Valuation and Investment Analysis

Übersicht

This course develops the core skills required for valuation and investment analysis in corporate finance and capital markets. You will learn how to value businesses, projects, and financial assets using both intrinsic valuation and relative valuation approaches.

The course begins with the time value of money, discounted cash flow (DCF) modeling, and the estimation of free cash flow, WACC, and terminal value. It then examines market-based methods, including EV/EBITDA, P/E ratios, comparables analysis, and precedent transactions.

Using Excel-based financial modeling, you will build DCF models, conduct sensitivity and scenario analysis, and interpret valuation outputs in the context of risk and investor expectations. The course also explores simulation techniques and the integration of AI tools in financial modeling, with attention to governance and model validation.

By the end, you will be able to apply valuation frameworks to assess corporate value, analyze investment risk, and support data-driven financial decision-making.

Lehrplan

• Time Value of Money and Discounting Fundamentals
This module lays the groundwork for all valuation techniques by focusing on the time value of money. Learners will explore present value, future value, annuities, and discounting methods. The goal is to develop comfort with applying basic financial math to solve real-world problems involving cash flows, rates of return, and opportunity cost.
• Valuation Models: DCF and Multiples
This module introduces the most commonly used valuation models: the Discounted Cash Flow (DCF) model and relative valuation using multiples. Students will learn how to construct a DCF model using forecasted free cash flows and terminal value, and how to derive firm value using comparables like EV/EBITDA and P/E ratios. Emphasis is placed on understanding each method’s assumptions and limitations, and when to use each in real-world scenarios.
• Financial Modeling with AI
This module explores how artificial intelligence is transforming financial modeling, from automating data gathering and cleaning to generating forecasts and stress-test scenarios. Learners will gain a practical understanding of AI tools available to finance professionals, learn how to apply them in model building, and understand the governance, validation, and risk considerations necessary for reliable results. By the end of this module, participants will be equipped to integrate AI into their workflows effectively while avoiding common pitfalls.

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Unterrichtet von

Michael McDonald and Starweaver

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Fachgebiete

Business