research on working capital management pdf

Working capital management: a literature review and research agenda

Purpose – The purpose of this paper is to review research on working capital management (WCM) and to identify gaps in the current body of knowledge, which justify future research directions. WCM has attracted serious research attention in the recent past, especially after the financial crisis of 2008. Design/methodology/approach – Using systematic literature review (SLR) method, the present study reviews 126 articles from referred journal and international conferences published on WCM. Findings – Detailed content analysis reveals that most of the research work is empirical and focuses mainly on two aspects, impact of working capital on profitability of firm and working capital practices. Major research work has concluded that WCM is essential for corporate profitability. The major issues with prior literature are lack of survey-based approach and lack of systematic theory development study, which opens all new areas for future research. The future research directions proposed in this paper may help develop a greater understanding of determinants and practices of WCM. Practical implications – Till date, literature on classification of WCM has been almost non-existent. This paper reviews a large number of articles on WCM and provides a classification scheme in to various categories. Subsequently, various emerging trends in the field of WCM are identified to help researchers specifying gaps in the literature and direct research efforts. Originality/value – This paper contains a comprehensive listing of publications on the WCM and their classification according to various attributes. The paper will be useful to researchers, finance professionals and others concerned with WCM to understand the importance of WCM. To the best of the authors’ knowledge, no detailed SLR on this topic has previously been published in academic journals.

• Related Documents

A systematic literature review on working capital management – an identification of new avenues

Purpose This paper aims to provide a review of the existing literature available on working capital (WC) and working capital management (WCM). Design/methodology/approach A systematic literature review (SLR) methodology is used to review 187 articles selected from referred journals, books and international conferences for the period 1980-2017. Findings This comprehensive review reveals that much of the focus in the existing literature is paid on investigating the empirical relationship between WCM and firm performance. Furthermore, the attention has been paid towards studying the WC practices. The behavioural aspects, qualitative studies, survey studies and systematic theory development have been ignored in most of the prior studies. These areas have a broader scope for future research. Research limitations/implications This study is based on literature review and theoretical in nature. Therefore, it does not have any empirical results. Practical implications So far, a limited literature review studies have been conducted in WCM perspective. This review provides various emerging trends, which may be considered in future research for providing a deep understanding of WCM. Originality/value This is the first time a detailed review of WCM literature has been conducted by using SLR for the period of 1980-2017. This review will be useful for researchers, business policymaker, finance professionals and all other having direct or indirect concerns with WCM study.

Process improvement project failure: a systematic literature review and future research agenda

Purpose Although scholars have considered the success factors of process improvement (PI) projects, limited research has considered the factors that influence failure. The purpose of this paper is to extend the understanding of PI project failure by systematically reviewing the research on generic project failure, and developing research propositions and future research directions specifically for PI projects. Design/methodology/approach A systematic literature review protocol resulted in a total of 97 research papers that are reviewed for contributions on project failure. Findings An inductive category formation process resulted in three categories of findings. The first category are the causes for project failure, the second category is about relatedness between failure factors and the third category is on failure mitigation strategies. For each category, propositions for future research on PI projects specifically are developed. Additional future research directions proposed lay in better understanding PI project failure as it unfolds (i.e. process studies vs cross-sectional), understanding PI project failure from a theoretical perspective and better understanding of PI project failure antecedents. Originality/value This paper takes a multi-disciplinary and project type approach, synthesizes the existing knowledge and reflects upon the developments in the field of research. Propositions and a framework for future research on PI project failure are presented.

Research in urban logistics: a systematic literature review

Purpose The last decades have witnessed an increased interest in urban logistics originating from both the research and the practitioners’ communities. Sustainable freight transports today are on the political, social and technological agenda of many actors operating in urban contexts. Due to the extent of the covered areas and the continuous progress in many fields, the resulting body of research on urban logistics appears quite fragmented. From an engineering management perspective, the purpose of this paper is to present a systematic literature review (SLR) that aims to consolidate the knowledge on urban logistics, analyse the development of the discipline, and provide future research directions. Design/methodology/approach The paper discusses the main evidence emerging from a SLR on urban logistics. The corpus resulting from the SLR has been used to perform a citation network analysis and a main path analysis that together underpin the identification of the most investigated topics and methodologies in the field. Findings Through the analysis of a corpus of 104 articles, the most important research contributions on urban logistics that represent the structural backbone in the development of the research over time in the field are detected. Based on these findings, this work identifies and discusses three areas of potential interest for future research. Originality/value This paper presents an SLR related to a research area in which the literature is extremely fragmented. The results provide insights about the research path, current trends and future research directions in the field of urban logistics.

Green competencies: insights and recommendations from a systematic literature review

PurposeThis study conceptualises the construct – green competencies. The concept is in the niche stage and needs further elaboration. Hence, to address the research gap, this study follows the steps proposed by Tranfield et al. (2003). The major part of the study comprises descriptive analysis and thematic analysis. Descriptive analysis of the selected 66 articles was examined with the classification framework, which contains year-wise distribution, journal-wise distribution, the focus of the concept, the economic sector, and dimensions of sustainable development. The paper conducts a thematic analysis of the following research questions. What are the green competencies and their conceptual definition? What are their dimensions?Design/methodology/approachThis paper applies a systematic literature review of green competencies literature, extends the state-of-the-art using the natural resource-based view, and discusses future research directions for academicians and practitioners.FindingsIn recent years, there was considerable interest in green competencies (GC), as reflected in the surge of articles published in this genre. This paper asserts that green competencies are a multidimensional construct comprised of green knowledge, green skills, green abilities, green attitudes, green behaviours, and green awareness.Originality/valueDespite the significance of green competencies, there has been a dearth of study to define the constructs and identify the dimensions. Hence, this study addresses the literature gap by conceptualisation and discusses dimensions of the construct.

Nexus between business process management (BPM) and accounting

Purpose Multidisciplinary business process management (BPM) research can reap significant impact. We can particularly benefit from incorporating accounting concepts to address some of the key BPM challenges, such as value-creation and return on investment of BPM activities. However, research which addresses a relationship between BPM and accounting is scarce. The purpose of this paper is to provide a detailed synthesis of the current literature that has integrated accounting aspects with BPM. The authors profile and thematically describe existing research, and derive evidence-based directions to guide future research. Design/methodology/approach A multi-staged structured literature review approach to search for the two broad themes, accounting and BPM, supported by NVivo (to manage the papers and the coding and analysis processes) was designed and followed. Findings The paper confirms the dearth of work that ties the two disciplines, despite the synergetic multidisciplinary results that can be attained. Available literature is mostly from the management accounting perspective and relates to describing how performance management, in particular performance measurement, can be applicable to process improvement initiatives together with tools such as activity-based costing and the balanced scorecard. There is a lack of research that examines BPM in relation to any financial accounting perspectives (such as external reporting). Future research directions are proposed together with implications for practitioners with the findings of this structured literature review. Research limitations/implications The paper provides a detailed synthesis of the existing literature on the nexus between accounting and BPM. It summarizes the implications for practitioners and provides directions for future research by identifying key gaps and opportunities with a sound contextual basis for extension and new work. Originality/value Effective literature reviews create strong foundations for future research and accumulate the otherwise scattered knowledge into a single place. This is the first structured literature review that provides a detailed synthesis of the research that ties together the accounting and BPM disciplines, providing a basis for future research directions together with implications for practitioners.

Organizational learning and Industry 4.0: findings from a systematic literature review and research agenda

PurposeIndustry 4.0 has been one of the most topics of interest by researches and practitioners in recent years. Then, researches which bring new insights related to the subjects linked to the Industry 4.0 become relevant to support Industry 4.0's initiatives as well as for the deployment of new research works. Considering “organizational learning” as one of the most crucial subjects in this new context, this article aims to identify dimensions present in the literature regarding the relation between organizational learning and Industry 4.0 seeking to clarify how learning can be understood into the context of the fourth industrial revolution. In addition, future research directions are presented as well.Design/methodology/approachThis study is based on a systematic literature review that covers Industry 4.0 and organizational learning based on publications made from 2012, when the topic of Industry 4.0 was coined in Germany, using data basis Web of Science and Google Scholar. Also, NVivo software was used in order to identify keywords and the respective dimensions and constructs found out on this research.FindingsNine dimensions were identified between organizational learning and Industry 4.0. These include management, Industry 4.0, general industry, technology, sustainability, application, interaction between industry and the academia, education and training and competency and skills. These dimensions may be viewed in three main constructs which are essentially in order to understand and manage learning in Industry 4.0's programs. They are: learning development, Industry 4.0 structure and technology Adoption.Research limitations/implicationsEven though there are relatively few publications that have studied the relationship between organizational learning and Industry 4.0, this article makes a material contribution to both the theory in relation to Industry 4.0 and the theory of learning - for its unprecedented nature, introducing the dimensions comprising this relation as well as possible future research directions encouraging empirical researches.Practical implicationsThis article identifies the thematic dimensions relative to Industry 4.0 and organizational learning. The understanding of this relation has a relevant contribution to professionals acting in the field of organizational learning and Industry 4.0 in the sense of affording an adequate deployment of these elements by organizations.Originality/valueThis article is unique for filling a gap in the academic literature in terms of understanding the relation between organizational learning and Industry 4.0. The article also provides future research directions on learning within the context of Industry 4.0.

Islamic social finance: a literature review and future research directions

Purpose This paper aims to study the main trends of scientific research in Islamic finance’s social aspects to clarify place, role and functions, especially in the context of increasing social problems. To achieve this goal, this paper focuses on the social component of Islamic finance, analyzes publications on social Islamic finance in the Web of Science database, covering the period from 1979 to 2020, specify the geographical localization of research networks, determines the most cited authors and their scientific position. Design/methodology/approach The authors have applied several literature review techniques, a bibliometric citation and co-citation analysis, a co-authorship analysis and a review of the most cited papers. The analyzes’ results allow us to offer five future questions in Islamic social finance, zakat and waqf, which have not been investigated before and could influence Islamic social finance and Islamic finance research. Findings The authors also derive and summarize five leading future research questions. Research limitations/implications This is a limitation of using only the Web of Science Core Collection database as the premier resource and the most trusted citation index for the world’s scientific and scholarly research. Further study might expand the types of analyzed units, include more keywords and include other databases, such as Scopus. Originality/value This paper can be considered as an inspirational one to future researchers and policymakers in Islamic social finance.

