Course coordinator
Dr Eric Tan
Course overview
- Study period
- Semester 1, 2025 (26/05/2025 - 27/06/2025)
- Study level
- Postgraduate Coursework
- Location
- Brisbane City
- Attendance mode
- Intensive
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Quantitative Investment Strategies will analyse strategies across multiple instruments and asset classes and describe the strategies employed by balance sheet managers, portfolio managers, hedge funds and proprietary traders to generate excess returns. Students will learn to develop, back test, and evaluate some of the most successful strategies employed by alpha seeking active managers. The course utilises different econometric software, widely used in the industry, to solve complex problems.
What combines the precision of mathematics with the unpredictability of financial markets and uses modelled data to predict market movements and make investment decisions? The answer is quantitative investment strategies. The objective of this course is to enhance students’ understanding of the quantitative world of finance. Students will be exposed to the key components of quantitative strategies. Furthermore, students will better understand strategies used in practice, the risk considerations and the trends and future direction of this less known investment technique. This is achieved through a blend of online learning and face-to-face workshops delivered by expert practitioners in quantitative funds management.
Course requirements
Companion or co-requisite courses
You'll need to complete the following courses at the same time:
FINM7101
Restrictions
Restricted to students enrolled in the MFinInvMgt and GCFinInvM.
Course contact
Course staff
Lecturer
Mr Adam Millhouse
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
This is an intensive course that will include both self-paced online and face-to-face teaching as part of its delivery. Important dates for this course that do not appear on the public timetable are as follows:
- Online Content Available: 26 May 2025
- Face-to-Face Delivery: 11, 12, and 13 June 2025
- End of Course: 27 June 2025
Aims and outcomes
The aim of this course is for students to develop the skills needed to solve complex industry problems using econometric techniques and software. Students will use data to analyse different strategies employed in the industry in order to assess the effectiveness and apply a quantitative finding.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Design and execute active quantitative investment strategies linked to factor models, macro strategies and behavioural finance.
LO2.
Use industry relevant software to devise investment strategies and solve complex problems.
LO3.
Assess the effectiveness and robustness of strategies based on large global data sets and the use of econometrics software.
LO4.
Critically analyse investments to effectively communicate data and decisions appropriately to a variety of stakeholders.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation | Quantitative Investment Strategies Report | 40% |
9/06/2025 5:00 pm |
| Paper/ Report/ Annotation | Critical Analysis | 60% |
27/06/2025 5:00 pm |
Assessment details
Quantitative Investment Strategies Report
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
9/06/2025 5:00 pm
- Learning outcomes
- L01, L03
Task description
The size effect was one of the earliest anomalies identified as a source of long-term outperformance. The quantification of a size risk premium was originally documented in academic studies in the early 1980’s (see, for example Banz 1981 and Keim 1983), which reported that stocks with small market capitalisations beat large stocks by more than market beta could explain. This was a noteworthy discovery, as it was arguably the first substantive challenge to CAPM theory.
Given its significance in the academic field and wide application in practice, as evidenced by the proliferation of small-cap investment funds, this assessment asks you to evaluate the efficacy of the size effect in generating excess returns.
Your evaluation should weigh heavily on the necessity of an idea or hypothesis as the foundation of a strategy, and the process of observation to ensure that strategies are robust and implementable. Additionally, you should consider how to determine genuine value in your discoveries and accurately assess your findings while avoiding misleading information, and outline lessons that need to be carried forward.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
To be submitted via Blackboard
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Critical Analysis
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 60%
- Due date
27/06/2025 5:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
This assessment asks you to choose a quantitative investment strategy as the basis of a detailed evaluation. You should attempt to augment an existing strategy based on your personal ideas or a specific style that aligns with your professional experience and interests. Assessment 2 will be released on the last day of the 3 face-to-face workshop.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
To be submitted via Blackboard
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Not scheduled |
Not Timetabled |
Online Learning / Self-Directed Learning 26 May - 10 June (Self-Directed Learning) Topics covered include historic development of quantitative investment industry, key theories behind the practice, prominent strategies, background quantitative methods, return forecasting, and breaking down the most common strategies employed in practice. Learning outcomes: L01, L02, L03 |
Not scheduled |
Workshop |
Face-to-Face Learning: Day 1 11 June 2025 Summary of online content, foundations of strategy building Learning outcomes: L01, L03 |
Not scheduled |
Workshop |
Face-to-Face Learning: Day 2 12 June 2025 Practical example of the process from idea to strategy implementation. Learning outcomes: L02, L03 |
Not scheduled |
Workshop |
Face-to-Face Learning: Day 3 13 June 2025 Lessons and learnings from observed episodes, future-proofing, and a case study on Long Term Capital Management (LTCM). Learning outcomes: L04 |
Not scheduled |
Not Timetabled |
Online / Assessment 14 June - 27 June (Self-Directed Learning) Students will be working individually on their assessment Learning outcomes: L01, L02, L03, L04 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.