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Portfolio and Investment Analysis with SAS : Financial Modeling Techniques for Optimization

Author: John B Guerard; Ziwei Wang; Ganlin Xu
Publisher: Cary, NC : SAS Institute, 2019.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Choose statistically significant stock selection models using SAS® Portfolio and Investment Analysis with SAS®: 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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Guerard, John B.
Portfolio and Investment Analysis with SAS : Financial Modeling Techniques for Optimization.
Cary, NC : SAS Institute, ©2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: John B Guerard; Ziwei Wang; Ganlin Xu
ISBN: 9781635266917 1635266912 9781635266894 1635266890
OCLC Number: 1096245574
Description: 1 online resource (230 pages)
Contents: Intro; Contents; About This Book; What Does This Book Cover?; Is This Book for You?; What Should You Know about the Examples?; We Want to Hear from You; Chapter 1: Why Do We Invest?; 1.1 Introduction; 1.2 Assumptions; 1.3 Annualized Return; 1.4 Average Return; 1.5 Expected Return; 1.6 Efficient Portfolio; 1.7 Minimum Variance Portfolio; 1.8 Market Portfolio; 1.9 Portfolio Optimization; 1.10 Summary and Conclusions; Chapter 2: An Introduction to Financial Statement Analysis; 2.1 Introduction; 2.2 Types of Businesses; 2.3 The Income Statement; 2.4 The Balance Sheet 2.5 Why Issue Debt? Calculating the Return on Equity2.6 Annual Cash Flow Statement; 2.7 Ratio Analysis and Working Capital; 2.8 General Analysis Ratios; 2.9 Corporate Exports; 2.10 Summary and Conclusions; Chapter 3: The Risk and Return of Equity and the Capital Asset Pricing Model; 3.1 Introduction; 3.2 Calculating Holding Period Returns; 3.3 Markowitz on Portfolio Risk; 3.4 An Introduction to Modern Portfolio Theory; 3.5 Estimating Stock Betas; 3.6 Multi-Beta Risk Models; 3.7 Summary and Conclusions; 3.8 Appendix: Robust Regression and SAS Implementation Chapter 4: Robust Regression and Stock Selection in Global Equity Markets4.1 Introduction and Efficient Markets; 4.2 Fundamental Variables for Stock Selection Modeling; 4.3 Fundamental Variables and Regression-Based Expected Returns Modeling; 4.4 Why Apply Robust Regression?; 4.5 SAS Robust Regression Estimations; 4.6 SAS PROC ROBUSTREG with M, S, and MM Estimations; 4.7 SAS Robust Regression with the Optimal Influence Function; 4.8 Summary and Conclusions; Chapter 5: The Theory of Risk, Return, and Performance Measurement; 5.1 Introduction 5.2 Risk and Return and Markowitz Optimization Analysis5.3 Capital Market Equilibrium; 5.4 The Barra Model: A Fundamental Risk Model; 5.5 APT and Statistical Risk Models: Constructing Mean-Variance Efficient Portfolios; 5.6 The Axioma Risk Model: Fundamental and Statistical Risk Models; 5.7 The Axioma Alpha Alignment Factor and Custom Risk Models; 5.8 Assessing Mutual Funds: The Treynor Index; 5.9 What Have You Done for Me Lately?; 5.10 Summary and Conclusions; Chapter 6: Data Mining Corrections; 6.1 Introduction to Data Mining; 6.2 Single Performance Measurement and Testing 6.3 Multiple Hypothesis Testing and False Discovery Rate6.4 Multiple Mean Comparison Test with ANOVA; 6.5 Regression to the Mean; 6.6 Empirical Bayes Estimation and Hypothesis Testing; 6.7 Summary and Conclusions; Chapter 7: Summary and Conclusions; References
Responsibility: John B. Guerard, Ganlin Xu, Ziwei Wang.

Abstract:

Choose statistically significant stock selection models using SAS® Portfolio and Investment Analysis with SAS®: 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.

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