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Plan Architecture and Portfolio Engineering

Copyright 1995, Institutional Investor Journals. Reproduced and republished from Journal of Investing with permission.  All rights reserved.

Copyright 1996, Institutional Investor Journals. Reproduced and republished from Journal of Portfolio Management with permission.  All rights reserved.

Engineering Portfolios: A Unified Approach
by Bruce I. Jacobs and Kenneth N. Levy, The Journal of Investing, Winter 1995

Residual Risk: How Much is Too Much?
by Bruce I. Jacobs and Kenneth N. Levy, The Journal of Portfolio Management, Spring 1996

The articles listed here focus on Jacobs Levy Equity Management's philosophy of portfolio management, including the scope of the security selection/portfolio engineering problem, the goal of portfolio management, and the place of an individual portfolio within the investor's overall investment scheme.

Our process considers a wide range of return predictors designed to capture economic and behavioral effects, as well as company-specific information and events. But the power of these predictors can differ across different types of stock. The selection process must thus include breadth in terms of coverage of stocks, as well as return predictors. This does not mean that one should ignore the very real differences in price behavior that distinguish particular market subsets, or that one cannot choose to focus on a particular subset, such as value, growth, or small-capitalization stocks. It simply means that the model used for analyzing individual stocks should incorporate all information available from a broad universe of stocks.

“Engineering Portfolios: A Unified Approach,” which appeared as the lead article in a Special Technology Issue of the Journal of Investing, discusses the many benefits of taking a broad, unified approach to the investment problem. Such an approach offers a coherent framework for analysis, one in which each stock in the universe has one and only one alpha, and in which each can be related to every other stock in the universe. A unified approach can also take advantage of more information than a narrower view of the market can provide. Of practical importance is the fact that a broad, unified approach allows the investment manager to “engineer” portfolios designed to outperform various client-specified mandates. 

A broad, unified approach, combined with the power of a security selection system based on an appropriate multivariate analysis of a large number of return predictors, allows for numerous insights into profit opportunities and improves the goodness of those insights; this in turn can lead to superior portfolio performance. The process of translating the insights into the performance is the process of portfolio engineering.  A portfolio optimization process that is customized to include exactly the same dimensions found relevant by the stock selection process helps to ensure that all the opportunities detected by the modeling process are exploited, while all the risks detected are accounted for and controlled. The aim of portfolio engineering should be to provide the maximum possible expected return for the desired level of risk. “Residual Risk: How Much Is Too Much?” considers the portfolio engineering problem within the broader context of the investor's risk policy. In particular, it demonstrates that the investor must factor into the portfolio selection decision the level of manager skill—the manager's ability to deliver incremental return for each unit of incremental risk taken. Taking too little risk may end up costing as much as taking too much!

Key Articles:

· “Smart Beta versus Smart Alpha,” by Bruce I. Jacobs and Kenneth N. Levy, Journal of Portfolio Management, Summer 2014. article
Smart beta strategies aim to outperform the capitalization-weighted market through relatively simple alternative weighting methods that emphasize a handful of factors such as size, value, momentum, or low volatility. Though similar in some respects to passive index investing, smart beta strategies are the product of active choices and should be compared with proprietary active multifactor investment strategies (“smart alpha”). Smart beta strategies exploit fewer return opportunities, tend to be more static, and have less control of risk exposures. Furthermore, because of their reliance on a small number of factors, smart beta strategies can run into liquidity and overcrowding problems that can adversely impact their performance. Smart alpha may be the smarter choice.

· “Residual Risk: How Much is Too Much?” by Bruce I. Jacobs and Kenneth N. Levy, The Journal of Portfolio Management, Spring 1996; and abstracted in The CFA Digest, Winter 1997. article
The optimal level of residual risk for a portfolio is the level that allows the portfolio to provide the highest expected return the manager can generate within the limits of the investor's risk tolerance parameters. As it is not always easy to determine investor risk tolerance or manager ability to add value, portfolios are often pigeonholed” according to residual risk levels alone. “Enhanced passive” or “index-plus” portfolios, for example, are expected to offer excess returns of up to 1% at residual risk levels not to exceed 2%. But such artificial constraints as a 2% bound on residual risk can lead to selection of suboptimal portfolios. In particular, they can lead investors to assume too little risk, hence allow too little expected return, for their actual risk tolerances, or to accept less skillful managers when more highly skilled managers are available. They may also encourage suboptimal manager behavior.