Defined strategies for financial working capital management

Purpose – The purpose of this paper is to develop strategies for financial working capital management and to present previous literature on financial working capital management and its measures. Design/methodology/approach – Qualitative comparative analysis is used to formulate the strategies, and the variables in the analysis have been selected from previous literature. Empirical data consists of 91 companies listed in the Helsinki Stock Exchange during 2008-2012. Findings – The results indicate 11 possible strategies for financial working capital management which all aim at increasing financial working capital. There are suitable strategies for all companies independent from their profitability, capital intensity or working capital requirements. Research limitations/implications – The presented strategies have been created theoretically and have not been tested in companies, which could be done in future research. Originality/value – This study has three contributions. First, previous literature on financial working capital management is reviewed. Second, a novel measure for financial working capital is developed. Third, strategies for financial working capital management are presented.

Life cycle assessment of anaerobic digestion systems

Purpose It is well known that sustainability is the ideal driving path of the entire world and renewable energy is the backbone of the ongoing initiatives. The current topic of argument among the sustainability research community is on the wise selection of processes that will maximize yield and minimize emissions. The purpose of this paper is to outline different parameters and processes that impact the performance of biogas production plants through an extensive literature review. These include: comparison of biogas plant efficiency based on the use of a diverse range of feedstock; comparison of environmental impacts and its reasons during biogas production based on different feedstock and the processes followed in the management of digestate; analysis of the root cause of inefficiencies in the process of biogas production; factors affecting the energy efficiency of biogas plants based on the processes followed; and the best practices and the future research directions based on the existing life cycle assessment (LCA) studies. Design/methodology/approach The authors adopted a systematic literature review of research articles pertaining to LCA to understand in depth the current research and gaps, and to suggest future research directions. Findings Findings include the impact of the type of feedstock used on the efficiency of the biogas plants and the level of environmental emissions. Based on the analysis of literature pertaining to LCA, diverse factors causing emissions from biogas plants are enlisted. Similarly, the root causes of inefficiencies of biogas plants were also analyzed, which will further help researchers/professionals resolve such issues. Findings also include the limitations of existing research body and factors affecting the energy efficiency of biogas plants. Research limitations/implications This review is focused on articles published from 2006 to 2019 and is limited to the performance of biogas plants using LCA methodology. Originality/value Literature review showed that a majority of articles focused mainly on the efficiency of biogas plants. The novel and the original aspect of this review paper is that the authors, alongside efficiency, have considered other critical parameters such as environmental emission, energy usage, processes followed during anaerobic digestion and the impact of co-digestion of feed as well. The authors also provide solid scientific reasoning to the emission and inefficiencies of the biogas plants, which were rarely analyzed in the past.

High-performance organization: a literature review

PurposeThis paper aims to review and synthesize notable literature on high-performance organization (HPO), from which future research directions can be recommended.Design/methodology/approachThis narrative literature review analyzes major HPO literature in popular books and peer-reviewed articles published in English in the period between 1982 and 2019.FindingsThe review revealed that HPO literature has evolved multiple times, illustrating the complex and multifaceted nature of this phenomenon. In particular, literature on HPO has evolved in four phases: (1) definitions and conceptual development of HPO; (2) exploration of approaches to achieve HPO; (3) empirical validation of HPO framework; and (4) complicated research models and designs on HPO. Several research gaps were identified, which definitely hold varying research value and can be seen as potential opportunities for future research.Research limitations/implicationsThe focus of this review is on HPO literature published in English rather than cover all existing literature.Originality/valueIt is among the first studies to review the HPO literature and its evolution. This review also recommends constructive areas for future research on HPO to focus on.

Export Citation Format

Share document.

To read this content please select one of the options below:

Please note you do not have access to teaching notes, working capital management: a literature review and research agenda.

Qualitative Research in Financial Markets

ISSN : 1755-4179

Article publication date: 29 July 2014

The purpose of this paper is to review research on working capital management (WCM) and to identify gaps in the current body of knowledge, which justify future research directions. WCM has attracted serious research attention in the recent past, especially after the financial crisis of 2008.

Design/methodology/approach

Using systematic literature review (SLR) method, the present study reviews 126 articles from referred journal and international conferences published on WCM.

Detailed content analysis reveals that most of the research work is empirical and focuses mainly on two aspects, impact of working capital on profitability of firm and working capital practices. Major research work has concluded that WCM is essential for corporate profitability. The major issues with prior literature are lack of survey-based approach and lack of systematic theory development study, which opens all new areas for future research. The future research directions proposed in this paper may help develop a greater understanding of determinants and practices of WCM.

Practical implications

Till date, literature on classification of WCM has been almost non-existent. This paper reviews a large number of articles on WCM and provides a classification scheme in to various categories. Subsequently, various emerging trends in the field of WCM are identified to help researchers specifying gaps in the literature and direct research efforts.

Originality/value

This paper contains a comprehensive listing of publications on the WCM and their classification according to various attributes. The paper will be useful to researchers, finance professionals and others concerned with WCM to understand the importance of WCM. To the best of the authors’ knowledge, no detailed SLR on this topic has previously been published in academic journals.

• Literature review
• Survey methods
• Operating cycle
• Working capital management

Pratap Singh, H. and Kumar, S. (2014), "Working capital management: a literature review and research agenda", Qualitative Research in Financial Markets , Vol. 6 No. 2, pp. 173-197. https://doi.org/10.1108/QRFM-04-2013-0010

Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

The General Model of Working Capital Management

• © 2022
• Rodrigo Zeidan 0

NYU Shanghai, Shanghai, China

You can also search for this author in PubMed   Google Scholar

• Aims to be the most comprehensive textbook about Working Capital Management and Trade Credit
• Enlightens the theoretical concepts about this important management field
• Consolidates all aspects of working capital management under one roof

4 Citations

20 Altmetric

This is a preview of subscription content, log in via an institution to check access.

Access this book

• Available as EPUB and PDF
• Read on any device
• Instant download
• Own it forever
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

Similar content being viewed by others, benefits of working capital sharing in supply chains.

Towards Efficient Financial Supply Chains: How to Leverage Inter-organizational Working Capital by Digitalizing the Financial Flows

The Role of Trade Credit and the Cost of Capital

• Trade Credit
• Working Capital Management
• Cash-to-cash Cycle
• Short-term finance
• Supply-Chain Finance

Table of contents (10 chapters)

Front matter, introduction.

Rodrigo Zeidan

Why the General Model of Working Capital Management? The U$1 Billion Question

The general dynamic model of trade credit, market power and working capital optimization, the archetypes: from mature, single-product companies to cash-constrained organizations, the managerial decisions, building a comprehensive working capital strategy, building managerial cash-flow statements for budgeting working capital investments, greening supply chains and the role of working capital management, formal models on trade credit, back matter, authors and affiliations, about the author, bibliographic information.

Book Title : The General Model of Working Capital Management

Authors : Rodrigo Zeidan

DOI : https://doi.org/10.1007/978-981-19-3334-9

Publisher : Palgrave Macmillan Singapore

eBook Packages : Business and Management , Business and Management (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022

Hardcover ISBN : 978-981-19-3333-2 Published: 19 August 2022

Softcover ISBN : 978-981-19-3336-3 Published: 20 August 2023

eBook ISBN : 978-981-19-3334-9 Published: 18 August 2022

Edition Number : 1

Number of Pages : XVIII, 265

Number of Illustrations : 14 b/w illustrations, 53 illustrations in colour

Topics : Supply Chain Management , Economics, general , International Finance

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Open access
• Published: 29 July 2022

The impact of working capital management on credit rating

• Ala’a Adden Abuhommous   ORCID: orcid.org/0000-0003-3914-4266 1 ,
• Ahmad Salim Alsaraireh 1 &
• Huthaifa Alqaralleh 2  

Financial Innovation volume  8 , Article number:  72 ( 2022 ) Cite this article

3 Citations

Metrics details

This study investigates the possible nonlinear relationship between working capital and credit rating. Furthermore, it examines the relationship between the three components of working capital (inventory, accounts receivable, and accounts payable) and a firm’s credit rating. Employing data for U.S listed firms for the period between 1985 and 2017, the results of our ordered probit model show a nonlinear relationship between working capital and its components and credit rating. Finally, we find that the deviation from the optimal working capital adversely affects the credit rating. The results of this study are of significant importance for policy makers, managers, decision makers, and credit-rating agencies, as they help highlight the importance of working capital management for a firm’s credit rating.

Introduction

In light of the 2008 financial crisis, credit-rating scores (CRSs) released by credit-rating agencies have grown in importance (Hung et al. 2017 ). Credit-rating agencies play a vital role in capital markets by reducing the moral hazard problem. In addition, credit ratings help investors assess the creditworthiness of issuers and the financial securities issued by them (Lee et al. 2021 ). Furthermore, credit ratings are used as a benchmark based on which investors manage their portfolios. Finally, they play an important monitoring role, as they may require firms with rating deteriorations to take corrective actions to minimize these deteriorations (Huan and Mohamed 2021 ). Additionally, CRSs have become a crucial tool that helps investors in their investment decision-making process, as they help investors identify risky assets, price their credit, and allocate their capital more efficiently (Amato and Furfine 2004 ). Investors are highly concerned about borrowers’ ability to fulfil their obligations (Haspolat 2015 ). Moreover, Bauer and Esqueda ( 2017 ) point out that banks rely on credit rating scales to compensate for information deficits when making loan decisions. Thus, a considerable amount of literature has been published on the impact of factors such as firm-specific characteristics [Return on Assets (ROA), size, and liquidity], corporate social responsibility (CSR), and operational leanness on the corporate credit rating (Attig et al. 2013 ; Hung et al. 2013 ; Bendig et al. 2017 ; Dong et al. 2021 ). However, to our knowledge, no previous study has investigated the impact of working capital management (WCM) on a firm’s credit rating. A key aspect of a WCM decision is its impact on a firm’s risk, return, and valuation (Smith 1980 ). Many researchers have attempted to investigate the impact of WCM on firms’ financial performance (e.g., Aktas et al. 2015 ; Jose et al. 1996 ; Shin and Soenen 1998 ; Deloof 2003 ; García-Teruel and Martínez-Solano 2007 ). These studies have mainly focused on the impact of WCM on a firm’s profitability performance. The interesting point here is the consensus that maintaining a high level of net working capital (NWC) reduces a firm’s risk and profitability.

