ObjectivesThe innovative new MSc in Financial Software Engineering will complement and build on the successful CCFEA Masters in Computational Finance. CCFEA has acquired a reputation of being a leading edge interdisciplinary centre for combining economic and financial modelling with computational implementation. The rigorous training the student receives in this Masters programme on software engineering for large, dynamic and automated financial systems and finance models, with over half of the degree focussing on software design in a number of real-world financial systems and expert lectures, will enable the student to be a leader in this field. The degree aims at introducing the student to information and communication technology (ICT) that underpins financial systems with special emphasis on the challenges for software design in an electronic market environment. These include design issues relating to automation and straight through processing (STP), parallel and distributed networks, encryption, security and real time constraints. The student is given training on the structure, instruments and institutional aspects of financial markets, as well as the option to acquire rigorous input in quantitative finance. A case study approach will be used to familiarise students with financial software architecture which include multi-lateral trading facilities (MTFs), examples of electronic markets such as LSE SETS, electronic trading engines and commercial financial decision support software.
Entry requirementsEntry Qualifications A 2.1 or first class BSc in Computer Science, Engineering or a BSc which provides a high level of programming expertise such as in Java, C++ and/or .NET.
Academic titleMSc Financial Software Engineering
Course description
Course Description
This MSc enables students to attain a high level of competence in software development in the area of financial software engineering. It aims to introduce students to quantitative finance, the ICT of financial systems, design issues relating to parallel and distributed networks, encryption and security and real time constraints for developments, such as Straight through Processing (STP).
Modules and Options
The lists of modules below represent the range of options available for each year of study. This may not be a complete list of the options you will study, and may be subject to change, so please contact the department for further details.
Stage 1
AGENT TECHNOLOGY FOR E-COMMERCE
AGENT-BASED ECONOMICS AND FINANCIAL MODELLING
Compulsory: ADVANCED RELATIONAL AND OBJECT-ORIENTED DATABASES
Compulsory: SOFTWARE ENGINEERING CHALLENGES FOR FINANCIAL SYSTEMS
Core: DISSERTATION
Core: HIGH FREQUENCY FINANCE AND COMPUATIONAL MARKET MICRO-STRUCTURE
Core: UNDERLYING TECHNOLOGY IN FINANCIAL SYSTEMS
CRITICAL MARKETING
DEVELOPMENT OF LARGE SOFTWARE SYSTEMS
ECONOMICS OF FINANCIAL MARKETS
FINANCIAL ENGINEERING AND RISK MANAGEMENT
INTRODUCTION TO COMPUATIONAL FINANCE AND MARKET ANALYSIS
INTRODUCTION TO E-COMMERCE LAW
OBJECT ORIENTED SOFTWARE DESIGN
SOFTWARE DESIGN AND ARCHITECTURE
TOPICS ON FINANCIAL MATHEMATICS AND MARKET ANALYSIS
Teaching and Assessment Methods
A: Knowledge and Understanding
Learning Outcomes
A1 : Knowledge of principles of software development for large dynamic systems
A2 : Knowledge of the main aspects of financial markets and instruments for asset pricing and risk management
A3 : Knowledge of market microstructure, agent computation for financial modelling and automated trading
A4 : Knowledge to implement core analytical and operational aspects of financial modelling C++ or .NET, with the view to real world financial software projects
A5 : Some knowledge of mathematical, statistical and econometric tools to deal with financial market modelling
Teaching Methods
Outcomes A1-A3 are acquired through lectures, classes and related course work.
Outcome A4 is achieved by specially devised laboratory based courses where students will be assisted in developing and running their own programmes for financial analysis and trading algorithms.
The development of the dissertation in consultation with a supervisor provides an additional opportunity for the acquisition of outcomes A1-A4.
Lectures are used to present materials - ideas, data and analytical tools - in a clear and structured manner. Lectures are also used to stimulate students' interest in learning financial research and operational methods. Classes and preparation for lectures and classes, provide an opportunity for students to develop their knowledge and understanding of the content of the courses.
A highlight of this degree scheme is the seminar module where visiting experts on financial systems and their software development give focussed coverage on core issues. Students could base their summer dissertation on topics covered there.
The dissertation provides an opportunity for students to develop their knowledge and understanding further through undertaking a piece of independent, though supervised, advanced research.
