Stock Market Analysis

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

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.

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;
describe and critically evaluate alternative equity investment strategies.
articulate the main theories for stock market valuation;
identify and apply the main tools of portfolio performance management;

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

discuss the valuation of equities and their organisation into portfolios;
how to understand important investment data.
critically analyse the impact of current financial market developments on asset allocation issues;

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

Study time
Type	Hours
Independent Study	126
Teaching	24
Total study time	150

Resources & Reading list

Journal Articles

Rasekhschaffe & Jones (2019). Machine Learning for Stock Selection. Financial Analysts Journal,, 75:3, 70-88,.

Cao (2019). AI Pioneers in Investment Management, CFA Institute.. CFA Institute Publications.

Fama, E.F. and French, K.R. (1996). Multifactor explanations of asset pricing anomalies.. The journal of finance, 51(1), pp.55-84..

Fama, E.F. (1995). Random walks in stock market prices.. Financial analysts journal,, 51(1), pp.75-80..

Textbooks

Maginn, Tuttle, McLeavey, and Pinto (2007). Managing Investment Portfolios: A Dynamic Process. Wiley.

Bodie, Z., Kane, A., Marcus, A. J. (2018). Investments. McGraw Hill.

Assessment

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Individual Coursework	100%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Individual Coursework	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Individual Coursework	100%

Repeat Information

Repeat type: Internal & External