The results of these previous studies led us to take the research on this topic a step further and investigate the impact of WCM on a firm’s credit rating. Understanding this relationship is of high importance for corporate managers in their quest for external financing, especially after the 2008 financial crisis (Hung et al. 2013 ).

One would expect that maintaining a high level of working capital would enhance a company’s credit rating since it reduces risk. However, is this always the case? Besides reducing risk, previous studies have shown that holding a high level of working capital reduces profits. This particular observation raises a major concern about the effect of low profits on a company’s ability to cover interest payments, especially since a company’s reliance on external financing would increase as the level of working capital increases (Kieschnick et al. 2013 ). This concern may indicate that maintaining a high level of working capital may enhance a company’s credit rating for a certain period but become harmful thereafter. This argument suggests a concave relationship between WCM and credit rating. Furthermore, this concave relationship postulates that firms could have an optimal working capital ratio to reduce risks and improve credit ratings. Therefore, we expect a positive or negative deviation from the optimal working capital to have an adverse effect on the evaluation of a firm’s risk and ultimately impact the firm’s credit rating.

To this end, this study distinguishes itself from previous studies in the following aspects: first, it examines the possible concave relationship between WCM and credit rating; second, it conducts a deeper analysis by investigating the impact of three important components of working capital, namely inventory (INV), accounts receivable (AR), and accounts payable (AP), on a firm’s credit rating; and finally, it investigates the impact of deviation from the optimal working capital on a firm’s credit rating. This study attempts to answer the following questions: (1) Does WCM and its components affect the credit rating? (2) If so, what is the nature of this relationship? Finally, (3) does deviation from optimal working capital affect a firm’s credit rating?

Center for research in security prices

Utilizing annual panel data of U.S. listed firms from Wharton Research Data Services (WRDS) merged with Center for Research in Security Prices (CRSP)/Compustat files for the period between 1985 and 2017, we find evidence to draw the following conclusions. First, there exists a positive relationship between WCM and credit rating. Second, we find that high investments in working capital have an inverse impact on CRSs; thus, our results support the non-linear relationship between WCM and credit rating. Third, there exists a nonlinear relationship between the components of working capital (inventory and accounts receivable) and CRSs, while accounts payable have a negative relationship with credit scoring. Finally, the results show that firms have an optimal level of working capital and deviation from this target harms their credit rating.

The rest of this paper is organized as follows: “ Literature review and hypotheses development ” section discusses previous studies on WCM and credit ratings; “ Methodology ” section presents the study methodology and research design; “ Data and descriptive statistics ” section presents the data and descriptive statistics sample and data resources; “ Empirical results ” section presents the empirical results; and “ Concluding remarks ” section concludes the paper.

Literature review and hypotheses development

In this section, we discuss the relevant literature on WCM and credit ratings, in addition to the development of the research hypotheses.

Working-capital management

According to Lewellen et al. ( 1980 ), decisions regarding working capital have no impact on a firm’s value in a perfect capital market. However, because of the nonexistence of a perfect capital market and the presence of an optimal level of each component of working capital, such as accounts receivable (Nadiri 1969 ; Emery 1984 ), inventories (Ouyang et al. 2005 ), and accounts payable (Nadiri 1969 ; Abuhommous 2017 ), one would expect firms to have a target or optimal level of working capital. Aktas et al. ( 2015 ) provide evidence for the presence of an optimal level of working capital.

The WCM concept pertains to how firms manage their current assets and liabilities, and this policy comprises two elements: (1) the level of investment in current assets and (2) the means of financing current assets. When selecting the most suitable policy, firms try to obtain an optimal level of working capital, depending on the trade-off between risk and return.

Focusing on the second element, firms may adopt one of three working capital strategies, namely that of a conservative, hedging, or aggressive strategy. In the conservative approach, firms try to maintain high levels of working capital (high investment in working capital), as they rely more on long-term financing compared with short-term financing. This strategy decreases both the risk and return of the firm due to the higher need for expensive external financing, which harms the firm’s profitability. In this regard, Baños-Caballero et al. ( 2014 ) state that a low level of working capital enhances a firm’s performance because of the lower need for expensive external financing. Furthermore, an increase in working capital may result in an increase in the opportunity cost of cash locked-up in accounts receivable and inventories (Tauringana and Afrifa 2013 ). However, a firm may adopt an aggressive working capital strategy by using more short-term sources of funds to finance its investments, which indicates low investment in working capital. By adopting such a strategy, both risk and returns increase. Finally, in the hedging strategy (matching strategy), the temporary amount of short-term assets is met with short-term financing, and the permanent amount of short-term assets is financed by long-term financing resources; thus, the investment in working capital may increase or decrease according to the firm’s activity.

Several attempts to investigate the impact of WCM on a firm’s profitability (e.g., Jose et al. 1996 ; Shin and Soenen 1998 ; Deloof 2003 ; García-Teruel and Martínez-Solano 2007 ) suggest that there exists a linear relationship between a firm’s investment in working capital and its profitability. The findings of such studies indicate that the lower the investment in working capital, the higher the profitability. Mohamad and Saad ( 2010 ) find a negative impact of working capital elements, such as cash conversion cycles (CCCs), current ratios, current-asset-to-total-asset ratios, current-liabilities-to-total-asset ratios, and debt-to-asset ratios, on firm performance, suggesting the importance of WCM in enhancing performance at both the accounting and market levels.

Kieschnick et al. ( 2013 ) investigate the relationship between net operating WCM and firm value. They find that for average firms, every additional dollar held in cash is worth more for shareholders than investing that dollar in net operating working capital. They add that, for the average firm, investing more in trade credit would add more value for shareholders than investing in inventory, which indicates the high importance of trade credit as part of WCM for shareholder wealth. In a more recent study, Aktas et al. ( 2015 ) document a nonlinear relationship between excess NWC and stock performance, finding that this relationship is negative for firms with positive excess NWC and positive for firms with negative excess NWC. These findings further support the idea of an optimal level of NWC, and firms that reach that level increase their stock value.

Another stream of research has focused on the impact of WCM on firm risk. For instance, maintaining a high working capital might lead a firm to rely more on long-term financing, which would result in a higher interest cost. Moreover, high working capital increases a firm’s opportunity cost (Kieschnick et al. 2013 ). On the other hand, adopting excessively aggressive WCM may increase the risk of stockouts, input price fluctuations, and supply costs (e.g., Blinder and Maccini 1991 ; Fazzari and Petersen 1993 ; Corsten and Gruen 2004 ). Therefore, the negative relationship between NWC and firm performance may be due to an increase in firm risk following a decrease in NWC.

• Credit rating

A firm’s credit rating is a statistic that summarizes a firm’s creditworthiness by considering several elements of the firm’s financial characteristics, such as debt ratio, priority and maturity structure of the firm’s debt, and the volatility of the firm’s cash flows (Bali and Hovakimian 2009 ).

The corporate credit rating has grown in importance especially after the 2008 financial crisis (Hung et al. 2013 ). Therefore, Amato and Furfine ( 2004 ) assert the important role of credit rating analysis in financing and investment decisions, such as in pricing credit, determining risky assets, and asset allocation. Furthermore, investors are highly concerned about borrowers’ ability to fulfil their obligations (Haspolat 2015 ). In this regard, Bauer and Esqueda ( 2017 ) point out the importance of credit rating scales in helping banks overcome information deficits when making loan decisions.

A considerable amount of the literature has been published on firms’ credit ratings. These studies can be classified into two streams. The first stream includes studies on the factors that influence the credit rating. The second concerns the impact of the credit rating on a firm’s decision-making. Attig et al. ( 2013 ) study the impact of CSR on a firm’s credit rating and find that firms with good social performance are rated relatively high. They also conclude that CSR investments can reduce financing costs owing to high credit ratings. Hung et al. ( 2013 ) offer evidence on the effect of firm-specific characteristics on the credit rating and find that ROA, size, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), and interest coverage are positively related to credit rating. However, the debt ratio and ratio of cash to current liabilities are negatively related to credit rating. In a large longitudinal study, Bendig et al. ( 2017 ) investigate the impact of operational leanness [relative inventory leanness and relative property, plant, and equipment (PPE) leanness] on a firm’s credit rating and find a concave positive relationship between inventory leanness and credit rating and a negative and concave relationship between PPE leanness and credit rating.

Turning to the impact of a firm’s credit rating on decision-making, Kisgen ( 2006 ) examine the relationship between credit ratings and capital structure decisions. He finds a negative relationship between a change in credit rating status and reliance on debt. As firms near a change in credit rating status (upgrade or downgrade), their reliance on debt decreases. Another study finds that firms pursue real earnings management activities when they have an upcoming credit rating change; however, just prior to the change, they reduce their discretionary accruals. Moreover, the study concludes that real activities management and credit rating upgrades are positively related, whereas there is no significant relationship between real activities management and credit rating downgrades (Kim et al. 2013 ).

Credit rating and WCM

Since 1941, the Standard & Poor’s (S&P) rating agency has assigned its CRSs based on two broad categories of risk: financial risk and business risk. In the business risk category, S&P is concerned with the firm’s ability to generate sufficient cash flow to cover operating revenues. However, in the financial risk category, the focus is on the firm’s ability to manage its financial leverage and debt.

