Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit

SUBJECT OUTLINE

25624 Financial Metrics for Decision Making

Subject description

This subject serves as an introduction to financial decision-making and the role of (big) data in financial decisions. It will enable students to make smart decisions, such as management finance decisions, personal savings decisions and investment decisions. The starting point is the traditional paradigm, which assumes that individuals have rational beliefs (with Bayesian updating when new information arrives) and the objective of maximising their expected utilities. This view is enriched, using models of decision-making based on research in psychology, which allow for beliefs that  are not fully rational, as well as for alternative preferences and limits to cognition. Using real data, students will calculate the metrics used to make financial decisions, they will learn how to apply decision-making rules to those metrics, and they will discover the systematic pitfalls and biases that plague decision-making, as well as techniques for overcoming them.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. describe various decision-making techniques and explain the roles of financial metrics, utility theory, prospect theory and systematic biases in financial decision-making

2. recommend a financial decision based on data, using appropriate financial and statistical concepts and techniques

3. identify cognitive biases and analyse their impact on corporate behaviour and asset prices

Contribution to the development of graduate attributes

This subject contributes to the development of the following graduate attributes:

. Intellectual rigour and innovative problem solving

. Communication and collaboration

· Professional and technical competence

This subject also contributes specifically to develop the following Program Learning Objectives:

· Apply evidence, creativity and critical reasoning to solve business problems (1.1)

· Communicate information clearly in a form appropriate for its audience (2.1)

. Make judgements and business decisions consistent with the principles of social responsibility, inclusion and knowledge of Indigenous peoples (3.1)

Teaching and learning strategies

The following strategies are used to facilitate student learning.

Preparation before class

Students will be required to watch online videos and read prescribed articles before coming to class. This will facilitate in-class interaction and productive discussion of case studies.

In-class activities

The lectures focus on practical examples. Following the discussion of an example, students will be required to answer questions about it, giving them immediate feedback on their understanding.

In-class data analysis

Students will be required to use data and financial metrics to solve decision-making problems in class. Their solutions will then be discussed in-class.

Case studies

Collaborative group case studies will enable students to prepare professional responses to realistic questions and to develop their critical thinking skills in a group context.

Online discussions

Students will be directed to the online discussion board, where they can lodge questions and suggest solutions to other students’ questions. This forum will stimulate debate and provide peer feedback.

Feedback

Feedback will be provided for all in-class presentations. Students will be given recommendations on how to improve their problem-solving techniques and their presentations.

An aim of this subject is to help you develop academic and professional language and communication skills to succeed at university and in the workplace. During the course of this subject, you will complete a milestone assessment task

that will, in addition to assessing your subject-specific learning objectives, assess your English language proficiency

Content (topics)

. Utility theory and prospect theory

. Financial decision-making

.   Financial metrics

. Heuristics and systematic biases

· Managerial decision-making

Additional information

The teaching delivery concept for this subject will be as follows:

Prior to every session, pre-recordings, and other materials will be made available to students. The material will consist of theoretical explanations of concepts and definitions.

During the lectures, we will apply these concepts in an interactive setting. Therefore, it is important that students go through and understand the pre-recordings and other materials before attending the live lectures.

There are two seminars each week. Please make sure you only attend the seminar you are registered in:

Friday from 2:00pm to 5:00pm

Friday from 6:00pm to 9:00pm

Each seminar has three components:

Lecture - 50 min;

In-class activity - 50 min;

Quiz and Q&A - 50 min

All topics are subject to change.

Assessment

Assessment task 1: Quizzes (Individual)*

Objective(s): This addresses subject learning objective(s): 1 and 3

Weight: 30%

Task: There will be 10 quizzes in weeks 3 to 12, inclusive:

Each quiz will be:

administered via Canvas during the third hour of each weekly seminar,

open for 22 minutes once started,

based on the topics of the previous week.

Only the top 6 quiz results will count towards the final grade.

Each of the top 6 quizzes will contribute 5% to the students’ final grade, with the assessment task having a total weight of 30%.

Due: Weeks 3 to 12. Based on topics of the previous week.

