- Hardcover: 272 pages
- Publisher: McGraw-Hill Education; 1 edition (September 17, 2013)
- Language: English
- ISBN-10: 007181793X
- ISBN-13: 978-0071817936
- Product Dimensions: 6.5 x 0.9 x 9.3 inches
- Shipping Weight: 1.2 pounds (View shipping rates and policies)
- Average Customer Review: 8 customer reviews
- Amazon Best Sellers Rank: #167,449 in Books (See Top 100 in Books)
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Risk-Return Analysis: The Theory and Practice of Rational Investing (Volume One) 1st Edition
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About the Author
HARRY M. MARKOWITZ is a Nobel Laureate and the father of Modern Portfolio Theory. Named "Man of the Century" by Pensions & Investments magazine, he is a recipient of the prestigious John von Neumann Theory Prize for his work in portfolio theory, sparse matrix techniques, and the SIMSCRIPT programming language.
KENNETH A. BLAY serves as the Director of Research for 1st Global. He leads asset allocation research and policy recommendations for the firm's investment management platform. He has played an instrumental role in the development of 1st Global's efforts in portfolio management, investment due diligence, capital markets analysis, and the establishment of the Investment Management Research Group, which today oversees the firm's portfolio management research. Kenneth has worked extensively with Dr. Harry Markowitz on portfolio analysis, risk management, and various research initiatives including tax-cognizant asset allocation.
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Top customer reviews
Essentially, This book justifies MVA as an application of rational decision-making under uncertainty and over time, synthesizing the approaches of von Neumann & Morgenstern (1944) "Theory of Games and Economic Behavior", Savage (1954) "The Foundations of Statistics", and Bellman (1957) "Dynamic Programming".
This volume addresses the "Great Confusion" regarding the necessary and sufficient conditions for the practical use of Markowitz' MVA, first described in 1952. After the financial crisis of 2008, some investors lost faith in the diversification benefits of modern portfolio theory, and this book addresses and corrects these mistaken beliefs. Markowitz clarifies that the premises of Expected Utility and the premises of MVA are identical, and discusses how mean-variance approximations to expected utility have been emphasized in a variety of publications by Markowitz and others since 1979. For example, after 2008, some investors have questioned the use of Gaussian return distributions in MVA. First, the financial dislocation of 2008 does not indicate non-normality; second, even if it did, normal return distributions are not necessary conditions for the use of MVA, as pointed out in Markowitz (1959) and re-emphasized in the present book, "Risk Return Analysis". Markowitz clarifies that while the normal distribution is SUFFICIENT to justify MVA as being approximately equivalent to maximization of Expected Utility across a wide variety of risk-averse utility functions, normal distributions are not NECESSARY. I will just also mention that diversification is not, and has never been presented by Markowitz as, protection from loss; rather, it is presented as a method of investing rationally.
I am going to humbly suggest the following course of readings by Professor Markowitz that you will want to review (if you have not done so already) prior to reading this book. All of the journal articles are available online either free or for a small fee, just google the title; and all of the books are available here on Amazon.
1952, "Portfolio Selection", Journal of Finance vol.7, No. 1 pp.77-91;
1952, "The Utility of Wealth", Journal of Political Economy vol. 60, pp. 151-158;
1956, "The Optimization of a Quadratic Function Subject to Linear Constraints", Naval Logistics Research Quarterly, vol. 3, Issue 1-2, pp. 111-133;
1959 (2nd edition 1991), Portfolio Selection: Efficient Diversification of Investments, Second edition, Cambridge: Basil Blackwell, Inc.
1981 (with Andre Perold), “Portfolio Analysis with Factors and Scenarios”. Journal of Finance Vol. 36, No. 4 (September), pp. 871-877.
1987 (2nd edition 2000 with G. Peter Todd), Mean–Variance Analysis in Portfolio Choice and Capital Markets. New Hope: Frank J. Fabozzi Associates.
2012, "The 'Great Confusion' Concerning MPT", The IEB International Journal of Finance, Vol. 4, pp. 8-27.
This book assumes the reader has high-school algebra as a basic math background. That said, it is no simple read for the beginning -- or even the average -- investor; those readers expecting the level of, say, Peter Lynch's "One Up on Wall Street" may be overwhelmed by the formulas. Even Wharton Professor Jeremy Siegel, in his book "Stocks for the Long Run", has made his information easily accessible to the layperson by avoiding mathematical formulas entirely. Professor Markowitz, on the other hand, provides a number of mathematical expositions and proofs in this book -- I urge readers uncomfortable with equations to just keep reading! Skim the equations if need be, but read the text, which describes the math in pretty simple terms. Although you will do yourself a favor by reading, thinking about and understanding the equations too.
This book has 5 chapters:
Chapter 1) Rational investors should maximize their expected utility; this chapter compares standard deviation vs. maximum loss as a measure of risk; it addresses rational decision-making vs. human decision-making and uses Weber's law to explain the human decision problems described by Kahneman & Tversky (behavioral finance) and reviews Allais' Nobel prize-winning paradox; Markowitz presents the two axiom systems from Markowitz (1959) in 4 axioms at the end of chapter 1.
Chapter 2) MVA approximates EU; this chapter compares utility of return to utility of wealth; Markowitz corrects the mistake of Loistl (1976) who criticized MVA; the chapter reviews the Levy & Markowitz (1979) paper on approximating EU with MVA; Markowitz discusses and agrees with Simaan's (1993) result that including a risk-free asset in the portfolio choices for highly risk averse investors is helpful; the chapter also discusses MVA utility with common stocks vs. call options and reviews several other relevant works including Young & Trent (1969), Dexter, Yu & Ziemba (1980), Kroll, Levy & Markowitz (1984), Hakansson (1971), Grauer (1986) and Pulley (1983).
