Customer Reviews

Equity Management: Quantitative Analysis for Stock Selection

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The most important thing that I (an individual mutual fund investor) came away with was an understanding of the reasons why actively managed mutual funds cannot consistently beat their underlying benchmarks if they take a "stock picking" approach. Since this is the approach taken by most mutual fund managers, it is not surprising that more than 50% (often much more than 50%) do not beat their benchmarks. I also found it interesting how with sufficient breath and depth, engineered portfolios offer higher return and lower risk than the S&P 500.

It is easy to see how "alpha transport", when properly used, can also help maximize returns while minimizing risk. However, I imagine there have been, more often than not, improper use of alpha transport which did just the opposite (minimize return and maximize risk). Obviously not included in the text is the "proper usage" or algorithms for "disentangling equity return regularities". I imagine the authors keep such information close to their chest and constantly update them as old algorithms become part of the efficient market.

The work provides a guide to modern methods of equity management, and for those interested in the state-of-the-art, I highly recommend this book.

The Good: It is a collection of some of the great papers by the authors over the years that have appeared in Journal of Portfolio Mgt., Journal of Investing and the Financial Analysts Journal. If you do any work in quant equity management you should know and have these papers. Buy the book and save time hunting down the papers.

The Bad: It is a collection of some of the great papers by the authors over the years that have appeared in Journal of Portfolio Mgt., Journal of Investing and the Financial Analysts Journal. If you do any work in quant equity management you know and have these papers. The introductions and "new" material that ties it all together is not worth the expense. What sort of got under my skin is that none of the work has been updated or brought current, even by using appendicies. Since some of their most interesting quant work was published 12 years ago we have no updates as to how the factors are doing. This would have been a great value to this reader.

Overall its a great body of work but nothing new.

Jacobs and Levy have assembled a body of work here centering on their stock picking techniques as well as their long-short portfolio construction techniques. Most of the chapters have their origins in various finance journals, though the articles themselves are not very heavy on mathematics. Overall, the book was interesting, though somewhat repititious. In retrospect I'd suggest that those familiar with long-short portfolios and the various market anomolies should just read chapter 2 about "Disentangling Equity Return Regularities" since that is where Jacobs & Levy's original work is outlined.

Chapter 2 focuses on the use of regression analysis to "disentangle" various stock market anomalies. The authors claim that simple rules such as "Buy low-P/E stocks" are appealing, but oversimplify the true source of stock returns. For example, low P/E stocks tend to have higher rates of return, as do small-capitalization stocks. But if a small capitalization stocks also tend to have low P/E's, then how much of their return is due to the low-P/E effect by itself, and how much is due to the small-capitalization effect by itself? Jacobs & Levy have done the analyses, and show which effects are genuine, and which effects are merely proxies for other effects. The effects that turn out to be the strongest when "disentangled" include low P/E, Earnings trend, Earnings Surprise, Residual Reversal, and Relative Strength.

The introductory chapters in the book make some interesting points. They argue that the stock market is not random, but then again it is also not simple. Although simple rules are appealing to humans, they oversimplify the complexity of the market. To gain an edge, one must use sophisticated, objective, multi-factor statistical computer models that capture the complex interactions in the market. Of course the authors are saying this to advocate the techniques they use, but nevertheless, they have some good points.

Finally, the second half of the book focuses on the construction of long-short portfolios, though there is not much fresh material here. They point out some of the logistical details of running a long-short portfolio, and give some examples. Also, they introduce the concept of "alpha-transport." That is, one can construct a long-short market neutral portfolio, then by buying buy an index (using SP500 futures, for example) one "transports" the gains from the long-short portfolio onto the gains/losses of the index position. Thus, if the stock picking for the long-short portfolio is done correctly, the total portfolio will beat the index picked. To me, this seemed like an obvious technique; I'm surprised they decided to focus on it and give it a fancy name ("alpha transport")

Overall, I found the book interesting, though somewhat repetitious. I was familiar with much of what was covered, however I did find that Chapter 2 was worth reading, since I wasn't familiar with Jacobs & Levy's work in detail.

This book is great for people who want an overview of the opportunities available in numerical evaluation of stocks. However to say that this is a book on quantitative techniques is probably not the mest description. There is extensive use of regression analysis but more hard-core 'quant' people will probabliy be disappointed. It is clear that the book is written by investment professionals focusing a lot on traditional evaluations parameters such as P/E, P/B etc. Interesting points include purification of return signals, and an extensive discussion of long-short techniques.

As a critic once said of Henry James, Jacobs and Levy have chewed more than they bit off. There is (as another reviewer points out) no follow-up on how their factors have behaved in the decade or more since they did some of the pioneering work on factor models. Overall, the book has about 10% as much information useful to a practitioner as appears in Grinold & Kahn's authoritative text.