Introduction to Modern Portfolio Optimization with NuOPT, S-PLUS and S+Bayes

Introduction to Modern Portfolio Optimization with NuOPT, S-PLUS and S+Bayes

By Bernd Scherer, R. Douglas Martin

* Publisher: Springer

* Pages: 410
* Publication Date: 2007-09-05
* ISBN-10: 0387210164
* ISBN-13: 9780387210162
* Binding: QQ: 7450911

In recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management, and this trend will only accelerate in the coming years. Unfortunately there is a large gap between the limited treatment of portfolio construction methods that are presented in most university courses with relatively little hands-on experience and limited computing tools, and the rich and varied aspects of portfolio construction that are used in practice in the finance industry. Current practice demands the use of modern methods of portfolio construction that go well beyond the classical Markowitz mean-variance optimality theory and require the use of powerful scalable numerical optimization methods. This book fills the gap between current university instruction and current industry practice by providing a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods. The computational aspect of the book is based on extensive use of S-Plus?, the S+NuOPT optimization module, the S-Plus Robust Library and the S+Bayes Library, along with about 100 S-Plus scripts and some CRSP? sample data sets of stock returns. A special time-limited version of the S-Plus software is available to purchasers of this book.

For money managers and investment professionals in the field, optimization is truly a can of worms rather left un-opened, until now! Here lies a thorough explanation of almost all possibilities one can think of for portfolio optimization, complete with error estimation techniques and explanation of when non-normality plays a part. A highly recommended and practical handbook for the consummate professional and student alike!

Steven P. Greiner, Ph.D., Chief Large Cap Quant & Fundamental Research Manager, Harris Investment Management

The authors take a huge step in the long struggle to establish applied post-modern portfolio theory. The optimization and statistical techniques generalize the normal linear model to include robustness, non-normality, and semi-conjugate Bayesian analysis via MCMC. The techniques are very clearly demonstrated by the extensive use and tight integration of S-Plus software. Their book should be an enormous help to students and practitioners trying to move beyond traditional modern portfolio theory.

Peter Knez, CIO, Global Head of Fixed Income, Barclays Global Investors

With regard to static portfolio optimization, the book gives a good survey on the development from the basic Markowitz approach to state of the art models and is in particular valuable for direct use in practice or for lectures combined with practical exercises.

Short Book Reviews of the International Statistical Institute, December 2005
Summary: If your copy did not include the web registration code..

Rating: 5
Some copies (especially used copies) of this book don't include the web registration key sticker. If you need it, you can contact Insightful Technical Support (keys at insightful dot com) to get a registration key and password.

Summary: Customer Service

Rating: 5
I have got a very good and prompt service and response from Amazon for the book ordered.

Summary: Excellent academic treatise a little less useful for practitioners.

Rating: 4
I will admit to being torn between four and five stars for this book. I ultimately deduct a star because of: the lack of any sign of the promised web registration key for downloading the 150 day trial software and data, the heavy use of NuOPT where vanilla S/R code would have been sufficient and possibly even easier to understand, and the frequent use by the authors of providing symbolic solutions from Scherer's 2000 book on optimization where implementation is "left as an excercise".

The book dispenses with traditional Markowitz mean-variance optimization in the first chapter, and then moves on to many other methods of optimization for different types of portfolios, asset classes, and investor utility functions. All of this is excellent, comprising the broadest treatment in a single title that I am aware of.

The book makes heavy use of NuOPT, an add-on package for S-Plus from Insightful, and the SIMPLE linear programming included with NuOPT. I was disappointed that the authors make no effort to work problems without NuOPT, even when simplex or other methods would solve the problems presented in more elegant manner.

I was most disappointed that the authors often leave implementation to the reader. Every chapter has "Exercises" at the end. This is fine. I don't think it is fine to discuss the symbolic solution of a problem (like several of the scenario optimization methods discussed in Chapter 5), and then leave as an excercise the implementation of those portfolio solutions in S-PLUS, SIMPLE, or NuOPT. Nearly every chapter has a significant section, usually lifted largely from Scherer's 2000 book, that suffers from this deficiency. It is almost as if the publishers were pushing for a draft, and the authors went through and "left as exercises" whatever they didn't have tested code for.

All my negatives left to the side, this is still the best treatment you'll find in a single title on many issues of portfolio optimization under varying conditions today. Buy this book if you work in portfolio optimization with S-Plus or R.

Summary: great reference

Rating: 5
The best book on this subject. It provides both an excellent up-to-date overview of the relevant literature and an application-oriented perspective. The chapter on robust estimation is outstanding.

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