Chapter 3) MVA approximates the geometric mean; this chapter discusses differences between geometric and arithmetic mean and explains why MVA requires arithmetic rather than geometric means as input; Markowitz presents empirical asset class results and real equity returns of 16 countries over the 20th century; he compares six methods of estimating geometric means from arithmetic means, and recommends use of three equations: equation 10f, the "HL" approximation named for Henry Latane, equation 10b, "QE" from Chapter 6 of Markowitz (1959), and equation 10e, "NLN" for near-lognormal.
Chapter 4) Risk measures; this chapter evaluates five alternative risk measures for potential use in MVA: variance, mean absolute deviation, semi-variance, value at risk and conditional value at risk. Markowitz uses the empirical data from Dimson, Marsh and Staunton's "Triumph of the Optimists" (Which I also highly recommend) to evaluate these measures and finds that variance is the superior measure of risk. Marketing people may find Figure 4.11, "Maximum Loss by 16 Equity Markets During the Twentieth Century" useful in terms of outcomes to avoid. The conclusion of this chapter bears direct quotation: "...proponents of alternative risk measures need to get beyond their current line of argument, which goes roughly as follows: return distributions are not normal; therefore mean-variance is inapplicable; therefore my risk measure is best."
Chapter 5) Return distributions; in this chapter, Markowitz explores a variety of return distributions (with co-authors Anthony Tessitore, Ansel Tessitore and Nilfur Usmen), evaluating the same empirical data from the prior chapter using Bayes' rule. If you like histograms (as I do), Figure 5.3 is for you! The chapter concludes that Canada, the U.S., and Australia appear to generate normally distributed returns, while most other countries appear to have infinite density functions (Pearson class IV distributions). All however, may be easily accommodated by MVA.
The book concludes with some practical recommendations; essentially, investors can maximize their utility of wealth by employing MVA. To do so,
1) calculate efficient frontiers as described in Markowitz (1959),
2) select portfolios from the frontiers,
3) compute return series for the portfolios,
4) find the distribution using Bayes' rule that likely generated the return series, and then
5) select one of the likely distributions to serve as the portfolio distribution.
I am going to simplify this [what may seem like convoluted and circular to the uninitiated] process for the financial practitioner: Follow steps 1 and 2, the market will perform step 3 for you, and the rest is academic (though interesting).
Step 1) How can you calculate the frontier? Most commercial optimizers I have found over the past quarter century employ nonparametric methods to estimate optimal portfolios. Markowitz' CLA is the only parametric method, aside from Wolfe's (1959) simplex method, available. Personally I like CLA the best, because it is simple, straightforward and robust. Open source methods of Markowitz' CLA are available, I recommend that you read the 2000 edition of Markowitz (1987) which provides Peter Todd's VBA code that you can implement in MS Excel. Also there is an excellent introductory article by Clarence Kwan, "A Simple Spreadsheet-Based Exposition of the Markowitz Critical Line Method for Portfolio Selection" in Spreadsheets in Education (2007) vol 2, Issue 3, Article 2 available free online if you google it. Do yourself a big favor, and walk through this example and build it in your own spreadsheet. I did that and then added some VBA to automate the procedure for large, dense matrices. Finally if you'd like to build a Python implementation, David Bailey and Marcos Lopez de Prado have a great paper with Python code for CLA in Algorithms (2013), volume 6, pp. 169-196, "An Open-Source Implementation of the Critical-Line Algorithm for Portfolio Optimization". Marcos also gives an excellent discussion of the Critical Line Algo in a presentation available on vimeo at vimeoDOTcom/61426601.
Step 2) OK, now you have the efficient portfolios, calculated as in step 1 above. Which portfolio do you select? Well, you can just choose one consistent with the level of risk you are willing to take. Since there will be estimation errors in your ex-ante inputs, you may want to account for that somehow, such as by using shrinkage estimators; or the resampling method described in Richard and Robert Michaud's book, "Efficient Asset Management"; or you could use a simpler method, by taking the average weight of all the assets included in all of the portfolios along the efficient frontier you have calculated -- I show how to do this in my paper, "Portfolio Selection: How to Construct and Use the Critical-Implied Reference Portfolio" available on SSRN at papers.ssrnDOTcom/sol3/papers.cfm?abstract_id=813131.
Summary: If you want an "Introduction to Investing" book, this may be over your head. However if you are already familiar with mean variance analysis and efficient frontiers, and would like an update based on the latest thinking and research of its inventor, Harry Markowitz, buy this book. You will find it to be a valuable addition to your knowledge base.
First of all, this is supposed to be the first of four volumes on the subject of risk-return analysis. That is to say, after you've read this book, you're one-quarter of the way through all the information that Markowitz intends to share with his readers. That means as thorough and helpful this book is, it is not complete. The next problem is the approachability of the subject matter. This is nothing short of a college textbook. Laden with heavy financial jargon and long, difficult equations that would challenge all but the most serious of math students, this book is not for the faint of heart. For those who can manage it, I'm sure the details are worth the price of admittance, but mere mortals (like myself), will have to (regrettably) skip large portions where the math comes in.
That said, many would wonder how I can rate this book so highly. Well, I do so because the parts I could understand were interesting and useful, and I don't penalize the book for my own shortcomings. If you happen to pick this book up and the weightiness starts to make your eyes glaze over, you can get a much more approachable overview of the subject by reading another McGraw Hill book (which like this one, was provided to me gratis by the publisher), Asset Allocation: Balancing Financial Risk, Fifth Edition. It covers much of the same material with less math and Nobel-worthy lingo. Either way, this is important subject matter for all investors, so I give this book a solid four stars.