University of Technology Sydney

25705 Financial Modelling and Analysis

Warning: The information on this page is indicative. The subject outline for a 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 2023 is available in the Archives.

UTS: Business: Finance
Credit points: 6 cp

Subject level:


Result type: Grade and marks

There are course requisites for this subject. See access conditions.


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)

Upon successful completion of this subject students should be able to:
1. Identify statistical methods appropriate for financial analysis and decision-making
2. Implement financial models and analyse data using spreadsheets
3. Construct and compare cross-sectional and time-series regression models
4. Evaluate models and diagnose their performance

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 computer lab workshops. The lectures and lab sessions will be supplemented with both printed and electronic learning materials and resources that allow students to interact with the subject outside the classroom. The subject also incorporates interactive in-class activities and authentic assignments where students work as a team to implement statistical models and analyze financial data.


Students are required to read assigned materials before class. Starting from the preparation weeks, instructive materials are available on the learning management system, including articles, lecture notes, examples, and worksheets. Students are required to read and reflect upon recommended reading from the textbook and on-line resources to familiarize the topics in the upcoming lecture and identify potential difficulties they need to resolve during the lecture.


Students are expected to complete the pre-lecture preparations and are encouraged to be proactive during the lecture to raise questions and resolve difficulties they had in pre-lecture preparation. Collaborative learning is a key component during the weekly computer lab session. Students form teams to complete their exercises. It gives an opportunity for students with complementary skills, e.g. Excel, statistics, and finance, to work together to resolve any difficulty they encounter.


Students are expected to complete the self-study problems to enhance their learning experience and improve their problem-solving skills. Students will also work in teams for the case study. Coordination, cooperation, and communication are the key for the completion and success of the case study.


Students receive regular feedback on their understanding of the key concepts, theories, and hands-on implementation during lecture and computer lab sessions. Two in-class quizzes in weeks 5 and 8 provide assessments on student understanding and progress on key components of the lecture content. Students will receive detailed comments on their mistakes in the quizzes and suggestions on how to improve their performances. The case study has two submissions. Students receive detailed comments on their first submission which will help to guide their second submission.

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 task 1: Quizzes (Individual)*


This addresses subject learning objective(s):

1, 2, 3 and 4

Weight: 20%

90 minutes each quiz


The assessment will be graded on the following criteria:

  • demonstration of the ability to apply the correct statistical concepts and models to answer the multiple-choice questions
  • accuracy of response

*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 the mid-semester quiz will have its weight added to the final examination conditional on the students submitting, receiving approval and complying with the requirements of special consideration application in accordance with the UTS rules. If the composite mark for the final exam is more than 50 percent and the student is in the final subject of their degree, the UTS rules on borderline result shall apply whereby students will be allowed to undertake a supplementary final examination (range of 45-49). Where a student completes and passes a supplementary examination, the maximum mark awarded for the subject will be 50 Pass.

Assessment task 2: Case Study (Individual)


This addresses subject learning objective(s):

1, 2, 3 and 4

Weight: 20%

See case instruction to be posted in week 4.


The group study case will be graded on the following criteria:

  • Students should demonstrate the ability to apply the correct statistical concepts and models to address the questions in the case study.
  • Students should demonstrate the ability to verbally explain the statistical findings.
  • The case report should have a clear structure that addresses the issues in the case study.
  • Paragraphs should be clearly connected and coherent. Each paragraph should start with a topic sentence.The sentences flow logically from point to point.Written expressions should be clear, complete, and grammatically correct.
  • Sources of information should be fully referenced in the text with details provided in the reference list.

Assessment task 3: Final Exam (Individual)


This addresses subject learning objective(s):

1, 2, 3 and 4

Weight: 60%

The assessment will be graded on the following criteria:

  • knowledge and understanding of statistical concepts and models
  • demonstration of the ability to apply the correct statistical concepts and models to answer the multiple-choice and short-answer questions
  • accuracy of responses.

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.


Lecture notes are available for download from Canvas.