Nonparametric testing for anomaly effects in empirical asset pricing models

In this paper, we propose a class of nonparametric tests for anomaly effects in empirical asset pricing models in the framework of nonparametric panel data models with interactive fixed effects. Our approach has two prominent features: one is the adoption of nonparametric functional form to capture...

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Main Authors: JIN, Sainan, SU, Liangjun, ZHANG, Yonghui
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2015
Subjects:
EIV
Online Access:https://ink.library.smu.edu.sg/soe_research/1874
https://ink.library.smu.edu.sg/context/soe_research/article/2874/viewcontent/Nonparametric_testing_Empirical_2014_pp.pdf
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spelling sg-smu-ink.soe_research-28742020-04-01T01:58:48Z Nonparametric testing for anomaly effects in empirical asset pricing models JIN, Sainan SU, Liangjun ZHANG, Yonghui In this paper, we propose a class of nonparametric tests for anomaly effects in empirical asset pricing models in the framework of nonparametric panel data models with interactive fixed effects. Our approach has two prominent features: one is the adoption of nonparametric functional form to capture the anomaly effects of some asset-specific characteristics and the other is the flexible treatment of both observed/constructed and unobserved common factors. By estimating the unknown factors, betas, and nonparametric function simultaneously, our setup is robust to misspecification of functional form and common factors and avoids the well-known "error-in-variable" problem associated with the commonly used two-pass procedure. We apply our method to a publicly available data set and divide the full sample into three subsamples. Our empirical results show that size and book-to-market ratio affect the excess returns of portfolios significantly for the full sample and two of the three subsamples in all five factor pricing models under investigation. In particular, nonparametric component is significantly different from zero, meaning that the constructed common factors (e.g., small minus big and high minus low) cannot capture all the size and book-to-market ratio effects. We also find strong evidence of nonlinearity of the anomaly effects in the Fama-French 3-factor model and the augmented 4-factor and 5-factor models in the full sample and two of the three subsamples. 2015-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1874 info:doi/10.1007/s00181-014-0846-2 https://ink.library.smu.edu.sg/context/soe_research/article/2874/viewcontent/Nonparametric_testing_Empirical_2014_pp.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Anomaly effects Asset pricing CAPM Common factors EIV Fama-French three-factor Interactive fixed effects Nonparametric panel data model Sieve method Specification test Econometrics Economic Theory
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Anomaly effects
Asset pricing
CAPM
Common factors
EIV
Fama-French three-factor
Interactive fixed effects
Nonparametric panel data model
Sieve method
Specification test
Econometrics
Economic Theory
spellingShingle Anomaly effects
Asset pricing
CAPM
Common factors
EIV
Fama-French three-factor
Interactive fixed effects
Nonparametric panel data model
Sieve method
Specification test
Econometrics
Economic Theory
JIN, Sainan
SU, Liangjun
ZHANG, Yonghui
Nonparametric testing for anomaly effects in empirical asset pricing models
description In this paper, we propose a class of nonparametric tests for anomaly effects in empirical asset pricing models in the framework of nonparametric panel data models with interactive fixed effects. Our approach has two prominent features: one is the adoption of nonparametric functional form to capture the anomaly effects of some asset-specific characteristics and the other is the flexible treatment of both observed/constructed and unobserved common factors. By estimating the unknown factors, betas, and nonparametric function simultaneously, our setup is robust to misspecification of functional form and common factors and avoids the well-known "error-in-variable" problem associated with the commonly used two-pass procedure. We apply our method to a publicly available data set and divide the full sample into three subsamples. Our empirical results show that size and book-to-market ratio affect the excess returns of portfolios significantly for the full sample and two of the three subsamples in all five factor pricing models under investigation. In particular, nonparametric component is significantly different from zero, meaning that the constructed common factors (e.g., small minus big and high minus low) cannot capture all the size and book-to-market ratio effects. We also find strong evidence of nonlinearity of the anomaly effects in the Fama-French 3-factor model and the augmented 4-factor and 5-factor models in the full sample and two of the three subsamples.
format text
author JIN, Sainan
SU, Liangjun
ZHANG, Yonghui
author_facet JIN, Sainan
SU, Liangjun
ZHANG, Yonghui
author_sort JIN, Sainan
title Nonparametric testing for anomaly effects in empirical asset pricing models
title_short Nonparametric testing for anomaly effects in empirical asset pricing models
title_full Nonparametric testing for anomaly effects in empirical asset pricing models
title_fullStr Nonparametric testing for anomaly effects in empirical asset pricing models
title_full_unstemmed Nonparametric testing for anomaly effects in empirical asset pricing models
title_sort nonparametric testing for anomaly effects in empirical asset pricing models
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/soe_research/1874
https://ink.library.smu.edu.sg/context/soe_research/article/2874/viewcontent/Nonparametric_testing_Empirical_2014_pp.pdf
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