Students are expected to extend and enhance the knowledge and understanding they acquire from lectures and classes by regularly consulting library materials relating to the course.
Assessment Methods
All courses taken from the different departments will be assessed by the rules of assessment applicable in the department responsible for the course. Learning outcomes A1-A5 will be assessed by compulsory end of year examinations, optional or compulsory term papers, class tests and the MSc dissertation.
B: Intellectual/Cognitive Skills
Learning Outcomes
B1 : Theoretical appraisal and understanding of challenges posed for software design by financial systems in an electronic and automated environment
B2 : Develop and implement financial models under different assumptions for empirical and computational testing
B3 : Acquire critical frame of reference regarding the inadequacies of traditional assumptions of financial modelling
B4 : Acquire theoretical and practical knowledge of market microstructure and multilateral trading systems
B5 : Carry out independent research
Teaching Methods
Skills B1-B4 are acquired and enhanced primarily through the work that students do for their courses, although lectures and lab demonstrations provide a means for teachers to demonstrate these skills through example.
Student preparation involves the reading, interpretation and evaluation of the finance literature, including texts and research papers, and the analysis of empirical evidence. Teachers provide feedback on students work through comment and discussion. In addition, teachers engage students outside the classroom through office hours, appointments and email.
Skill B2 is honed in the lab based classes.
The dissertation is additionally used to develop a student's mastery of the combined application of financial principles and empirical methods, as well as their analytical ability and understanding of the complete research process.
Assessment Methods
Skills B1-B5 are assessed throughout the courses comprising the degree by means of written examinations with optional term papers.
Skills B1-B4 are also assessed in certain courses through written tests.
The MSc dissertation provides a further opportunity to assess skills B1-B5.
Skill B5 is assessed through the dissertation, group project and optional term papers.
C: Practical Skills
Learning Outcomes
C1 : Identify, select and gather information using relevant sources, including the library and online searches
C2 : Organise ideas in a systematic and critical fashion
C3 : Present and critically assess advanced financial ideas and arguments coherently in writing
C4 : Acquire advanced programming skills for financial software development
C5 : Implement computational testbeds for competing hypothesis regarding financial markets
C6 : Implement appropriate financial decision support software
C7 : Acquire experience to formulate financial problems and then program and run in C++ or .NET
Teaching Methods
Skills C1-C5 are acquired and enhanced primarily through the work that students do for their courses. Lectures also provide a means of teachers demonstrating these skills through example. Skills C4-C6 are acquired to a greater degree in courses that focus on econometrics, evolutionary computation and the lab based compulsory courses especially designed for this degree scheme. These skills are reinforced or supplemented depending on the optional courses taken.
The dissertation is additionally used to provide an opportunity for students to acquire skills C1-C5.
Assessment Methods
Skills C1-C6 are assessed throughout the courses comprising the degree by means of written examinations with optional term papers. The dissertation also provides a further opportunity to assess skills C1-C7.
Skills C4-C7 are also informally assessed by student's class presentations in the lab based courses.
D: Key Skills
Learning Outcomes
D1 : Communication in writing, using appropriate terminology and technical language
D2 : Production of word-processed research dissertation, term papers and group project. Use of C+ or .NET for financial software programming.
D3 : Use of mathematical techniques to construct financial models and the use of econometric/statistical and other computational methods to analyse financial data
D4 : Application of analytical and computational simulation techniques to address financial market phenomena
D5 : Joint problem solving in lab oriented courses and classes and group project
D6 : Capacity to: (a) organise and implement a plan of independent study; (b) reflect on his or her own learning experience and adapt in response to feedback; and (c) recognise when he or she needs to learn more and appreciate the role of additional research
Teaching Methods
Students are guided in acquiring skills D1-D5 through lectures, classes and individual advice from teachers. These skills are further developed as students pursue the learning activities associated with their courses.
The dissertation enables students to acquire skill D2 and also assists them in acquiring skills D1, D4 and D5.
Students also have the opportunity to develop skills in working in groups through their participation in classes for courses, especially the applied ones.
Assessment Methods
Skills D1, D3, D4 and D5 are assessed throughout the courses comprising the degree by means of examinations with optional term papers or written tests and a group project. Both the group projects and the dissertation provide further means for an overall assessment of communication (D1), using IT (D2), problem-solving skills (D4), and self-learning (D6).