A "hands-on" guide to applied equity analysis and portfolio management From asset allocation to modeling the intrinsic value of a stock, Applied Equity Analysis and Portfolio Management + Online Video Course offers readers a solid foundation in the practice of fundamental analysis using the same tools and techniques as professional investors. Filled with real-world illustrations and hands-on applications, Professor Weigand's learning system takes a rigorous, empirical approach to topics such as analyzing the macro-finance environment, sector rotation, financial analysis and valuation, assessing a company's competitive position, and reporting the performance of a stock portfolio. Unlike typical books on this subject—which feature chapters to read and exercises to complete—this resource allows readers to actively participate in the learning experience by completing writing exercises and manipulating interactive spreadsheets that illustrate the principles being taught. The learning system also features instructional videos that demonstrate how to use the spreadsheet models and excerpts from the author's blog, which are used to depict additional examples of the analysis process. Along the way, it skillfully outlines an effective approach to creating and interpreting outputs typically associated with a top-down money management shop — including a macroeconomic forecasting newsletter, detailed stock research reports, and a portfolio performance attribution analysis. Covers topics including active and passive money management, fundamental analysis and portfolio attribution analysis Companion streaming videos show how to use free online data to create yourown analyses of key economic indicators, individual stocks, and stock portfolios A valuable resource for universities who have applied equity analysis and portfolio management courses Practical and up-to-date, the book is an excellent resource for those with a need for practical investment expertise.

An informative resource focused on applied equity analysis and portfolio management The Applied Equity Analysis and Portfolio Management Video Course contains a forty- to fifty-minute video lecture for each of the seven chapters found in the actual book, as well as Appendix A: The Time Value of Money and the Basics of Valuation. Engaging and accessible, this course provides investors and students alike with a solid grounding in the timeless principles of investing. Outlines a structured approach to analyzing popular economic indicators—from leading to lagging, and more Addresses key finance principles regarding asset valuation and expected returns Offers a framework for analyzing a company's strategic and competitive positioning Skillfully discusses portfolio risk and performance attribution Informative and up to date, Applied Equity Analysis and Portfolio Management Video Course is an excellent resource for those with both a practical and theoretical need for investment expertise.

Trading and Pricing Financial Derivatives is an introduction to the world of futures, options, and swaps. Investors who are interested in deepening their knowledge of derivatives of all kinds will find this book to be an invaluable resource. The book is also useful in a very applied course on derivative trading. The authors delve into the history of options pricing; simple strategies of options trading; binomial tree valuation; Black-Scholes option valuation; option sensitivities; risk management and interest rate swaps in this immensely informative yet easy to comprehend work. Using their vast working experience in the financial markets at international investment banks and hedge funds since the late 1990s and teaching derivatives and investment courses at the Master's level, Patrick Boyle and Jesse McDougall put forth their knowledge and expertise in clearly explained concepts. This book does not presuppose advanced mathematical knowledge, though it is presented for completeness for those that may benefit from it, and is designed for a general audience, suitable for beginners through to those with intermediate knowledge of the subject.

Applied Equity Analysis treats stock valuation as a practical, hands-on tool rather than a vague, theoretical exercise—and covers the entire valuation process from financial statement analysis through the final investment recommendation. Its integrated approach to valuation builds viable connections between a firm’s competitive situation and the ultimate behavior of its common stock. Techniques explained include EVA, newer hybrid valuation techniques, and relative multiple analysis.

"Aswath Damodaran is simply the best valuation teacher around. If you are interested in the theory or practice of valuation, you should have Damodaran on Valuation on your bookshelf. You can bet that I do." -- Michael J. Mauboussin, Chief Investment Strategist, Legg Mason Capital Management and author of More Than You Know: Finding Financial Wisdom in Unconventional Places In order to be a successful CEO, corporate strategist, or analyst, understanding the valuation process is a necessity. The second edition of Damodaran on Valuation stands out as the most reliable book for answering many of today?s critical valuation questions. Completely revised and updated, this edition is the ideal book on valuation for CEOs and corporate strategists. You'll gain an understanding of the vitality of today?s valuation models and develop the acumen needed for the most complex and subtle valuation scenarios you will face.

Aswath Damodaran, distinguished author, Professor of Finance, and David Margolis, Teaching Fellow at the NYU Stern School of Business, has delivered the newest edition of Applied Corporate Finance. This readable text provides the practical advice students and practitioners need rather than a sole concentration on debate theory, assumptions, or models. Like no other text of its kind, Applied Corporate Finance, 4th Edition applies corporate finance to real companies. It now contains six real-world core companies to study and follow. Business decisions are classified for students into three groups: investment, financing, and dividend decisions.

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

This book provides a manual on quantitative financial analysis. Focusing on advanced methods for modelling financial markets in the context of practical financial applications, it will cover data, software and techniques that will enable the reader to implement and interpret quantitative methodologies, specifically for trading and investment. Includes contributions from an international team of academics and quantitative asset managers from Morgan Stanley, Barclays Global Investors, ABN AMRO and Credit Suisse First Boston. Fills the gap for a book on applied quantitative investment & trading models Provides details of how to combine various models to manage and trade a portfolio