This module aims to equip students with both theoretical and practical knowledge of computational methods. Upon successful completion of this module, the students will be able to use Python to solve a number of computational challenges pertaining to the financial industry.

(LO1) Students will be able to appreciate the importance of computational methods.

(LO2) Students will be able to compare and contrast different computational methods.

(LO3) Students will be able to implement the relevant computational methods to solve a financial problem.

(LO4) Students will be able to discuss the limitations of computational methods

(S1) Resiliency and adaptability

(S2) Communication

(S3) IT Literacy

(S4) Lifelong Learning

(S5) Numeracy

(S6) Problem Solving

(S7) Organisation

Teaching Method - Lectures
Description: Lecture (12 Lectures of 2 hours each)
Scheduled Directed Student Hours: 24 hours
Attendance Recorded: Yes
Students will attend the weekly 2 hours lecture during which the key concepts will be introduced.

Teaching Method – Seminar (6 seminars of 2 hour each)
Description: Face to face sessions
Scheduled Directed Student Hours: 12 hours
Attendance Recorded: Yes
The seminar will take place over 6 weeks. During these sessions, students will use python to apply their knowledge.

Self-Directed Learning Hours: 114 hours
Description: These independent learning hours are aimed at supporting the directed student learning. The module leader will provide guidance in the form of suggested readings or topics to complete with the expectation that students are well prepared to contribute to the tutorial activities and to understand the content of lectures.

This module is a pre-requisite for the follow ing modules:
ACFI233 Econometrics for Finance II
ACFI234 Theory of Finance II
ACFI232 Database Management
ACFI235 Financial Data Visualisation

Skills/Other Attributes Mapping

Skills / attributes: Resiliency and adaptability skills
How this is developed: In the lectures and seminars, by using their knowledge of programming and computational methods to deal with a range of data-related situations, e.g. missing data, simulation analysis.
Mode of assessment (if applicable): Individual project

Skills / attributes: Communication
How this is developed: By contributing to in-class discussions (lectures and seminars), and by preparing the individual project.
Mode of assessment (if applicable): Individual project

Skills / attributes: IT Literacy
How this is developed: By using the computers during the lectures and seminars to implement the relevant computational methods. The students will also develop their skills by using digital too ls and specialist software to engage with the course material, collaborate and communicate with others, e.g. Jupyter notebook.
Mode of assessment (if applicable): Mid-term and individual project

Skills / attributes: Lifelong Learning
How this is developed: In lectures and seminars by critically thinking about the way to approach datasets and handle data issues such as missing observations and interpolation.
Mode of assessment (if applicable): Mid-term and individual project

Skills / attributes: Numeracy
How this is developed: During the lectures & seminars, the students will be analysing new problems and writing computer codes to analyse datasets.
Mode of assessment (if applicable): Mid-term and individual project

Skills / attributes: Problem solving
How this is developed: During the lectures & seminars, the students will gather and synthesise information, compare different computational methods to solve problems, and use their knowle dge of programming to effectively solve the problems.
Mode of assessment (if applicable): Mid-term and individual project

Skills / attributes: Organization
How this is developed: In lectures & seminars, the students will learn to manage their time carefully by prioritising and completing tasks within specific deadlines. They will also develop their organization skills by effectively managing their time when working on individual projects.
Mode of assessment (if applicable): Mid-term and individual project

The module outline:
• Series expansions: Calculus and computation
• Interpolation and extrapolation
• Optimisation in finance
• Numerical differentiation and integration
• Simulation methods
• Game theory in financial analysis

These topics will be instrumental to other programme modules and content. For example, interpolation/extrapolation will be useful to compute a term-structure of interest rates, numerical differentiation is useful to compute the sensitivity of a security’s returns to a change in variable, root-finding is useful in the derivatives module to learn how to find the Black-scholes implied volatility, and simulation will help in the financial risk management module to understand how to compute the Value at Risk (VaR).