This advanced undergraduate/graduate textbook teaches students in finance and economics how to use R to analyse financial data and implement financial models. It demonstrates how to take publically available data and manipulate, implement models and generate outputs typical for particular analyses. A wide spectrum of timely and practical issues in financial modelling are covered including return and risk measurement, portfolio management, option pricing and fixed income analysis. This new edition updates and expands upon the existing material providing updated examples and new chapters on equities, simulation and trading strategies, including machine learnings techniques. Select data sets are available online.

Valuation is part art and part science. While there are wrong ways to value a stock, there may be no single correct way to value a stock. Applied Valuation: A Pragmatic Approach helps to bridge theory and how valuations can be implemented in practice. It offers pragmatic solutions that are in line with valuation principles, and explains the implications of certain approaches and rules of thumb that are commonly used in practice, so the reader understands why or when such methods make sense. Valuation is a highly case-specific exercise and slight changes in the conditions at the time of the valuation could change the approach and inputs that an analyst should be using. This book discusses how to develop the intuition and skills that would allow you to determine the appropriate or reasonable approach to take regardless of what situation may arise in the future. Also including in-depth case studies of Walmart and Tesla, this book examines concepts like projections, discount rates, terminal value, and relative valuation to equip students, practitioners, and the general reader with a better understanding of the methods that will help them build their own framework to value businesses and analyze valuation issues.

This advanced undergraduate/graduate textbook teaches students in finance and economics how to use R to analyse financial data and implement financial models. It demonstrates how to take publically available data and manipulate, implement models and generate outputs typical for particular analyses. A wide spectrum of timely and practical issues in financial modelling are covered including return and risk measurement, portfolio management, option pricing and fixed income analysis. This new edition updates and expands upon the existing material providing updated examples and new chapters on equities, simulation and trading strategies, including machine learnings techniques. Select data sets are available online.