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The main purpose of this research is to investigate the impact of changes in cash flow measures and metrics on firm financial performance. The study uses generalized estimating equations (GEEs) methodology to analyze longitudinal data for sample of 20288 listed Chinese non-financial firms from the period 2018:q2-2020:q1. The main advantage of GEEs method over other estimation techniques is its ability to robustly estimate the variances of regression coefficients for data samples that display high correlation between repeated measurements. The findings of study show that the decline in cash flow measures and metrics bring significant positive improvements in the financial performance of firms. The empirical evidence suggests that performance improvement levers (i.e. cash flow measures and metrics) are more pronounced in low leverage firms, suggesting that changes in cash flow measures and metrics bring more positive changes in low leverage firms’ financial performance relatively to high leveraged firms. The results hold after mitigating endogeneity based on dynamic panel system generalized method of moments (GMM) and sensitivity analysis considering the robustness of main findings. The paper makes significant contribution to the literature related to cash flow management and working capital management. Since, this paper is among few to empirically study, how cash flow measures and metrics are related to firm performance from dynamic stand point especially from the context of Chinese non-financial firms. Abstract
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Most cited references120
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Signaling Theory: A Review and Assessment
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Author and article information
Contributors
Fahmida Laghari: Role: Investigation
Farhan Ahmed:
ORCID: https://orcid.org/0000-0002-1147-8347
Role: Investigation
María de las Nieves López García:
ORCID: https://orcid.org/0000-0002-9892-5006
Role: Investigation
Chenguel Mohamed Bechir: Role: Editor
Journal
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS One
Journal ID (publisher-id): plos
Title: PLOS ONE
Publisher: Public Library of Science (San Francisco, CA USA )
ISSN (Electronic): 1932-6203
Publication date (Electronic): 20 June 2023
Publication date Collection: 2023
Volume: 18
Issue: 6
Electronic Location Identifier: e0287135
Affiliations
[1 ] School of Accounting, Xijing University, Xi’an City, Shaanxi Province, People’s Republic of China
[2 ] Department of Economics and Management Sciences, NED University of Engineering & Technology, Karachi City, Pakistan
[3 ] Department of Business and Economics, University of Almeria, Almería, Spain
Author notes
* E-mail: mlg252@ 123456ual.es
Author information
Farhan Ahmed https://orcid.org/0000-0002-1147-8347
María de las Nieves López García https://orcid.org/0000-0002-9892-5006
Article
Publisher ID: PONE-D-23-04379
DOI: 10.1371/journal.pone.0287135
PMC ID: 10281586
SO-VID: 4e6f33c6-b4e2-4f32-8eec-4052fb5cf27b
Copyright © © 2023 Laghari et al
License:
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
History
Date received : 23 February 2023
Date accepted : 31 May 2023
Page count
Figures: 1, Tables: 7, Pages: 26
Funding
Funded by: Spanish Ministry of Science and Innovation and FEDER
Award ID: PID2021-127836NB-I00
Award Recipient :
ORCID: https://orcid.org/0000-0002-9892-5006
María de las Nieves López García
Funded studies the grant has been awarded to the author María de la Nieves López García from the grant PID2021-127836NB-I00 (Spanish Ministry of Science and Innovation and FEDER). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Categories
Subject: Research Article
Subject: Social Sciences
Subject: Economics
Subject: Finance
Subject: Physical Sciences
Subject: Physics
Subject: Classical Mechanics
Subject: Continuum Mechanics
Subject: Fluid Mechanics
Subject: Fluid Dynamics
Subject: Fluid Flow
Subject: Social Sciences
Subject: Economics
Subject: Commerce
Subject: Payment
Subject: Social Sciences
Subject: Economics
Subject: Finance
Subject: Public Finance
Subject: Money Supply and Banking
Subject: People and Places
Subject: Geographical Locations
Subject: Asia
Subject: China
Subject: Research and analysis methods
Subject: Mathematical and statistical techniques
Subject: Statistical methods
Subject: Monte Carlo method
Subject: Physical sciences
Subject: Mathematics
Subject: Statistics
Subject: Statistical methods
Subject: Monte Carlo method
Subject: Social Sciences
Subject: Economics
Subject: Financial Markets
Subject: Computer and Information Sciences
Subject: Data Management
Custom metadata
Data Availability The data used in this study is taken from China Stock Market and Accounting Research (CSMAR) database.
ScienceOpen disciplines: Uncategorized
Data availability:
ScienceOpen disciplines: Uncategorized
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