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 a comprehensive introduction to financial management and financial analysis. Focus is on creating shareholder value. Topics include financial modelling, the time value of money, stock and bond valuation, capital budgeting and net present value, risk and diversification, and the Capital Asset Pricing Model.
This introductory course lays out the main foundations of financial management. We begin by developing a suite of tools for converting cash flows between present and future values, which is followed by applications to stock and bond valuation and mortgages, as well as applications to the evaluation of new projects in a corporate setting. Investment diversification and a range of asset pricing models that can be used to estimate investment returns conditional on risk are also investigated.
Course requirements
Assumed background
Students are assumed to be proficient in Mathematics to Year 12 level.
Incompatible
You can't enrol in this course if you've already completed the following:
FINM2401 or 2412 or 7401 or 7805
Restrictions
Restricted to students enrolled in the BAdvFinEcon(Hons)
Course contact
Course staff
Lecturer
Professor Stephen Gray
Tutor
Mr Noah Greenaway
Mr Dominic Byrne
Ms Mariella Bourke
Miss Brianne Major
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 provide you with a solid grounding in the key components of financial management. The skills developed in this course will enable students to determine the value of various financial instruments, evaluate proposed new capital investments, and determine a fair return given the risk of an investment. These skills will be further developed and expanded in subsequent courses.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Value assets and assess proposed investments by converting cash flows between present and future values.
LO2.
Value financial instruments such as stocks and bonds and understand the markets in which they trade.
LO3.
Determine whether a proposed new project creates value for shareholders and justify your reasoning through the application of rigorous financial analysis.
LO4.
Implement a range of asset pricing models in order to determine a fair return given the risk of a particular investment.
LO5.
Assess the merits of a proposed new capital investment and prepare a detailed financial analysis and report.
LO6.
Demonstrate basic proficiency in accessing data from Bloomberg.
LO7.
Work constructively as part of a team.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Paper/ Report/ Annotation |
Python case project
|
10% |
27/03/2025 3:00 pm |
| Examination | In-Semester Exam | 30% |
Week 6, Fri
During Class |
| Presentation, Project |
NPV case project
|
20% |
Report - 3PM - 1/05/2025 Presentation times arranged via Blackboard Week 10 - Week 11 |
| Computer Code, Project |
Efficient frontier case
|
10% |
29/05/2025 3:00 pm |
| Examination | Final exam | 30% |
End of Semester Exam Period 7/06/2025 - 21/06/2025 |
Assessment details
Python case project
- Team or group-based
- Mode
- Product/ Artefact/ Multimedia
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 10%
- Due date
27/03/2025 3:00 pm
- Other conditions
- Peer assessed.
- Learning outcomes
- L03, L04, L05, L06, L07
Task description
Python-Based Financial Calculations and Analysis: In a group, develop and apply Python functions to solve key financial tasks, including basic valuation computations and the analysis of historical trading strategies. Results should be concisely summarized in a PDF report, with code appended and properly documented.
All materials are to be submitted electronically via Blackboard. Detailed instructions, guidelines, and supporting resources, including the peer assessment process, will be available on the course Blackboard site.
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
Online via Blackboard. Only one team member is to submit. All team member names and student numbers must appear on the cover page.
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
- Mode
- Written
- Category
- Examination
- Weight
- 30%
- Due date
Week 6, Fri
During Class
- Learning outcomes
- L01, L02
Task description
The in-semester exam covers material from classes 1-4 inclusive.
The exam will consist of three questions, each of which has a number of parts. Questions will cover time value of money, and valuation of stocks, bonds and mortgages. All questions will require numerical calculations.
More information about the in-semester exam will be shared in class and posted to the course Blackboard site.
AI Statement:
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) tools will not be permitted. Any attempted use of Generative AI may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Closed Book examination - specified written materials permitted |
| Materials | Two A4 pages, both sides, written or typed. |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
NPV case project
- Team or group-based
- Mode
- Oral, Product/ Artefact/ Multimedia
- Category
- Presentation, Project
- Weight
- 20%
- Due date
Report - 3PM - 1/05/2025
Presentation times arranged via Blackboard Week 10 - Week 11
- Other conditions
- Peer assessed.
Task description
Part 1: NPV Case Submission
Description:
Working in teams, students will develop a regulatory financial model for a large-scale infrastructure project, encompassing both regulated and unregulated revenue streams. The objective is to ensure that investors receive a fair return on capital (the “just made whole” principle), while also conducting a full NPV analysis under chosen discount rates. Teams should compare real-world returns against regulated assumptions, incorporate relevant parameters (e.g., required returns, tax, gamma), and identify key sensitivities using data tables. A concise written report and accompanying Excel model will be submitted electronically.
