financial performance related research papers

A meta-analysis: capital structure and firm performance

Journal of Economics and Development

ISSN : 1859-0020

Article publication date: 29 April 2020

Issue publication date: 29 May 2020

The paper aims at providing insights on the relationship between capital structure and performance of the firm by employing meta-analytical approach to obtain a synthesized result out of controversial studies as well as the sources for such inconsistency.

Design/methodology/approach

Using secondary data, the analysis is divided into two main parts with concerns to the overall strength of the relationship, the effect size and the potential paper-specific characteristics influencing the magnitude of impacts between leverage and firm performance (moderators of the relationship). Overall, a total number of 32 journals, reviews and school presses were selected besides online libraries and publishing platforms. There were 50 papers with 340 studies chosen from 2004 to 2019, of which data range from 1998 to 2017.

Using Hedges et al. (1985,1988), descriptive and quantitative analysis have been conducted to confirm that corporate performance is negatively related to capital decisions, which inclines toward trade-off model with agency costs and pecking order theory. The estimation induces rather small effect size that implies sufficiently large sample size to be effectively investigated. In terms of moderator analysis, random-effects meta-regression models of three different techniques are used to increase the robustness in research findings, showing statistically significant elements as publication status, factor of industry and proxy of firm performance.

Originality/value

This paper is one of the first papers presenting meta-analysis in capital structure and performance for two languages, Vietnamese and English, providing a consistent result with previous worldwide papers.

• Capital structure
• Firm performance
• Meta-analysis

Dao, B.T.T. and Ta, T.D.N. (2020), "A meta-analysis: capital structure and firm performance", Journal of Economics and Development , Vol. 22 No. 1, pp. 111-129. https://doi.org/10.1108/JED-12-2019-0072

Emerald Publishing Limited

Copyright © 2019, Binh Thi Thanh Dao and Tram Dieu Ngoc Ta

Published in Journal of Economics and Development . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Capital structure of the firm, as defined by Baker and Martin (2011) , is the mixture of debt and equity that the firm employs to finance its productive assets, operations and future growth. It is a direct determinant of the overall costs of capital and contributes to the firm's total level of risks. The choice of different proportions of debt among mixed financing resources can impose major influences on the firm value, and thus on the wealth of the shareholders ( Baker and Martin, 2011 ). Since capital decision is one of the most important elements in corporate finance, it has attracted considerable concern of both academics and practitioners over the past few decades.

At the beginning of its theory development, capital structure was convinced to be irrelevant to the performance of corporations, as suggested by Modigliani and Miller (1958, 1963) .

However, given the existence of an imperfect market's conditions and behaviors, the concept of optimal capital structure emerges with the proposal of trade-off theory that integrates the effect of corporate taxes, financial distress and agency problems. On the other hand, the recognition of information asymmetry also leads to the appearance of signaling hypothesis and the pecking order theory, which neglect the term of an optimal leverage. Each theory, despite concerning the same relation of capital structure and firm performance, suggests quite a divergent collection of outcomes toward the sign of impacts between the two subjects of interest.

Myriad empirical studies have been conducted to confirm if the market is more inclined to the most suitable theories, but none of them has come close to a consensus. It is due to the fact that practices observed from the real marketplace are rather sophisticated and influenced by many relevant factors. Since the final outcomes of each study remain fractional and inconsistent, the need for a generalized conclusion comes into consideration as one of the most fundamental issues. Moreover, conventional research tends to focus on answering whether a significant relation between two variables exists, rather than reporting how much influence they have on one another, which underestimates the true value that a research is expected to contribute.

Originally used in medical study, meta-analysis has become more widespread in the field of finance and economics. However, these papers mostly work on the determinants of capital structure or firm performance separately and have rarely been investigated under the view of a relationship. Besides, in addition to the mutual relation between capital structure and firm performance, other accountable factors such as industry, business strategy of the firm or even paper-specific characteristics of each study can also be potential sources of controversial results, yet they have not been evaluated with appropriate level of emphasis. In fact, these third elements, besides providing insights on how the relationship of interest changes under different contexts, also offer solutions for the improvement in research design and sampling technique if they are properly scrutinized.

In general, the study is expected (1) to determine the strength of relationship between leverage and performance of the firm, both in terms of direction and quantified intensity, and (2) to explore possible factors that influence the magnitude of relationship between capital structure and firm performance.

The paper is divided into seven major sections. The first part of introduction will provide background knowledge and general idea of how the analysis manages to address the problem of controversial results in a coherent and logical way. Next, in literature review , five major theories of capital structure will be discussed to demonstrate the possible influence of leverage on the firm value. Around 15 empirical researches will be summarized, based on which hypotheses of this paper will be developed for future testing, including one on the relationship of interest and seven others concerning the moderating effect of potential third factors. The methodology is then explained with the basis of meta-analytical approaches as well as data collection and processing methods. After that, descriptive analysis will classify different groups of paper-specific features and exhibit descriptive statistics of the regression outcomes from the selected studies. In the fifth section of quantitative analysis , the strength of relationship between capital structure and firm performance, or the overall effect size, will be measured and combined according to the standardized framework proposed by Hedges and his colleagues. Then, moderator analysis will investigate the potential sources of heterogeneity among individual studies by performing different meta-regression techniques. It helps to explore possible moderating elements that impose certain influence on the magnitude of effect from leverage to the firm value; thus, the second purpose of this research will be fulfilled by this section. Besides, further test for small-study effect will also be conducted as a complementary analysis to examine if the quality of data implies any probability of the bias problem. Finally, significant remarks on the empirical findings will be summarized in the conclusion along with several limitations of the study and future opportunities of research.

