Robustness of Smart Beta Strategies

Amenc, Noël; Goltz, Felix; Sivasubramanian, Sivagaminathan; Lodh, Ashish

doi:10.3905/jii.2015.6.1.017

Cited by 14 publications

(3 citation statements)

References 29 publications

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“…Ledoit, Wolf, and Zhao (2019) and Hsu, Kalesnik, and Surti (2010) have shown that factor underperformance may be tackled by using more sophisticated weighting criteria. Amenc et al (2015) also recognise that index providers do not always efficiently deal with the issue of ensuring out-of-sample robustness of factor investing strategies. They suggest certain practices that index creators can follow to improve the robustness of factor portfolios.…”

Section: Resultsmentioning

confidence: 99%

Are Factor Investing Strategies Successful Out-of-Sample: Evidence From the Nifty Indices

Lalwani¹

2023

AFL

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Do factor investment strategies that have generated superior returns in the past continue to do so out-of-sample? To test this hypothesis, I check the performance of nine factor-based indices of the National Stock Exchange (NSE) of India. My results show that the performance of most indices falls considerably in the out-of-sample period, i.e. the period after the launch of an index. The results hold for absolute as well as excess and risk-adjusted returns. In additional tests, I find that none of the factor strategies generates significant alpha after controlling for standard factors such as size, value, and momentum.

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Section: Resultsmentioning

confidence: 99%

Are Factor Investing Strategies Successful Out-of-Sample: Evidence From the Nifty Indices

Lalwani¹

2023

AFL

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“…Smart betas create new markets and their ultimate goal is to deliver superior performance. Amenc et al (2015) evaluate the robustness of various smart beta strategies and suggest that single-factor indices have high robustness only in similar market conditions, while multi-beta allocations have high robustness regardless of prevailing market conditions. However, Malkiel (2014) argues that the excess returns generated by smart beta funds are compensations of the additional risks they assume.…”

Section: Introductionmentioning

confidence: 99%

Are smart beta funds really smart? Evidence from rational and quasi-rational investor sentiment data

Verma

Soydemir

Huang

2019

RBF

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Purpose The purpose of this paper is to examine the relative effects of rational and quasi-rational sentiments of individual and institutional investors on a set of smart beta fund returns. The magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact). These results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals. Design/methodology/approach The results of the impulse functions generated from a multivariate model suggest that the smart beta fund returns are negatively (positively) impacted by individual (institutional) investor sentiments. Findings The magnitudes of the impacts of institutional investor sentiments are greater than those of individual investor sentiments. In addition, both rational and quasi-rational sentiments of individual and institutional investors have significant impacts on smart beta fund returns. The magnitudes of the impacts of quasi-rational sentiments are greater than those of the rational sentiments for both types of investors (quasi-rational sentiments of institutional investors have the maximum impact). Originality/value These results are consistent with the arguments that professional investors consider the sentiments of individual investors as contrarian leading indicators which are mainly driven by noise while conform the sentiments of institutional investors which are driven by more rational factors. A majority of smart beta funds in the sample outperform the S&P500 returns in the short term but fail to consistently beat the market. The authors find evidence that smart beta funds with consistently high returns are relatively less (more) driven by individual (institutional) investor sentiments. Overall, the authors argue that smart beta funds appear to follow quasi-rational sentiments of both individual and institutional investors that are not rooted in economic fundamentals.

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“…The robustness of treating factors as investable indices, presented as a reasonable alternative to cap-weighted indices has been evaluated by e.g. Amenc et al [2015]. Cazalet, Grison and Roncalli [2013] evaluated construction of "smart beta" indices based on risk factors.…”

Section: Introductionmentioning

confidence: 99%

Can we invest on the basis of equity risk premia and risk factors from multi-factor models?

Sakowski¹,

Ślepaczuk²,

Wywiał³

2016

EBR

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: We examine two investment algorithms built on the weekly data of world equity indices for emerging and developed countries in the period 2000-2015. We create seven risk factors using additional data about market capitalization, book value, country GDP and betas of equity indices. The first strategy utilizes the theoretical value of equity risk premium from the seven-factor Markov-switching model with exogenous variables. We compare theoretical with the realized equity risk premium for a given index to undertake the buy/sell decisions. The second algorithm works only on eight risk factors and applies them as input variables to Markowitz models with alternative optimization criteria. Finally we note that the impact of risk factors on the final results of investment strategy is much more important than the selection of a particular econometric model in order to correctly evaluate the equity risk premium.

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Robustness of Smart Beta Strategies

Cited by 14 publications

References 29 publications

Are Factor Investing Strategies Successful Out-of-Sample: Evidence From the Nifty Indices

Are Factor Investing Strategies Successful Out-of-Sample: Evidence From the Nifty Indices

Are smart beta funds really smart? Evidence from rational and quasi-rational investor sentiment data

Can we invest on the basis of equity risk premia and risk factors from multi-factor models?

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