Portfolio Optimization

Michael J. Best is a professor in the Department of Combinatorics and Optimization at the University of Waterloo in Ontario, Canada. He received his Ph.D. from the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. Dr. Best has authored over 37 papers on finance and nonlinear programming and co-authored a textbook on linear programming. He also has been a consultant to Bank of America, Ibbotson Associates, Montgomery Assets Management, Deutsche Bank, Toronto Dominion Bank, and Black Rock-Merrill Lynch.

Michael Best’s book is the ideal combination of optimization and portfolio theory. Mike has provided a wealth of practical examples in MATLAB to give students hands-on portfolio optimization experience. The included stand-alone MATLAB code even provides its own quadratic solver, so that students do not need to rely on any external packages.—David Starer, Stevens Institute of TechnologyOverall, this is a nice book that would be ideal as a textbook for one-semester portfolio optimization courses. It can also be good as a supplementary text for courses in operations research and/or financial engineering. The book is self-contained enough to be used as study material for those who want to teach themselves portfolio optimization and related computer programming, be they advanced undergraduate students, graduate students, or financial practitioners.—Youngna Choi, Mathematical Reviews, Issue 2012a… an excellent companion text for the course ‘Discrete-Time Models in Finance’ that I have been teaching in the past years. … I think adding your text can make the course more lively. This is what I plan to do in the coming (fall) semester.—Edward P. Kao, University of Houston, Texas, USA

• Optimization. The Efficient Frontier. The Capital Asset Pricing Model. Sharpe Ratios and Implied Risk-Free Returns. Quadratic Programming Geometry. A QP Solution Algorithm. Portfolio Optimization with Linear Inequality Constraints. Determination of the Entire Efficient Frontier. Sharpe Ratios under Constraints and Kinks. Appendix. References.