A Framework for Assessing Factors and Implementing Smart Beta Strategies

Hsu, Jason C.; Kalesnik, Vitali; Viswanathan, Vivek

doi:10.3905/jii.2015.6.1.089

Cited by 19 publications

(9 citation statements)

References 26 publications

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“…Moreover, overall perception of the mutual fund performance and performance persistence by and large remains unchanged when the 'missing factors' are added. Further, the most recent academic literature raises concerns on the exploding number of the priced factors proposed and claim that some published studies are the result of statistical biases (McLean and Pontiff, 2016) and data mining (Hsu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Review of new trends in the literature on factor models and mutual fund performance

Mateus¹,

Mateus

Todorović

2019

International Review of Financial Analysis

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In this paper we provide critical review of recent developments in the mutual fund performance evaluation literature. The new literature centres around two main themes: enhancing explanatory power of the standard Fama-French-Carhart factor models by augmenting them with different factors and altering standard models to account for presence of non-zero alphas in passive indices used as fund benchmarks.The latter includes the literature providing solutions for scenarios in which those benchmarks do not match fund objectives. We find that in the plethora of suggested 'missing' factors, not one can be universally used to explain all anomalies or price all stocks. We also find that new models that adjust a fund's standard Carhart alpha for alpha of its benchmark or for commonalities in its peer-group, provide additional information on fund performance to that given by the standard models. Specifically, these models give account of fund's relative performance -to the benchmark or the peer-group, which is of use to investors.

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Section: Introductionmentioning

confidence: 99%

Review of new trends in the literature on factor models and mutual fund performance

Mateus¹,

Mateus

Todorović

2019

International Review of Financial Analysis

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“…The "low volatility" premium measures the outperformance of low volatility stocks over highly volatile stocks. Blitzand Vliet ( 2007) and Hsu et al (2015) examined the low-volatility effect and found that low-risk stocks substantially outperformed the market benchmark. The low volatility anomaly and its persistence may be explained by a variety of cognitive and behavioral factors.…”

Section: Low Volatility Factormentioning

confidence: 99%

Smart Beta Investing: An Alternative Investment Paradigm in Emerging Indian Equity Market

Monga

Aggrawal

Singh

2022

OMEE

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This paper fundamentally looks at the novel concept of Smart Beta investing in constructing a more efficient and well-diversified alternative investment. Smart beta has been a popular investment philosophy, although emerging countries have been slower to adopt and execute it. In this way, the study investigates the existence, performance, and robustness of smart beta strategies in a divergent financial market. Moreover, it is an initial attempt to integrate the framework of stock selection and stock weighting to construct and test smart beta strategies against the traditional Indian market benchmark (S&P BSE 500). The findings show that smart beta investing results in a better risk-return profile on an absolute and risk-adjusted basis. Furthermore, the results demonstrate the consistency and robustness of smart beta strategies in different market conditions and display their outperformance even in bearish market conditions.

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“…We use a different set of proxy variables to check the robustness of particular investment styles to the definition of their factors. Hsu et al [2015] also recommend to slightly perturb the definition of the factors while evaluating smart beta strategies. Panel A of Exhibit 8 shows the definition of the new set of factors.…”

Section: Robustness Checkmentioning

confidence: 99%

Analyzing the Performance of Multifactor Investment Strategies under a Multiple Testing Framework

Vincent¹,

Hsu²,

Lin³

2018

JPM

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This study aims at risk-scaled returns for multi-factor portfolios, many of which appear to systematically have a superior Sharpe ratio. However, given the prevalent use of standalone backtesting research designs in the finance field to evaluate portfolio strategies that are potentially based on numerous combinations of factors, the significance of data mining bias may be among the most pronounced in this area of studies. Therefore, we aim to discriminate against the competing explanation of spuriously significant risk-scaled returns. Our empirical tests provide evidence that the stock picking strategies with certain combined criteria of firm characteristics outperform both value-weighted index and smallcap value portfolio even after we adjust for the multiple testing bias. The occurrences of winning for the superior multi-factor portfolios appear to be more stable than those for the single factor portfolios over different subsamples. Moreover, the outperformance of multifactor rules is robust to alternative definitions of factors.

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A Framework for Assessing Factors and Implementing Smart Beta Strategies

Cited by 19 publications

References 26 publications

Review of new trends in the literature on factor models and mutual fund performance

Review of new trends in the literature on factor models and mutual fund performance

Smart Beta Investing: An Alternative Investment Paradigm in Emerging Indian Equity Market

Analyzing the Performance of Multifactor Investment Strategies under a Multiple Testing Framework

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