2. Literature review

2.1 theoretical framework, modigliani and miller first proposition (1958).

This research is among the pioneers attempting to unravel the relationship between capital structure and firm value. Their proposition, usually referred to as MM theorem , was first introduced in 1958, and it brought up the most intriguing question about the relevance of funding decisions toward corporate performance. In particular, they argue that any changes in the current proportions of debt and equity cannot affect the value of the firm, which means no capital structure is better or worse, and firm values remain irrelevant to different levels of leverage ( Modigliani and Miller, 1958 ).

Modigliani and Miller alternative propositions (1963)

Using tax-deductible expenditure, the appearance of interest promotes lower tax payments and thus improves the firm's general cash flows ( Miller and Modigliani, 1963 ). Indeed, the two economists also discovered that the firm value is now positively related to financial leverage, which implies that corporations are fully capable of maximizing their values by raising their debt levels.

Trade-off Theory

states that the capital decision of one firm involves a trade-off between the tax benefit of debts and the costs of financial distress ( Kraus and Litzenberger, 1973 ).

When adopting the trade-off theory, each firm tends to set its own targeted debt-to-equity ratio and strives to achieve the expected optimum which varies with the characteristics of different firms ( Myers, 1984 )

Agency Theory

proposed by Jensen and Meckling (1976) and Myers (1977) investigates the influence of capital structure under a new perspective of corporate governance. Since the theory is developed on the basis of previous models, it shows consistent results with the trade-off theory. In general, agency problems involve the participation of three parties including managers, shareholders and creditors.

Agency problems between shareholders and managers

The first type of conflict is rooted when the managers own less than 100% of the share of the firm's assets, which induces less motivation behind their acts to maximize the firm value for shareholder's best interest ( Jensen and Meckling, 1976 ) With a low level of debt, managers will own more freedom to spend the firm's free cash flows, and hence they easily take on low-return projects and acquire unnecessary physical assets to enlarge the firm size, which is believed to reflect their own reputation. For such reasons, managers increase the agency costs of equity , which is detrimental to the firm performance. On the contrary, if the firm is funded by higher amount of leverage, the commitment to fulfill interest payments leaves managers with less freedom to distribute the cash flows; therefore, they are required to be more efficient in choosing investments and generally improve the firm performance.

Agency problems between shareholders and creditors

The second conflict arises when two groups of investors prefer different levels of risk-taking behaviors. In particular, shareholders may have the incentive to either take considerably risky projects or move toward underinvestment ( Ross et al. , 2013 ; Westerfield and Jaffe, 2013). Regarding the former motive in which shareholders take part in high-risk investments, they shall receive extra return if the projects succeed and share losses with their counterpart in any case of failure ( Jensen and Meckling, 1976 ). Concerning the second incentive, if a firm owns excessive amount of leverage, the significant probability of bankruptcy would discourage shareholders to take on new investments despite positive NPVs; hence, the firm becomes underinvested ( Myers, 1977 ).

Pecking Order Theory

is an alternative to the trade-off model that declares a negative relationship between firm's performance and its decision of financing. There are two rules as proposed by the pecking order ( Myers, 1984 ): (1) use internal financing and (2) issue safer securities first. In other words, the preference of financial instruments shall be prioritized as follows: internally generated funds, debt and equity. The driving force behind this arrangement generally stems from the problems of information asymmetry. According to Ross et al. (2013) , in some cases where the managers wish to embark on a risky project but the lenders, due to discrepancy of information, stay rather optimistic about the venture, the issuance of debt would be much likely to be overpriced just as the equity issuance. It leads to a major problem in which investors eventually recognize the pattern of issuing decisions for both equity and debt whenever they are overvalued under the managers' perspective. As a result, any public offering can then become less than a success since this phenomenon creates a never-ending cycle of skepticism between investors and managers of the firm.

Signaling Theory

is proposed by Ross (1977) in which the choice of debt-to-equity ratio is independent of the optimum concept and rather represented by the willingness of a firm in sending certain messages to the investors. Profitable firms sometimes attempt to push up the stock price by excessively increasing debt over its optimal level and mislead the market to believe in its inflated growth opportunity in the future. Indeed, they believe that the extra cost of issuing debts shall prevent less profitable firms from taking advantages of higher leverage as compared to those with better performance, despite the managers' attempt to fool the public ( Ross et al. , 2013 ). Additionally, Myers and Majluf (1984) propose the tendency in which managers are rather reluctant to issue equity when it is believed to be undervalued; consequently, investors tend to perceive issuance of stocks as a bad signal, assuming that managers offer equity to the public only if it is fairly priced or overpriced. In short, the relationship between leverage and firm performance is found positive under the signaling theory.

Among the five theories, only MM and Signaling support the positive relationship between leverage and firm performance, while the other three theories – Agency, Trade-off and Pecking order – support the negative relationship.

2.2 Empirical research

As a majority of theoretical frameworks provide equivalently credible arguments, it requires remarkable effort and profound knowledge to convince that one of them should be more competent and appropriate than the others, not to mention the influence of an inefficient market and different aspects of behavioral finance. For such reasons, myriad of empirical researches have been conducted to obtain statistical conclusions by representative observations in the market. Since the number of studies is clearly substantial, Table 1 in Appendix only includes several recently published articles to examine their main ideas and empirical results. In our knowledge, the paper of Hang et al. (2018) is the first publication on meta-analysis of factors influencing the capital structure, and a bit different from ours is the relationship between firm leverage and performance.

