John B. Guerard, Jr.
Director of Quantitative Research
McKinley Capital Management, LLC.
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.
By This Author
Portfolio and Investment Analysis with SAS®: Financial Modeling Techniques for Optimization
Portfolio and Investment Analysis with SAS®: Financial Modeling Techniques for Optimization is designed to show readers how use SAS to choose a statistically significant stock selection model, create Mean-Variance efficient portfolios, and be aggressive in investing to maximize the Geometric Mean. Based on the pioneering portfolio selection techniques of Harry Markowitz and others, this book shows how to maximizing the Geometric Mean maximizes the utility of final wealth to achieve the greatest level of terminal wealth in the shortest time possible.
- Marie Gaudard is a consultant specializing in statistical training with the use of JMP. She is currently a statistical writer with the JMP documentation team
- Satish Garla is a former Analytical Consultant in Risk Practice at SAS.
- Sam Gardner is a Senior Research Scientist at Eli Lilly and Company where he is focusing on business analytics and using statistical modeling.