Are Smart Beta strategies suitable for hedge fund portfolios?

Hitaj, Asmerilda; Zambruno, Giovanni

doi:10.1016/j.rfe.2016.03.001

Cited by 14 publications

(12 citation statements)

References 19 publications

(31 reference statements)

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“…six months). The effect of n is in line with the results derived in [15]. However, also for this dataset, the network-based approaches over-perform the GMV strategy, in terms of both average performance and risk-adjusted measures.…”

Section: Alternative Datasets and Different Rolling Windowssupporting

confidence: 86%

“…In the majority of the cases, papers [20,24]. Empirical analyses have shown that the use of improved estimators for moments and co-moments leads to higher out-of-sample performance compared to the sample estimation, see among others [15]. both long-term performances and risk-adjusted measures tend to provide the best behaviour at least for one of the network-based approaches.…”

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confidence: 99%

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Asset allocation: new evidence through network approaches

2019

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The main contribution of the paper is to employ the financial market network as a useful tool to improve the portfolio selection process, where nodes indicate securities and edges capture the dependence structure of the system. Three different methods are proposed in order to extract the dependence structure between assets in a network context. Starting from this modified structure, we formulate and then we solve the asset allocation problem. We find that the portfolios obtained through a network-based approach are composed mainly of peripheral assets, which are poorly connected with the others. These portfolios, in the majority of cases, are characterized by an higher trade-off between performance and risk with respect to the traditional Global Minimum Variance (GMV) portfolio. Additionally, this methodology benefits of a graphical visualization of the selected portfolio directly over the graphic layout of the network, which helps in improving our understanding of the optimal strategy. IntroductionModern portfolio theory, which originated with Harry Markowitz's seminal paper in 1952 (see [23]) has stood the test of time and continues to be the intellectual foundation for real-world portfolio management. In this static framework, investors optimally allocate their wealth across a set of assets considering only the first and second moment of the returns' distribution. Despite the profound changes derived from this publication, the out-of-sample performance of Markowitz's prescriptions is often not as promising as expected. The poor performance of Markowitz's rule stems from the large estimation errors on the vector of expected returns (see [26]) and on the covariance matrix (see [17]) leading to the well-documented error-maximizing property discussed in [27]. The magnitude of this problem is evident when we acknowledge the modest improvements achieved by those models specifically designed to tackle the estimation risk (see [10]). Moreover, the evidence indicates that the simple yet effective equally-weighted portfolio rule has not been consistently out-performed by more sophisticated alternatives, as reported in [2,10]. The literature on portfolio selection has been extended on several directions. On one hand, some extensions modify the optimal problem, considering higher moments of returns' distribution (see [24] and the reference therein), exploiting alternative risk measures (see [6,19]) and as well as utility functions (see [37,13]), or proposing dynamic approaches (see among others [39,4]). On the other hand, there is now a vast literature on how to deal with the problem, that focuses on improved estimation procedures, considering that the optimal allocation is very sensitive to the estimation of moments and co-moments 1 .1 This sensitivity has generally been attributed to the tendency of the optimization to magnify the effects of estimation error. Michaud in [27] referred to "portfolio optimization "as "error maximization ". Efforts to improve parameters estimation procedure include among others the arXiv:18...

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Section: Alternative Datasets and Different Rolling Windowssupporting

confidence: 86%

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confidence: 99%

Asset allocation: new evidence through network approaches

2019

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“…To overcome these main drawbacks, several variations and extensions of the original methodology have been proposed in the literature. In [20], a higher out-of-sample performance is derived by imposing specific constraints, and these results have been further confirmed in [4,18]. Alternative approaches deal with the problem of optimal portfolio choice by employing a Bayesian methodology to estimate unknown mean-variance parameters reducing the estimation errors.…”

Section: Introductionmentioning

confidence: 68%

“…The authors in [26] propose the shrinkage estimator toward the constant correlation, while in [31] this approach has been extended to higher moments such as skewness and kurtosis. Empirical analyses have shown that the use of shrinkage estimators for the mean-variance parameters often improves the out-of-sample performance (see [18,19]).…”

Section: Introductionmentioning

confidence: 99%

Smart network based portfolios

Clemente,

Grassi,

Hitaj

2019

Preprint

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In this article we deal with the problem of portfolio allocation by enhancing network theory tools. We use the dependence structure of the correlations network in constructing some well-known risk-based models in which the estimation of correlation matrix is a building block in the portfolio optimization. We formulate and solve all these portfolio allocation problems using both the standard approach and the network-based approach. Moreover, in constructing the network-based portfolios we propose the use of two different estimators for the covariance matrix: the sample estimator and the shrinkage toward constant correlation one. All the strategies under analysis are implemented on two high-dimensional portfolios having different characteristics, covering the period from January 2001 to December 2017. We find that the network-based portfolio consistently better performs and has lower risk compared to the corresponding standard portfolio in an out-of-sample perspective.

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“…In our analysis, the in-sample and the out-of-sample windows involve 104 and four observations respectively. These parameter choices are typical settings for portfolio selection problems (Hitaj & Zambruno, 2016).…”

Section: Methodsmentioning

confidence: 99%

Group Risk Parity Strategies for ETFs Portfolios

Kaucic¹,

Valentinuz²

2018

CBR

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This research aims to compare different strategies that a non-professional investor in exchange-traded funds (ETFs) could employ to reach a good performance both from profits and from a risk perspective. In recent years, especially after the 2008 crisis, a new technique to evaluate the risk has become more popular, the so-called risk parity, which seeks to equalise the contributions to risk of the portfolio constituents. Our study analyses 17 variants of risk parity portfolio design for groups with the minimum variance strategy and equally weighted portfolio over a pool of 56 ETFs-listed on the Italian Stock Exchange-of eight different categories of specialisation. Empirical results confirm the usefulness of the group risk parity strategies in improving outcomes regarding diversification of risks among classes with good out-of-sample performance with respects to the target models.

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Are Smart Beta strategies suitable for hedge fund portfolios?

Cited by 14 publications

References 19 publications

Asset allocation: new evidence through network approaches

Asset allocation: new evidence through network approaches

Smart network based portfolios

Group Risk Parity Strategies for ETFs Portfolios

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