2.3 Hypothesis development

There is a negative relationship between capital structure and firm performance.

Regarding the second purpose of this meta-analysis, in general, the variation in each study can be traced to different qualitative features involving research designs, sampling methods or analytical techniques. As can be seen from Table 2 , many outcomes are reported with specific notes on the three elements that potentially influence the final conclusion on the relationship, such as the choice of indicators for firm performance, the condition of sample firms being listed or the relevance of business strategies and industrial factors accounted in each study. Indeed, Sánchez-Ballesta and García-Meca (2007) suggest that the contextual characteristics of analysis, proxies for firm value, econometric methods and types of firm can contribute further insights to explain the inconsistency in the prevailing impact of capital structure on the firm performance. Since the paper is expected to explore potential sources of heterogeneity that lead to divergent results, based on the empirical evidence discussed above, seven categorical characteristics of each paper are chosen to be scrutinized as potential moderators on the relation between firm value and leverage, namely: (1) publication status, (2) country development, (3) company ' s listed status, (4) industry factor, (5) business strategy, (6) proxy for firm performance and (7) econometric method for analyzing . In short, all the hypotheses included in this paper are summarized in Table 1 .

3. Research methodology

3.1 research design, 3.1.1 meta-analysis.

Meta-analysis , as explained by Borenstein et al. (2011) , refers to the statistically synthesized results from a series of studies collected through a methodological procedure. According to Glass (1976) , meta-analysis can be considered as “the analysis of analyses” where individual researches are gathered with the aim to integrate their knowledge and findings. In particular, meta-analysis allows separate empirical outcomes of different papers to be aggregated and compared after being transformed into one common metric called the effect size .

3.1.2 Meta-regression

Besides the purpose of obtaining a generalized empirical evidence on the relation of two variables, meta-analysis can also be advanced into meta-regression, or meta-regression analysis, which performs closer scrutiny on the third elements that potentially influence the strength of relationship.

According to Higgins and Green (2011) , meta-regression is quite similar in essence to simple regressions where a dependent variable is forecasted by one or more explanatory variables. However, meta-regression should be distinguished from simple regressions by two means. Firstly, the weight of each study is assigned based entirely on the precision of its effect estimates, in which larger studies tend to have stronger influence as compared to the smaller ones. Secondly, the existence of residual heterogeneity that cannot be explained by independent variables should be recognized and allowed in the analysis, giving rise to the term “random-effects meta-regression” ( Thompson and Sharp, 1999 ).

3.1.3 Generalized models and assumptions

Fixed-effects meta-regression is the extension of fixed-effect meta-analysis where the mean effect, θ , is developed into a linear predictor, β x i , such that.

(2) Random-effects meta-regression , similarly, is extended from the random-effects meta-analysis with consideration of the covariates.

y i =   β x i + u i +   ϵ i , where u i   ∼   N ( 0 ,     τ 2 ) and ϵ i   ∼   N ( 0 ,   σ i 2 ) .

3.2 Data selection method

3.2.1 data collection.

The process of collecting and evaluating data for a meta-analysis is of critical importance since it is one of the most significant factors that can contribute to the analytical success. Overall, a total number of 32 journals, reviews and school presses were selected [1] besides online libraries and publishing platforms, namely, Elsevier, JSTOR, ResearchGate, Wiley, SSRN and Springer. There were 50 papers with 340 studies chosen from 2004 to 2019, of which data ranged from 1998 to 2017.

3.2.2 Data evaluation and final sample size

After the first stage of massive data collection, four additional standards were established as predetermined requirements for the following screening procedure.

First of all, the general search for papers on relationship between capital structure and firm performance leads to two ways of defining main dependent variables where a minority of 7.4% choose leverage ratios and the other 92.6% choose firm value indicators. While there is no threshold on the number of studies needed for a meta-analysis ( Pigott and Terri, 2012 ), it remains more preferable to keep the data collected at its potential maximum.

Secondly, proxy for firm value can be divided into two main groups: accounting-based measures including return on asset (ROA), return on equity (ROE) and market-based ratio such as Tobin's Q.

Thirdly, further steps of data processing require the provision of at least two following figures: (1) beta coefficients of regression, and (2) t -statistics or p -value, which means studies without these numbers are also excluded.

Lastly, statistically significant outcomes tend to be utilized repeatedly in multiple works of the same authors under different forms such as dissertations, working papers and journal articles. At the end of the screening process, the final data officially consist of 34 papers which propose 245 studies served as observations for this meta-analysis. The time period also changed, as it now covers researches during 2012–2019, with a data set dated from 2000 to 2017.

4. Descriptive analysis

4.1 descriptive analysis of paper-specifics.

Since the purpose of meta-analysis is to examine the effect sizes as well as the potential impact of other qualitative characteristics on the intervention effects, it is essential to take a look at the descriptive summary of these paper-specific data.

As stated in Table 2 , the data collection takes into account all papers with no regard to publication status. Consequently, 71% of studies were published as review and journal articles, while 29% were not, since they are either graduate dissertations or master thesis (See Table 3 ).

Out of 245 studies, 17.1% analyze the relationship between capital structure and firm performance by classifying each group of firms by the industry that they are operating in. For the remaining researches, external environments such as industrial factors are neglected during analysis (See Table 4 ).

In terms of firm value indicators, number of studies employing accounting measures (ROA, ROE) amount up to 73.1% compared with 26.9% using market ratio (Tobin's Q). The prevalence of accounting-based indices is nearly three times higher than its counterpart, which means ROA and ROE are generally more favorable as representatives for firm performance than Tobin's Q (See Table 5 ).

