Postgraduate Course: Forecasting Financial Markets (CMSE11281)
Course Outline
| School | Business School |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 15 |
ECTS Credits | 7.5 |
| Summary | Many investors like mutual fund managers or hedge funds try to forecast the behaviour of assets in financial markets and given the advantages of explicit quantitative models in forecasting, these are becoming increasingly popular. Many of the strategies underlying these models originate in academic research. This course introduces students to recent findings in the academic literature on the predictability of financial markets and trains students on how they can develop, test and benchmark automated trading strategies that forecast financial market behaviour based on these academic insights. This course is intended to be a hands-on module as students will develop their own forecasting system. |
| Course description |
Not entered
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | For Business School PG students only, or by special permission of the School. Please contact the course secretary. |
Course Delivery Information
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| Academic year 2014/15, Not available to visiting students (SS1)
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Quota: 20 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
150
(
Lecture Hours 20,
Summative Assessment Hours 100,
Programme Level Learning and Teaching Hours 3,
Directed Learning and Independent Learning Hours
27 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
There is one group assignment which counts for 100% of the total mark.
The group assignment consists of the following three components:
In-class presentations (20%)
A group written report (40%) and
A developed trading system (40%)
There will be no exam. A peer review system (WebPA) will also be in place and 15% of the total mark can be adjusted in this way.
From the start students will get organized in teams. These teams will all work on developing their own trading system. During different stages teams will have to present how far they got. |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- a) have learned how to gather information and use common sense and academic thinking to solve new problems;
b) have a good detailed understanding of the academic literature that deals with the predictability of financial markets;
c) be able to build, test and benchmark an automated trading strategy;
d) get a better understanding of forecasting principles in general.
- Intellectual Skills and personal development
a) have developed a critical understanding of the main principles of forecasting as applied in financial markets;
b) have developed their ability to understand complex lines of argument and reasoning in forecasting financial markets;
c) be able to develop the links between academic literature and professional practice;
d) have improved their skills in critical information gathering relevant to a topic;
e) have improved their information searching skills to find solutions to new problems and improved their common sense and (creative) thinking to solve and innovate themselves out of problems.
f) have improved their writing skills;
g) have developed skills in collaboration and teamwork.
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Reading List
| The Signal and the Noise: The Art and Science of Prediction¿ by Nate Silver, Penguin (2013). ISBN: 0141975652. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | fin-FFM |
Contacts
| Course organiser | Prof Seth Armitage
Tel:
Email: Seth.Armitage@ed.ac.uk |
Course secretary | Miss Rachel Allan
Tel: (0131 6)51 3757
Email: Rachel.Allan@ed.ac.uk |
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