Description: 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.
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EAN: 9781642951936
UPC: 9781642951936
ISBN: 9781642951936
MPN: N/A
Book Title: Portfolio and Investment Analysis with SAS: Financ
Item Length: 27.9 cm
Publisher: Sas Institute
Item Height: 280 mm
Subject: Computer Science
Publication Year: 2019
Number of Pages: 230 Pages
Publication Name: Portfolio and Investment Analysis with Sas: Financial Modeling Techniques for Optimization
Language: English
Type: Textbook
Item Weight: 826 g
Author: Ganlin Xu, John B Guerard, Ziwei Wang
Item Width: 216 mm
Format: Hardcover