Equity Investing with Artificial Intelligence

Module overview

This module provides an introduction to modern finance theory and its applications to equity investing, incorporating artificial intelligence and data analytics in investment analysis. It begins with an overview of asset classes and equity markets, fundamentals of security analysis, and equity valuation models. Building on this foundation, students examine portfolio optimisation and portfolio management, including passive and active investment strategies, portfolio construction techniques, and rigorous approaches to performance measurement and evaluation. An important aspect of the module is how artificial intelligence, and data analytics are transforming equity investing. Students explore how these techniques can be used for return forecasting, risk modelling, factor discovery, and portfolio construction, while critically assessing their assumptions, limitations, and practical challenges. The module also considers stock-market efficiency, investor rationality, and selected insights from behavioural finance. By integrating established finance theory with modern artificial intelligence approaches, the module equips students with a comprehensive understanding of contemporary equity investment analysis and management.

Aims and Objectives

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

evaluate and compare alternative theories and models for equity valuation;
analyse economic and financial data and evaluate recent developments in equity markets;
apply and assess artificial intelligence models for security selection, equity portfolio optimisation and performance evaluation;
describe and critically evaluate alternative equity investment strategies.

Transferable and Generic Skills

Having successfully completed this module you will be able to:

demonstrate advanced analytical and communication skills through the critical analysis, interpretation, and reporting of investment analysis results.

Knowledge and Understanding

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

critically analyse the impact of current financial market developments on equity investments;
critically discuss the role, scope, and limitations of artificial intelligence models in equity investing.
discuss the valuation of equities and their organisation into portfolios;

Learning and Teaching

Teaching and learning methods

This module will involve 24 contact hours over a six-week period. Lectures account for 24 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
Teaching	24
Independent Study	126
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.

Textbooks

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

Bodie, Kane, and Marcus (2024). Investments. McGraw Hill.

CFA Institute (2025). Handbook of Artificial Intelligence and Big Data Applications in Investments. CFA Institute Research Foundation..

CFA Institute (2025). AI in Asset Management: Tools, Applications, and Frontiers, . CFA Institute Research Foundation.

Dixon, Halperin, and Bilokon (2020). Machine learning in finance. New York, NY, USA:: Springer International Publishing.

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