Financial Modelling (FINM3422)

Course overview

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

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:

Assessment

Assessment summary

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.

See the conditions definitions

Learning outcomes

L01, L02, L04

Task description

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

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

• In-person

Mode

Activity/ Performance, Product/ Artefact/ Multimedia

Category

Computer Code, Examination, Tutorial/ Problem Set

Weight

17.5%

Due date

11/10/2024

During Class - more information about the due date will be provided in class and via the course Blackboard site.

Other conditions

Time limited.

See the conditions definitions

Learning outcomes

L01, L02, L03

Task description

Exam details

Submission guidelines

Deferral or extension

You may be able to defer this exam.

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

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.

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.

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.