Regarding statistical approaches, pooled OLS is a dominant method with the use of nearly 41% of the selected papers. Next, fixed-effects model ranks second in popularity with 30.2%, closely followed by its counterpart. Meanwhile, a modest 3% of the studies use GMM as their preferable method.

4.2 Descriptive analysis of study results

The development of meta-analysis is to provide a comparison and synthesis on the findings of individual researches; hence, it is no surprise to see inconsistent results collected from 245 separate studies. Table 6 shows a summary of conclusions according to their statistical outcomes at 5% level of significance.

As illustrated in Table 6 , negative relationship between capital structure and firm performance seems to be a prevalent result, accounting for nearly 50% of the consequences, whereas the proportions of positive and insignificant outcomes similarly vary around 26%.

Descriptive analysis of study results supports H1 : There is a negative relationship between capital structure and firm performance.

5. Quantitative analysis: overall effect size

Quantitative analysis is a crucial part of meta-analysis which generally concerns the determination of effect sizes. With regard to the rapid increase in the total number of studies and the evolution of statistics means, Gene Glass, an American statistician and researcher who originated the term “meta-analysis,” believed that “statistical significance is the least interesting thing about the results” as they should be able to answer not just the question of whether or not a relationship between two variable exists, but rather how strong the relation can be.

In general, the following section of quantitative analysis will cover two main parts, described below.

5.1 Hedges et al. 's method (1985,1988)

Based on the framework of Hedges et al. , effect sizes are represented by the Pearson “r” correlation coefficient of individual studies, which is appropriate and widely used for comparing results of two continuous variables.

The procedure from analyzing to interpreting the overall effect size is demonstrated in Figure 1 .

In general, each study is expected to produce one Pearson “ r ” correlation which will be transformed into its z -scale statistic by Fisher's method. Then, the combined effect size represented by z -score is obtained and converted back to receive the overall correlation for further interpretation ( Borenstein and Hedges, 2011 ; Higgins and Green, 2011 ).

5.1.1 Standardized effect sizes

It is noted that the values of “ r ” obtained from separate papers remain dependent on different research designs and not yet synthesized; thus, they are not directly interpretable. It explains why Pearson “ r ” should be transformed into a standardized measure of Fisher score “ Zr ” before combining the average true effect. According to Hedges and Olkin (1985) , Rosenthal (1991) and Hedges and Vevea (1998) , the transformation of “ r ” into “ Zr ” is proved to be capable of correcting skewness problems in the distribution of Pearson correlation coefficient. This statement is also supported by prior research of Silver and Dunlap (1987) who also observed a less distorted distribution in “ r ” with the complement of Fisher standardization.

One noticeable problem detected during data collection is that not all studies in management and finance provide Pearson “ r ” correlation in their analysis ( Rocca, 2010 ). Fortunately, Cooper and Hedges (1994) suggested a way of retrieving “ r ” using the t -Students as illustrated by Eqn 1 . (1) r i =   t i 2 t i 2 + ⅆ f i where r i is the correlation coefficient of study i ; t i is the t -statistic of beta coefficients of study i ; df i is the degree of freedom that equals to n − ( k ′ + 1 ) ; n is the sample size and k ′ is the number of independent variables of study i .

Next step is to convert r i into Fisher Z -score by Eqn 2 ( Field and Gillett, 2010 ). (2) Z r i =   1 2 ln ( 1 + r i 1 − r i ) where Z r i is the standardized Z -score of the corresponding r i in study i; r i is the correlation coefficient of study i .

5.1.2 Weights under fixed-effects model

The first approach is based on a model which states that if the sample size is large enough, residual errors will converge toward 0 ( Hedges and Olkin, 1985 ), thus indicating an increase in the level of accuracy as more subjects are added to the sample of interest: (3) w i = n i − 3 where w i is the weight of study i among a total of k studies; n i is the sample size of study i.

In the second approach, it is recalled that fixed-effects model assumes one true effect size θ for every study, and its only source of error is reflected in the within-study variances, σ i 2 . In particular, with a smaller standard error, the estimation of effect size is appraised as more rigorous. Consequently, it leads to Eqn 4 , which simply shows the reverse relation between within-study variances and weights allocated to selected studies ( Hedges and Vevea, 1998 ). (4) w i =   1 σ i 2 =   1 SE i 2   where w i is the weight of study i among a total of k studies; SE i is the standard error of the estimate in study i.

5.1.3 Weights under random-effects model

While fixed-effects model allows no heterogeneity, random-effects model does the exact opposite, which results in the appearance of second variance component, τ 2 , during the computation of weights. Accordingly, the value of between-study variance must be incorporated as illustrated in Eqn 5 ( Hedges and Olkin, 1985 , Hedges and Vevea, 1998 ). (5) w i =   1 σ i 2 +   τ 2      

The estimation of between-study variance, τ 2 , proposed by Hedges and Olkin (1985) , is provided below. (6) τ HO 2 = max { 0 ,   1 k − 1 ∑   ( y i − y ¯ ) 2 − 1 k ∑   σ i 2 }  

where k is the total number of studies; y i is the effect size in study i ; y ¯ is the average effect size of k studies; σ i 2 is the within-study variance in study i.

