutions, including investment banks, hedge funds, and fintech companies.
• Proximity to London: Offers access to a hub of financial activity and potential career opportunities.
• Connections with employers: The program fosters relationships with employers in the financial sector, providing networking opportunities and potential job leads.

• Home/UK fee £11,550
• International fee £22,400