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About the Author
Harry M. Markowitz is a Nobel Laureate and the father of Modern Portfolio Theory. He divides his time between consulting and teaching as an adjunct professor at the Rady School of Management at the University of California at San Diego.
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Product details
Hardcover: 400 pages
Publisher: McGraw-Hill Education; 1 edition (May 23, 2016)
Language: English
ISBN-10: 007183009X
ISBN-13: 978-0071830096
Product Dimensions:
6.6 x 1.4 x 9.3 inches
Shipping Weight: 1.4 pounds (View shipping rates and policies)
Average Customer Review:
5.0 out of 5 stars
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Amazon Best Sellers Rank:
#555,882 in Books (See Top 100 in Books)
This is the second volume of Harry's planned 4-volume set, “Risk-Return Analysisâ€. This book is essentially an in-depth exposition of Chapter 11, “Utility Analysis Over Timeâ€, in Markowitz' 1959 book, "Portfolio Selection: Efficient Diversification of Investments". The previous volume (Volume 1) covered Chapter 10, “The Expected Utility Maximâ€, and the remaining two volumes will likewise refresh chapters 12 “Probability Beliefsâ€, and 13 “Applications to Portfolio Selection†of Markowitz (1959). These four chapters of Markowitz (1959), and the present four-volume set explain the fundamental assumptions for the use of mean-variance analysis (MVA).Essentially, this book (Volume 2) explores MVA beyond the single-period choice framework that was covered in Volume 1, into a multi-period setting, still assuming known odds (a given return distribution). Volume 3 will move both single-period and multi-period analyses into a setting with unknown odds -- investor choice under uncertainty. Volume 4 will wrap up the series with important practical and theoretical considerations not addressed in the first three volumes.Volume 2 of Risk-Return Analysis includes the following 7 chapters:Chapter 6) "The Portfolio Selection Context"; this chapter introduces the current volume as an evolution from Volume 1, which focused on single period choice with known odds, to a focus on multi-period choice still with known odds. Rational Decision Makers vs. Human Decision Makers, first introduced in chapter 1 of Volume 1, are briefly reviewed; the joint distribution of current period return and future period opportunities is introduced as a game (expounded upon in more detail in Chapter 11); The traditional concept of a single utility-maximizing “Investor†is expanded into a “Stakeholder†in order to accommodate multi-person households and institutional investors aggregating the interests of many investors; the critical concept of diversification is briefly reviewed; and a preview of chapters 7-12 is provided.Chapter 7) "Modeling Dynamic Systems"; this chapter introduces simulation in financial analysis, covering Decision Support Systems (DSS) and Optimization Simulators. Examples covered include the GuidedChoice DSS; the Kim and Markowitz (1989) Simulator; the Markowitz and van Dijk (2003) heuristic algorithm; the Blay and Markowitz “Tax Cognizant Portfolio Analysis†simulator (TCPA) developed for the book’s commercial sponsor, 1st Global; the Jacobs Levy and Markowitz (2004) JLMSim re-optimizing simulator; and the Markowitz (1991) Game of Life simulator. The chapter reviews the EAS-E DSS approach used in Markowitz’ preferred programming language, SIMSCRIPT. A history and detailed description of EAS-E and SIMSCRIPT is followed by a description of GuidedChoice’s commercial implementation of a DSS including Markowitz’ 1991 Game of Life simulator. This led to commercial products that are similar to Bill Sharpe’s Financial Engines, providing savings and consumption advice to investors participating in 401(k) plans. The chapter concludes with further detail and discussion of the history and attributes of SIMSCRIPT.Chapter 8) "Game Theory and Dynamic Programming", introduces optimization. Beginning with von Neumann and Morgenstern (1944) game theory, then introducing Bellman’s Dynamic Programming with an example of a tic-tac-toe solver, then generalizes Dynamic Programming as an application of conditional expected values with a coin-flipping, card-playing, dice shooting, beer drinking example. Two types of games are described, those in which players can calculate conditional probability distributions of current possible states of the world without guessing other players’ strategies, and those in which they cannot. (Got that?) The latter type of game is beyond the scope of the book, as it requires Nash equilibrium approaches to solve. Markowitz’ mind is beautiful, but thankfully he spares readers incredible difficulty by focusing exclusively on the former type of game. The chapter concludes with an introduction to the Curse of Dimensionality, in which the number of state variables in a state space is typically too high for optimizers to solve without the use of heuristics.Chapter 9) "The Mossin-Samuelson Model", covers a dynamic investment model in which utility is a function of final wealth. It results in the counterintuitive recommendation that investors should not become more cautious as they approach retirement. This is discussed viz. “glide path†and “target-date†investment strategies. A discussion of Markowitz’ views versus Samuelson’s views on maximizing utility over the long run presents the pros and cons