Academic, Practitioner, and Investor Perspectives on Factor Investing

Cerniglia, Joseph A.; Fabozzi, Frank J.

doi:10.3905/jpm.2018.44.4.010

Cited by 10 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2019) , and Fama and French (2020) among others. As addressed by Cerniglia and Fabozzi (2018) returns from factor strategies decay as investors learn about factor mispricings. 30…”

Section: Resultsmentioning

confidence: 99%

“…11 Profitability is, then, measured through three different metrics: 1) Gross profit on total assets (GPA), 2) Return on capital (ROC_Green), as defined by Greenblatt (2010). 3) Return on capital (ROC_Det) including 4 Factor investment strategies exhibit in this sense also characteristics of passive investment by using algorithms based on static rules (see Cerniglia and Fabozzi, 2018). 5 In what follows, we will use the term "iterative", "recursive" and "conditional" interchangeably.…”

Section: Building Portfoliosmentioning

confidence: 99%

“…The decay in the value of alpha is especially notable for the GPA and EVEBITDA_ROC_Green strategies and is consistent with the existence of factor crowding widely discussed in the literature: see Linnainmaa et al (2018), Arnott et al (2019), and Fama and French (2020) among others. As addressed by Cerniglia and Fabozzi (2018) returns from factor strategies decay as investors learn about factor mispricings. 30…”

Section: Time Series Evolution Of Factor Outperformancementioning

confidence: 99%

See 2 more Smart Citations

Factor investing: A stock selection methodology for the European equity market

Bermejo

Figuerola‐Ferretti

Hevia³

et al. 2021

Heliyon

View full text Add to dashboard Cite

This paper uses European high capitalization corporate data for the 1991-2019 period to demonstrate that a systematic active management portfolio based on the identification of value, profitability, and momentum factors can outperform competing benchmark strategies. Factor investment methodologies received significant attention in the literature in the U.S. market but their application to European corporates is more limited. The authors construct several systematic investment strategies combining different metrics measuring the three factors. Reported results show that a) combined (mixed/conditional) strategies deliver positive alphas and significantly outperform their pure strategy counterparts and b) while there exists a time changing performance of selected metrics the iterative combination of factors delivers the highest performance with average annualized compounded returns of up to about 17%.Three Key Takeaways: 1. This paper documents the existence of alpha-generating factor strategies based on a combination of value, profitability, and momentum metrics. Combined (mixed/conditional) portfolios significantly outperform their pure strategy counterparts.2. The iterative combination of factors delivers the highest performance with annualized compounded returns of up to 17%.3. In line with the recent literature, we find decaying returns to factor strategies.

show abstract

“…(2019) , and Fama and French (2020) among others. As addressed by Cerniglia and Fabozzi (2018) returns from factor strategies decay as investors learn about factor mispricings. 30…”

Section: Resultsmentioning

confidence: 99%

Section: Building Portfoliosmentioning

confidence: 99%

Section: Time Series Evolution Of Factor Outperformancementioning

confidence: 99%

See 1 more Smart Citation

Factor investing: A stock selection methodology for the European equity market

Bermejo

Figuerola‐Ferretti

Hevia³

et al. 2021

Heliyon

View full text Add to dashboard Cite

show abstract

“…Factor investing has gained increased popularity over the past decades among academics and market participants (Cerniglia & Fabozzi, 2018). This is based on the fact that investors believe that portfolio returns' expectations should be evidence based and that factor-based portfolios are considered a solid example of long-term investment (Briere & Szafarz, 2018;Dimson, Marsh, & Staunton, 2017).…”

Section: Introductionmentioning

confidence: 99%

Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization

2019

View full text Add to dashboard Cite

This study is investigating the predictability of the five Fama–French factors and explores their optimal portfolio allocation for factor investing during 2000–2017. Firstly, we forecast each factor with a pool of linear and nonlinear models. Next, the individual forecasts are combined through dynamic model averaging, and their performance is benchmarked by the best performing individual predictor and other forecast combination techniques. Finally, we use the generalized autoregressive score model and the skewed t copula method to estimate the correlation of assets. The generalized autoregressive score performance is also compared with other traditional approaches such as dynamic conditional correlation model and asymmetric dynamic conditional correlation. The performance of the constructed portfolios is assessed through traditional metrics and ratios accounting for the conditional value‐at‐risk and the conditional diversification benefits approach. Our results show that combining Bayesian forecast combinations with copulas is leading to significant improvements in the portfolio optimization process, and forecasting covariance accounting for asymmetric dependence between the factors adds diversification benefits to the obtained portfolios.

show abstract

“…Further, factors are not directly investable, but factor exposures are an offshoot of investing in assets. With relatively more stable returns over time than those obtained from asset classes, depending on the market environment some factors perform better than others, see Ang (2014), Cerniglia and Fabozzi (2018), Dimson, Marsh, and Staunton (2017), Naik, Devarajan, Nowobilski, Page, and Pedersen (2016).…”

Section: Introductionmentioning

confidence: 99%

Diversification benefits of cat bonds: An in‐depth examination

Demers-Bélanger

Lai

2020

Financial Market

View full text Add to dashboard Cite

We investigate whether the inclusion of Cat Bonds in portfolios composed of traditional assets and common factors is beneficial to investors. Various mean‐variance spanning tests performed for the period of 2002 to 2017 show that under different market conditions, the addition of Cat Bonds gives rise to previously unattainable portfolios. Using the Engle (2002) Dynamic Conditional Correlation (DCC) model, we find that including Cat bonds increases significantly the time‐varying Sharpe ratio and the Choueifaty and Coignard (2008) maximum diversification ratio. Cat Bonds provide needed diversification during critical times particularly during episodes of crisis and of high volatility. Under the second‐order stochastic dominance efficiency (SDE) tests, the null hypothesis that portfolios without Cat Bonds are efficient cannot be rejected. Out‐of‐sample analyses indicate that the performance of portfolios with Cat Bonds included varies depending on the performance measures employed, the portfolio construction techniques used and the assets or factors considered.

show abstract

Academic, Practitioner, and Investor Perspectives on Factor Investing

Cited by 10 publications

References 18 publications

Factor investing: A stock selection methodology for the European equity market

Factor investing: A stock selection methodology for the European equity market

Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization

Diversification benefits of cat bonds: An in‐depth examination

Contact Info

Product

Resources

About