Investigating linear multi-factor models in asset pricing: considerable supplemental evidence*

The literature has offered an interesting debate about whether the performance of Fama-French’s three-factor benchmark model is inadequate because it fails to pass some model specification tests and its R2 is not convincingly high in cross-sectional estimations. Previous studies have been quite limi...

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Main Authors: Shi, Q., Cheung, Adrian, Li, B.
Format: Journal Article
Published: 2018
Online Access:http://hdl.handle.net/20.500.11937/72674
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author Shi, Q.
Cheung, Adrian
Li, B.
author_facet Shi, Q.
Cheung, Adrian
Li, B.
author_sort Shi, Q.
building Curtin Institutional Repository
collection Online Access
description The literature has offered an interesting debate about whether the performance of Fama-French’s three-factor benchmark model is inadequate because it fails to pass some model specification tests and its R2 is not convincingly high in cross-sectional estimations. Previous studies have been quite limited, since they only focused on the time-series procedure with many models. We extend their work by providing a more robust investigation of the performance of several well-regarded pricing models in pooled portfolios and other portfolios sorted by new and important anomalies, using cross-sectional GMM tests for robustness. Finally, we find that, in addition to Fama and French’s five-factor model proposed in 1993, Fama-French’s three-factor model augmented by other factors usually outperforms Fama-French’s three-factor model across a significant proportion of different portfolios. In particular, Frazzini, Kabiller, and Pedersen’s model shows the best overall performance and consistency across different portfolios.
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spelling curtin-20.500.11937-726742019-02-08T05:36:54Z Investigating linear multi-factor models in asset pricing: considerable supplemental evidence* Shi, Q. Cheung, Adrian Li, B. The literature has offered an interesting debate about whether the performance of Fama-French’s three-factor benchmark model is inadequate because it fails to pass some model specification tests and its R2 is not convincingly high in cross-sectional estimations. Previous studies have been quite limited, since they only focused on the time-series procedure with many models. We extend their work by providing a more robust investigation of the performance of several well-regarded pricing models in pooled portfolios and other portfolios sorted by new and important anomalies, using cross-sectional GMM tests for robustness. Finally, we find that, in addition to Fama and French’s five-factor model proposed in 1993, Fama-French’s three-factor model augmented by other factors usually outperforms Fama-French’s three-factor model across a significant proportion of different portfolios. In particular, Frazzini, Kabiller, and Pedersen’s model shows the best overall performance and consistency across different portfolios. 2018 Journal Article http://hdl.handle.net/20.500.11937/72674 10.1080/16081625.2017.1419878 restricted
spellingShingle Shi, Q.
Cheung, Adrian
Li, B.
Investigating linear multi-factor models in asset pricing: considerable supplemental evidence*
title Investigating linear multi-factor models in asset pricing: considerable supplemental evidence*
title_full Investigating linear multi-factor models in asset pricing: considerable supplemental evidence*
title_fullStr Investigating linear multi-factor models in asset pricing: considerable supplemental evidence*
title_full_unstemmed Investigating linear multi-factor models in asset pricing: considerable supplemental evidence*
title_short Investigating linear multi-factor models in asset pricing: considerable supplemental evidence*
title_sort investigating linear multi-factor models in asset pricing: considerable supplemental evidence*
url http://hdl.handle.net/20.500.11937/72674