(MSc) Master of Science
Algorithmic Trading
Current
University of Essex
University of Essex
Computational Finance and Economic Agents (Centre for)
Colchester Campus
Masters
Part-time
None
MSC N35024
10/05/2023
Details
Professional accreditation
None
Admission criteria
Applicants with a 2:2 degree in one of the following subjects (with no module requirements):
- Computer Science
- Computer Engineering
- Computer Networks
- Computer Games
- Computing
- Software Engineering
- Finance
- Economics
- Physics
- Mathematics
We will consider applicants with any other 2:2 degree or above which include one or more modules from this list:
At least one Maths modules such as:
- Mathematics
- Calculus
- Statistics
- Differential Equations
- Probability
- Stochastic Processes
AND at least one Programme module, such as:
- C
- C#
- C++
- Java
- Python
- MATLAB
- Object Oriented Programming aka "OOP"
- Introduction to Programming
- Programming Languages
- Advanced Programming
IELTS (International English Language Testing System) code
IELTS 6.0 overall with a minimum component score of 5.5
If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.
Additional Notes
The University uses academic selection criteria to determine an applicant’s ability to successfully complete a course at the University of Essex. Where appropriate, we may ask for specific information relating to previous modules studied or work experience.
Course qualifiers
A course qualifier is a bracketed addition to your course title to denote a specialisation or pathway that you have achieved via the completion of specific modules during your course. The
specific module requirements for each qualifier title are noted below. Eligibility for any selected qualifier will be determined by the department and confirmed by the final year Board of
Examiners. If the required modules are not successfully completed, your course title will remain as described above without any bracketed addition. Selection of a course qualifier is
optional and student can register preferences or opt-out via Online Module Enrolment (eNROL).
None
Rules of assessment
Rules of assessment are the rules, principles and frameworks which the University uses to calculate your course progression and final results.
Additional notes
Please refer to the full time version of this course for information on Core and Compulsory modules.
External examiners
Dr Anna Jordanous
Senior Lecturer
University of Kent
External Examiners provide an independent overview of our courses, offering their expertise and help towards our continual improvement of course content, teaching, learning, and assessment.
External Examiners are normally academics from other higher education institutions, but may be from the industry, business or the profession as appropriate for the course.
They comment on how well courses align with national standards, and on how well the teaching, learning and assessment methods allow students to develop and demonstrate the relevant knowledge and skills needed to achieve their awards.
External Examiners who are responsible for awards are key members of Boards of Examiners. These boards make decisions about student progression within their course and about whether students can receive their final award.
Programme aims
- To equip students with the core concepts and quantitative methods in high-frequency finance along with the operational skills to use state-of-the-art computational methods for financial modelling.
- The main objective of this degree scheme is to enable students to attain an understanding of financial markets at the level of individual trades occurring over sub-millisecond timescales, and apply this to the development of real-time approaches to trading and risk-management.
- In addition to traditional topics in financial econometrics and market microstructure theory, there will be special emphasis on statistical and computational methods for modelling trading strategies and predictive services that are deployed by hedge funds, algorithmic trading groups, derivatives desks, and risk management departments.
- The student will have the opportunity to study the use of financial market simulators for stress testing trading strategies, and designing electronic trading platforms.
Learning outcomes and learning, teaching and assessment methods
On successful completion of the programme a graduate should demonstrate knowledge and skills as follows:
A: Knowledge and understanding
A1: Knowledge of advanced mathematical principles of modern quantitative finance
A2: Knowledge of the main aspects of financial markets and instruments for asset pricing and risk management
A3: Knowledge of market microstructure, computational methods for financial modelling and automated trading
A4: Knowledge to implement the core analytical and operational aspects of financial theory using empirical data.
A5: Knowledge of mathematical and statistical tools to deal with financial market modelling.
Learning methods
Outcomes A1-A3 and A5 are acquired through lectures, classes and related course work.
Outcome A4 is achieved by specially devised laboratory based modules where students will be assisted in developing and running their own Matlab programs for financial data analysis.
The development of the dissertation in consultation with a supervisor provides an additional opportunity for the acquisition of outcomes A1-A5.
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.
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 term papers, class tests and the MSc dissertation.
B: Intellectual and cognitive skills
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 electronic trading systems
B5: Carry out independent research
Learning 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, coursework and optional term papers.
C: Practical skills
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 ideas and arguments coherently in writing
C4: Understand statistical and econometric tools for financial data analysis
C5: Implement econometric tests for testing hypotheses regarding financial markets
C6: Develop appropriate algorithms for financial decision support
C7: Acquire experience to formulate financial problems and then program and execute in Matlab
Learning methods
Skills C1-C5 are acquired and enhanced primarily through the work that students complete in 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-C6.
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
D1: Acquire skills in technical writing.
D2: Production of word-processed research dissertation, term papers and project reports. Use software for financial analysis.
D3: Use of mathematical techniques to construct financial models and the use of statistical and computational methods to analyse financial data
D4: Application of analytical and computational simulation methods to model financial market phenomena
D5: Collaborative problem solving.
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.
Learning 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).