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.
Guera.
Dr.
He is also a former faculty member at the Rutgers University Graduate School of Management and at Lehigh University.
He is the author of numerous books and articles, including An Introduction to Financial Forecasting in Investment Analysis and Quantitative Corporate Finance.
Harry Markowitz.
Guerard held a number of senior-level positions, including Vice President at Daiwa Securities Trust Co., where he co-managed the Japan Equity Fund with Nobel Prize winner Dr.
Before joining Mc Kinley Capital in 2005, Dr.
Guerard focuses on maintaining and enhancing the firm\'s quantitative capabilities and Investment models.
Dr.
Guerard, Jr., Ph D, is the Director of Quantitative Research at Mc Kinley Capital Management, LLC.
About the Author John B.
Finally, readers learn how to perform hypothesis testing using Bayesian methods to add confidence when data mining from large Financial databases.
They then show how to construct and manage portfolios by combining the expected return and risk models.
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.
Using real-world data, the book illustrates the concept of risk-return Analysis and explains why intelligent investors prefer stocks over bonds.
The authors draw on decades of experience as teachers and practitioners of Financial Modeling to bridge the gap between theory and application.
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.
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