As mentioned earlier, maintaining high levels of working capital reduces a firm’s risk and returns. To this point, one would expect that firms with high levels of working capital would have higher CRSs because of the low risk of such firms. However, considering the above two risk categories may change the rules. For instance, Baños-Caballero et al. ( 2012 ) suggest that excessive investment in working capital may negatively impact a firm’s operating performance, consequently reducing the firm’s cash inflows (Deloof 2003 ; García Teruel and Martínez-Solano 2007 ; Shin and Soenen 1998 ). Such studies conclude that reducing the CCC and inventory amount would lead to higher operating performance. Thus, over time, firms that suffer a decrease in their operating performance due to their high levels of working capital would be less able to repay interest payments. Kieschnick et al. ( 2013 ) suggest that these payments would be high for firms with high levels of working capital due to their high reliance on external financing, which would lead to higher bankruptcy costs according to Kieschnick et al. ( 2013 ). This may result in lower CRSs for such firms. Shin and Soenen ( 1998 ) report a good example on this point: They mention that despite the similarity of two firms in capital structure, namely Kmart and Walmart, Kmart faced higher financial troubles than Walmart due to its high NWC relative to sales, which led Kmart to close 110 stores in 1994, and in 2002 the firm filed for Chapter 11 bankruptcy protection. This example supports the argument that the relationship between working capital level and credit rating is concave. To this end, one may expect that neither over-investment in working capital nor an aggressive working capital policy would be favorable for a firm’s credit rating. This is because increasing investment in working capital is preferable for credit rating (due to its role in reducing risk) to some point, after which the rating starts to drop (due to its negative impact on cash flows). Consequently, we hypothesize a concave relationship between WCM and credit rating.

Furthermore, the above discussion leads to the conception that firms should efficiently manage their working capital by maintaining it at an optimal level. According to Gill et al. ( 2019 ), efficient WCM positively impacts the bond quality rating. Therefore, we conjecture that holding working capital on target enhances a firm’s credit rating.

Methodology

In “ Estimation framework ” section, we develop a regression model to examine whether WCM affects credit rating decisions. In “ The concave relationship between NWC and credit rating ” and “ Components of working capital and credit rating ” sections, we expand our analysis to examine the nonlinear relationship between the working capital level and its components (i.e., accounts receivable, inventory, and accounts payable) and credit rating. In the final section of the analysis, we attempt to determine the optimal working capital level and examine whether deviation from the optimal working capital adversely affects the credit rating score.

Estimation framework

In this section, we augment the previous models of Ashbaugh-Skaife et al. ( 2006 ), Alissa et al. ( 2013 ), Attig et al. ( 2013 ), Oikonomou et al. ( 2014 ), and Bendig et al. ( 2017 ) by including our main independent variable (WCM) in the credit rating model. We use an ordered probit regression, where we add the working capital proxy to the model, as follows:

where RATING is the S&P credit rating for firm i at time t . We consider the following to be determinants of credit rating: NWC ratio, log of assets, firm’s leverage, interest coverage ratio, whether the firm has losses (indicated by a dummy variable), whether the firm has fixed assets in its asset structure (measured by capital intensity), whether the firm has subordinate loans, and whether the firm’s external auditor is considered as one of the “big four” auditing firms (indicated by a dummy variable). We present the definitions of these variables in Table 2 . We also include IndustryEffects to control for differences across industries and YearEffects to control for the time-specific effect, which captures economic factors that affect all firms in the same year but vary over time, and ε i,t is the error term.

Key variables measurement

In this section, we present the key variables included in the estimated model, including their definitions and measurement.

Dependent variable (credit rating)

Credit ratings are based on opinions about credit risk and are considered a quantified forward-looking assessment of the debt issuer’s creditworthiness, which measures firm’s ability to meet its financial commitment in terms of time and fullness. They can also be used to measure the likelihood of default and can thus be assigned to individual debt issues or, as an overall credit rating, to corporations, governments, and municipalities, or to a sovereign government (Standard and Poor’s 2022 ). Following the literature (e.g., Kisgen 2009 , 2006 ; Hovakimian and Li 2009 ; Alissa et al. 2013 ) we use Standard & Poor’s Long-Term Domestic Issuer Credit Rating (RATING), a long-term scale for firms according to their overall creditworthiness, in which the rating range is assigned from “AAA” for an extremely strong obligor (highest rating) to “D” for an obligor in default (lowest rating). Consistent with Attig et al. ( 2013 ) and Bendig et al. ( 2017 ), we transform the credit rating to an ordinal scale for the purpose of our regression. Thus, we assign eight values starting from one for a “CC” rating to eight for an “AAA” rating (see Table 1 ). As a robustness check, we follow Alissa et al. ( 2013 ) and Bendig et al. ( 2017 ), who measure credit rating based on 20 micro rating classes. We calculate MICRORATING and transform credit rating to an ordinal scale with 20 categories, ranging from the value of 1 for the “CC” class to 20 for the “AAA” class. Footnote 1 Table 1 reports the number of firm-year observations for each credit-rating category. The number of firms per year ranges from 98 to 5265 (Table 2 ).

Main independent variable (WCM)

Our main independent variable that measures a firm’s WCM investment is based on the literature (e.g., Shin and Soenen 1998 ; Aktas et al. 2015 ; Kieschnick et al. 2013 ). Following these works, we use the net operating working capital to sales ratio (NWC); the dependent variable is measured as (INV) plus (AR) minus (AP), all divided by total sales. Furthermore, as a robustness check, we follow Baños-Caballero et al. ( 2012 ) and measure working capital policy as (inventory/cost of sales) × 365 + (accounts receivable/sales) × 365 − (accounts payable/cost of sales) × 365; we call this the CCC.

Control variables and firms’ characteristics

A firm’s characteristics are included in the credit rating regression model, based on the literature (e.g., Ashbaugh-Skaife et al. 2006 ; Kisgen 2006 ; Bendig et al. 2017 ; Alissa et al. 2013 ; Attig et al. 2013 ). With respect to firm size (SIZE), we expect large firms to have more leverage because of the high volume of information available about the firm; usually, large firms tend to have less asymmetric information in the market. We use the natural logarithm of total assets to measure size. Therefore, larger firms are expected to have higher credit ratings than smaller firms. Firm’s leverage (LEV) has an inverse relationship with credit rating, since firms with high leverage are more likely to suffer from financial crises and bankruptcy probability increases. We use the ratio of long-term debt-to-total-assets as an indicator of a firm’s leverage. Interest coverage ratio (COVERAGE) is used as a proxy for a firm’s default risk, which demonstrates a firm’s ability to pay its debt interest; the more able the firm is to pay its debt interest, the more likely the firm will receive a higher credit rating. This ratio is calculated by dividing the operating income before depreciation by interest expenses. A firm’s losses (LOSS) are an indicator of the firm’s likelihood of default; unprofitable firms tend to have a high probability of bankruptcy and therefore have a lower credit rating. Firm’s capital intensity (CAP_INTEN) is included as a control variable because firms with high capital intensity present a lower risk for debt providers; thus, firms with high capital intensity are expected to have a higher credit rating. This variable is measured using the ratio of property, plant, and equipment to total assets. Subordinate debt (SUBORD) is included as a control variable to capture the differences in firms’ debt structure; firms with a debt structure that includes subordinated debt are considered riskier and are expected to have a lower credit rating. Bendig et al. ( 2017 ), Ashbaugh-Skaife et al. ( 2006 ), and Alali et al. ( 2012 ) find an inverse relationship between subordinate debt and credit rating. We measure subordinate debt using a dummy variable that takes the value of one if the firm has subordinate debt and zero otherwise. We also include external auditors to control for their role in monitoring a firm’s actions (Alissa et al. 2013 ); BIG4 is the variable used as a proxy for corporate governance, which reduces opportunistic managerial behavior (Bhandari and Golden 2021 ).

Data and descriptive statistics

This section presents the details of the selection criteria and sample descriptive statistics. Our sample is drawn from the population of U.S. listed firms, and to serve our study aim, we select firms that have a credit rating. Thus, we exclude any firms with missing values for credit rating or working capital. Consistent with previous studies (e.g., Hovakimian and Li 2009 ; Attig et al. 2013 ; Bendig et al. 2017 ), this study utilizes annual panel data of listed firms from WRDS merged with CRSP/Compustat files for the period between 1985 and 2017. Footnote 2 Following the literature, we exclude firms with a standard industry classification (SIC) code between 6000 and 6799. Thus, we exclude all firms operating in the financial sectors. All firms should have positive total assets and net sales, because these variables are used to deflate other variables, and the results may not be consistent when they have negative or zero values. We also only consider observations without missing values. These criteria yield 43,183 firm-year observations.

Summary statistics

Table 3 presents the descriptive statistics for the explanatory and control variables. The NWC cycle has a mean of 56.94 days, while the median is 47.70 days. The financial leverage to total assets ratio is on average 33.3% (the median is 29.7%). For unreported data, the number of firm-year observations in which the external auditor ranks among the “big four” (BIG4) accounting firms is 31,603. The total number of firm-year observations for reported loss is 9,955.

Table 4 shows the correlation matrix between the variables of interest; the table shows that the correlation is not very high, the maximum value between LOSS and COVERAGE is 33.3%. Therefore, we can conclude that the multicollinearity problem is not a serious concern.

Empirical results

This section focuses on our regression on the effect of the operating working capital policy on a company’s credit rating. We examine the main hypothesis of this study, namely, that a conservative working capital policy is associated with a high credit rating. A number of different estimates are calculated using our proposed model. This enables more robust results by controlling for firm and industry effects.

