This module integrates economics with financial statement analysis to enable a fundamental analysis of a company, which can then be applied to equity or fixed income security valuation in other modules. It covers microeconomic and macroeconomic principles, financial reporting procedures and disclosure rules, with emphasis on basic financial statements and how alternative accounting methods affect those statements and their analysis. The module adopts a top-down analytical framework — spanning macroeconomic analysis, industry analysis, corporate strategy analysis, and financial statement analysis — to provide a structured and integrated approach to investment research and corporate valuation. This module is compulsory for MSc Investment Management and optional for MSc Business Analytics. It provides an integrated framework for conducting thorough company-level financial analysis by incorporating a range of economic factors and principles, developing candidates' capabilities in qualitative and quantitative analysis. A distinctive feature of the module is its emphasis on AI-oriented data analytical skills, introducing students to the concept and practical construction of AI agents as analytical tools to support industry analysis, and how artificial intelligence can augment the research process in real-world financial practice. The module also develops key soft and transferable skills, including critical thinking, structured analytical communication, and the ability to present investment research in the format of a professional sell-side analyst report.
Students completing the module successfully should be able to: A Demonstrate a systematic understanding of knowledge and a critical awareness of current issues in the area of professional practice related to financial analysis, including how AI-assisted tools and data analytics are increasingly shaping investment research practice. B Apply the top-down analytical framework and AI agent tools to conduct industry analysis and company valuation, demonstrating both technical data analytical competencies and transferable research communication skills. C Evaluate methodologies, develop critiques, and propose new practices, including critically assessing the capabilities and limitations of AI agents, and the appropriateness of the DCF model and other valuation methodologies in different investment contexts. D Communicate financial analysis effectively in the style of a professional sell-side research report, synthesising macroeconomic, industry, and company-level information into coherent investment conclusions.
This module will be taught using a combination of lectures, tutorials and practicals in the Thomson Reuters financial lab. Learning will also be reinforced by readings from the textbook and industry reports and analysis. Extensive use will be made of case studies and highlight the various ethical and professional standards in the industry related to this market activity.