Course coordinator
Mr Jon Aster
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Provides students with a comprehensive set of financial modelling skills. Covers corporate and project valuation, sensitivity and scenario analysis, optimisation, Monte Carlo simulation, value-at-risk.
Financial modelling is a quantitative task to build scientific mathematical models to reflect real-world financial situations. This subject will be focused on learning skills practically during class time and will require the use of MS Excel and Python. FINM3422 offers a practical and comprehensive guide for students to construct robust, practical and readily understandable financial models tailored for real-life examples.
The first third of the course will focus on learning about financial turnarounds of businesses in crisis. This will cover strategy, operations and financial management. You will be required to translate management and financial accounting data into a detailed operational cashflow forecast model. This part of the course will be delivered by our industry partner - Vantage Performance Group Pty Ltd.
The remainder of the course focusses on how to use Python efficiently and insightfully in a range of finance domains including quantitative trading and investing, asset and derivative valuation, portfolio construction, and sensitivity analysis. The purpose of this part of the course is to develop applied modelling skills that will differentiate students from others by providing tools which leverage their finance knowledge in ways that will be valued by industry. Compared to MS Excel, Python allows for faster computation time involving stochastic based models and very large datasets can be handled with relative ease. Classes will combine discussion of theoretical finance topics, the industry application of this theory, and the Python tools and modelling techniques that bring this application to life. This part of the course has been developed in collaboration with and will be taught by our industry expert - Richard Howes.
No prior coding experience is necessary, but students are encouraged to participate during the class. Students will have the opportunity to become familiar with Python, learn to modify existing code, create their own code and be able to evaluate results. In order to foster students’ understanding, all issues and techniques discussed in the course will be supported with practical examples. Python is a computing language that is used extensively in many real-life applications in finance, investing, banking, machine learning and numerous other fields. Python and associated libraries can be obtained free online. We will teach Python from within the VSCode, a leading integrated development environment (IDE) developed by Microsoft and also available free online.
Sustainable Development Goals - UQ Business School is a proud supporter and Advanced Signatory of the United Nations Principles for Responsible Management Education (UN PRME). As part of the largest global collaboration between business schools and the UN, the school emphasises its role in empowering students to drive societal transformation through the Sustainable Development Goals. The SDGs highlight that a thriving economy relies on a healthy environment, aiming to balance economic growth, social well-being, and environmental protection for a sustainable future.
Course requirements
Assumed background
Students are assumed to be familiar with using Microsoft Excel. Prior knowledge in using Python is desirable but not essential.ﾠ
In addition, students should have previously completed FINM2411. Students will also benefit from having completed FINM3405.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
FINM2411 or 2416 or 3402
Restrictions
Restricted to students enrolled in the BAdvFinEcon(Hons) and BCom(Hons) programs
Course contact
Course staff
Lecturer
Professor Richard Howes
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 aims to acquaint students with the latest state-of-the-art techniques in financial modelling. In particular,ﾠstudents will be equipped with a strong understanding in modelling issues and concepts related to finance and statistics.ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Demonstrate how to extract and translate raw financial data into useful information
LO2.
Design and implement robust financial models
LO3.
Audit, validate and interpret financial models
LO4.
Develop competencies needed to function well in a team
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation |
Turnaround Case Study
|
30% |
2/04/2025 4:00 pm |
| Computer Code |
Coding Project - Part 1
|
30% |
30/04/2025 4:00 pm |
| Computer Code |
Coding Project - Part 2
|
40% |
29/05/2025 4:00 pm |
Assessment details
Turnaround Case Study
- Team or group-based
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Paper/ Report/ Annotation
- Weight
- 30%
- Due date
2/04/2025 4:00 pm
- Other conditions
- Peer assessment factor.
- Learning outcomes
- L01, L02, L04
Task description
This will be a team based assignment (4 members per team). You will take on the role of a turn-around consultant tasked with creating a research report analysing a fictional case study for an in-distress business that covers:
- Identification of key issues
- Development of key interview questions
- Proposal of strategic options
- Proposal of recommendations to the company board
You will also be tasked with building a quantitative model in Excel to support your analysis.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
One designated team member will submit both your report and Excel model via the submission link on Blackboard.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 14 days. Extensions are given in multiples of 24 hours.
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.
Coding Project - Part 1
- Team or group-based
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code
- Weight
- 30%
- Due date
30/04/2025 4:00 pm
- Other conditions
- Peer assessment factor.
- Learning outcomes
- L01, L02, L03, L04
Task description
This will be a team-based assignment (4 members per team). You will download an existing codebase written in Python by forking a GitHub repo and then modify that code to meet a set of objectives. This assignment draws on the material from Lecture 6 on Equity Research.