Table 5 presents the credit rating regression; the dependent variable is the firm credit rating class of Standard & Poor’s domestic long-term issuer rating (RATING). The variable of interest is NWC. Columns 1 and 2 show the regression results for an ordered probit for NWC and the control variables. In column 2, we add year and industry effects, while in columns 3 and 4, we use MICRORATING as a proxy for credit rating. The relationship between NWC and credit rating is positive and statistically significant in all models in the columns; a P value of < 0.01 is found in both ordered probit regressions. These results do not change when we include the year and industry effects, as shown in column 2. Thus, the results support the hypothesis that investment in working capital (conservative working capital policy) enhances the probability of a firm having a good credit rating. In columns 5, 6, 7, and 8, we use the CCC as a proxy for the dependent variable; the results are also qualitatively similar. Our results support the findings of Blinder and Maccini ( 1991 ), Corsten and Gruen ( 2004 ), and Baños-Caballero et al. ( 2012 ), who find that a firm’s risk increases with an aggressive working capital policy because of the loss of sales due to possible stock-outs, which reduces market share and creates interruptions in the production process, or a loss of customers due to an aggressive accounts receivable strategy. The signs of the control variables are consistent with those in prior research. We find a positive and statistically significant relationship between credit rating and size (SIZE), due to the lower asymmetric information of larger firms; interest coverage ratio (COVERAGE), which indicates that firms with a higher ability to pay their debt interest are less likely to default; capital intensity (CAP_INTEN), which indicates that firms with a higher capital intensity present lower risk since these firms can use their fixed assets as collateral; and the BIG4 coefficient, showing that firms that reduce managerial opportunistic behavior have a better credit rating. However, credit rating has a negative relationship with leverage (LEV), losses (LOSS), and subordinate debt (SUBORD), implying that firms with a high leverage ratio, unprofitable firms, and firms with subordinated debt have a higher probability of receiving a lower credit score.

As a robustness check, we control for the firm-specific effect by using a random-effect ordered probit regression; the results are qualitatively similar (for brevity, the results are not included).

The concave relationship between NWC and credit rating

This section examines whether high levels of working capital decrease the credit rating. As postulated by Soenen ( 1993 ) and Baños-Caballero et al. ( 2012 ), high investment in working capital might lead firms to bankruptcy since a high level of inventory incurs costs such as rent, insurance, and security. A high level of accounts receivable is associated with a high probability of a customer default. Specifically, we expect a U-shaped relationship between a firm’s credit rating and investment in working capital. Thus, we examine the nonlinear relationship by including the square of NWC into Eq. ( 1 ). Table 6 presents the results. The results postulate that the coefficient of NWC is positive and its square (NWC 2 ) is negative and statistically significant ( P value < 0.01), and both coefficients are statistically significant. This confirms that an overly conservative working capital policy (high investment in working capital) increases the probability of bankruptcy, which adversely affects a firm’s credit rating. Thus, our results show that when working capital is below the optimal level, the benefits from low production disruption and stimulating sales enhance the credit rating of firms. On the other hand, high investment in working capital might suggest a high risk of uncollectibility and impose high financing costs for these receivables; furthermore, high inventory investment is subject to the risk of obsolescence, spoilage, and greater financing and holding costs.

Components of working capital and credit rating

In the previous section, an NWC proxy is used to examine the relationship between the operating working capital policy and credit rating. Therefore, the positive relationship between NWC and credit rating is due to the management policy of the NWC components. A useful exercise is to examine the relationship between each component of working capital and credit rating. Thus, in Table 7 , we examine the relationship between (AR), (INV), and (AP) and rating. Consistent with our prediction, the results in Table 7 show a positive and significant relationship between credit rating and (INV) and (AR), showing that higher levels of investment in inventory and accounts receivable will increase the probability of a firm receiving a higher credit score. However, the negative and significant impact of (AP) on credit rating indicates that increasing the level of accounts payable on a firm’s balance sheet will increase the likelihood of this firm receiving a lower credit rating. Furthermore, we examine the U-shaped relationship between the components of working capital and credit rating. The coefficients of AR 2 and INV 2 are negative and significant. In addition, AP 2 is negative but not significant. These results indicate that when AR and INV are below the target level, the influence on credit rating is positive. Conversely, above-optimal investment in the components of working capital has a negative relationship with credit rating.

Deviation from the optimal working capital level and credit rating

The U-shaped relationship between working capital level and credit rating is confirmed in Table 8 , due to the quadratic relationship between NWC and credit rating. In this section, we extend our regression and attempt to determine whether deviation from the optimal working capital inversely affects the credit rating; the cost of holding an amount of working capital lower than the target (such as stock sold out and losing on credit sales) may send a signal of the firm’s riskiness. In addition, a firm’s bankruptcy cost may increase as the firm increases its investment in working capital. In the first step, we examine the relationship between deviation from the optimal working capital and credit rating. In the next stage, we examine whether the deviation on the upper and lower sides of the optimal NWC adversely affects the firm’s credit rating; the deviation from NWC is interacted with a dummy variable that is equal to one if the deviation is above the optimal deviation. Thus, in the second stage, we examine whether these deviations (i.e., negative or positive) from the target working capital adversely affect the credit rating. We estimate the optimal working capital using the following equation:

Equation ( 2 )—optimal NWC

Equation ( 3 )—deviation from optimal target

To calculate the optimal NWC, we follow the model of Baños-Caballero et al. ( 2014 ), where \(CASH_{it}\) is cash flow, and is calculated by the ratio of depreciation plus net income to total assets; \(LEV_{it}\) is the firm’s leverage, and is calculated by total debt to total assets; \(GROWTH_{it}\) is measured by the percentage change in total revenue; \(TANG_{it}\) is the firm’s investment in fixed assets, and is calculated by the ratio of net fixed assets to total assets; \(PROF_{it}\) is the firm’s profitability, and is measured by earnings before interest and taxes to total assets; and \(SIZE_{it}\) is the firm’s size, and is calculated by the natural logarithm of total assets.

We use the residual from Eq. ( 2 ) and replace the NWC variable with it. The next step is to use the regression residual as a proxy for deviation from the optimal working capital (Tong 2008 ). The residual value can be positive or negative. Therefore, we use the absolute value as a proxy for deviation. We use Eq. ( 1 ) and replace NWC with the absolute value of the residual deviation. We expect the credit rating to be adversely affected as the NWC of the firm deviates from the optimal NWC level; thus, our expectation is \(\delta_{1} < 0\) in Eq. ( 3 ).

The results in Table 8 from estimating Eq. ( 3 ) are based on replacing the variables NWC and NWC 2 in Eq. ( 1 ) with the absolute residual from Eq. ( 2 ). Consistent with our expectations, the findings in Table 8 show that the coefficient of DEV is negative and statistically significant. This confirms that there is a point at which working capital has a positive relationship with credit rating, and moving from this point adversely affects this relationship. Since the results from Eq. ( 3 ) do not indicate whether deviations on both sides have an adverse effect on credit rating, we include in Eq. ( 4 ) a new variable (interaction term), which takes the value of one if the deviation is positive and zero if the deviation is negative. In Eq. ( 4 ), our main interest is to measure how the coefficients of DEV(δ 1 ) and DEV + DEV* above (δ 1  + δ 2 ) affect credit rating. Thus, we expect δ 1  < 0 and δ 1  + δ 2  < 0; this enables us to examine the negative effect of both positive and negative deviation from the optimal working capital on credit rating.

The interaction term DEV* above is defined as a dummy variable that takes the value of one for a positive residual and zero for a negative residual from the estimation in Eq. ( 2 ), in which (δ 1  + δ 2 ) represents the influence of an above-optimal working capital investment level on credit rating. If the deviation from the optimal working capital negatively affects the firm’s credit rating, we expect the value of δ 1 ˂0, and (δ 1  + δ 2 )˂0 if the deviation both above and below optimal working capital have an adverse impact on the firm’s credit rating.

Column 2 of Table 8 shows the results from the interaction term; the results show that the deviation coefficient (DEV) is negative and statistically significant at a conventional level, and the interaction term DEV* above is also statistically significant at the 1% level and positively related to credit rating. As mentioned by Tong ( 2008 ), the interaction term DEV* above may have a positive value because the positive and negative residuals may offset each other. However, the main concern here is that the sum of (δ 1  + δ 2 ) would be lower than zero, and the results, as predicted, are lower than zero; in column 2, (δ 1  + δ 2 ) is (− 0.01 + 0.002) =  − 0.008. We also conduct a likelihood-ratio test on the null hypothesis that the sum of the estimates of DEV and DEV* above is zero. The test rejects the null hypothesis at a conventional level, which supports that the deviation on both sides of the optimal working capital has a negative effect on credit rating. A likelihood-ratio test ratio for the joint significance levels of (δ 1  + δ 2 ) is lower than zero and statistically significant at the 1% level.

Finally, credit rating may have an impact on a firm’s ability to buy goods on credit and finance their accounts receivable using low-cost external financing. Thus, using the same approach as Attig et al. ( 2013 ) and Bendig et al. ( 2017 ), we test for any potential endogeneity bias caused by reverse causality. Thus, we repeat the regression model in Table 5 using the lagged values of NWC and CCC. The results are qualitatively similar, in which the coefficients of the lagged value of NWC and CCC are positive and statistically significant at a conventional level (the results are available from the authors upon request), which suggests that the endogeneity problem may have no impact on our results.

Concluding remarks

WCM is considered to be one of the most important factors in a firm’s success or failure. Therefore, in this study, we investigate whether a firm’s WCM can affect the perceived riskiness of external evaluators, such as the credit-rating agency S&P. The rationale for this relationship is that inappropriate WCM may increase a firm’s riskiness through under- or over-investment in working capital components. Based on U.S. panel data of 43,141 firm-year observations from 1985 to 2017, we find evidence of a relationship between WCM and credit rating. In particular, we find that this relationship is concave, in which firms have an optimal working capital level that balances costs and benefits to reduce firms’ riskiness; hence, credit is improved. Further, we find that the concave relationship is applicable to working capital components (inventory, accounts receivable, and accounts payable) and that deviation from the optimal working capital level may decrease a firm’s credit rating score.

This study aims to fill the gap in the existing literature by offering for the first time direct evidence of a relationship between WCM and credit rating. Based on theory and our empirical evidence, this study will help policy makers, managers, decision makers, and credit-rating agencies recognize that WCM can affect a firm’s riskiness, which in turn is reflected in its credit rating. This study highlights the advantages and disadvantages of over- and under-investment in working capital and its relationship with credit rating. However, a limitation of this study that should be taken into consideration is that we were unable to test the exact cost of over- and under-investment in working capital, which could have helped us understand the relationship more accurately.