However, this method only works when τ 2 is non-negative. In practice, several researches have shown the possibility of negative value of τ 2 . It is then set back to 0 according to the rule stated above and seemingly denies the existence of heterogeneity. To promote a more effective measure, Chung et al. (2013) suggested the use of DerSimonian and Laird's (1986) estimate that employs method of moment estimator as follows: (7) τ DL 2 =   ∑ i s i − 2 ( y i − μ ˆ ) 2 − ( n − 1 ) ∑ i s i − 2 − ∑ i s i − 4 ∑ i s i − 2   where s i is the standard error of the estimate [2] in study i ;

y i is the effect size in study i ;

n is the total number of studies;

μ ˆ is defined by the formula μ ˆ =   ∑ i y i / s i 2 ∑ i 1 / s i 2

5.1.4 Overall effect size

Eqn 8 provides the calculation of “ Zr ” as suggested by Hedges and Olkin (1985) and Hedges and Vevea (1998) , which takes into account the distribution of the weights: (8) Z r ¯ =   ∑ i = 1 k w i Z r i ∑ i = 1 k w i   where Z r ¯ is the weighted mean of effect sizes from k studies ;

Z r i is the standardized effect size of study i;

w i is the corresponding weight of study i among a total of k studies.

The standard error for weighted average “ Zr ” is calculated as below. (9) SE ( Z r ¯ ) = 1 ∑ i = 1 k w i   where SE ( Z r ¯ )   is the standard error of the weighted mean of effect sizes from k studies ;

After achieving the mean value of “ Zr ,” it must be converted into its correlation form for final conclusions on the strength of relationship between capital structure and firm performance. Borenstein et al. (2011) introduced the conversion formula for “ r ” in the following equation. (10) r overall = e ( 2 × Z r ¯ ) − 1 e ( 2 × Z r ¯ ) + 1 where r overall is the overall effect size as measured by correlations;

Z r ¯ is the weighted mean of effect sizes from k studies.

For the interpretation of results, Cohen (1977) proposed the “rules of thumb” as Table 8 .

5.2 Discussion of findings

Given all essential elements, the calculation of overall effect size (ES) between capital structure and firm value was performed on MS Excel spreadsheets in several different ways with the aim to provide diverse perspectives on the same subject. The main statistics are summarized in Table 9 .

It is evident that the combined effect sizes under z -scale, despite standardized or unstandardized measurements, are all negative. Five out of six 95% confidence intervals stay below zero, except for case (3) where the upper limit of confidence surpasses this value. However, the third method only accounts for unweighted outcomes from statistically significant studies.

Interestingly, the confidence interval under random-effects model is closely similar to that of fixed-effects model weighted by the within-study variances, while it is generally expected to be larger. However, as compared to method (5) where “ Zr ” is weighted based on adjusted sample size, the random-effects approach indeed provides a wider interval, hence showing a more conservative result (See Table 10 ).

Quantitative analysis of overall effect size confirms H1 : There is a negative relationship between capital structure and firm performance.

6. Moderator analysis

While the main interest of a simple meta-analysis is the combination of an overall effect size, moderator analysis is rather an extension which performs meta-regression to investigate relevant factors that may be influential to the relationship of interest ( Rocca, 2010 ). In particular, the magnitude of impact measured between two variables is expected to diverse from study to study, partially due to the differences in paper-specific characteristics, such as clinical diversity and methodological diversity ( Harbord, 2010 ). By the use of meta-regression, the amount of statistical heterogeneity among empirical results can be examined to further understand how much of the variation stems from one or more elements of paper-specifics ( Thompson and Higgins, 2002 ).

6.1 Specification of variables and methods

6.1.1 moderating variables.

In moderator analysis, the standardized effect size of leverage on firm performance, “ Zr ”, becomes the dependent variable since it represents the magnitude of impacts and is sensitive to different strength across studies ( Rocca, 2010 ). Meanwhile, other paper-specific features that potentially induce controversial results should be chosen as the explanatory variables ( Wolf, 1986 ; Rosenthal, 1991 ). In particular, the examination of heterogeneity utilizes dichotomous covariates and subgroups of observations according to various categorical characteristics. Since dummy variables are employed in the regression, the coefficients would emphasize on the differences of effect sizes between subgroups in comparison with another nominated subgroup of which all dummy variables are assigned to 0 ( Higgins and Green, 2011 ). We use the moderator variables as dummy variable. For example, D-publication = 1 if the study is published, and = 0 otherwise. Theses moderating variables are based on hypotheses H2 - H8 .

6.1.2 Econometric method

Many researchers suggest the use of random-effects model as the proper method for meta-regression, such as Hedges and Olkin (1985) , Cooper and Hedges (1994) and Hedges and Vevea (1998) . This method considers both within-study variance, σ i 2 , and between-study variance, τ 2 , which means two sources of errors due to two levels of sampling are addressed simultaneously . Furthermore, in contrast to fixed-effect model that assumes homogeneity across studies, random-effects model accepts “residual heterogeneity,” which is the between-study variance component that cannot be explained by the covariates. In conclusion, for the reasons above, random-effects meta-regression is selected as the appropriate method for moderator investigation.

In fact, the default estimation method for τ 2 by “metareg” is the restricted maximum likelihood (REML) since this model takes into account the problem of autocorrelation and works well with unbalanced or correlated data ( Rocca, 2010 ). Hence, it is suggested by both Thompson and Sharp (1999) and Viechtbauer (2005) , who also perform comparison among methods and conclude that REML is generally the preferable approach in meta-regression. Therefore, based on the aforementioned opinions, REML is decided to be the benchmark model for this moderator analysis. However, two other options of moment-estimator and empirical Bayes will also be included to increase the robustness of investigation.

