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. |
| Course description |
This course is intended to be a hands-on module as students will develop their own forecasting system. We teach this investment course based on a changed premise that financial market returns are predictable. This is contrary to the old view that markets are informationally efficient and therefore unpredictable. In the new view markets are no longer fully informationally efficient all the time and we will use unpredictability as a benchmark to which we can compare the success of forecasting systems. By changing the premise we intend to move our students ahead of the curve and give Edinburgh students a competitive edge compared to their peers both in an academic sense and in a practical sense.
Syllabus:
Forecasting Financial Markets, Market Efficiency as a Benchmark, Calendar anomalies as an example
Forecasting Financial Markets, Market Efficiency as a Benchmark, Gradual Information Diffusions as an example
The Art and Science of Building an Automated Trading System: An example
Students present their first proposal for an automated trading systems
Testing Variables (Datastream)
Issues that pop up (Datamining, Robustness)
Presenting the first stage automated trading models (Datastream)
Further issues (Back testing) (Datastream)
Further Issues (Multiple Variables, What to predict?) (Datastream)
Final System Presentations (Datastream)
Student Learning Experience
From the start students will get organised into teams. These teams will all work on developing their own trading system. During different stages teams will have to present how far they got.
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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. |
Course Delivery Information
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| Academic year 2015/16, 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 organised 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 |
All students will be given at least one formative feedback or feedforward event for every course they undertake, provided during the semester in which the course is taken and in time to be useful in the completion of summative work on the course. Such feedback may be at course or programme level, but must include input of relevance to each course in the latter case. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand complex lines of argument and reasoning in forecasting financial markets.
- Understand and critically discuss the academic literature that deals with the predictability of financial markets.
- Build, test and benchmark an automated trading strategy.
- Understand and critically discuss forecasting principles.
- Apply skills of information searching to find solutions to new problems and apply creative thinking to solve problems innovatively.
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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 |
Intellectual Skills and personal development
On completion of the module, students should:
* Have developed a critical understanding of the main principles of forecasting as applied in financial markets;
* Have developed their ability to understand complex lines of argument and reasoning in forecasting financial markets;
* Be able to develop the links between academic literature and professional practice;
* Have improved their skills in critical information gathering relevant to a topic;
* 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.
* Have improved their writing skills;
* Have developed skills in collaboration and teamwork.
Subject Specific Skills
Students will learn the latest developments and issues in forecasting financial markets and forecasting in general. |
| Keywords | finForecastingFinancialMarkets |
Contacts
| Course organiser | Prof Berend Jacobsen
Tel:
Email: Ben.Jacobsen@ed.ac.uk |
Course secretary | Miss Rachel Allan
Tel: (0131 6)51 3757
Email: Rachel.Allan@ed.ac.uk |
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