Availability of data and materials

The data supporting the findings of this study are available from the corresponding author upon reasonable request. The data are available at https://wrds-www.wharton.upenn.edu/

We also include “D” credit rating firms in our regression; the results are qualitatively similar.

Compustat cover credit ratings from the year 1985 onwards.

Abuhommous A (2017) Partial adjustment toward target accounts payable ratio. Int J Islam Middle East Finance Manag 10(4):484–502

Article   Google Scholar  

Aktas N, Croci E, Petmezas D (2015) Is working capital management value-enhancing? Evidence from firm performance and investments. J Corp Finan 30:98–113

Alali F, Anandarajan A, Jiang W (2012) The effect of corporate governance on firm’s credit ratings: further evidence using governance score in the United States. Account Finance 52(2):291–312

Alissa W, Bonsall SB IV, Koharki K, Penn MW Jr (2013) Firms’ use of accounting discretion to influence their credit ratings. J Account Econ 55(2–3):129–147

Amato JD, Furfine CH (2004) Are credit ratings procyclical? J Bank Finance 28(11):2641–2677

Ashbaugh-Skaife H, Collins DW, LaFond R (2006) The effects of corporate governance on firms’ credit ratings. J Account Econ 42(1–2):203–243

Attig N, El Ghoul S, Guedhami O, Suh J (2013) Corporate social responsibility and credit ratings. J Bus Ethics 117(4):679–694

Bali TG, Hovakimian A (2009) Volatility spreads and expected stock returns. Manag Sci 55(11):1797–1812

Baños-Caballero S, García-Teruel PJ, Martínez-Solano P (2012) How does working capital management affect the profitability of Spanish SMEs? Small Bus Econ 39(2):517–529

Baños-Caballero S, García-Teruel PJ, Martínez-Solano P (2014) Working capital management, corporate performance, and financial constraints. J Bus Res 67(3):332–338

Bauer K, Esqueda OA (2017) Credit ratings, relationship lending and loan market efficiency. Stud Econ Finance 34(1):122–142

Bendig D, Strese S, Brettel M (2017) The link between operational leanness and credit ratings. J Oper Manag 52:46–55

Bhandari A, Golden J (2021) CEO political preference and credit ratings. J Corp Finance 68:101909

Blinder AS, Maccini LJ (1991) Taking stock: a critical assessment of recent research on inventories. J Econ Perspect 5(1):73–96

Corsten D, Gruen TW (2004) Stock-outs cause walkouts. Harv Bus Rev 82(5):26–28

Google Scholar  

Deloof M (2003) Does working capital management affect profitability of Belgian firms? J Bus Finance Acc 30(3–4):573–588

Dong Y, Hou Q, Ni C (2021) Implicit government guarantees and credit ratings. J Corp Finance 69:102046

Emery GW (1984) A pure financial explanation for trade credit. J Financ Quant Anal 19(3):271–285

Fazzari SM, Petersen BC (1993) Working capital and fixed investment: new evidence on financing constraints. RAND J Econ 24:328-342:

García-Teruel JP, Martínez-Solano P (2007) Effects of working capital management on SME profitability. Int J Manag Finance 3(2):164–177

Gill A, Amiraslany A, Obradovich J, Mathur N (2019) Efficient working capital management, bond quality rating, and debt refinancing risk. Manag Financ 45(7):869–885

Haspolat FB (2015) Analysis of Moody’s sovereign credit ratings: criticisms towards rating agencies are still valid? Procedia Econ Finance 30:283–293

Hovakimian A, Li G (2009) Do firms have unique target debt ratios to which they adjust? SSRN 1138316

Huan ANS, Mohamed MAS (2021) Credit ratings controversy: a Review. J Insur Financ Manag 4(3):49–64

Hung CHD, Banerjee A, Meng Q (2017) Corporate financing and anticipated credit rating changes. Rev Quant Finance Acc 48(4):893–915

Hung K, Cheng HW, Chen SS, Huang YC (2013) Factors that affect credit rating: an application of ordered Probit models. Roman J Econ Forecast 16(4):94–108

Jose ML, Lancaster C, Stevens JL (1996) Corporate returns and cash conversion cycles. J Econ Finance 20(1):33

Kieschnick R, Laplante M, Moussawi R (2013) Working capital management and shareholders’ wealth. Rev Finance 17(5):1827–1852

Kim YS, Kim Y, Song KR (2013) Credit rating changes and earnings management. Asia Pac J Financ Stud 42(1):109–140

Kisgen DJ (2006) Credit ratings and capital structure. J Financ 61(3):1035–1072

Kisgen DJ (2009) Do firms target credit ratings or leverage levels? J Financ Quant Anal 44(6):1323–1344

Lee WC, Shen J, Cheong TS, Wojewodzki M (2021) Detecting conflicts of interest in credit rating changes: a distribution dynamics approach. Financ Innov 7(1):1–23

Lewellen W, McConnel J, Scott J (1980) Capital market influences on trade credit policies. J Financ Res 3(2):105–113

Mohamad NEAB, Saad NBM (2010) Working capital management: the effect of market valuation and profitability in Malaysia. Int J Bus Manag 5(11):140

Nadiri MI (1969) The determinants of trade credit in the US total manufacturing sector. Econom J Econom Soc 37:408–423

Oikonomou I, Brooks C, Pavelin S (2014) The effects of corporate social performance on the cost of corporate debt and credit ratings. Financ Rev 49(1):49–75

Ouyang LY, Chang CT, Teng JT (2005) An EOQ model for deteriorating items under trade credits. J Oper Res Soc 56(6):719–726

Shin HH, Soenen L (1998) Efficiency of working capital management and corporate profitability. Financ Pract Educ 8:37–45

Smith K (1980) Profitability versus liquidity tradeoffs in working capital management. In: Smith KV (ed) Readings on the management of working capital. West Publishing Company, Eagan, pp 549–562

Soenen LA (1993) Cash conversion cycle and corporate profitability. J Cash Manag 13:53–53

Standard & Poor's (2022) Standard & poor's ratings definitions. https://www.spglobal.com/ratings/en/about/understanding-credit-ratings . Accessed 14 May 2022

Tauringana V, Afrifa GA (2013) The relative importance of working capital management and its components to SMEs’ profitability. J Small Bus Enterp Dev 24(3):453–469

Tong Z (2008) Deviations from optimal CEO ownership and firm value. J Bank Finance 32(11):2462–2470

Download references

There is no funding for this research.

Author information

Authors and affiliations.

College of Business, Banking and Finance Department, Mutah University, Al-Karak, Jordan

Ala’a Adden Abuhommous & Ahmad Salim Alsaraireh

College of Business, Department of Economics, Business and Finance, Mutah University, Al-Karak, Jordan

Huthaifa Alqaralleh

You can also search for this author in PubMed   Google Scholar

Contributions

AA analysed and interpret the results. AS made the theoretical framework. HQ drafted the work. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ala’a Adden Abuhommous .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Abuhommous, A.A., Alsaraireh, A.S. & Alqaralleh, H. The impact of working capital management on credit rating. Financ Innov 8 , 72 (2022). https://doi.org/10.1186/s40854-022-00376-z

Download citation

Received : 20 November 2021

Accepted : 22 July 2022

Published : 29 July 2022

DOI : https://doi.org/10.1186/s40854-022-00376-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Working capital management
• Trade credit

JEL Classification

McKinsey Global Private Markets Review 2024: Private markets in a slower era

At a glance, macroeconomic challenges continued.

McKinsey Global Private Markets Review 2024: Private markets: A slower era

If 2022 was a tale of two halves, with robust fundraising and deal activity in the first six months followed by a slowdown in the second half, then 2023 might be considered a tale of one whole. Macroeconomic headwinds persisted throughout the year, with rising financing costs, and an uncertain growth outlook taking a toll on private markets. Full-year fundraising continued to decline from 2021’s lofty peak, weighed down by the “denominator effect” that persisted in part due to a less active deal market. Managers largely held onto assets to avoid selling in a lower-multiple environment, fueling an activity-dampening cycle in which distribution-starved limited partners (LPs) reined in new commitments.

About the authors

This article is a summary of a larger report, available as a PDF, that is a collaborative effort by Fredrik Dahlqvist , Alastair Green , Paul Maia, Alexandra Nee , David Quigley , Aditya Sanghvi , Connor Mangan, John Spivey, Rahel Schneider, and Brian Vickery , representing views from McKinsey’s Private Equity & Principal Investors Practice.

Performance in most private asset classes remained below historical averages for a second consecutive year. Decade-long tailwinds from low and falling interest rates and consistently expanding multiples seem to be things of the past. As private market managers look to boost performance in this new era of investing, a deeper focus on revenue growth and margin expansion will be needed now more than ever.

Perspectives on a slower era in private markets

Global fundraising contracted.

Fundraising fell 22 percent across private market asset classes globally to just over $1 trillion, as of year-end reported data—the lowest total since 2017. Fundraising in North America, a rare bright spot in 2022, declined in line with global totals, while in Europe, fundraising proved most resilient, falling just 3 percent. In Asia, fundraising fell precipitously and now sits 72 percent below the region’s 2018 peak.

Despite difficult fundraising conditions, headwinds did not affect all strategies or managers equally. Private equity (PE) buyout strategies posted their best fundraising year ever, and larger managers and vehicles also fared well, continuing the prior year’s trend toward greater fundraising concentration.

The numerator effect persisted

Despite a marked recovery in the denominator—the 1,000 largest US retirement funds grew 7 percent in the year ending September 2023, after falling 14 percent the prior year, for example 1 “U.S. retirement plans recover half of 2022 losses amid no-show recession,” Pensions and Investments , February 12, 2024. —many LPs remain overexposed to private markets relative to their target allocations. LPs started 2023 overweight: according to analysis from CEM Benchmarking, average allocations across PE, infrastructure, and real estate were at or above target allocations as of the beginning of the year. And the numerator grew throughout the year, as a lack of exits and rebounding valuations drove net asset values (NAVs) higher. While not all LPs strictly follow asset allocation targets, our analysis in partnership with global private markets firm StepStone Group suggests that an overallocation of just one percentage point can reduce planned commitments by as much as 10 to 12 percent per year for five years or more.

