25705 Financial Modelling and Analysis
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particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2025 is available in the Archives.
Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksThere are course requisites for this subject. See access conditions.
Description
This subject provides students with the tools necessary to describe and analyse financial data. It uses Excel as a tool for spreadsheet analysis using forecasting and modelling techniques. An applied approach is taken in the finance context to ensure students are able to understand and apply critique modelling and forecasting techniques.
Subject learning objectives (SLOs)
| 1. | Build financial models using Microsoft Excel and following best practices |
|---|---|
| 2. | Analyse financial data and produce forecasts using appropriate statistical and analytical methods |
| 3. | Recommend evidence-based solutions to complex financial problems |
| 4. | Communicate technical financial information and analysis in a form appropriate to the audience |
Contribution to the development of graduate attributes
The subject teaches students statistical methods commonly used in economic and financial analyses. Students will learn to design, evaluate, and apply statistical models to identify financial relationships and support financial decisions. It allows students to develop critical thinking and analytical skills through practice-oriented assessments such as real-world case studies and in-class hands-on applications. It complements the other finance subjects by providing students with statistical knowledge necessary for understanding financial risk and pricing.
This subject also contributes to the following Graduate Attribute(s) in the following ways:
- Intellectual rigour and innovative problem solving will be enhanced by designing, evaluating, and conducting statistical analyses of economic and financial relationships.
- Professional and technical competence will be enhanced by learning high level technical skills necessary for professional practice in the finance industry
This subject develops the following program learning objective(s) for the Master of Finance and Master of Financial Analysis
- Critically analyse and apply innovative and integrated solutions to address complex business decisions (1.1)
- Integrate high-level technical skills and knowledge necessary for professional practice in the finance industry (4.2)
Teaching and learning strategies
The subject is delivered as a combination of interactive lectures and in-class activities in Microsoft Excel.
The lectures and in-class activities will be supplemented with readings and short videos. To help students prepare for the subject, a set of Excel Basics instructive materials are provided in the learning management system.
Pre-lecture
On a weekly basis, students are required to read and watch assigned materials before class.
Lectures
Students are encouraged to be proactive during the lecture to raise questions and resolve difficulties they had in pre-lecture preparation.
Post-lecture
Students are expected to complete the component of the in-class activities that is left unfinished at the end of each lecture. This work will help improve their problem-solving skills and Excel proficiency.
Feedback
Students receive regular feedback on their understanding of the key concepts, theories, and hands-on implementation during the in-class activities. Students will receive timely feedback after each assessment and ongoing feedback on their progress during class and in-class activities.
Content (topics)
- Introduction to descriptive statistics and analysis
- Use and presentation of data using Excel
- Probabilities and distributions
- Hypothesis testing
- Regression analysis and applications in finance
- Forecasting with time series data
- Comparing forecasting models
- Using Excel for spreadsheet modelling and analysis
Assessment
Assessment task 1: Quizzes (Individual)*
| Objective(s): | This addresses subject learning objective(s): 1 and 2 |
|---|---|
| Weight: | 20% |
| Length: | 60 minutes |
| Criteria: | The assessment will be graded on the following criteria:
*Note: Late submission of the assessment task will not be marked and awarded a mark of zero. There are no other opportunities to sit the mid-semester quiz. Students who do not attend a quiz will have its weight added to the final assessment conditional on the students submitting, receiving approval and complying with the requirements of special consideration application in accordance with the UTS rules. |
Assessment task 2: Financial Modelling Case Study (Individual)
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 4 |
|---|---|
| Weight: | 40% |
| Length: | Excel model and dashboard with a 500-word elaboration |
| Criteria: | The assessment will be graded on the following criteria:
|
Assessment task 3: Financial Analysis Case Study & Presentation (Individual)
| Objective(s): | This addresses subject learning objective(s): 2, 3 and 4 |
|---|---|
| Weight: | 40% |
| Length: | Excel workings and a 5-minute recorded presentation |
| Criteria: | The assessment will be graded on the following criteria:
|
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
- McEvoy, David M., A Guide to Business Statistics, John Wiley & Sons, Inc
- Charles W. Chase Jr., Demand-Driven Forecasting: A Structured Approach to Forecasting, 2nd Edition, John Wiley & Sons, Inc
Recommended texts
- Levine D. F., Stephan D. F. and Szabat K.A., Statistics for Managers Using Microsoft Excel, 7th Edition, 2014,
Pearson. This is very helpful for students with basic Excel knowledge.
References
Lecture notes are available for download from Canvas.