Criteria: *Note: Late submission of the assessment task will not be marked and awarded a mark of zero.

Further information:

Submission of assessment via Canvas.

The following conditions will apply to late submissions: no marks awarded.

Assessment task 2: Business case (Individual)

Objective(s): This addresses subject learning objective(s): 2

Weight: 40%

Task: In this assignment, each student will use Microsoft Excel to examine a set of problems.

The instructions for the assessment task will be made available in Week 5.

Submissions will consist of a written report, formatted as a PDF document, together with an Excel workbook containing the quantitative portion of the analysis.

The assessment will be graded on the quality of both the written report and the quantitative analysis in Excel.

This assignment will contribute 40% to the overall grade of each student.

Due: 11.59pm Monday 22 April 2024

Further information:

Submission of assessment via Canvas. All submissions will be checked for plagiarism via Turnitin.

The following conditions will apply to late submissions: no marks awarded.

This task includes a milestone assessment component that evaluates English language proficiency. You may be guided to further language support after the completion of this subject if your results in this milestone task indicate you need more help with your language skills.

Assessment task 3: Final Exam (Individual)

Objective(s): This addresses subject learning objective(s): 1, 2 and 3

Weight: 30%

Task: This will be a take-home exam administered via Canvas:

The exam will consist of interpretation questions and the use of Excel for data analysis.

This exam will contribute 30% to the overall grade for the subject.

Due: Formal exam period

Further information:

Submission of final exam via Canvas. All submissions will be checked for plagiarism via Turnitin.

The following conditions will apply to late submissions: no marks awarded.

Students who are granted a special alternative final exam based on approved special consideration will be given 3 working days’ notice of the scheduled special alternative final exam. Special alternative final exam will not be rescheduled if the student fails to attend the special alternative final exam on the designated schedule.

Minimum requirements

Students must achieve at least 50% of the subject’s total marks.

Other resources

. Camm, J. D., Cochran, J. J., Fry, M. J., & Ohlmann, J. W. (2020). Business analytics. Cengage AU . Guerrero, H. (2019). Excel data analysis. Springer Berlin Heidelberg

. Alexander, M., Kusleika, R., & Walkenbach, J. (2018). Excel 2019 Bible. John Wiley & Sons

Assessment: faculty procedures and advice

Extensions and late assessment protocol

Any assessment task (excluding take-home final exams, frequent assessment tasks that must be submitted on a

regular basis prior to or during a synchronous class such as weekly homework preparation, pre-class or in-class

quizzes or single assessments that are performed and assessed during class time such as presentations) submitted after the due date and time, will be either:

· penalised by way of loss of marks where 10 per cent (10%) of the marks for the assessment task will be deducted

per day for assessment tasks submitted after the due date. A day is defined as a 24-hour period or part thereof following the published due date and time of the assignment, and

. will be rejected and not marked if the assessment is submitted more than five (5) calendar days for subjects offered in the Main Calendar (Autumn, Spring and Summer) and seven (7) calendar days for subjects offered in the UTS Online (Sessions 1 to 6), after the stated submission date, unless a formal short-term extension has been granted   by the Subject Coordinator, or

. rejected without marking (where the subject outline states that this will be the consequence of an assessment task being submitted after the due time on the due date without an approved extension)

The maximum penalty that can be applied is 50% of the assessment marks. Students cannot receive a negative score for the assessment task after a penalty is levied.

A penalty for late work will not apply in cases of approved extensions by the Subject Coordinator. Approved

extensions cannot be made without a request for extension sent directly to the coordinator (or nominated approving

tutor as designated by the subject coordinator) for short-term extensions (within the timeframe set out above) or via an application for special consideration (within the UTS time frames for submission). Any direct requests must be

received by the Subject Coordinator at least 24 hours prior to the due date and time.

Students enrolled in UTS Online courses must complete the Request for Short Extension without Academic Penalty form to apply for a short-term extension.

A penalty may not apply after due consideration of any submission (request for extension or application for special  consideration) by the Academic Liaison Officer (ALO), on behalf of students registered with Accessibility Services .