Portfolio Constraints and the Fundamental Law of Active Management

Clarke, Roger; Silva, Harindra de; Thorley, Steven

doi:10.2469/faj.v58.n5.2468

Cited by 224 publications

(92 citation statements)

References 8 publications

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“…Examples are marketcap-neutral constraints, turnover constraints, and dividend-yield neutrality with respect to the benchmark. Adding further constraints generally reduces the ex post information ratio; see Clarke et al (2002). Nevertheless, the manager will still benefit from using a superior risk model.…”

Section: Empirical Studymentioning

confidence: 99%

Honey, I Shrunk the Sample Covariance Matrix

Ledoit¹,

Wolf²

2004

JPM

919

383

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The central message of this paper is that nobody should be using the sample covariance matrix for the purpose of portfolio optimization. It contains estimation error of the kind most likely to perturb a mean-variance optimizer. In its place, we suggest using the matrix obtained from the sample covariance matrix through a transformation called shrinkage. This tends to pull the most extreme coefficients towards more central values, thereby systematically reducing estimation error where it matters most. Statistically, the challenge is to know the optimal shrinkage intensity, and we give the formula for that. Without changing any other step in the portfolio optimization process, we show on actual stock market data that shrinkage reduces tracking error relative to a benchmark index, and substantially increases the realized information ratio of the active portfolio manager. *

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Section: Empirical Studymentioning

confidence: 99%

Honey, I Shrunk the Sample Covariance Matrix

Ledoit¹,

Wolf²

2004

JPM

919

383

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show abstract

“…Generalizations of the Grinold and Kahn (1994) theory of active management by Clarke, De Silva, and Thorley (2002) and by others focused on the role of formal constraints in portfolio construction, particularly the negative impact of the long-only constraint. At the same time, innovations in prime brokerage practices and the acceptance of shorting by institutional fiduciaries led to a proliferation of long-short strategies and products.…”

Section: Long-short Extension Strategies Such As 130-30 Allow Portfmentioning

confidence: 99%

Long–Short Extensions: How Much Is Enough?

Clarke

Silva

Sapra

et al. 2008

Financial Analysts Journal

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Long-short extension strategies, such as 130-30, allow ne of the major innovations in portfolio construction during the past several years has been the adoption of long-short extension strategies that allow managers to fully exploit the cross-sectional variation in forecasted security returns. Generalizations of the Grinold and Kahn (1994) theory of active management by Clarke, De Silva, and Thorley (2002) and by others focused on the role of formal constraints in portfolio construction, particularly the negative impact of the long-only constraint. At the same time, innovations in prime brokerage practices and the acceptance of shorting by institutional fiduciaries led to a proliferation of long-short strategies and products. Because long-short extensions are new to many market participants, Jacobs and Levy (2007) addressed misconceptions about the strategies. The analytical model we develop here will further improve investors' conceptual understanding of the factors that determine the size of the short (and equivalent long) extension in long-short strategies.The short extension model is based on the concept of the expected short weight for individual securities in the benchmark, similar to Sorensen, Hua, and Qian (2006). We also use the assumption of a constant correlation matrix and other modeling techniques used in an early analytical treatment of long-short strategies by Jacobs, Levy, and Starer (1998). In this article, we describe how the expected short weight for a security depends on the relative size of the security's benchmark weight and the active weight assigned to that security by the portfolio management process. The formal mathematical model and approximations enhance perspectives from previous studies that depended on time period-specific numerical examples or on insights from simulations.The derivation of the long-short extension model rests on the assumption of an unconstrained portfolio optimization and, therefore, gives an upper bound on possible long-short ratios in practice. In the language of the fundamental law of active management, we assume a transfer coefficient of 1 and thus the maximum possible expected information ratio. 1 As discretionary constraints are imposed, the long-short ratio declines from the upper bound suggested by the model, with a corresponding decline in information ratio. As a result, empirical illustrations that use the S&P 500 Index and other common equity benchmarks have longshort ratios that are generally higher than applied strategies, in which a variety of additional constraints are often used. In addition to being difficult Roger Clarke is chairman and Harindra de Silva, CFA, is president

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“…Kahn, in particular, has just published an interesting article in a recent issue of the Journal of Portfolio Management (Johnson, Kahn, and Petrich 2007). Of course, the connection of the 120/20 idea with the transfer coefficient appeared in an article by Clarke, de Silva, and Thorley (2002) a few years ago. Considering the amount of discussion about the process and the theory behind the concept of 120/20, it is curious that general acceptance of the method has taken so long.…”

Section: Cfa Institute Conference Proceedings Quarterlymentioning

confidence: 99%

Active Extensions in the Fund-Level Context

LeibowitzMartin¹

2008

CFA Institute Conference Proceedings Quarterly

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Portfolio Constraints and the Fundamental Law of Active Management

Cited by 224 publications

References 8 publications

Honey, I Shrunk the Sample Covariance Matrix

Honey, I Shrunk the Sample Covariance Matrix

Long–Short Extensions: How Much Is Enough?

Active Extensions in the Fund-Level Context

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