Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group activities on campus, and most will keep an online version available to those students unable to return or those who choose to continue their studies online.
To check the availability of face-to-face and online activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit Convenor
Abhay Singh
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
ACST603 or ACST6003 or AFIN6012 or AFIN613 or AFIN6013 or AFIN858
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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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. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Assessment criteria for all assessment tasks will be provided on the unit iLearn site.
It is the responsibility of students to view their marks for each within-session-assessment on iLearn within 20 days of posting. If there are any discrepancies, students must contact the unit convenor immediately. Failure to do so will mean that queries received after the release of final results regarding assessment tasks (not including the final exam mark) will not be addressed.
Late submissions and extensions
Tasks 10% or less – No extensions will be granted. Students who have not submitted the task prior to the deadline will be awarded a mark of 0 for the task, except for cases in which an application for special consideration is made and approved.
Tasks above 10% - No extensions will be granted. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late (for example, 25 hours late in submission – 20% penalty). This penalty does not apply for cases in which an application for special consideration is made and approved. No submission will be accepted after solutions have been posted.
Name | Weighting | Hurdle | Due |
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Online Quiz | 5% | No | Week 4 |
Modelling Task 1 | 35% | No | Week 6 |
Modelling Task 2 | 60% | No | Week 13 |
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 2 hours
Due: Week 4
Weighting: 5%
The online quiz will consist of 10 to 15 multiple choice and/or short answer questions. Please use the quiz result as an indicator of whether you are progressing satisfactorily in the unit.
Assessment Type 1: Modelling task
Indicative Time on Task 2: 20 hours
Due: Week 6
Weighting: 35%
The assignment requires students to employ selected financial modelling techniques discussed in the class lectures to analyse real world data. The task involves using Excel and R software to generate financial analytics for Financial Portfolio Modelling and Financial Risk Modelling.
Assessment Type 1: Modelling task
Indicative Time on Task 2: 30 hours
Due: Week 13
Weighting: 60%
The assignment requires students to employ selected financial risk forecasting techniques discussed in the class lectures to analyse real world data. The task involves using R software and econometrics methods to quantify financial risk for individual assets and portfolios. The analysis will need to be discussed and presented in a document. An oral presentation may also be required.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Required Text: |
The unit will utilise various library resources, including research papers, book chapters, case studies etc., and relevant material will be made available on ilearn. |
Unit Web Page: |
Log in via https://ilearn.mq.edu.au |
Technology Used and Required: |
Necessary technology: Computer with MS Excel, R and RStudio software, scientific or business calculator without alphanumeric capabilities, internet access. Useful technology: The MATLAB and Python software environment are also very useful if you intend doing this sort of work professionally. |
Delivery Format and Other Details: |
Classes A typical class will be structured as recorded lecture(s) and a live lecture with hands on example. The two parts will mostly flow together and not separately. Please feel free to ask (and answer!) questions throughout the class. Attendance at the live sessions is expected. Teaching and Learning Activities The teaching in the unit will be interactive case study style delivery where financial modelling and forecasting methods will be discussed along with hands on examples using Excel and R. You are strongly advised to attempt all examples before the weekly lectures, and before consulting the solutions. You are encouraged to submit your workings of the class examples for further feedback. |
Recommended Readings: |
We will supplement the lecture materials with readings from journals and other textbooks. A list of relevant material will be provided on iLearn site. Following are some of the recommended readings:
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Date | Description |
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08/02/2021 | Unit is co-taught with AFIN7090 |
Unit information based on version 2021.02 of the Handbook