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 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.
John B. Guerard, Jr., PhD, is the Director of Quantitative Research at McKinley Capital Management, LLC. Dr. Guerard focuses on maintaining and enhancing the firm’s quantitative capabilities and investment models. Before joining McKinley Capital in 2005, Dr. 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. Harry Markowitz. He is the author of numerous books and articles, including An Introduction to Financial Forecasting in Investment Analysis and Quantitative Corporate Finance. He is also a former faculty member at the Rutgers University Graduate School of Management and at Lehigh University. Dr. Guerard earned an AB degree in Economics from Duke University, an MA degree in Economics from the University of Virginia, an MSIM in Finance from the Georgia Institute of Technology, and a PhD in Finance from the University of Texas at Austin.
Ziwei Wang is a senior quantitative research analyst at McKinley Capital Management, LLC. Her research work has focused on developing alpha models, risk factors, quantitative strategies, and portfolio construction methods. She has worked on building stock prediction models using regression models, machine learning models, and NLP techniques. She has also worked extensively on portfolio optimization and construction in historical tests and live implementation. She earned a BS in Economics and Science from the Peking University School of Economics & School of Mathematical Sciences and an MS in Quantitative and Computational Finance from the Georgia Institute of Technology.
Ganlin Xu, PhD, is the Chief Investment Officer at GuidedChoice, Inc. Dr. Xu has more than two decades of investment industry experience and currently serves as a Quantitative Research Consultant at McKinley Capital Management, LLC. He has worked on various theoretical and practical aspects of portfolio management and published papers in the SIAM Journal of Control and Optimizations and Annals of Applied Probability. He has also written numerous articles on domestic and global stock selection and the role of earnings forecasting in stock selection modeling. Dr. Xu was a co-recipient of the RIIA Practitioner Thought Leadership Award in 2012. He earned a BS in Mathematics from the University of Science and Technology of China and a PhD in Mathematics from Carnegie Mellon University.