6.2 Regression models

6.2.1 initial regression models.

After performing “metareg” command in Stata 14, the initial regression model uses eight independent variables such as D_publication, D_development, D_listed, D_industry, D_strategy, D_proxy, D_ols, D_fem and D_rem. In general, the moderating effect on the relationship between capital structure and firm performance is the joint contribution of publication status, factor of industry and proxy of firm performance . Hence, three hypotheses with respect to these moderators, including H2, H5 and H7, are statistically supported, while the remaining statements are rejected.

6.2.2 Final regression models

The final models are conducted with the participation of three significant variables discovered in previous section, including D_publication, D_industry and D_proxy.

In comparison with Table 11 , all values of the adjusted R 2 generally increase, especially in the case of moments method where it turns from an abnormal negative figure to a positive number despite remaining extremely low (0.32%), confirmed together with the F -statistics, which implies a considerable rise in overall significance of each model.

On the other hand, VIF test shows remarkable reduction in value for all regressors, and hence produces smaller mean VIF at only 2.02, much below 10, confirming the absence of multicollinearity in the regression.

Meanwhile, no change is observed in the index of variability, I 2 . It is understandable since the proportion of variation due to between-study variance is independent of the moderators taken into account.

7. Conclusion

As indicated in the Introduction, the paper is expected to answer the following research questions: What is the overall effect size between capital structure and firm performance?

In particular, two analyses are included to address the first inquiry: a descriptive analysis to predict the sign that should be expected from the relationship of interest, and a standard meta-analysis, or quantitative analysis, to standardize individual outcomes and estimate the overall effect size that leverage imposes on the firm performance. These two approaches are employed to test Hypothesis 1 which states that there is a negative relationship between the two variables of concern.

At first, the descriptive analysis of study results has clearly shown the number of studies proposing negative outcomes dominate those with positive and insignificant conclusions. Hence, H1 is initially supported. Consequently, based on Hedges and his colleagues' framework, the quantitative analysis of the overall effect size is conducted, which produces confidence intervals with the upper limits generally below 0. Thus, as a matter of fact, values of the mean effect size are negative despite the use of standardized or unstandardized methods, fixed-effects or random-effects models. The consistent results statistically confirm H1 , and possibly imply the prevailing relevance of trade-off theory with agency costs as well as the theory of pecking order in financial practices. In addition, Cohen's “rule of thumbs” ( 1977 ) suggests that the combined effect between capital structure and firm performance is relatively small, which does not mean it is insignificant in the real market, but rather recommends future research concerning this subject affords a sufficiently large sample size of 452 participants to investigate the underlying impacts in the most effective way. In this part, Q -test for homogeneity is also performed, and the result indicates the existence of heterogeneity across studies, which emphasizes the need of meta-regression for the next question to obtain appropriate answers.

7.1 Moderator analysis confirms the following hypotheses:

There is a negatively statistically significant effect of publication status as a moderator on the relationship between capital structure and firm performance.

There is a positively statistically significant effect of industry as a moderator on the relationship between capital structure and firm performance.

There is a negatively statistically significant effect of proxy of firm performance as a moderator on the relationship between capital structure and firm performance.

The analysis of the paper still encounters some limitations. Firstly, besides small-study effects, the concept of publication bias in meta-analysis also refers to many other problems as well, including bias during the process of data collection. In fact, all the studies collected are either in English or in Vietnamese, indicating a language-bias issue. Furthermore, they are completely free of charge due to financial capability, which implies the possibility of selection bias in which the collection of data is dependent on free academic resources.

Secondly, the estimation of effect sizes in quantitative analysis requires the presence of t -statistics. However, after the evaluation of data, 30 studies were excluded due to zero p -values, which make it impossible to infer the corresponding t -statistics by all means. In other words, 30 studies with statistically significant results were omitted from the analysis.

Procedure to analyze overall effect size on correlation.

Hypothesis testing on the relationship between leverage and firm performance

Number of studies categorized by publication status

Number of studies considering influence of industry

Number of studies categorized by proxies of firm performance

Number of studies categorized by statistical methods

Study results on the relationship between leverage and firm performance

Descriptive statistics of beta coefficients for the effect of capital structure

Benchmarks for the magnitude of effect and suggested sample size

Overall effect sizes by correlation

Random-effects meta-regression final results

Source(s) : Author's summary (2019)

Please refer to TableA1 for the list of journals, reviews and university presses originally collected.

Note that s i − 2 = 1 / s i 2 and s i − 4 = 1 / s i 4 .

Appendix 2List of journals for data collection

Indian Journal of Finance.

Review of European Studies.

Review of Finance.

The Singapore Economic Review.

Journal of Marine Science and Technology.

External Economics Review.

Journal of Science.

Science of Management and Economics Review.

Economics and Business Review.

University of Twente Press Journal.

Journal of Economics and Finance.

Accounting and Taxation Review.

Applied Economics and Finance.

Proceedings of the Academy of Finance.

International Journal of Business and Commerce.

Journal of Competitiveness.

Journal of Risk and Financial Management.

Journal of Natural and Social Science.

Journal of Business Perspective.

Global Journal of Management and Business Research.

Science Review of Ho Chi Minh Open University.

The Quarterly Review of Economics and Finance.

International Journal of Academic Research in Economics and Management Sciences.

Journal of Emerging Trends in Economics and Management.

Eurasian Journal of Business and Management.

Turkish Journal of Economics and Administrative Sciences.

Global Illuminators Publishing.

International Journal of Accounting and Financial Reporting.

