Postgraduate Course: Financial Machine Learning (CMSE11475)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course provides a complete and systematic overview of machine learning methods on finance, including introduction of neural network, financial data structure, cross-validation, bootstrap and boosting, autoencoder in asset pricing, interpretability. |
| Course description |
Machine learning is changing all aspects of our lives and has been adopted in finance area as a disruptive technology that may lead to profound changes in how everyone invests and manages the risks in the future. Many financial institutions, including huge investment banks on sell-side, cutting-edge hedge funds on buy-side and leading consulting firms, have heavily invested in this machine learning techniques in finance.
Whether we use support vector machine, random forest, AdaBoost, recurrent neural network, and so on, there are many shared generic problems we will face: data structuring, labelling, weighting, stationary transformations, cross-validation, bootstrap and boosting, model interpretability. In the context of financial modelling, answering these questions is non-trivial, and framework-specific approaches need to be developed. That's the focus of this course.
This course aims to provide a complete and systematic treatment of machine learning methods specific for finance. It contains these primary parts:
- An introduction of deep neural network and recurrent neural network
- Financial data structure and feature engineering
- Bootstrap and boosting in financial problems
- Autoencoder and its application in asset pricing
- Interpretability of machine learning model
On the finance side, this course will enable students to understand the structures of different financial data, transformation of raw data into informative features and further into asset pricing factors. On the technical side, it equips the student with capability to correctly use machine learning method in asset pricing and other relevant financial problems and to use Python (or R/MATLAB) to implement the model.
|
Course Delivery Information
|
| Academic year 2022/23, Not available to visiting students (SS1)
|
Quota: 44 |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Seminar/Tutorial Hours 4,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
84 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% coursework (individual) - assesses all course Learning Outcomes |
| Feedback |
Formative: will be provided on an ongoing basis through class discussions.
Summative: students will be provided with feedback on the assignment. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Apply the knowledge of financial theories and skills of pre-processing the financial data to construct useful features for machine learning methods.
- Transform the constructed financial features into informative investment signals with predictive powers.
- Transform the informative features into the actual profitable investment strategy with formulated theory that explains and backs the theory as a white box.
- Understand the importance of the back-testing and the over-fitting, and be able to assess the profitability of an investment strategy under various scenarios.
|
Reading List
Advances in Financial Machine Learning, ISBN: 978-1-119-48210-9
Machine Learning in Finance: From Theory to Practice, ISBN : 3-030-41068-4
Deep Learning, ISBN : 9780262035613 (hardback) |
Additional Information
| Graduate Attributes and Skills |
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Convey meaning and message through a wide range of communication tools, including digital technology and social media; to understand how to use these tools to communicate in ways that sustain positive and responsible relationships.
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Cognitive Skills
After completing this course, students should be able to:
Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to quality.
Knowledge and Understanding
After completing this course, students should be able to:
Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact.
Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore and solve them responsibly. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Yi Cao
Tel: (0131 6)51 5338
Email: |
Course secretary | Mrs Kelly-Ann De Wet
Tel: (0131 6)50 8071
Email: |
|
|
University Homepage