· “Engineering Portfolios: A Unified Approach,” by Bruce I. Jacobs and Kenneth N. Levy, The Journal of Investing, Winter 1995; and abstracted in The CFA Digest, Summer 1996.(1) article
Many traditional equity managers focus on particular subsets of the investment universe—value or growth stocks, for example—and structure their portfolios from preselected groups. By contrast, a “unified” approach starts with a blank slate, having no built-in biases regarding any particular type of stock, and searches the widest possible stock universe and the largest number of investment variables. At the same time, it recognizes differences in stock price behavior across different types of stocks and over time, as well as possible nonlinearities in stock price response to gradations in exposure to a given variable. A unified approach to stock valuation is poised to take advantage of more information and to discover a greater number of potentially profitable investment opportunities. These opportunities are maximized by a portfolio optimization process that is customized along the same dimensions as the valuation process. This ensures a portfolio whose risks and return opportunities are balanced in accordance with the insights garnered from the unified valuation approach. Given its range and depth of coverage, a unified approach provides a firm with substantial flexibility to engineer portfolios to meet a variety of client risk/return requirements.

Other Articles:

· “Alpha Transport With Derivatives,” by Bruce I. Jacobs and Kenneth N. Levy, The Journal of Portfolio Management, May 1999; and abstracted in The CFA Digest, Fall 1999.(2) article
Investors can use derivatives to transport the excess returns available from the selection of securities within a given asset class or subclass to virtually any other asset class. For example, an investor can pursue the return possibilities in small-cap stocks, while using futures or a swap to neutralize exposure to the small-cap asset subclass and establish exposure to the large-cap segment. The investor can thus benefit from both the security selection opportunities in small-cap stocks and the asset class performance of large-cap stocks. Using derivatives in conjunction with market-neutral long-short portfolios can offer further performance enhancement.

· “The Law of One Alpha,” by Bruce I. Jacobs and Kenneth N. Levy, The Journal of Portfolio Management, Summer 1995. article
Firms that use one valuation model for their core portfolio and different models for subsets of that core may end up with multiple estimates of alpha. But as every asset has only one price, doesn't it follow that the asset should have only one mispricing? It is argued here that it hardly makes sense for a single firm to begin the investment selection process with an approach that allows for the possibility of multiple mispricings for a given stock over a given horizon.


Copyright © 2017

Copyright © 2000

Chinese Translation

Copyright © 2006

· Equity Management: The Art and Science of Modern Quantitative Investing, Second Edition, by Bruce I. Jacobs and Kenneth N. Levy, forewords by Harry M. Markowitz, Nobel Laureate, McGraw-Hill, New York, 2017.

· Equity Management: Quantitative Analysis for Stock Selection, by Bruce I. Jacobs and Kenneth N. Levy. McGraw-Hill, New York, NY, 2000. Authorized Chinese translation from English language edition, McGraw-Hill, China Machine Press, 2006.

This new edition of Equity Management reflects 30 years of research and investment practice by two pioneers of quantitative equity investing. In the 1980s, Bruce Jacobs and Ken Levy published in peer-reviewed journals a series of articles on detecting and exploiting the factors that significantly influence stock returns. Since then, they have examined short selling in the context of long-short portfolios, optimization of portfolios with short sales or other leveraged positions, markets in crisis, and models that can simulate realistic market behavior.

Equity Management: The Art and Science of Modern Quantitative Investing includes the classic 15 articles from the original edition plus 24 articles that were published since the first edition appeared. Together, they present a compelling argument for the benefits of a quantitative approach in a complex, multidimensional, and dynamic factor world.

The chapters are grouped into eight parts, with introductory material that places each section within the broader context of the investment body of knowledge. Part 1 examines the intricacies of stock price behavior and focuses on detecting the characteristics, or factors, behind them. Security prices are neither efficient nor random and unpredictable. Rather, the market is a complex system, permeated by a web of return regularities. These regularities must be "disentangled" to arrive at the real sources of return. This requires analyzing numerous promising return-predictor relationships simultaneously.

Part 2 looks at how best to exploit the investment opportunities detected. The chapters outline a holistic approach that is multidimensional and dynamic. Viewing the market as integrated allows for greater breadth of investigation and greater depth of analysis, hence enhances the potential for more and better insights. A dynamic, multidimensional, proprietary approach that can adapt to changes in the underlying environment is better poised to capture opportunities than an approach that restricts itself to a small number of well-known and static factors.

Part 3 examines how short sales can expand investment opportunities and improve performance. Balancing long and short positions within a portfolio creates a market-neutral portfolio whose performance should reflect the returns and risks of the constituent securities, but not the performance of the overall market. The return from security selection can be transported to virtually any asset class via derivatives, allowing the investor to take advantage of manager skill, wherever it lies, while maintaining any desired asset allocation.

