Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- 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. ﾠ
The first half of the course will focus on learning about financial turn arounds of business 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 half of the course will be delivered by our industry partner - Vantage Performance Group Pty Ltd .
For the second half of the course, we will turn our attention to modelling asset prices and derivatives using Python. The purpose of the second half of the course is gain exposure to quantitative finance modelling and learn techniques which are often in practice only undertaken using coding software or a programming language. Compared to MS Excel, Python allows for faster computation time involving stochastic based models and very large datasets can be handled with relative ease. The specific quantitative finance topics using Python we will cover will be modelling random asset prices, creating Monte Carlo simulations for scenario testing, constructing efficient stock portfolios using optimisation techniques andﾠstress testing a portfolio’s value at risk (VaR). Each class will start with a brief presentation to provide a theoretical grounding on the topic that we will be modelling in class using Python. This half of the course has been developed in collaboration with our industry expert - Richard Howes and he will deliver the last two topics in this half of the course. With the first four topics taught by our own internal team.
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 through the latest available version of Anaconda Individual (Window or Mac) and contains all the Python related software required to undertake this course.
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 |
|---|---|---|---|
| Computer Code, Paper/ Report/ Annotation, Product/ Design, Role play/ Simulation |
Turn-around Case Study
|
35% |
27/08/2024 2:00 pm |
| Computer Code, Product/ Design, Tutorial/ Problem Set |
Data Cleaning and Analysis Exercise
|
7.5% |
2/09/2024 2:00 pm |
| Computer Code, Examination, Tutorial/ Problem Set |
In-Semester Exam
|
17.5% |
7/10/2024
During Class - more information about the due date will be provided in class and via the course Blackboard site. |
| Computer Code, Paper/ Report/ Annotation, Project | Project - Financial markets | 40% |
4/11/2024 4:00 pm |
Assessment details
Turn-around Case Study
- Team or group-based
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation, Product/ Design, Role play/ Simulation
- Weight
- 35%
- Due date
27/08/2024 2:00 pm
- Other conditions
- Peer assessed.
- Learning outcomes
- L01, L02, L04
Task description
This will be a team based assignment with 2-3 members per team and you will be tasked with creating a research report that covers:
- Identify key issues
- Develop key interview questions
- Propose strategic options and recommendations
- Create a working model
Please see Blackboard for additional details on the Case Study.
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
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.
Data Cleaning and Analysis Exercise
- Online
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code, Product/ Design, Tutorial/ Problem Set
- Weight
- 7.5%
- Due date
2/09/2024 2:00 pm
- Learning outcomes
- L01, L02, L03
Task description
This assessment is an introductory level python assignment.
You will be tasked with creating a python notebook that includes:
- Data Exploration
- Data Cleaning
- Data Analysis and presentation
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
Submit through the Blackboard Assessment link.
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.
In-Semester Exam
- Online
- Mode
- Activity/ Performance, Product/ Artefact/ Multimedia
- Category
- Computer Code, Examination, Tutorial/ Problem Set
- Weight
- 17.5%
- Due date
7/10/2024
During Class - more information about the due date will be provided in class and via the course Blackboard site.
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03
Task description
The will be a time limited coding exam.
You will be tasked with answering a set of questions using a python notebook.
Answers will be supplied as an Excel file with the optional submission of your notebook.
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.
Exam details
| Planning time | no planning time minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | (In person) Casio FX82 series or UQ approved , labelled calculator only |
| Open/closed book | Closed Book examination - specified written materials permitted |
| Materials | All materials created during the course (i.e., code from lectures/workshops etc.). Students are not permitted to access external resources during the exam. |
| Exam platform | Learn.UQ |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Late submission
Exams submitted after the end of the submission time will incur a late penalty.
Project - Financial markets
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation, Project
- Weight
- 40%
- Due date
4/11/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L04
Task description
More details to be provided on Blackboard during semester.
AI Statement
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
The assignment must be submitted electronically through Turnitin, located in the Blackboard Assessment link.
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 |
|---|---|---|
Week 1 |
Seminar |
Introduction to Course & Modelling General overview of key topics for the semester, introduction to turnaround assignment and seminar on modelling basics. Learning outcomes: L01, L02 |
Week 2 |
Seminar |
Self-directed study Self-directed study (Turnaround) - see Blackboard Learning Pathway Learning outcomes: L01, L02, L03 |
Week 3 |
Seminar |
Self-directed study Self-directed study (Turnaround) - see Blackboard Learning Pathway Learning outcomes: L01, L02, L03, L04 |
Week 4 |
Seminar |
Vantage Guest Lecture Vantage Guest Lecture - Recap of Learning Pathway - Principles of Turnaround advice and Case Study Examples Learning outcomes: L01, L02, L03 |
Week 5 |
Seminar |
Assignment Workshop Learning outcomes: L01, L02, L03, L04 |
Week 6 |
Seminar |
Self-directed study Self-directed study on coding topics 1-4. Learning outcomes: L01, L02, L03, L04 |
Week 7 |
Lecture |
Coding topic 1 Learning outcomes: L01, L02, L03 |
Week 8 |
Seminar |
Coding topic 2 Learning outcomes: L01, L02, L03 |
Week 9 |
Seminar |
Coding topic 3 Learning outcomes: L01, L02, L03 |
Mid Sem break |
Seminar |
In-Semester Break No classes during In-Semester break. |
Week 10 |
Seminar |
Coding topic 4 Learning outcomes: L01, L02, L03 |
Week 11 |
Seminar |
Self-directed study Self-directed study - prepare for topics 5 & 6. Learning outcomes: L01, L02, L03 |
Week 12 |
Seminar |
Topics 5 and 6 Learning outcomes: L01, L02, L03 |
Week 13 |
Seminar |
Self-directed study Self-directed study Learning outcomes: L01, L02, L03 |
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.