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FINC3017: Investments and Portfolio Management

Overview

This unit is designed to provide a comprehensive analytical approach to the modern theory of investments. Topics covered include: mean-variance analysis; Markowitz type portfolio analysis; portfolio construction; asset pricing theories; market efficiency and anomalies; hedge funds and investment fund performance evaluation. Although analytical aspects of investments theory are stressed, there is also an equal amount of coverage on the practical aspects of portfolio management. Current research on investments is emphasised in the course.

· Assignments:  Assignments are to be completed individually and will require you to prepare a report that contains responses to a combination of written and numerical problems drawn from real-world application of the topics studied in class. You will be assessed on your technical application to quantitative questions as well as your critical discussion of key issues.

· Final online exam: The final exam will cover the topics studied throughout the semester. There will be a mix of quantitative and conceptual questions.

· Further details on all assessments will be provided on Canvas

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

· Deduction of 5% of the maximum mark for each calendar day after the due date.

· After ten calendar days late, a mark of zero will be awarded.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

Weekly schedule

WK

Topic

Learning activity

Learning outcomes

Week 01

Introduction & Math Preliminaries

Lecture (2 hr)

LO1 LO2 LO3

Week 02

Financial Assets & Decisions under Uncertainty

Lecture and tutorial (3 hr)

LO1 LO2 LO3 LO5

Week 03

Markowitz Portfolio Theory

Lecture and tutorial (3 hr)

LO1 LO2 LO3 LO5

Week 04

CAPM

Lecture and tutorial (3 hr)

LO1 LO3 LO5

Week 05

Factor Models

Lecture and tutorial (3 hr)

LO1 LO2 LO3 LO5

Week 06

Arbitrage Pricing Theory

Lecture and tutorial (3 hr)

LO1 LO2 LO3 LO5

Week 07

Screening & Factor Replication

Lecture and tutorial (3 hr)

LO1 LO2 LO3 LO4LO5

Week 08

Anomalies & Smart Beta

Lecture and tutorial (3 hr)

LO1 LO2 LO3 LO4LO5

Week 09

Performance evaluation

Lecture and tutorial (3 hr)

LO1 LO2 LO3 LO4LO5

Week 10

Frictions, Rebalancing and Risk Management

Lecture and tutorial (3 hr)

LO1 LO2 LO3 LO4LO5

Week 11

Asset Pricing

Lecture and tutorial (3 hr)

LO1 LO2 LO3 LO4LO5

Week 12

Trading Volatility

Lecture and tutorial (3 hr)

LO1 LO2 LO3 LO4LO5

Week 13

Review

Lecture and tutorial (3 hr)

LO1 LO2 LO4 LO5

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

See Canvas for weekly required reading list.

Prescribed textbook: Bodie, Z., Kane, A. and Marcus, A.J. (2018), Investments, 11th edition,McGraw Hill, ISBN 9781259277177 (denoted BKM on the reading list).

Chapters from the textbook will be supplemented by journal articles and other online materials. Journal articles can be accessed through the Library.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

· Outcomes

· Graduate qualities

At the completion of this unit, you should be able to:

· LO1. apply the fundamentals of investment theory to construct portfolios and evaluate their performance

· LO2. interpret current academic research and identify how it guides investment decision making and portfolio construction in practice

· LO3. use Microsoft Excel to solve and analyse investment problems

· LO4. communicate clearly and succinctly in writing

· LO5. critique asset pricing models and portfolio management strategies

Closing the loop

The weekly topics have been slightly updated to reflect recent developments in the field.

· Key dates

Key dates through the academic year, including teaching periods, census, payment deadlines and exams.

· 

· Student administration

Enrolment, course planning, fees, graduation, support services, student IT

· 

· Expectations of student conduct

Code of Conduct for Students, Conditions of Enrollment, University Privacy Statement, Academic Integrity

· 

· Academic appeals

Academic appeals process, special consideration, rules and guidelines, advice and support

· 

· Learning and teaching policy

Policy register, policy search

· 

· Financial support

Scholarships, interest free loans, bursaries, money management

· 

· Study resources

Learning Centre, faculty and school programs, Library, online resources

· 

· Health and support

Student Centre, counselling & psychological services, University Health Service, general health and wellbeing

· 

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

This unit of study outline was last modified on 05 Jul 2022.

To help you understand common terms that we use at the University, we offer an online glossary.