Despite these headwinds, recent surveys indicate that LPs remain broadly committed to private markets. In fact, the majority plan to maintain or increase allocations over the medium to long term.

Investors fled to known names and larger funds

Fundraising concentration reached its highest level in over a decade, as investors continued to shift new commitments in favor of the largest fund managers. The 25 most successful fundraisers collected 41 percent of aggregate commitments to closed-end funds (with the top five managers accounting for nearly half that total). Closed-end fundraising totals may understate the extent of concentration in the industry overall, as the largest managers also tend to be more successful in raising non-institutional capital.

While the largest funds grew even larger—the largest vehicles on record were raised in buyout, real estate, infrastructure, and private debt in 2023—smaller and newer funds struggled. Fewer than 1,700 funds of less than $1 billion were closed during the year, half as many as closed in 2022 and the fewest of any year since 2012. New manager formation also fell to the lowest level since 2012, with just 651 new firms launched in 2023.

Whether recent fundraising concentration and a spate of M&A activity signals the beginning of oft-rumored consolidation in the private markets remains uncertain, as a similar pattern developed in each of the last two fundraising downturns before giving way to renewed entrepreneurialism among general partners (GPs) and commitment diversification among LPs. Compared with how things played out in the last two downturns, perhaps this movie really is different, or perhaps we’re watching a trilogy reusing a familiar plotline.

Dry powder inventory spiked (again)

Private markets assets under management totaled $13.1 trillion as of June 30, 2023, and have grown nearly 20 percent per annum since 2018. Dry powder reserves—the amount of capital committed but not yet deployed—increased to $3.7 trillion, marking the ninth consecutive year of growth. Dry powder inventory—the amount of capital available to GPs expressed as a multiple of annual deployment—increased for the second consecutive year in PE, as new commitments continued to outpace deal activity. Inventory sat at 1.6 years in 2023, up markedly from the 0.9 years recorded at the end of 2021 but still within the historical range. NAV grew as well, largely driven by the reluctance of managers to exit positions and crystallize returns in a depressed multiple environment.

Private equity strategies diverged

Buyout and venture capital, the two largest PE sub-asset classes, charted wildly different courses over the past 18 months. Buyout notched its highest fundraising year ever in 2023, and its performance improved, with funds posting a (still paltry) 5 percent net internal rate of return through September 30. And although buyout deal volumes declined by 19 percent, 2023 was still the third-most-active year on record. In contrast, venture capital (VC) fundraising declined by nearly 60 percent, equaling its lowest total since 2015, and deal volume fell by 36 percent to the lowest level since 2019. VC funds returned –3 percent through September, posting negative returns for seven consecutive quarters. VC was the fastest-growing—as well as the highest-performing—PE strategy by a significant margin from 2010 to 2022, but investors appear to be reevaluating their approach in the current environment.

Private equity entry multiples contracted

PE buyout entry multiples declined by roughly one turn from 11.9 to 11.0 times EBITDA, slightly outpacing the decline in public market multiples (down from 12.1 to 11.3 times EBITDA), through the first nine months of 2023. For nearly a decade leading up to 2022, managers consistently sold assets into a higher-multiple environment than that in which they had bought those assets, providing a substantial performance tailwind for the industry. Nowhere has this been truer than in technology. After experiencing more than eight turns of multiple expansion from 2009 to 2021 (the most of any sector), technology multiples have declined by nearly three turns in the past two years, 50 percent more than in any other sector. Overall, roughly two-thirds of the total return for buyout deals that were entered in 2010 or later and exited in 2021 or before can be attributed to market multiple expansion and leverage. Now, with falling multiples and higher financing costs, revenue growth and margin expansion are taking center stage for GPs.

Real estate receded

Demand uncertainty, slowing rent growth, and elevated financing costs drove cap rates higher and made price discovery challenging, all of which weighed on deal volume, fundraising, and investment performance. Global closed-end fundraising declined 34 percent year over year, and funds returned −4 percent in the first nine months of the year, losing money for the first time since the 2007–08 global financial crisis. Capital shifted away from core and core-plus strategies as investors sought liquidity via redemptions in open-end vehicles, from which net outflows reached their highest level in at least two decades. Opportunistic strategies benefited from this shift, with investors focusing on capital appreciation over income generation in a market where alternative sources of yield have grown more attractive. Rising interest rates widened bid–ask spreads and impaired deal volume across food groups, including in what were formerly hot sectors: multifamily and industrial.

Private debt pays dividends

Debt again proved to be the most resilient private asset class against a turbulent market backdrop. Fundraising declined just 13 percent, largely driven by lower commitments to direct lending strategies, for which a slower PE deal environment has made capital deployment challenging. The asset class also posted the highest returns among all private asset classes through September 30. Many private debt securities are tied to floating rates, which enhance returns in a rising-rate environment. Thus far, managers appear to have successfully navigated the rising incidence of default and distress exhibited across the broader leveraged-lending market. Although direct lending deal volume declined from 2022, private lenders financed an all-time high 59 percent of leveraged buyout transactions last year and are now expanding into additional strategies to drive the next era of growth.

Infrastructure took a detour

After several years of robust growth and strong performance, infrastructure and natural resources fundraising declined by 53 percent to the lowest total since 2013. Supply-side timing is partially to blame: five of the seven largest infrastructure managers closed a flagship vehicle in 2021 or 2022, and none of those five held a final close last year. As in real estate, investors shied away from core and core-plus investments in a higher-yield environment. Yet there are reasons to believe infrastructure’s growth will bounce back. Limited partners (LPs) surveyed by McKinsey remain bullish on their deployment to the asset class, and at least a dozen vehicles targeting more than $10 billion were actively fundraising as of the end of 2023. Multiple recent acquisitions of large infrastructure GPs by global multi-asset-class managers also indicate marketwide conviction in the asset class’s potential.

Private markets still have work to do on diversity

Private markets firms are slowly improving their representation of females (up two percentage points over the prior year) and ethnic and racial minorities (up one percentage point). On some diversity metrics, including entry-level representation of women, private markets now compare favorably with corporate America. Yet broad-based parity remains elusive and too slow in the making. Ethnic, racial, and gender imbalances are particularly stark across more influential investing roles and senior positions. In fact, McKinsey’s research  reveals that at the current pace, it would take several decades for private markets firms to reach gender parity at senior levels. Increasing representation across all levels will require managers to take fresh approaches to hiring, retention, and promotion.

Artificial intelligence generating excitement

The transformative potential of generative AI was perhaps 2023’s hottest topic (beyond Taylor Swift). Private markets players are excited about the potential for the technology to optimize their approach to thesis generation, deal sourcing, investment due diligence, and portfolio performance, among other areas. While the technology is still nascent and few GPs can boast scaled implementations, pilot programs are already in flight across the industry, particularly within portfolio companies. Adoption seems nearly certain to accelerate throughout 2024.

Private markets in a slower era

If private markets investors entered 2023 hoping for a return to the heady days of 2021, they likely left the year disappointed. Many of the headwinds that emerged in the latter half of 2022 persisted throughout the year, pressuring fundraising, dealmaking, and performance. Inflation moderated somewhat over the course of the year but remained stubbornly elevated by recent historical standards. Interest rates started high and rose higher, increasing the cost of financing. A reinvigorated public equity market recovered most of 2022’s losses but did little to resolve the valuation uncertainty private market investors have faced for the past 18 months.

Within private markets, the denominator effect remained in play, despite the public market recovery, as the numerator continued to expand. An activity-dampening cycle emerged: higher cost of capital and lower multiples limited the ability or willingness of general partners (GPs) to exit positions; fewer exits, coupled with continuing capital calls, pushed LP allocations higher, thereby limiting their ability or willingness to make new commitments. These conditions weighed on managers’ ability to fundraise. Based on data reported as of year-end 2023, private markets fundraising fell 22 percent from the prior year to just over $1 trillion, the largest such drop since 2009 (Exhibit 1).

The impact of the fundraising environment was not felt equally among GPs. Continuing a trend that emerged in 2022, and consistent with prior downturns in fundraising, LPs favored larger vehicles and the scaled GPs that typically manage them. Smaller and newer managers struggled, and the number of sub–$1 billion vehicles and new firm launches each declined to its lowest level in more than a decade.

Despite the decline in fundraising, private markets assets under management (AUM) continued to grow, increasing 12 percent to $13.1 trillion as of June 30, 2023. 2023 fundraising was still the sixth-highest annual haul on record, pushing dry powder higher, while the slowdown in deal making limited distributions.

Investment performance across private market asset classes fell short of historical averages. Private equity (PE) got back in the black but generated the lowest annual performance in the past 15 years, excluding 2022. Closed-end real estate produced negative returns for the first time since 2009, as capitalization (cap) rates expanded across sectors and rent growth dissipated in formerly hot sectors, including multifamily and industrial. The performance of infrastructure funds was less than half of its long-term average and even further below the double-digit returns generated in 2021 and 2022. Private debt was the standout performer (if there was one), outperforming all other private asset classes and illustrating the asset class’s countercyclical appeal.

Private equity down but not out

Higher financing costs, lower multiples, and an uncertain macroeconomic environment created a challenging backdrop for private equity managers in 2023. Fundraising declined for the second year in a row, falling 15 percent to $649 billion, as LPs grappled with the denominator effect and a slowdown in distributions. Managers were on the fundraising trail longer to raise this capital: funds that closed in 2023 were open for a record-high average of 20.1 months, notably longer than 18.7 months in 2022 and 14.1 months in 2018. VC and growth equity strategies led the decline, dropping to their lowest level of cumulative capital raised since 2015. Fundraising in Asia fell for the fourth year of the last five, with the greatest decline in China.

Despite the difficult fundraising context, a subset of strategies and managers prevailed. Buyout managers collectively had their best fundraising year on record, raising more than $400 billion. Fundraising in Europe surged by more than 50 percent, resulting in the region’s biggest haul ever. The largest managers raised an outsized share of the total for a second consecutive year, making 2023 the most concentrated fundraising year of the last decade (Exhibit 2).