of repeatedly rebalancing to a M-V efficient portfolio that approximates the maximization of expected log wealth.Chapter 10) "Portfolio Selection as a Social Choice", delves into normative aspects of portfolio choice -- rather than attempting to describe how decisions are actually made (human decision makers), this chapter focuses on how decisions should be made (rational decision makers). It presents value judgements. Arithmetic mean versus geometric mean utility is reviewed, and Markowitz then corrects an error in the Nash solution to the two-person bargaining problem.Chapter 11) "Judgement and Approximation", covers maximization of expected utility for dynamic practical games, along the lines of chapter 13 of Markowitz (1959), which uses implicit maximization of expected utility. Next, Markowitz turns to explicit maximization of expected utility, relying on, and explaining, the heuristic approaches of Markowitz and van Dijk (2003) and Kritzman, Myrgren and Page (2009) optimization techniques. Regarding the superiority of the two heuristic approaches, Markowitz concludes “time will tellâ€. Finally, chapter 11 covers taxable accounts, utilizing the Blay-Markowitz Net Present Value methodology to motivate the “Tax Cognizant Portfolio Analysis†(TCPA). This process transforms pre-tax return sequences into vectors of present values and estimates the inputs required by the critical line algorithm from the present values. Because this procedure is overly sensitive to outliers in the data, the vector of means, and the matrix of variances and covariances are inferred from a sample-parameterized normal distribution rather than from the sample present values directly. Efficient frontiers and resampled frontiers are discussed, and the chapter addresses Merrill Lynch Wealth Management’s asset allocation approach. Bucketing generally is discussed, including as a viable way to approach partially convex utility functions. Markowitz suggests such cases, not well served by mean-variance analysis, may be accommodated with mixed-integer linear programming.Chapter 12) "The Future", proposes that even more robust financial simulators and decision support systems will be created as computers evolve into massively parallel architectures. Essentially this chapter is an in-the weeds continuation of chapter 7. It makes a detailed case for the continued development of SIMSCRIPT as a programming language well suited to future development of simulators and DSS. While SAP is acknowledged to be the most commonly-used current enterprise DSS generator, Markowitz lays out his vision for possible future development of the SIMSCRIPT language, with a detailed discussion of how SIMSCRIPT II could evolve into a future SIMSCRIPT M (“Modernizedâ€). This chapter also reviews Moore’s law, and points out that current computer architecture was invented by John von Neumann -- the same genius who was a principal developer of game theory described earlier in the book. Markowitz concludes this volume with comments on the analogy of computers as brains, learning, emulation, processes, and parallelized processes. I especially appreciated the Brahms "would have driven me mad" quotation about J.S. Bach's "Chaconne", a complicated (up to 4 parts) piece for solo unaccompanied violin, and looked up the video of Perlman playing this beautiful piece on YouTube. I agree with Harry, it is extraordinary -- a musical metaphor for Markowitz' own Portfolio Selection, in my humble view.Volume 2 of Risk-Return Analysis is a bigger tome than Volume 1. To me it was more difficult to work through, and I ascribe this to the fact that I am not a hardcore programmer. I personally use VBA, Python and R and could follow the discussion just fine, but I felt like chapters 7 and 12 in particular were overly specific to a language, SIMSCRIPT, that while clearly dear to Markowitz’ heart, is essentially a proprietary RAND Corporation project which has since been commercially developed by defense contractor CACI, again, in a proprietary mode, while IBM left its version to wither. I am grateful that Markowitz, who co-developed the SIMSCRIPT language in the early 1960s, has provided his detailed history and vision of the language. However I feel like the free, open source languages such as Python [1991 origin with 2016 current stable release], R [1993 origin with 2016 current stable release] , and (at least at Jane Street) OCaml [1996 origin with 2016 current stable release], all of which have broad-based, self-organized, thriving volunteer development initiatives, are quants' preferred languages of choice today. That said, I did learn a lot from the SIMSCRIPT chapters.The classic Markowitz (1959), "Portfolio Selection", runs 384 pages in total. Its Chapter 10, “The Expected Utility Maximâ€, is 37 pages long; the exposition and extension of Chapter 10 as Volume 1 of "Risk-Return Analysis" is 230 pages. Chapter 11 of Markowitz (1959), “Utility Analysis Over Timeâ€, is 13 pages long; the exposition and extension of Chapter 11 as Volume 2 of Risk Return Analysis is 376 pages (nearly 100 of which are devoted to SIMSCRIPT). Markowitz has put in an incredible effort to bring us up-to-date on these topics. Personally I think it would be great if the open-source developer community reignited the SIMSCRIPT cause.I am eagerly anticipating Volume 3 of Risk-Return Analysis. You might, too.
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