International Journal of Environment, Agriculture and Biotechnology.

Management Science and Engineering.

Journal of Finance and Economics Research.

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Baker , H. and Martin , G. ( 2011 ), Capital Structure and Corporate Financing Decisions: Theory, Evidence, and Practice , John Wiley and Sons .

Borenstein , M. , Hedges , L.V. , Higgins , J.P. and Rothstein , H.R. ( 2011 ), Introduction to Meta-Analysis , John Wiley and Sons .

Chadha , S. and Sharma , A.K. ( 2016 ), “ Capital structure and firm performance: empirical evidence from India ”, Vision , Vol. 19 No. 4 , pp. 295 - 302 .

Chung , Y. , Rabe-Hesketh , S. and Choi , I.-H. ( 2013 ), “ Avoiding zero between-study variance estimates in random-effects meta-analysis ”, Statistics in Medicine , pp. 4071 - 4089 .

Cohen , J. ( 1977 ), Statistical Power Analysis for the Behavioral Sciences , Academic Press , New York, NY .

Cooper , H. and Hedges , L. ( 1994 ), The Handbook of Research Synthesis , Russell Sage Foundation , New York, NY .

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Hedges , L. and Olkin , I. ( 1985 ), Statistical Methods for Meta-Analysis , Academic Press , FL .

Hedges , L. and Vevea , J. ( 1998 ), “ Fixed- and random-effects models in meta-analysis ”, Psychological Methods , pp. 486 - 504 .

Higgins , J.P. and Green , S. ( 2011 ), Cochrane Handbook for Systematic Reviews of Interventions , The Cochrane Collaboration .

Hoang , T.T. ( 2015 ), “ The effect of capital structure on corporate performance: evidence in Vietnam ”, Proceeding GSTAR , Global Illuminators Publishing , pp. 140 - 155 .

Jensen , M.C. and Meckling , W.H. ( 1976 ), “ Theory of the firm: managerial behavior, agency costs and ownership structure ”, Journal of Financial Economics , pp. 305 - 360 .

Jiahui , M.A. ( 2015 ), “ Relationship between capital structure and firm performance: evidence from growing enterprise market in China ”, Management Science and Engineering , pp. 45 - 49 .

Kraus , W. and Litzenberger , R. ( 1973 ), “ A state-preference model of optimal financial leverage ”, Journal of Finance , pp. 911 - 922 .

Mehmood , R. , Hunjra , A.I. and Chani , M.I. ( 2019 ), “ The impact of corporate diversification and financial structure on firm performance: evidence from South Asian countries ”, Journal of Risk and Financial Management .

Miller , M. and Modigliani , F. ( 1963 ), “ Taxes and the cost of capital: a correction ”, American Economic Review , pp. 433 - 443 .

Modigliani , F. and Miller , M. ( 1958 ), “ The cost of capital, corporate finance and the theory of investment ”, American Economic Review , pp. 261 - 297 .

Myers , S. ( 1984 ), “ The capital structure puzzle ”, Journal of Finance , pp. 575 - 592 .

Myers , S.C. ( 1977 ), “ Determinants of corporate borrowing ”, Journal of Financial Economics , pp. 147 - 175 .

Myers , S. and Majluf , N. ( 1984 ), “ Corporate financing and investment decisions when firms have information that investors do not have ”, Journal of Financial Economics , pp. 31 - 49 .

Nguyen , T.M. and Dang , T.L. ( 2017 ), “ Impact of ownership structure on the performance of Vietnam's listed companies on stock exchange ”, VNU Journal of Science: Economics and Business , pp. 23 - 33 .

Olajide , O.S. , Funmi , S.R. and Olayemi , S.O. ( 2017 ), “ Capital structure - firm performance relationship: EMpirical evidence from African countries ”, Journal of Emerging Trends in Economics and Management Sciences , pp. 82 - 95 .

Phan , T.H. ( 2016 ), “ Impact of capital structure on firm performance ”, Tạp Chí Tài Chính- Journal of Finance .

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Rocca , M.L. ( 2010 ), Is Ownership a Complement to Debt in Affecting Firm's Value? A Meta-Analysis , University of Calabria .

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Ross , S.A. , Westerfield , R.W. and Jaffe , J.F. ( 2013 ), Corporate Finance , 10th ed. , McGraw-Hill Irwin , New York, NY .

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Research Article

Cash flow management and its effect on firm performance: Empirical evidence on non-financial firms of China

Roles Investigation

Affiliation School of Accounting, Xijing University, Xi’an City, Shaanxi Province, People’s Republic of China

Affiliation Department of Economics and Management Sciences, NED University of Engineering & Technology, Karachi City, Pakistan

* E-mail: [email protected]

Affiliation Department of Business and Economics, University of Almeria, Almería, Spain

• Fahmida Laghari, 
• Farhan Ahmed, 
• María de las Nieves López García

• Published: June 20, 2023
• https://doi.org/10.1371/journal.pone.0287135
• Reader Comments

The main purpose of this research is to investigate the impact of changes in cash flow measures and metrics on firm financial performance. The study uses generalized estimating equations (GEEs) methodology to analyze longitudinal data for sample of 20288 listed Chinese non-financial firms from the period 2018:q2-2020:q1. The main advantage of GEEs method over other estimation techniques is its ability to robustly estimate the variances of regression coefficients for data samples that display high correlation between repeated measurements. The findings of study show that the decline in cash flow measures and metrics bring significant positive improvements in the financial performance of firms. The empirical evidence suggests that performance improvement levers (i.e. cash flow measures and metrics) are more pronounced in low leverage firms, suggesting that changes in cash flow measures and metrics bring more positive changes in low leverage firms’ financial performance relatively to high leveraged firms. The results hold after mitigating endogeneity based on dynamic panel system generalized method of moments (GMM) and sensitivity analysis considering the robustness of main findings. The paper makes significant contribution to the literature related to cash flow management and working capital management. Since, this paper is among few to empirically study, how cash flow measures and metrics are related to firm performance from dynamic stand point especially from the context of Chinese non-financial firms.