AI Statement:
The use of Artificial Intelligence (AI) is permitted as a code writing aid in this assignment for in-line code completion and troubleshooting. If you use any code generated by AI technologies, including in-line completion, then the use of those AI technologies must be acknowledged, and the extent of use described, in a comment at the head of each file. Students are advised that the use of AI technologies to generate sections of code other than through in-line completion, or troubleshooting code written by the student, is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
One designated team member will upload a folder containing the repo of their code through the submission link in Blackboard, and will also provide a link to their repo on GitHub.
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.
Coding Project - Part 2
- Team or group-based
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code
- Weight
- 40%
- Due date
29/05/2025 4:00 pm
- Other conditions
- Peer assessment factor.
- Learning outcomes
- L01, L02, L03, L04
Task description
This will be a team-based assignment (4 members per team). You will download an existing codebase written in Python by forking a GitHub repo and then modify that code to meet a set of objectives. This assignment will draw on the material from one or more lectures between Lecture 7 on Portfolio Management and Lecture 10 on Option Pricing, inclusive.
AI Statement:
The use of Artificial Intelligence (AI) is permitted as a code writing aid in this assignment for in-line code completion and troubleshooting. If you use any code generated by AI technologies, including in-line completion, then the use of those AI technologies must be acknowledged, and the extent of use described, in a comment at the head of each file. Students are advised that the use of AI technologies to generate sections of code other than through in-line completion, or troubleshooting code written by the student, is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
One designated team member will upload a folder containing the repo of their code through the submission link in Blackboard, and will also provide a link to their repo on GitHub.
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
Find the required and recommended resources for this course 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 |
|---|---|---|
Week 1 |
Lecture |
Introduction to Course & Modelling General overview of course structure, logistics and assessment. Introduction of external resources. Introduction to Python. Learning outcomes: L01, L02 |
Multiple weeks From Week 2 To Week 13 |
Tutorial |
Tutorials In each tutorial you will practice the material covered in the previous lecture. See Blackboard for details on tutorial content and timeline. Good Friday Public Holiday - Friday 18 April 2025 - Check Blackboard for announcements about affected classes. Labour Day Public Holiday - Monday 5 May 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L01, L02, L03, L04 |
Week 2 |
Lecture |
Turnaround (Self-Directed) Self-directed study on distressed business turnaround - see Blackboard Learning Pathway. Excel modelling assignment released. Learning outcomes: L01, L02, L03 |
Week 3 |
Lecture |
Turnaround (Vantage Seminar) Vantage Guest Lecture. Recap of Learning Pathway. Principles of Turnaround advice and Case Study Examples Learning outcomes: L01, L02, L03 |
Week 4 |
Lecture |
Excel Modelling Cover best tips and practices in the use of Excel in financial modelling. Preparation for the Excel modelling assignment. Learning outcomes: L01, L02, L03, L04 |
Week 5 |
Lecture |
GitHub for Finance Professionals Learn how to use GitHub for version control and collaboration in a finance setting. Learning outcomes: L01, L02, L03, L04 |
Week 6 |
Lecture |
Equity Research Use programming tools to create an equity analyst research report with financials, ratios, and commentary. Part 1 of coding assignment released. Learning outcomes: L01, L02, L03 |
Week 7 |
Lecture |
Portfolio Management Use programming tools to undertake a Markowitz portfolio optimisation. Learning outcomes: L01, L02, L03 |
Week 8 |
No student involvement (Breaks, information) |
Good Friday No lectures this week. |
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester Break No classes during In-Semester break. |
Week 9 |
Lecture |
Quant Funds Management Use programming tools to implement, back-test and monitor a momentum trading strategy. Learning outcomes: L01, L02, L03 |
Week 10 |
Lecture |
Yield Curves Use programming tools to bootstrap a fixed income yield curve. Learning outcomes: L01, L02, L03 |
Week 11 |
Lecture |
Option Pricing Use programming tools to implement numerical methods for option pricing, including binomial pricing models and the Monte-Carlo method. Learning outcomes: L01, L02, L03 |
Week 12 |
Lecture |
AI as Coding Partner Showcase of Claude MCP creating GitHub pull requests. Learning outcomes: L01, L02, L04 |
Week 13 |
Lecture |
Course Review Learning outcomes: L01, L02, L03 |
Additional learning activity information
Sustainable Development Goals.
This course integrates the following Sustainable Development Goal (SDG) throughout course learning activities.
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.