Students

AFIN8090 – Financial Modelling and Forecasting

2026 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convenor and Lecturer
Hieu Nguyen
h.nguyen@mq.edu.au
Contact via Email
Room 536, 4ER Building, 4 Eastern Road
Thursday, 2:15 PM-3:15 PM (by appointment)
Credit points Credit points
10
Prerequisites Prerequisites
(Admission to MActPrac or MFin or GradCertResMQBS or GradDipResMQBS) or (ACST6003 and (BUSA6004 or ECON6034))
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This unit applies financial modelling and forecasting principles to various methods and theories covered in the corporate finance and financial statement analysis fields. This is an excellent course for students with an interest in a career in corporate finance or financial statement analysis. The modelling and forecasting principles covered in this course are not simply an application of extrapolative techniques to historical data. Rather, there is an emphasis on modelling the uncertainty, and alerting decision makers, of corporate change as the forecast horizon increases. This is very much a hands-on course and the lectures use worked examples throughout, requiring students to be at computer terminals with access to excel and industry standard simulation packages. The worked examples are designed to reinforce the financial modelling and forecasting principles covered in the course.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Evaluate and summarise with appropriate statistics the empirical properties of financial time series data.
  • ULO2: Build and estimate a range of quantitative, statistical models used by financial analysts and forecasters using software tools.
  • ULO3: Present a complex model in simple and credible terms, understandable by decision makers.
  • ULO4: Model uncertainty in the financial markets to include these effects in their analysis.

General Assessment Information

Late Submission Penalties

If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days. Submissions more than 7 days late will receive a mark of 0.

Example 1 (out of 100):

If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.

Example 2 (out of 30):

If you score 27/30 but submit 20 hours late, you will lose 1.5 marks and receive 25.5/30.

Extensions

Automatic short extension: Some assessments are eligible for automatic short extension. You can only apply for an automatic short extension before the due date.

Special Consideration: If you need more time due to serious issues and for any assessments that are not eligible for Short Extension, you must apply for Special Consideration. Need help? Review the Special Consideration page for further details.

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Professional practice: Industry database analysis 40% No 24/04/2026 Individual Yes Open AI
Formal examination: Test 20% No 15/05/2026 Individual No Observed
Professional practice: Financial data analytics 40% No 05/06/2026 Individual Yes Open AI

Professional practice: Industry database analysis

Assessment Type 1: Professional task
Indicative Time on Task 2: 25 hours
Due: 24/04/2026
Weighting: 40%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open AI

The purpose of this assessment is for you to develop expertise in applying quantitative analysis techniques to real-world data.

You will extract data from an industry database, perform data analysis tasks and provide insights based on the information obtained.

Skills in focus:

  • Communication skills
  • Critical thinking and problem solving
  • Digital skills
  • Discipline knowledge

Deliverable(s): Written submission [max 2,500 words] Individual assessment
On successful completion you will be able to:
  • Evaluate and summarise with appropriate statistics the empirical properties of financial time series data.
  • Build and estimate a range of quantitative, statistical models used by financial analysts and forecasters using software tools.
  • Present a complex model in simple and credible terms, understandable by decision makers.
  • Model uncertainty in the financial markets to include these effects in their analysis.

Formal examination: Test

Assessment Type 1: Examination
Indicative Time on Task 2: 15 hours
Due: 15/05/2026
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

The purpose of this assessment is for you to demonstrate your understanding and knowledge of key topics from the unit.

You will participate in a formal mid-session test. Feedback on your performance will help you assess your progress through the unit content.

Deliverable(s): Test Individual assessment
On successful completion you will be able to:
  • Evaluate and summarise with appropriate statistics the empirical properties of financial time series data.
  • Build and estimate a range of quantitative, statistical models used by financial analysts and forecasters using software tools.
  • Model uncertainty in the financial markets to include these effects in their analysis.

Professional practice: Financial data analytics

Assessment Type 1: Professional task
Indicative Time on Task 2: 25 hours
Due: 05/06/2026
Weighting: 40%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open AI

The purpose of this assessment is for students to demonstrate the expertise they have acquired in the unit by analysing real-world financial data.

You will conduct financial data analytics tasks and provide insights based on the information obtained.

Skills in focus:

  • Communication
  • Critical thinking and problem solving
  • Digital skills
  • Discipline knowledge

Deliverable(s): Written submission [max 2,500 words] Individual assessment
On successful completion you will be able to:
  • Evaluate and summarise with appropriate statistics the empirical properties of financial time series data.
  • Build and estimate a range of quantitative, statistical models used by financial analysts and forecasters using software tools.
  • Present a complex model in simple and credible terms, understandable by decision makers.
  • Model uncertainty in the financial markets to include these effects in their analysis.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • Academic Success for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

Delivery: In-person, weekly seminar (2 hours) and workshop (1 hour).

Resources:

RECOMMENDED READINGS

  • Rachev, S. T., Hoechstoetter, M., Fabozzi, F. J., & Focardi, S. M. (2010). Probability and Statistics for Finance. Wiley.
  • Kronthaler, F. (2022). Statistics Applied with Excel: Data Analysis Is (Not) an Art (1st ed.). Springer Berlin
  • McKinney, W. (2022). Python for Data Analysis : Data Wrangling With Pandas, NumPy, and Jupyter (Third edition). O’Reilly.
  • Nussbaumer Knaflic, C. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals (1st edition). Wiley. 
  • Wooldridge, J., Wadud, I. K. M. M., & Lye, J. N. (2020). Introductory Econometrics (Seventh edition). Cengage Learning Australia.
  • Müller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python : A Guide for Data Scientists (1st edition). O’Reilly Media, Incorporated.
  • Yan, Y. (2017) Python for fiance (2nd edition). Packt Publishing. 

TECHNOLOGY NEEDS

  • Computer
  • Excel and Python (Install Anaconda)
  • Factset account

Unit Schedule

Two-hour seminar: Friday 13:00 Week 1-13 (No in-person class in Week 6 due to public holiday, recording will be avaiable)

One-hour workshop: Week 1-13

Week Topic
1 Introduction to Python, financial modelling and forecasting
2 Financial data, cleaning and manipulation
3 Descriptive statistics and plotting
4 Market risk modelling 1
5 Market risk modelling 2
6 Portfolio analysis 1
7 Portfolio analysis 2 
8 Regression analysis 1
9 Regression analysis 2
10 Mid-term test
11 Time series analysis 1
12 Time series analysis 2
13 Revision

This schedule is subject to minor revisions when needed

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Academic Success

Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via the Service Connect Portal, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Unit information based on version 2026.06 of the Handbook