37008 Quantitative Portfolio Analysis
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Subject handbook information prior to 2025 is available in the Archives.
Credit points: 8 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): 24 credit points of completed study in spk(s): STM91543 Core Subjects (Mathematics)
These requisites may not apply to students in certain courses. See access conditions.
Anti-requisite(s): 25876 Quantitative Portfolio Analysis
Description
This subject equips students with a rigorous understanding of portfolio analysis through quantitative tools. It covers a comprehensive mathematical treatment of the Markowitz framework for portfolio optimisation, the index-tracking problem, Arbitrage Pricing Theory (APT) and factor models for asset pricing, portfolio performance measurement, portfolio risk measures, and capital allocation. The subject also tackles a continuous-time portfolio optimisation problem (the Merton problem) via a stochastic optimal control approach. The theoretical discussion of concepts is complemented by practical problems involving statistical estimation and computational implementation.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Apply modern portfolio theory to the construction of portfolios |
|---|---|
| 2. | Explain risk and asset pricing models |
| 3. | Apply factor models to construct equity portfolios |
| 4. | Devise asset allocation strategies |
| 5. | Understand and apply standard and alternative performance measures |
Contribution to the development of graduate attributes
This subject provides students a solid foundation in modern portfolio theory and its extensions. As such, it provides a detailed exposition of the classical Markowitz portfolio optimization framework, the Capital Asset Pricing Model (CAPM), Arbitrage Pricing Theory, factor models, and empirical methods for portfolio construction, performance measurement, and risk management. The disciplinary knowledge acquired in this subject contributes to the students’ toolkit as a quantitative finance practitioner. Furthermore, students are provided opportunities to apply and implement portfolio construction and optimization techniques using financial data and communicate their findings and insights through written and/or oral reports.
This subject contributes to the development of the following graduate attributes:
GA 1. Disciplinary Knowledge – acquire detailed and specialized quantitative finance knowledge and professional competency required to work as a quantitative finance professional in the modern finance industry.
GA3. Professional, Ethical, and Social Responsibility – develop an enhanced capacity to work ethically and professionally using collaborative skills in the workplace.
GA5. Communication – develop professional communication skills for a range of technical and non-technical audiences.
Teaching and learning strategies
The subject is presented in a seminar format, complemented by online subject materials. The theoretical concepts are presented in lectures and are supplemented by on-campus seminar sessions in which the students’ conceptual understanding is reinforced by hands-on exercises. During the seminars, students can work collaboratively and receive feedback on their solutions. The teaching and learning strategies in this subject enable students to experience a seamless integration of online and face-to-face learning.
Relevant and challenging problem sets will also be provided for each topic and students are required to solve these during the on campus seminars. The problems sets will prepare students for the successful completion of the subject assessment tasks and will encourage critical thinking and innovation.
Students will receive verbal feedback on their work during the seminars. Students will receive summative feedback through marked assessment activities.
Content (topics)
The subject tackles and investigates quantitative methods in modern portfolio theory, continuous-time portfolio optimization, portfolio risk measurement, and capital allocation. Emphasis will be placed on the mathematical foundations of these portfolio analysis frameworks and on the implementation of basic quantitative portfolio analyses using software.
- Markowitz’s mean-variance portfolio optimization framework
- Non-mean-variance portfolio analysis
- Index tracking
- Arbitrage Pricing Theory and factor models
- Performance and diversification indicators
- Portfolio risk measures and capital allocation
- Continuous-time portfolio optimization (the Merton Problem)
Assessment
Assessment task 1: In-Class Test
| Intent: | This assessment task contributes to the development of the following graduate attributes: |
|---|---|
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 10% |
Assessment task 2: Project
| Intent: | This assessment task contributes to the development of the following graduate attributes: |
|---|---|
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 45% |
Assessment task 3: Final Examination
| Intent: | This assessment task contributes to the development of the following graduate attributes: |
|---|---|
| Type: | Examination |
| Groupwork: | Individual |
| Weight: | 45% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks to pass the subject.
Required texts
There are no required texts for this subject. Lecture notes and other materials shall be made available on the subject Canvas site.
Recommended texts
Rogers, L. C. G. (2013). Optimal Investment. Springer.
Brugiere, P. (2020). Quantitative Portfolio Management: With Applications in Python. Springer.