Aurora Borealis
5 September 2017 Financial Analysts Journal Book Review

Quantitative Equity Portfolio Management: An Active Approach to Portfolio Construction and Management (a review)

  1. Mark S. Rzepczynski
Practitioners who are serious about quantitative investing and want to focus on the details of running the numbers should have this book on their shelves.

Very few good practitioners’ cookbooks are available on quantitative equity portfolio management (QEPM). Ludwig Chincarini and Daehwan Kim’s book reduces this dearth. No previous volume has combined depth and breadth on the subject in a unified framework by a single set of authors. A book of this type usually consists of articles by different authors with varying strengths, a fair amount of redundancy, and no consistent notation. Having just two authors is invaluable, especially when they possess a rare knack of writing about the practical aspects of quantitative equity investing in a clear style.

While paying homage to the academic literature,  Quantitative Equity Portfolio Management: An Active Approach to Portfolio Construction and Management  focuses on how to get things done. It does not shortchange the reader, however, on the technical aspects. After reading this work, all a practitioner will need to construct a quantitative-based portfolio is some statistical software and a database. Naturally, there is a difference between reading a cookbook and becoming a chef, but readers of this book will know their way around the “quant kitchen.”

Chincarini and Kim begin with seven basic tenets for quantitative investment that form a strong foundation for all their work:

  1. Markets are mostly efficient.
  2. Pure arbitrage opportunities do not exist.
  3. Quantitative analysis creates statistical arbitrage opportunities.
  4. Quantitative analysis combines all of the available information in an efficient way.
  5. Quantitative models should be based on sound economic theories.
  6. Quantitative models should reflect persistent and stable patterns.
  7. Deviations of a portfolio from the benchmark are justified only if the uncertainty is small enough.

The first two tenets underscore the challenge of going up against market efficiency. Numbers 3–5 focus on the fundamental law of active management: The information ratio is related to the breadth of the portfolio manager’s universe and the information coefficient of trades. 1  The final two tenets deal with statistical issues. Seldom do quantitative books clearly describe their underlying philosophical assumptions to their modeling approach in such an accessible manner.

Quantitative Equity Portfolio Management  is divided into five parts. The first part lays out the fundamental assumptions of QEPM. Part 2 reviews portfolio construction and maintenance. The third part, with the pithy title “Alpha Mojo,” shows how quantitative techniques can be used to enhance alpha generation. Part 4 provides information about performance analysis and attribution, and the final part discusses practical applications and real-life issues in portfolio management.

Quant work is clearly not for everyone. This book’s overview section discusses the advantages and disadvantages of QEPM as well as how a quantitative or qualitative analyst will look at similar situations differently. Together with providing the seven tenets for QEPM, the authors explain in great detail how the tenets apply to their thought processes. The tenets are supported with a breakdown of quantitative relationships that have been exploited in the past and that fit their criteria. For example, the authors provide a list of market anomalies and the references for research done in each area. Following the same procedure for behavioral influences, they describe the resulting biases and give examples.

Some quantitative analysts may quibble with the composition of Chincarini and Kim’s lists, but the lists provide a good breakdown of the focal areas of quantitative methods and highlight the biases that systematic investing tries to minimize. The authors offer informative discussions of the difference between screening and ranking stocks and of the use of normalized  Z-scores and fundamental values. The authors also explain how various modeling approaches differ and provide a methodology for choosing the right model in a given situation.

Although this support information is valuable, the book’s greatest benefit is a detailed structure for combining different approaches to QEPM, such as fundamental and economic factor analysis. The comparisons of these methods in Part 1 are rich in detail, although a more precise discussion of how to implement and test models would have been useful.

Part 2 dives into the details of model building; the authors explain factor models and how to select factors. They divide the factors into categories to explain the choices. Their categories are valuation, solvency, operating efficiency, profitability, financial risk, liquidity, economics, and technical considerations. The importance of choosing the right model and the econometric traps surrounding the selection of factors are often overlooked, yet these areas are where most investors encounter frustration. Also in this section, Chincarini and Kim describe procedures for using  Z-scores to screen and rank stocks.

The difference between fundamental and economic factors, as well as procedures for forecasting factor premiums and exposures, are explored in detail. These clear descriptions relieve the reader of the need to go to an original source to obtain the specific steps on modeling. The presentation on forecasting factor premiums and parameter uncertainty is particularly clear and comprehensive. Understanding these aspects is essential if a reader wants to actually build usable models. The authors also discuss outliers and robustness testing of models.

Construction of a portfolio through stock picking is integrated by the determination of portfolio weights under optimization with constraints. Issues of factor targeting and tracking error are addressed, but following up on “cookbook issues” of how to check the optimization would have been helpful. The important practical aspects of rebalancing, transaction costs, and tax management are thoroughly addressed, which is unusual in a quantitative treatment but vital for actual portfolio management.

Note that, although the book is comprehensive, a solid knowledge of regression analysis and optimization is needed to understand the presentation fully. The devil is in the econometric details when striving for useful results.

The book’s third part is a wide-ranging discussion of techniques for adding alpha. Construction of leveraged portfolios through derivatives is explained. Market-neutral investing, which focuses on isolating alpha, is developed as an extension of factor modeling. The authors also provide a review of Bayesian techniques, which can be used in the search for alpha through setting prior probabilities. Although this specialized approach is interesting, I would have preferred the authors spending more time on the main topics to ground the reader thoroughly.

Part 4, dealing with performance analysis, tackles issues of measurement and attribution. This section could have been placed with the discussion of tracking error because a description of errors is essential in measuring performance.

The book’s final part addresses such practical applications as backtesting, analyzing model performance, and real-life portfolio management issues, such as taxes and transaction costs. In addition, a CD-ROM of data and examples is provided.

Throughout the book, the authors do a superb job of pointing out potential pitfalls with quantitative modeling. These complexities as well as extensions of the core principles are made through the end-of-chapter questions. Ordinarily, we would not look at a book’s end-of-chapter questions, but in this case, they effectively test the reader’s understanding of the key concepts.

Our only major criticism of  Quantitative Equity Portfolio Management  is that more practical examples would have been helpful. It is unusual, certainly, for a reader to ask for more of anything after plowing through 650 pages of quantitative material. Still, a cookbook needs specific instructions on how to turn a recipe into a palatable dish. Furthermore, because so many topics are covered, a number of important issues have received only limited attention. A two-page treatment of a complex topic, such as forming the optimal portfolio with transaction costs, cannot provide all the details needed to generate an effective quantitative program.

Like any overview of a large subject, this one favors the authors’ preferences, yet the authors display little bias in their presentation of the material. Reading  Quantitative Equity Portfolio Management  in conjunction with Grinold and Kahn’s thorough 1999 explication of theory provides a powerful amalgamation of academic theory and practical reality. Practitioners who are serious about quantitative investing and want to focus on the details of running the numbers should have this book on their shelves.


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