Despite the drop in aggregate fundraising, PE assets under management increased 8 percent to $8.2 trillion. Only a small part of this growth was performance driven: PE funds produced a net IRR of just 2.5 percent through September 30, 2023. Buyouts and growth equity generated positive returns, while VC lost money. PE performance, dating back to the beginning of 2022, remains negative, highlighting the difficulty of generating attractive investment returns in a higher interest rate and lower multiple environment. As PE managers devise value creation strategies to improve performance, their focus includes ensuring operating efficiency and profitability of their portfolio companies.

Deal activity volume and count fell sharply, by 21 percent and 24 percent, respectively, which continued the slower pace set in the second half of 2022. Sponsors largely opted to hold assets longer rather than lock in underwhelming returns. While higher financing costs and valuation mismatches weighed on overall deal activity, certain types of M&A gained share. Add-on deals, for example, accounted for a record 46 percent of total buyout deal volume last year.

Real estate recedes

For real estate, 2023 was a year of transition, characterized by a litany of new and familiar challenges. Pandemic-driven demand issues continued, while elevated financing costs, expanding cap rates, and valuation uncertainty weighed on commercial real estate deal volumes, fundraising, and investment performance.

Managers faced one of the toughest fundraising environments in many years. Global closed-end fundraising declined 34 percent to $125 billion. While fundraising challenges were widespread, they were not ubiquitous across strategies. Dollars continued to shift to large, multi-asset class platforms, with the top five managers accounting for 37 percent of aggregate closed-end real estate fundraising. In April, the largest real estate fund ever raised closed on a record $30 billion.

Capital shifted away from core and core-plus strategies as investors sought liquidity through redemptions in open-end vehicles and reduced gross contributions to the lowest level since 2009. Opportunistic strategies benefited from this shift, as investors turned their attention toward capital appreciation over income generation in a market where alternative sources of yield have grown more attractive.

In the United States, for instance, open-end funds, as represented by the National Council of Real Estate Investment Fiduciaries Fund Index—Open-End Equity (NFI-OE), recorded $13 billion in net outflows in 2023, reversing the trend of positive net inflows throughout the 2010s. The negative flows mainly reflected $9 billion in core outflows, with core-plus funds accounting for the remaining outflows, which reversed a 20-year run of net inflows.

As a result, the NAV in US open-end funds fell roughly 16 percent year over year. Meanwhile, global assets under management in closed-end funds reached a new peak of $1.7 trillion as of June 2023, growing 14 percent between June 2022 and June 2023.

Real estate underperformed historical averages in 2023, as previously high-performing multifamily and industrial sectors joined office in producing negative returns caused by slowing demand growth and cap rate expansion. Closed-end funds generated a pooled net IRR of −3.5 percent in the first nine months of 2023, losing money for the first time since the global financial crisis. The lone bright spot among major sectors was hospitality, which—thanks to a rush of postpandemic travel—returned 10.3 percent in 2023. 2 Based on NCREIFs NPI index. Hotels represent 1 percent of total properties in the index. As a whole, the average pooled lifetime net IRRs for closed-end real estate funds from 2011–20 vintages remained around historical levels (9.8 percent).

Global deal volume declined 47 percent in 2023 to reach a ten-year low of $650 billion, driven by widening bid–ask spreads amid valuation uncertainty and higher costs of financing (Exhibit 3). 3 CBRE, Real Capital Analytics Deal flow in the office sector remained depressed, partly as a result of continued uncertainty in the demand for space in a hybrid working world.

During a turbulent year for private markets, private debt was a relative bright spot, topping private markets asset classes in terms of fundraising growth, AUM growth, and performance.

Fundraising for private debt declined just 13 percent year over year, nearly ten percentage points less than the private markets overall. Despite the decline in fundraising, AUM surged 27 percent to $1.7 trillion. And private debt posted the highest investment returns of any private asset class through the first three quarters of 2023.

Private debt’s risk/return characteristics are well suited to the current environment. With interest rates at their highest in more than a decade, current yields in the asset class have grown more attractive on both an absolute and relative basis, particularly if higher rates sustain and put downward pressure on equity returns (Exhibit 4). The built-in security derived from debt’s privileged position in the capital structure, moreover, appeals to investors that are wary of market volatility and valuation uncertainty.

Direct lending continued to be the largest strategy in 2023, with fundraising for the mostly-senior-debt strategy accounting for almost half of the asset class’s total haul (despite declining from the previous year). Separately, mezzanine debt fundraising hit a new high, thanks to the closings of three of the largest funds ever raised in the strategy.

Over the longer term, growth in private debt has largely been driven by institutional investors rotating out of traditional fixed income in favor of private alternatives. Despite this growth in commitments, LPs remain underweight in this asset class relative to their targets. In fact, the allocation gap has only grown wider in recent years, a sharp contrast to other private asset classes, for which LPs’ current allocations exceed their targets on average. According to data from CEM Benchmarking, the private debt allocation gap now stands at 1.4 percent, which means that, in aggregate, investors must commit hundreds of billions in net new capital to the asset class just to reach current targets.

Private debt was not completely immune to the macroeconomic conditions last year, however. Fundraising declined for the second consecutive year and now sits 23 percent below 2021’s peak. Furthermore, though private lenders took share in 2023 from other capital sources, overall deal volumes also declined for the second year in a row. The drop was largely driven by a less active PE deal environment: private debt is predominantly used to finance PE-backed companies, though managers are increasingly diversifying their origination capabilities to include a broad new range of companies and asset types.

Infrastructure and natural resources take a detour

For infrastructure and natural resources fundraising, 2023 was an exceptionally challenging year. Aggregate capital raised declined 53 percent year over year to $82 billion, the lowest annual total since 2013. The size of the drop is particularly surprising in light of infrastructure’s recent momentum. The asset class had set fundraising records in four of the previous five years, and infrastructure is often considered an attractive investment in uncertain markets.

While there is little doubt that the broader fundraising headwinds discussed elsewhere in this report affected infrastructure and natural resources fundraising last year, dynamics specific to the asset class were at play as well. One issue was supply-side timing: nine of the ten largest infrastructure GPs did not close a flagship fund in 2023. Second was the migration of investor dollars away from core and core-plus investments, which have historically accounted for the bulk of infrastructure fundraising, in a higher rate environment.

The asset class had some notable bright spots last year. Fundraising for higher-returning opportunistic strategies more than doubled the prior year’s total (Exhibit 5). AUM grew 18 percent, reaching a new high of $1.5 trillion. Infrastructure funds returned a net IRR of 3.4 percent in 2023; this was below historical averages but still the second-best return among private asset classes. And as was the case in other asset classes, investors concentrated commitments in larger funds and managers in 2023, including in the largest infrastructure fund ever raised.

The outlook for the asset class, moreover, remains positive. Funds targeting a record amount of capital were in the market at year-end, providing a robust foundation for fundraising in 2024 and 2025. A recent spate of infrastructure GP acquisitions signal multi-asset managers’ long-term conviction in the asset class, despite short-term headwinds. Global megatrends like decarbonization and digitization, as well as revolutions in energy and mobility, have spurred new infrastructure investment opportunities around the world, particularly for value-oriented investors that are willing to take on more risk.

Private markets make measured progress in DEI

Diversity, equity, and inclusion (DEI) has become an important part of the fundraising, talent, and investing landscape for private market participants. Encouragingly, incremental progress has been made in recent years, including more diverse talent being brought to entry-level positions, investing roles, and investment committees. The scope of DEI metrics provided to institutional investors during fundraising has also increased in recent years: more than half of PE firms now provide data across investing teams, portfolio company boards, and portfolio company management (versus investment team data only). 4 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023.

In 2023, McKinsey surveyed 66 global private markets firms that collectively employ more than 60,000 people for the second annual State of diversity in global private markets report. 5 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023. The research offers insight into the representation of women and ethnic and racial minorities in private investing as of year-end 2022. In this chapter, we discuss where the numbers stand and how firms can bring a more diverse set of perspectives to the table.

The statistics indicate signs of modest advancement. Overall representation of women in private markets increased two percentage points to 35 percent, and ethnic and racial minorities increased one percentage point to 30 percent (Exhibit 6). Entry-level positions have nearly reached gender parity, with female representation at 48 percent. The share of women holding C-suite roles globally increased 3 percentage points, while the share of people from ethnic and racial minorities in investment committees increased 9 percentage points. There is growing evidence that external hiring is gradually helping close the diversity gap, especially at senior levels. For example, 33 percent of external hires at the managing director level were ethnic or racial minorities, higher than their existing representation level (19 percent).

Yet, the scope of the challenge remains substantial. Women and minorities continue to be underrepresented in senior positions and investing roles. They also experience uneven rates of progress due to lower promotion and higher attrition rates, particularly at smaller firms. Firms are also navigating an increasingly polarized workplace today, with additional scrutiny and a growing number of lawsuits against corporate diversity and inclusion programs, particularly in the US, which threatens to impact the industry’s pace of progress.

Fredrik Dahlqvist is a senior partner in McKinsey’s Stockholm office; Alastair Green  is a senior partner in the Washington, DC, office, where Paul Maia and Alexandra Nee  are partners; David Quigley  is a senior partner in the New York office, where Connor Mangan is an associate partner and Aditya Sanghvi  is a senior partner; Rahel Schneider is an associate partner in the Bay Area office; John Spivey is a partner in the Charlotte office; and Brian Vickery  is a partner in the Boston office.

The authors wish to thank Jonathan Christy, Louis Dufau, Vaibhav Gujral, Graham Healy-Day, Laura Johnson, Ryan Luby, Tripp Norton, Alastair Rami, Henri Torbey, and Alex Wolkomir for their contributions

The authors would also like to thank CEM Benchmarking and the StepStone Group for their partnership in this year's report.

This article was edited by Arshiya Khullar, an editor in the Gurugram office.

Explore a career with us

Related articles.

CEO alpha: A new approach to generating private equity outperformance

Private equity turns to resiliency strategies for software investments

The state of diversity in global private markets: 2022