Aspects of Constrained Long-Short Equity Portfolios

Sorensen, Eric H.; Shi, Jing; Hua, Ronald; Qian, Edward E.

doi:10.3905/jpm.2007.674790

Cited by 36 publications

(14 citation statements)

References 3 publications

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“…Legal restrictions and specific costs aside, long-short strategies are evidently superior to long-only ones because they capture investment opportunities that are otherwise inaccessible. This point has been made repeatedly in the literature, along with various performance indicators such as higher alphas and lower tracking errors (Sorensen et al, 2007), diversification benefits (Krusen et al, 2008) and higher efficiency (Johnson et al, 2007). With respect to this literature stream, our methodological innovation stems from using intermediate situations which depart from both the highly restrictive long-only case and the (too?)…”

Section: Resultsmentioning

confidence: 80%

“…We use as a benchmark the efficient frontier composed of portfolios that combine the market index with optimized proportions of the five historical longshort factors taken from Fama and French (2015), which we call the FF frontier, 11 and assess alternative investment rules, including the 130/30 option, with respect to this benchmark. Our derivations follow the line of logic proposed by Clark et al (2004) and Sorensen et al (2007), 10 who examine the consequences of imposing various realistic restrictions to portfolios, including short-selling limitations. The next section explains in more details how we proceed.…”

Section: Short-selling and Factor Investingmentioning

confidence: 96%

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Factor Investing: The Rocky Road from Long Only to Long Short

Brière

Szafarz

2017

SSRN Journal

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This paper examines how restrictions on short positions affect the financial attractiveness of factor investing. To fill the gap between unconstrained longshort allocations and restricted long-only portfolios, we consider two in-between strategies. The first imposes that only the market can be shorted; the second is the so-called "130/30" or "active extension" trading strategy, which caps total short exposure at 30%. The takeaways from our research are twofold. First, short sales contribute significantly to the mean-variance performance of efficient factor-based portfolios. Second, the factor portfolios built originally by Fama and French (1992) with the purpose of developing asset pricing are impressively clear-sighted when it comes to portfolio management. Indeed, combining these portfolios generates mean-variance performances similar to those of optimized long-short portfolios, except for low levels of volatility.Keywords: Investment, asset allocation, factor investing, long-only, long-short, 130-30. 2 ABSTRACT This paper examines how restrictions on short positions affect the financial attractiveness of factor investing. To fill the gap between unconstrained long-short allocations and restricted longonly portfolios, we consider two in-between strategies. The first imposes that only the market can be shorted; the second is the so-called "130/30" or "active extension" trading strategy, which caps total short exposure at 30%. The takeaways from our research are twofold. First, short sales contribute significantly to the mean-variance performance of efficient factor-based portfolios.Second, the factor portfolios built originally by Fama and French (1992) with the purpose of developing asset pricing are impressively clear-sighted when it comes to portfolio management.Indeed, combining these portfolios generates mean-variance performances similar to those of optimized long-short portfolios, except for low levels of volatility.3

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Section: Resultsmentioning

confidence: 80%

Section: Short-selling and Factor Investingmentioning

confidence: 96%

Factor Investing: The Rocky Road from Long Only to Long Short

Brière

Szafarz

2017

SSRN Journal

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“…Such studies-by Sorensen, Hua, and Qian (2006) and Clarke, De Silva, and Sapra (2004)-allow for consideration of a wide range of implementation issues, including discretionary constraints, but lack the generality of an analytical model. For example, numerical analyses of S&P 500-benchmarked long-short extension portfolios that are only a few years old may already be outdated because of shifts in key market parameters.…”

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

confidence: 99%

“…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).…”

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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“…An EAE portfolio maintains a 100% exposure to an underlying stock market benchmark while relaxing the long-only constraint to allow for short sales equal to some percentage of capital and for use of the shortsale proceeds to buy additional securities long (see A number of researchers have analyzed the optimal level of enhancement for long-short portfolios, including Johnson, Kahn, and Petrich (2007); Sorensen, Hua, and Qian (2007); and Clarke, de Silva, Sapra, and Thorley (2008). Johnson, Kahn, and Petrich showed that the optimal value of "gearing," a concept akin to enhancement, is a linearly increasing function of risk tolerance, forecast accuracy, and the number of securities in the portfolio; it is inversely proportional to security volatility.…”

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confidence: 99%

Leverage Aversion and Portfolio Optimality

Jacobs

Levy

2012

Financial Analysts Journal

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Portfolio volatility is the only source of risk in mean-variance optimality, but it fails to capture all the risks faced by leveraged portfolios. These risks include the possibility of margin calls and forced liquidations at adverse prices and losses beyond the capital invested. To recognize these risks, the authors incorporated leverage aversion into the optimization process and examined the effects of volatility and leverage aversion on optimal long-short portfolios.

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Aspects of Constrained Long-Short Equity Portfolios

Cited by 36 publications

References 3 publications

Factor Investing: The Rocky Road from Long Only to Long Short

Factor Investing: The Rocky Road from Long Only to Long Short

Long–Short Extensions: How Much Is Enough?

Leverage Aversion and Portfolio Optimality

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