37008 Quantitative Portfolio Analysis
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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
Designed for specifically for quantitative finance students, this subject provides a rigorous understanding of portfolio management using quantitative tools. The subject presents advanced techniques and applications in quantitative investment including portfolio construction, portfolio implementation, factor models and performance measurement. The subject also considers implementation issues on portfolio construction, backtesting and statistical estimation. The subject combines rigorous treatment of the theoretical concepts with extensive practical problems in quantitative portfolio analysis.
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 |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Appraise advanced knowledge and critically evaluate the information's source and relevance, with a focus on applications of mathematical methodologies to quantitative finance problem solving. (1.1)
- Practice professionally adhering to confidentiality requirements, ethical conduct, data management, and organisation and collaborative skills in the context of applying mathematical and statistical modelling to quantitative finance problems. (3.1)
- Develop and present complex ideas and justifications using appropriate communication approaches from a variety of methods (oral, written, visual) to communicate with mathematicians, data analysts, scientists, industry, and the general public. (5.1)
Contribution to the development of graduate attributes
This subject provides with a solid grounding in modern portfolio theory and its extensions. The subject will provide a detailed exposition of modern portfolio theory covering standard portfolio optimization, asset pricing models, quantitative portfolio management models for portfolio construction and the theory and application of standard performance measures. The subject will show how theoretical results are applied in practice using real world portfolios. Students will use portfolio construction and optimization techniques and develop the tool box required for a working knowledge of quantitative portfolio management. Students will also apply performance measurement tools to determine the performance of their portfolios on a risk-adjusted basis. Students will be required to produce high quality reports detailing the approach used to construct their portfolios and select their assets. The report will also show how the portfolios constructed tracked the benchmarks used.
Furthermore, students will engage in several class presentations of applaud problems using real-world data.
This subject contributes to the development of the following graduate attributes:
GA 1 - Disciplinary Knowledge –acquire detailed specialised quantitative finance knowledge and professional competency required to work as a quantitative finance analyst in the modern finance industry.
GA 3 - Professional, ethical and social responsibility – develop an enhanced capacity to work ethically and professionally using collaborative skills in the workplace.
GA 5 - 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 students work on an extensive set of applied problems. Students have the opportunity to work collaboratively and will 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.
Students will have access to online learning resources and will undertake preliminary learning tasks prior to coming to classes where they engage in further learning and practical applications. Off campus, students will have access to resources to help introduce theory and concepts before class. These learning resources can be accessed by students at their convenience. In the seminars, the theory and concepts are further reinforced with additional discussions and real-world examples.
Relevant and challenging problem sets will also be provided for each lecture and students are required to solve these after class. 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 applied work. Students will receive summative feedback on their assignment solutions.
Content (topics)
This subject tackles and investigates quantitative methods in modern portfolio theory, continuous-time portfolio optimization, portfolio risk management, 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.
- Continuous-time portfolio optimization (The Merton Problem)
- Markowitz's mean-variance portfolio analysis
- Non-mean-variance portfolio analysis
- Index tracking
- Performance and diversification indicators
- Arbitrage pricing theory and factor models
- Risk measures and capital allocation
Assessment
Assessment task 1: Assignment 1
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1 - Disciplinary Knowledge. 3 - Professional, ethical and social responsibility. 5 - Communication. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 3.1 and 5.1 |
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 25% |
| Criteria: | Disciplinary knowledge is demonstrated by the accuracy of the solutions to the applied problems and the mastery of the mathematical and quantitative methods used to address the applied problems. Professional, ethical and social responsibility is demonstrated by the implementation of the solution on real-world data, i.e. by way of authentic assessment. This is also demonstrated through the integration of practical insights and the acknowledgments of the practical limitations of the mathematical and quantitative methods used. Communication is demonstrated by clearly communicating mathematical steps in resolving the applied problems and qualitatively interpreting results, as required. |
Assessment task 2: Assignment 2
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1 - Disciplinary Knowledge. 3 - Professional, ethical, and social responsibility. 5 - Communication. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 3.1 and 5.1 |
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 25% |
| Criteria: | Disciplinary knowledge is demonstrated by the accuracy of the solutions to the applied problems and the mastery of the mathematical and quantitative methods used to address the applied problems. Professional, ethical and social responsibility is demonstrated by the implementation of the solution on real-world data, i.e. by way of authentic assessment. This is also demonstrated through the integration of practical insights and the acknowledgments of the practical limitations of the mathematical and quantitative methods used. Communication is demonstrated by clearly communicating mathematical steps in resolving the applied problems and qualitatively interpreting results, as required. |
Assessment task 3: Final Assessment
| Intent: | This assessment task contributes to the development of the following graduate attributes: 1 - Disciplinary Knowledge. 3 - Professional, ethical and social responsibility. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2, 3, 4 and 5 This assessment task contributes to the development of course intended learning outcome(s): 1.1 and 3.1 |
| Type: | Examination |
| Groupwork: | Individual |
| Weight: | 50% |
| Criteria: | The final exam assesses disciplinary knowledge in terms of the correctness of the answers and the accuracy of explanations provided. Professional, ethical and social responsibility is demonstrated through the integrity of the work conducted during the final examination and through critical engagement and thinking around the examination problems. |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks to pass the subject.
References
Rogers, L. C. G. (2013). Optimal Investment. Springer.
Brugiere, P. (2020). Quantitative Portfolio Management: With Applications in Python. Springer.