BUSFIN 711 : Financial Analytics Applications

Business and Economics

2024 Quarter Three (1246) (15 POINTS)

Course Prescription

Critically examines how advanced modelling features can be applied to obtain enhanced analytical insights from spreadsheet-based financial models. Develops skills in applying both non-programming (e.g., PowerBi, Alteryx) and programming (e.g., R, Python) based tools to real-world financial challenges. Applies these tools both to obtain analytical insight and communicate information effectively.

Course Overview

This course complements BUSFIN 710 Financial Modelling Techniques but focuses on financial modelling using programming. Students will learn to design and code financial models using programming languages such as Python. The course will cover other important aspects of programming such as setting up environments and sourcing, manipulating and storing data. Modelling will be applied to real-world financial settings. Classes will be designed to provide a hands-on, interactive experience for students.

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Graduate Profile: Master of Applied Finance

Learning Outcomes

By the end of this course, students will be able to:
  1. Examine the advantages and disadvantages of programming versus non-programming based modelling applications in the context of finance (Capability 3)
  2. Apply programming language(s) (eg Python) and associated components (eg environments, packages) to write standalone programmes (Capability 3)
  3. Design and build models using programming (eg Python) to analyse real-world corporate finance and investment related problems (Capability 5)
  4. Apply techniques to retrieve, manipulate and store financial data in various formats (Capability 3)
  5. Evaluate model outputs and communicate results to decision makers using written and visual formats (Capability 4 and 6)

Assessments

Assessment Type Percentage Classification
Assignment 1 30% Individual Coursework
Assignment 2 30% Individual Coursework
Assignment 3 40% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5
Assignment 1
Assignment 2
Assignment 3

The assessments for this course will comprise a series of individual assignments involving applied programming and analysis.

Workload Expectations

Following University workload guidelines, a standard 15 point course represents approximately 150 hours of study.

For this course, you can expect 4 hours of lectures and workshops per week (for 10 weeks). Since the course as a whole represents approximately 150 hours of study, that leaves a total of 110 hours across the entire quarter for independent study, e.g. reading, reflection, preparing for and completing assessments etc.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including lectures and workshops to complete components of the course.

Some class sessions may be available as recordings to the extent possible. Other learning activities including workshops may not be available as recordings.

The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Student feedback will be incorporated into future courses where relevant. 

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 27/08/2024 12:20 p.m.