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Cornell University

CORPORATE STRATEGY, EARNINGS MANIPULATION, AND SHORT SELLING

Abstract

dc:description.abstract

Earnings manipulation has perplexed many investors and regulators for decades. Factors such as CEO’s characteristics, macro environment, behavioral biases, and regulation rules are discussed in the literature. In this paper, corporate strategy aggressiveness is proposed to be one of the factors that leads to earnings manipulation. Short interest ratio is used to indicate how the market perceive and react to company’s earnings manipulation probability. Based on the data from Russell 2000 constituents during 2015 to 2019, my hierarchical regression results indicate that firms with the aggressive strategy are more likely to be engaged in earnings manipulation activities, resulting in a higher short interest ratio. The findings are significant when the model uses a binary variable instead of a continuous variable to measure the corporate strategy aggressiveness in the robust test. This paper will interest investors that focus on fundamental investing and researchers who study accounting, corporate strategy, and financial market.

Degree

thesis:*
Name thesis:degree_name
M.S., Applied Economics and Management
Level thesis:degree_level
Master of Science
Discipline thesis:degree_discipline
Applied Economics and Management
Grantor
Cornell University
Year dc:date.issued
2021

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Su, Xingbang
Committee member dc:contributor.committeemember
  • Gomes, Carla P.

Subjects

dc:subject × 3

Rights

Language dc:language.iso
en

Identifiers

dc:identifier.*
Dc Identifier Other
ProQuest Submission ID: 11267
ProQuest Publication ID: 28652622
OAI identifier oai:identifier
oai:ecommons.cornell.edu:1813/110463

Chain of custody

source
Harvested from
Cornell University
Base URL
ecommons.cornell.edu/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Su, Xingbang. CORPORATE STRATEGY, EARNINGS MANIPULATION, AND SHORT SELLING. Master of Science thesis, Cornell University, 2021. https://hdl.handle.net/1813/110463