Part 2: NPV Project Presentation
Description:
After submitting the NPV analysis, each team will deliver a 15-minute presentation summarizing their findings. This includes key financial conclusions, the impact of critical assumptions, and potential strategic considerations. The presentation should be clear, visually supported, and professional, with teams prepared to address questions regarding assumptions, regulatory conditions, and potential adjustments to improve project viability.
Please Note: The presentation will be recorded for marking purposes per UQ Policy.
AI Statement - both tasks:
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.
Extensions or deferrals are not available for an in-class presentation. An extension may be available for the submitted material only.
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.
10% Late Penalty applies to submitted material only. Late submissions are not accepted for in-class presentations. Failure to present at the scheduled time will result in a mark of zero for the presentation portion of this assessment.
Efficient frontier case
- Team or group-based
- Mode
- Written
- Category
- Computer Code, Project
- Weight
- 10%
- Due date
29/05/2025 3:00 pm
- Learning outcomes
- L04, L06, L07
Task description
Construction of efficient frontier using real-world data: In a group, use historical stock returns to construct an efficient frontier. Use that frontier to perform calculations about the balance of superannuation accounts. Results should be concisely summarized in a PDF report, with code appended and properly documented.
All materials are to be submitted electronically via Blackboard. Detailed instructions, guidelines, and supporting resources, including the peer assessment process, will be available on the course Blackboard site.
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
Online via Blackboard. Only one team member is to submit. All team member names and student numbers must appear on the cover page.
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.
Final exam
- Mode
- Written
- Category
- Examination
- Weight
- 30%
- Due date
End of Semester Exam Period
7/06/2025 - 21/06/2025
- Learning outcomes
- L01, L02, L03, L04
Task description
The final-term exam will cover all of the course material, but with a bias towards topics that were covered after the in-semester exam. All questions will require numerical calculations.
Further detail will be provided on Blackboard.
AI Statement:
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) tools will not be permitted. Any attempted use of Generative AI may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Closed Book examination - specified written materials permitted |
| Materials | Two A4 pages of notes, written or typed, both sides. |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
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 |
Present value mechanics I Present value, future value, annuities, perpetuities, compound interest. Learning outcomes: L01, L06 |
Week 2 |
Tutorial |
T1 - Present Value Mechanics I Learning outcomes: L01, L06 |
Lecture |
Present value mechanics II Advanced present value calculations Learning outcomes: L01 |
|
Week 3 |
Tutorial |
T2 - Present Value Mechanics Learning outcomes: L01 |
Lecture |
Bond valuation and credit ratings Learning outcomes: L02 |
|
Week 4 |
Tutorial |
T3 - Bond Valuation and Credit Ratings Learning outcomes: L02 |
Lecture |
Stock valuation Learning outcomes: L02 |
|
Week 5 |
Tutorial |
T4 - Stock Valuation Learning outcomes: L02 |
Lecture |
Net present value and in-semester exam review Learning outcomes: L01, L02, L03, L05 |
|
Week 6 |
Tutorial |
T5 - In-Semester Review Learning outcomes: L01, L02 |
Lecture |
In-Semester Exam |
|
Week 7 |
Tutorial |
T6 Case assignment Learning outcomes: L03, L05, L07 |
Lecture |
Stock return dynamics No Classes - Working on Case Study Assignment Learning outcomes: L03, L05, L07 |
|
Week 8 |
Tutorial |
T7 Stock return dynamics Good Friday Public Holiday - Friday 18 April 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L03, L05, L07 |
Lecture |
Investor utility and mean variance preferences Good Friday Public Holiday - Friday 18 April 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L04 |
|
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester break |
Week 9 |
Tutorial |
T8: Investor utility and mean variance preferences Thursday 25th April - Public Holiday. Affected students please attend am alternate tutorial for this week only. Learning outcomes: L04 |
Workshop |
The Capital Asset Pricing Model I Portfolio diversification. Learning outcomes: L03, L05, L07 |
|
Week 10 |
Tutorial |
T9: The Capital Asset Pricing Model I Portfolio diversification. Labour Day Public Holiday - Monday 5 May 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L04 |
Workshop |
The Capital Asset Pricing Model II Learning outcomes: L04 |
|
Week 11 |
Tutorial |
T10: The Capital Asset Pricing Model II Learning outcomes: L04, L07 |
Workshop |
The Capital Asset Pricing Model III Presentation times to be arranged via Blackboard. Learning outcomes: L04, L05, L07 |
|
Week 12 |
Tutorial |
T11: The Capital Asset Pricing Model III Presentation times to be arranged via Blackboard. Learning outcomes: L04, L07 |
Workshop |
CAPM parameter estimation Learning outcomes: L06 |
|
Week 13 |
Tutorial |
T12: CAPM parameter estimation Learning outcomes: L06 |
Workshop |
Review session |
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 for Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.