Name: Portfolio and Investment Analysis with SAS: Financial Modeling Techniques for Optimization (en Inglés)
Price: 89.49 USD
Availability: InStock
Author: Guerard, John B. ; Wang, Ziwei ; Xu, Ganlin
ISBN: 9781635266924

portada Portfolio and Investment Analysis with SAS: Financial Modeling Techniques for Optimization (en Inglés)

Formato

Libro Físico

Autor

Editorial

Idioma

Inglés

N° páginas

230

Encuadernación

Tapa Blanda

Dimensiones

27.9 x 21.0 x 1.2 cm

Peso

0.53 kg.

ISBN13

9781635266924

Categorías

Software empresarial
Software de matemáticas y estadísticas

Portfolio and Investment Analysis with SAS: Financial Modeling Techniques for Optimization (en Inglés)

John B. Guerard (Autor) · Ziwei Wang (Autor) · Ganlin Xu (Autor) · SAS Institute · Tapa Blanda

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Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "Portfolio and Investment Analysis with SAS: Financial Modeling Techniques for Optimization (en Inglés)"

Choose statistically significant stock selection models using SAS(R) Portfolio and Investment Analysis with SAS(R): Financial Modeling Techniques for Optimization is an introduction to using SAS to choose statistically significant stock selection models, create mean-variance efficient portfolios, and aggressively invest to maximize the geometric mean. Based on the pioneering portfolio selection techniques of Harry Markowitz and others, this book shows that maximizing the geometric mean maximizes the utility of final wealth. The authors draw on decades of experience as teachers and practitioners of financial modeling to bridge the gap between theory and application. Using real-world data, the book illustrates the concept of risk-return analysis and explains why intelligent investors prefer stocks over bonds. The authors first explain how to build expected return models based on expected earnings data, valuation ratios, and past stock price performance using PROC ROBUSTREG. They then show how to construct and manage portfolios by combining the expected return and risk models. Finally, readers learn how to perform hypothesis testing using Bayesian methods to add confidence when data mining from large financial databases.