FIN42000

Learning Outcomes:

Upon completion of this module, students should be able to:

Describe and apply fundamental principles and concepts in Investment Finance
Use key skills in financial decision making, including knowing how to use 'hands-on' computational optimisation tools
Contrast fundamental asset pricing theories and understand the crucial role of Information Sets
Critically assess financial theories and models of Investment
Appraise the tradeoff between risk and return.
Demonstrate research skills such as the use of computational optimisation tools and the understanding of published research, both practical and theoretical.
Recognise potential pitfalls of applying methods based on this theory too literally in real markets, both at the Agent level and the Regulation level.

Indicative Module Content:

Topic 1: An Introduction to Mean-Variance Portfolio Theory in Investments and
Computational Methods for Constrained Optimisation (in Julia), plus the CAPM
and APT.

Topic 2: Review of the Microeconomic Foundations of Financial Economics
Consumption and Investment without and with Capital Markets

Topic 3: Investment Decisions: The Certainty Case
Fisher Separation Theorem
Shareholder Wealth Maximisation

Topic 4: Making Choices in Risky Situations
Utility Theory
Risk Aversion
Stochastic Dominance
State Preference Theory
Mean Variance Portfolio Theory (Revisited)

Topic 5: Equilibrium Pricing
Capital Asset Pricing Model (Revisited)
Arbitrage Pricing Theory (Revisited)

Topic 6: Applying Market Theory too Literally: Pitfalls
Wealth Inequality (Piketty)
Cooperative and Non-cooperative games (Nash)

Topric 7: (time permitting, TBC)
Fundamentals of Derivatives Pricing

Student Effort Hours:

Approaches to Teaching and Learning:

Delivery will be via a combination of in-class, online, and pre-recorded Lectures (the composition of which may
be adjusted as Public Health requirements dictate), plus in-class and online or pre-recorded practical computing
and problem-solving Tutorials. Additionally students will submit both individual and group-based homework.
With regard to lecture attendance, the course will be double-delivered, with both sessions of lectures and tutorials
covering the same material, and (except where announced, such as in-class quizzes) students may attend either lecture session.


The module will rely heavily on students' learning and applying the methods of Constrained Optimisation,
which may be used to address nearly all practical applications and to motivate nearly all concepts considered in the module.
The Computation Platform to be used in the class will be the JuMP (Mathematical Programming for Julia) optimisation package
within the Julia programming language
It should be emphasised that this is not a class in programming, so students will only be expected to achieve facility for a limited
range of methods such as are required for the class (primarily, constrained optimisation).

Julia (version 1.9.3 or higher) is supported by MIT and is freely downloadable/easily installed for Windows, MAC, and Linux computers.
Use of ('Jupyter') Notebooks within Visual Studio Code (also freely downloadable from Microsoft) will be required following an introduction to the basics of pure Julia. Chatbot guides are of considerable help in getting started and in self-tutoring. In particular, JuliaHub is accessible via email account or free registration, and has an 'Ask AI' Assistant which has been trained specifically on Julia coding, syntax, and concepts.
[Also, if you have particular expertise in another programming language (like Python or MATLAB) and feel that translation from that language would be of help to you, you will find that most major chatbots are familiar with Julia and are quite competent at this.]

Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles

Not applicable to this module.
 

Assessment Strategy

 

Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

After each assessment the lecturer will provide group feedback to students.

Required Text:

Financial Theory and Corporate Policy: Pearson New International Edition, 4/E
Thomas E. Copeland, J. Fred Weston, Kuldeep Shastri, (CWS) (ISBN: 9781292021584)

Required Software

Before the first class, students should download and install the Julia programming language (current stable release v 1.9.3 as of Aug 2023):
https://julialang.org/downloads/

We will begin using Julia as a standalone package, but most of our computer work will be in Notebook format [Visual Studio Code (VSCode) or JupyterLab installation required]

https://code.visualstudio.com/download

No prior programming or language is assumed, but students with experience with Python and Jupyter Notebooks should notice considerable similarity. Students should bring laptops to class.
The basics of Getting Started with Julia will be demonstrated in class, and there is a very helpful dedicated chatbot for Julia (though GPT4, Bard, or Claude are fairly good)

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.