Module overview
This module provides an introduction to the modern finance theory and its applications to equity investing. It will start by giving a short preamble of the asset classes and stock markets, security analysis, and equity evaluation models. The course will then focus on stock portfolio optimisation, stock portfolio management including passive strategies, active strategies, and managing a portfolio of managers, and performance evaluation of stock portfolios. The module will also briefly consider the equilibrium asset pricing models such as the CAPM, APT and multi-factor models. The module will cover the stock market efficiency and briefly consider the assumptions of investor rationality and some relevant aspects of behavioural finance. Finally, the module will consider some advanced topics in stock market analysis such as the use of machine learning and big data analytics in equity investing.
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- analyse financial market data and evaluate recent development and future prospects for different asset classes;
- identify and apply the main tools of portfolio performance management;
- articulate the main theories for stock market valuation;
- describe and critically evaluate alternative equity investment strategies.
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- how to understand important investment data.
- critically analyse the impact of current financial market developments on asset allocation issues;
- discuss the valuation of equities and their organisation into portfolios;
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- demonstrate skills in written and oral communication regarding the analysis of issues relating to contemporary asset allocation, through the examination, group discussions, and assignments.
Syllabus
The material introduced in this module will cover the following topics:
- Topic 1: Introduction to Stock Markets
- Topic 2: Stock Valuation Theories
- Topic 3: Stock Portfolio Optimisation
- Topic 4: Stock Portfolio Management
- Topic 5: Performance Evaluation of Stock Portfolios
- Topic 6: Stock Market Efficiency and Some Other Advanced Topics
Learning and Teaching
Teaching and learning methods
This module will involve 24 contact hours over a six-week period. Lectures account for 14 hours of contact time and are used to introduce individual topics.
Teaching methods include:
- Lectures
- PowerPoint
- Presentations
- Mini class exercises
- Role-playing games
- Discussion
- Case studies
- Class discussion of relevant academic papers
- Videos
Learning activities include:
- Case study
- Role-playing games
- Problem solving activities
- Class discussion
- Independent study
| Type | Hours |
|---|---|
| Independent Study | 126 |
| Teaching | 24 |
| Total study time | 150 |
Resources & Reading list
Journal Articles
Cao (2019). AI Pioneers in Investment Management, CFA Institute.. CFA Institute Publications.
Fama, E.F. (1995). Random walks in stock market prices.. Financial analysts journal,, 51(1), pp.75-80..
Rasekhschaffe & Jones (2019). Machine Learning for Stock Selection. Financial Analysts Journal,, 75:3, 70-88,.
Fama, E.F. and French, K.R. (1996). Multifactor explanations of asset pricing anomalies.. The journal of finance, 51(1), pp.55-84..
Textbooks
Bodie, Z., Kane, A., Marcus, A. J. (2018). Investments. McGraw Hill.
Maginn, Tuttle, McLeavey, and Pinto (2007). Managing Investment Portfolios: A Dynamic Process. Wiley.
Assessment
Summative
Summative assessment description
| Method | Percentage contribution |
|---|---|
| Individual Coursework | 100% |
Referral
Referral assessment description
| Method | Percentage contribution |
|---|---|
| Individual Coursework | 100% |
Repeat
Repeat assessment description
| Method | Percentage contribution |
|---|---|
| Individual Coursework | 100% |
Repeat Information
Repeat type: Internal & External