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Course profile

Financial Modelling (FINM3422)

Study period
Sem 1 2025
Location
St Lucia
Attendance mode
In Person

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

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
  • Team or group-based
30%

2/04/2025 4:00 pm

Computer Code Coding Project - Part 1
  • Team or group-based
30%

30/04/2025 4:00 pm

Computer Code Coding Project - Part 2
  • Team or group-based
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.

See the conditions definitions

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.

See the conditions definitions

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.

See the conditions definitions

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.

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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.

Goal 17: Partnerships for the goals

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:

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.