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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 2018 is available in the Archives.

UTS: Business: Finance
Credit points: 6 cp

Subject level:


Result type: Grade and marks

Requisite(s): 25742 Financial Management OR 25746 Financial Management: Concepts and Applications
These requisites may not apply to students in certain courses.
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 ably 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 Attributes (GA):

  • GA 2: Critical thinking, creativity and analytical skills: will be enhanced by designing, evaluating and conducting statistical analyses of economic and financial relationships
  • GA 3: Communication and interpersonal skills: will be enhanced by coordinating, communicating and working in a team environment
  • GA 5: Business practice-oriented stills: will be enhanced by learning high level technical skills necessary for professional practice in the finance industry

This subject also adrresses the following program learning objectives:

  • 1.1 Research and critically analyse complex information for business decisions
  • 5.1 Apply high level technical skills necessary for professional practice in the finance industry

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.

Pre-lecture: Students are required to read assigned materials before class. Starting from the preparation weeks, instructive materials are available on UTSOnline, 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 lecture.

Lectures: Students are expected to complete the pre-lecture preparations, and are encouraged to be proactive during 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.

Post-lecture: 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.

Feedback: 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: Case Study (Group)


This addresses subject learning objective(s):

1, 2, 3 and 4

Weight: 20%
  • 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 2: Quizzes (Individual)


This addresses subject learning objective(s):

1, 2, 3 and 4

Weight: 20%

50 minutes


Assessment will be based on:

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

Assessment task 3: Final Exam (Individual)


This addresses subject learning objective(s):

1, 2, 3 and 4

Weight: 60%

Assessment will be based on:

  • 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

Hanke, J. E., Wichern, D. W., Business Forecasting, Pearson New International Edition, 9th Edition, Pearson.

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 limit Excel knowledge.


Lecture notes are available for download from UTSOnline.