Course coordinator
Associate Professor Jacquelyn Humphrey
Course overview
- Study period
- Semester 1, 2025 (28/01/2025 - 03/03/2025)
- Study level
- Postgraduate Coursework
- Location
- Brisbane City
- Attendance mode
- Intensive
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Contemporary Financial Modelling and Analytics concerns itself with the development of students' abilities to use modelling, quantitative and other analytical methods and tools, with a focus on those widely used in industry. The course uses comprehensive sets of data analysis and financial modelling skills including projects, corporate and portfolio forecast valuation simulations incorporating sensitivity analysis.
This course seeks to achieve two main objectives: Exposing students to a range of asset classes and developing a set of technical skills. The course will consider a range of assets for example equities, fixed income and derivatives. It will also develop a broad set of technical skills using Excel modelling and Python coding where applicable. Applications will include data analytics, scenario modelling, and advanced valuation.
The course has a very strong practical focus with a philosophy of learning by doing. Throughout the course students will build several models and templates that will be used in subsequent courses, and which have direct application in practice.
Course requirements
Restrictions
Restricted to students enrolled in the MFinInvMgt and GCFinInvM.
Course contact
Course staff
Lecturer
Associate Professor Jacquelyn Humphrey
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
- Full Name
- Student ID
- Course Code
Aims and outcomes
This course provides students with an advanced set of financial modelling skills applied in industry (banks/corporates balance sheets, funds management). Students will cover topics such as advanced corporate and project valuation, sensitivity and scenario analysis, optimisation, and value-at-risk.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Analyse data utilising widely used analytics methods and tools.
LO2.
Design robust financial models using various programming languages.
LO3.
Implement a process to validate and audit financial models while being able to interpret the meaning and robustness of model outputs.
LO4.
Demonstrate core collaborative competencies needed to work effectively in a team.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Paper/ Report/ Annotation, Tutorial/ Problem Set | A1: Data Analytics Task | 30% |
14/02/2025 5:00 pm |
| Computer Code, Paper/ Report/ Annotation | A2: Financial valuation | 30% |
7/03/2025 5:00 pm |
| Computer Code, Paper/ Report/ Annotation |
A3: Team financial modelling exercise
|
40% |
7/03/2025 5:00 pm |
Assessment details
A1: Data Analytics Task
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation, Tutorial/ Problem Set
- Weight
- 30%
- Due date
14/02/2025 5:00 pm
- Learning outcomes
- L01, L02
Task description
This individual assessment is designed to develop and evaluate students' ability to apply financial modelling and portfolio management principles in the context of real-world investment decisions. Students will perform a portfolio optimization task using Python and historical market data.
The deliverable includes a detailed report that explains the methodology, findings, and implications of the analysis, as well as a technical appendix containing Python code used in the computations. This task emphasizes critical thinking, fostering a practical understanding of investment portfolio management under real-world conditions.
AI Statement:
This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Due to the intense nature of the course, the maximum extension time will be limited.
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.
A2: Financial valuation
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 30%
- Due date
7/03/2025 5:00 pm
- Learning outcomes
- L01, L03
Task description
This individual assessment focuses on applying methods developed in class to valuing assets. Students will work independently to value assets using real-world date.
The deliverable is a concise report that outlines the methodology, assumptions, and results for each task. Supporting computations should be included as an appendix.
This assessment emphasizes individual analytical skills and the application of financial concepts to solve practical challenges, aligning with the course's learning outcomes.
Details to be provided via Blackboard.
AI Statement:
This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Due to the intense nature of the course, the maximum extension time will be limited.
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.
A3: Team financial modelling exercise
- Team or group-based
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 40%
- Due date
7/03/2025 5:00 pm
- Other conditions
- Peer assessed.
- Learning outcomes
- L02, L03, L04
Task description
This team-based assessment challenges students to evaluate and analyse the valuation and performance of a project. Students will apply financial modelling, sensitivity analysis, and market data to make an informed investment recommendation.
The deliverable is a concise team report summarizing the methodology, findings, and investment recommendations, supported by Excel or Python computations provided as an appendix.
This assessment develops teamwork, critical thinking, and advanced financial modelling skills, preparing students to navigate and analyse complex investment products in a dynamic market environment.
Full details and relevant data will be made available on Blackboard.
Students will be given an opportunity for Peer Assessment of their group members. The results of this assessment may impact an individuals overall score for this assessment.
AI Statement:
This task has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Due to the intense nature of the course, the maximum extension time will be limited.
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 - 27/01/2025 - 11/02/2025 Topics: Introduction, PV Mechanics, Bonds, Equity Valuation, Options Learning outcomes: L01, L02, L03 |
Week 1 |
Workshop |
Day 1 - Wednesday 12/02 Topics: Stock return analytics, diversification, recap of NPV and WACC, dividend imputation. Learning outcomes: L01, L02, L03 |
Workshop |
Day 2 - Thursday 13/02 Topics: Arbitrage, yield curves, structured products, efficient frontier. Learning outcomes: L01, L02, L03, L04 |
|
Workshop |
Day 3 - Friday 14/02 Topics: Option valuation, Black-Scholes, Monte Carlo valuation techniques. Assessment task. 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.