Slope Factors Outperform: Evidence from a Large Comparative Study

Chib, Siddhartha; Lin, Yi Chun; Pukthuanthong, Kuntara; Zeng, Xiaming

doi:10.2139/ssrn.3966807

Cited by 2 publications

(8 citation statements)

References 15 publications

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“…The analysis so far has run the model scan using all the available data. Chib et al. (2021a, b) argues that the choice of the relevant factor model might change for a number of reasons for example technological change, or the adaptive efficient market hypothesis (Lo, 2004) among others.…”

Section: Resultsmentioning

confidence: 99%

“…The overall sample period is split into two subperiods for a given change point. Chib et al . (2021a, b) use December 1996, June 1997, December 1997, June 1998, December 1998, June 1999, and December 1999 due to the Internet revolution.…”

Section: Methodsmentioning

confidence: 99%

“…Our study suggests that the efficient factor transformation should be performed and included in model comparison tests in the asset pricing literature. Our analysis has used the Bayesian model scan under the multivariate normal distribution of Chib et al (2021a). Further work could be extended to include the efficient factors in a classical asset pricing framework.…”

Section: Us Efficient Factors In Amentioning

confidence: 99%

“…First, we are the only authors to examine if the efficient factors developed by Ehsani and Linnainmaa (2022) have an impact on model comparison tests in U.S stock returns. Second, we complement the Bayesian model scan studies of Barillas and Shanken (2018), Chib et al . (2021a, b), and Chib et al .…”

Section: Introductionmentioning

confidence: 99%

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US efficient factors in a Bayesian model scan framework

O'Connell

2024

JES

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PurposeThe author examines the impact these efficient factors have on factor model comparison tests in US returns using the Bayesian model scan approach of Chib et al. (2020), and Chib et al.(2022).Design/methodology/approachEhsani and Linnainmaa (2022) show that time-series efficient investment factors in US stock returns span and earn 40% higher Sharpe ratios than the original factors.FindingsThe author shows that the optimal asset pricing model is an eight-factor model which contains efficient versions of the market factor, value factor (HML) and long-horizon behavioral factor (FIN). The findings show that efficient factors enhance the performance of US factor model performance. The top performing asset pricing model does not change in recent data.Originality/valueThe author is the only one to examine if the efficient factors developed by Ehsani and Linnainmaa (2022) have an impact on model comparison tests in US stock returns.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Us Efficient Factors In Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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US efficient factors in a Bayesian model scan framework

O'Connell

2024

JES

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“…In addition, some contributions have compared the efficiency of characteristics-based versus factor-based models in asset pricing. 3 For instance, Hou et al (2011), Goyal and Jegadeesh (2018), Chordia et al (2019), Fama and French (2020), Raponi et al (2020) and Chib et al (2021)) all report that the former have a better explanatory power over stock returns. Jacobs and Levy (2021) provide some historical perspectives on the differences between the two approaches.…”

Section: Introductionmentioning

confidence: 99%

Characteristics-driven returns in equilibrium

Coqueret¹

2022

Preprint

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We reverse-engineer the equilibrium construction process of asset prices in order to obtain returns which depend on firm characteristics, possibly in a linear fashion. One key requirement is that agents must have demands that rely separately on firm characteristics and on the log-price of assets. Market clearing via exogenous (non-factor driven) supply, combined with linear demands in characteristics, yields the sought form. The coefficients in the resulting linear expressions are scaled net aggregate demands for characteristics, as well as their variations, and both can be jointly estimated via panel regressions. Conditions underpinning asset pricing anomalies are derived and underline the theoretical importance of the links between characteristics. Empirically, when the number of characteristics is small, the value and momentum anomalies are mostly driven by firm-specific fixed-effects, i.e., latent demands, which highlights the shortcomings of low-dimensional models.

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Slope Factors Outperform: Evidence from a Large Comparative Study

Cited by 2 publications

References 15 publications

US efficient factors in a Bayesian model scan framework

US efficient factors in a Bayesian model scan framework

Characteristics-driven returns in equilibrium

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