Cross-Section of Returns, Predictors Credibility, and Method Issues

Zhimin (Jimmy) Yu ()
Additional contact information
Zhimin (Jimmy) Yu: Marilyn Davies College of Business, University Houston Downtown, Houston, TX 77002, USA

JRFM, 2023, vol. 16, issue 1, 1-12

Abstract: The paper focuses on the relationship between firms’ characteristics and cross-section returns. The author reviews and critically assesses the most recent contributions in the literature. After comparing the abnormal returns (Alpha) and t statistics of the original works with those of replication works, the author concludes that 94 characteristics are robust. The limitation of the paper is that measurement errors in the COMPUSTAT could affect the predictability of cross-section returns. The practical implication of the paper is that the author validates the practice of fundamental analysis. Investors could benefit from those discovered characteristics. The author validates the policy consequence and connects the theoretical frameworks with empirical results. The author evaluates the empirical methodology and proposes several methods to improve future research.

Keywords: accounting anomalies; abnormal return; characteristics; fundamental analysis; return predictors; risk factors (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1911-8074/16/1/34/pdf (application/pdf)
https://www.mdpi.com/1911-8074/16/1/34/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:16:y:2023:i:1:p:34-:d:1025770

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().