Postgraduate Course: Forecasting Financial Markets (CMSE11281)
Course Outline
School | Business School |
College | College of Humanities and Social Science |
Course type | Standard |
Availability | Not available to visiting students |
Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 15 |
Home subject area | Common Courses (Management School) |
Other subject area | None |
Course website |
None |
Taught in Gaelic? | No |
Course description | 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. |
Entry Requirements (not applicable to Visiting Students)
Pre-requisites |
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Co-requisites | |
Prohibited Combinations | |
Other requirements | For Business School PG students only, or by special permission of the School. Please contact the course secretary. |
Additional Costs | None |
Course Delivery Information
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Delivery period: 2014/15 Semester 2, Not available to visiting students (SS1)
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Learn enabled: Yes |
Quota: None |
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Web Timetable |
Web Timetable |
Course Start Date |
12/01/2015 |
Breakdown of 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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Additional Notes |
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Breakdown of Assessment Methods (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
1. Intended Learning Outcomes
On completion of the module, students should:
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.
2. Intellectual Skills and personal development
On completion of the module, students should:
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. |
Assessment Information
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. |
Special Arrangements
None |
Additional Information
Academic description |
Not entered |
Syllabus |
Not entered |
Transferable skills |
Not entered |
Reading list |
¿The Signal and the Noise: The Art and Science of Prediction¿ by Nate Silver, Penguin (2013). ISBN: 0141975652. |
Study Abroad |
Not entered |
Study Pattern |
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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© Copyright 2014 The University of Edinburgh - 29 August 2014 3:42 am
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