Part 4 focuses on another long-short approach—enhanced active equity, or 130-30 type portfolios. These portfolios retain full exposure to the market return, while pursuing excess returns via short positions and leveraged long positions. The development of 130-30 type portfolios was motivated by Jacobs and Levy’s research into optimization of long-short portfolios, which showed that the optimization process should consider long positions, short positions, and any benchmark holding simultaneously.

The authors also tackled a problem that arises when optimizing portfolios that contain both long and short positions. As the chapters in Part 5 explain, the factor or scenario models of covariance that simplify the optimization process for long-only portfolios do not necessarily apply to long-short portfolios. Jacobs and Levy, working with Harry Markowitz, provide a solution they call “trimability.”

Part 6 addresses the unique risks of leverage, which are distinct from the risk captured by standard deviation, or volatility; most notable is the risk that a margin call can force the unwinding of positions. The mean-variance model central to modern portfolio theory does not consider these unique risks and can thus lead to “optimal” portfolios with very high leverage. The authors present an alternative model—mean-variance-leverage optimization—that allows an investor who is both volatility-averse and leverage-averse to assess the utility of a portfolio.

High levels of leverage almost led to the demise of hedge fund Long-Term Capital Management in 1998 and to the disruption of the entire financial system in 2008-2009. Part 7 examines these episodes and other periods of market crisis, including the 1987 stock market crash. One conclusion is that products and strategies that promise increased returns at reduced risk have attracted investors, encouraged leverage, and too often precipitated not only their own demise, but also the near-collapse of the global economy.

Part 8 presents work undertaken with Harry Markowitz on a model for simulating market behavior. The Jacobs Levy Markowitz Market Simulator (JLMSim) allows users to create their own market models from the bottom up by specifying the numbers and types of market entities, including portfolio analysts, traders, and investors, as well as their decision rules. The results so far suggest that types of investors (value versus momentum), as well as trading rules, can have significant impacts on market stability.

Book Chapters:

· “An Architecture for Equity Portfolio Management,” by Bruce I. Jacobs and Kenneth N. Levy, in Frank J. Fabozzi and Harry M. Markowitz, Eds. Equity Valuation and Portfolio Management. John Wiley & Sons, Hoboken, NJ, September 2011. Earlier versions appeared as “Investment Management: An Architecture for the Equity Market,” in Frank J. Fabozzi, Ed. Handbook of Finance, Volume II: Investment Management and Financial Management. John Wiley & Sons, Hoboken, NJ, 2008; Chapter 1 in Frank J. Fabozzi, Ed. Active Equity Portfolio Management. Frank J. Fabozzi Associates, New Hope, PA, 1998; and in Frank J. Fabozzi, Ed. Handbook of Portfolio Management. Frank J. Fabozzi Associates, New Hope, PA, 1998.
A blueprint of the U.S. equity market reveals three basic building blocks—a comprehensive core representing all U.S. equity issues; static style subsets, comprising large-cap growth stocks, large-cap value stocks, and small-cap stocks; and a dynamic entity reflecting differing relative performance in different market environments. Investment approaches, too, can be categorized into three groups—passive, traditional active, and engineered active. Engineered active management has the potential to provide the best match between client risk/return goals and investment returns, because it can offer consistent performance relative to the equity market core or its various subsets.

Industry Press Publications:

· “How to Build a Better Equity Portfolio,” by Bruce I. Jacobs and Kenneth N. Levy, Pension Management, June 1996.
Investors in U.S. equity can choose among a variety of selection universes, from the broad core including all stocks to various style subsets. They can also choose from a variety of investment approaches, from passive to traditional active to engineered active. Investors may be able to make more informed decisions if they understand the “architecture” of investing that links selection universes and investment approaches to their potential risks and returns.

· “Broader Indexes Widen Horizons,” by Kenneth N. Levy and Bruce I. Jacobs, Pensions & Investments, August 20, 1984.
The S&P 500 is not truly representative of the broader U.S. equity market. It is biased toward large-cap stocks, for example, and exhibits less earnings variability, growth and market variability than the broader universe. This has implications for passive investors in search of a proxy for the U.S. equity market return.

Other Research Categories:

Security Selection

Long-Short Investing

Portfolio Optimization, Short Sales, and Leverage Aversion

Market Simulation

Market Crises


(1)The Journal of Portfolio Management Special 25th Anniversary Issue.
(2)The Journal of Investing Special Technology Issue, lead article.

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