Citation: Laghari F, Ahmed F, López García MdlN (2023) Cash flow management and its effect on firm performance: Empirical evidence on non-financial firms of China. PLoS ONE 18(6): e0287135. https://doi.org/10.1371/journal.pone.0287135

Editor: Chenguel Mohamed Bechir, Universite de Kairouan, TUNISIA

Received: February 23, 2023; Accepted: May 31, 2023; Published: June 20, 2023

Copyright: © 2023 Laghari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data used in this study is taken from China Stock Market and Accounting Research (CSMAR) database.

Funding: Funded studies the grant has been awarded to the author María de la Nieves López García from the grant PID2021-127836NB-I00 (Spanish Ministry of Science and Innovation and FEDER). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Firms’ efficient cash flow management is significant tool to enhance financial performance [ 1 , 2 ]. Exercising proper management of cash flow is vital to the persistence of business [ 3 ]. Cash flow management is primarily concerned with identifying effective policies that balance customer satisfaction and service costs [ 4 ]. Firms manage efficiently of cash flows via working capital by balancing liquidity and profitability [ 5 – 7 ]. Working capital management, which is the main source of firm cash flow has significant importance in the context of China, where firms are restricted with limited access to external capital markets. In order to fulfill their cash flow needs firms heavily depend on internal funds, short-term bank loans, and trade credit in order to finance their undertakings [ 5 ]. For such firms’ working capital plays the role of additional source of finance. Consistent with this view, KPMG China [ 8 ] declared that effective management of working capital has played a vital role to alleviate the effects of recent financial crisis. Additionally, in recent times the remarkable growth of China roots to Chinese private firms’ effective management of working capital in general and their accounts receivables in particular [ 9 ]. Therefore, efficient management of working capital is an avenue that highly influence firm profitability [ 10 – 12 ], liquidity [ 7 , 13 ], and value. Since corporates cash flow management policies settle working capital by account receivables, inventories and accounts payables. Hence, existing theories of working capital management support the view that by cash flow manipulation firms can enhance liquidity and competitive positioning [ 6 , 14 , 15 ]. Therefore, firms manipulate cash flows through its measures, as by way speedy recovery of accounts receivables, reducing inventories, and delaying accounts payables [ 16 ]. Hence, the first research question is whether changes in cash flow measures are the tools that could bring positive changes in firm financial performance.

From the accounting perspective, liquidity management evaluates firm’s competence to cover obligations with cash flows [ 17 , 18 ], as uncertainty about cash flow increases the risk of collapse in most regions, industries, and other subsamples [ 19 ]. There are two extents: static or dynamic views, through which corporate liquidity can be inspected. The balance sheet data at some given point of time is a basis for static view. This comprises of traditional ratios such as, current ratios and quick ratios, in order to evaluate firms ability to fulfill its obligations through assets liquidation [ 20 ]. The static approach is commonly used to measure corporate liquidity, however, authors also declare that financial ratio’s static nature put off their capability to effectively measure liquidity [ 21 , 22 ]. The dynamic view is to be utilized to capture the firms’ ongoing liquidity from firm operations [ 16 , 21 ]. Therefore as a dynamic measure, the cash conversion cycle (CCC) is used by authors to measure liquidity in empirical studies of corporate performance [ 23 ]. For instance; Zeidan and Shapir [ 24 ] and Amponsah-Kwatiah and Asiamah [ 25 ] find that reducing the CCC by not affecting the sales and operating margin increases share price, profits and free cash flow to equity. Accordingly, Farris and Hutchison [ 20 ] find that shorter cash conversion cycle leads to higher present value of net cash flows generated by asset which contribute to higher firm value. Moreover, Kroes and Manikas [ 1 ] used operating cash cycle as a measure for cash flow metrics, which combines accounts receivables and firm inventory. As explained by Churchill and Mullins [ 26 ] that all other things being constant shorter the operating cash cycle faster the company can reassign its cash and can have growth from its internal resources. The second research question therefore is that whether changes in cash flow metrics bring positive improvements in firm financial performance.

Study uses CSMAR database of Chinese listed companies from the period 2018:q2-2020:q1. In the study, measure of firm performance is Tobin’s-q. Study uses three cash flow measures; accounts receivables turning days, inventory turning days and accounts payable turning days, and cash conversion cycle and operating cash cycle as measure for cash flow metrics. Consistent with the prediction, study finds that changes in cash flow measures and metrics bring positive improvements in firm financial performance. In particular decline in cash flow measures (ARTD, ITD, and APTD) to one unit would increase firm performance approximately 6.8%, 0.03%, and 7.2%; respectively. Additionally, one unit decline in cash conversion cycle would increase firm performance approximately 3.8%. Furthermore, study uses GMM estimator to alleviate the endogeneity and observe that the main estimation results still hold. In addition, study also employs a sensitivity analysis specifications to better isolate the impact of changes in cash flow measures and metrics on firm financial performance in previous period and observe that negative association is still sustained.

The sizable number of listed firms in China enable the study to divide sample into two subsamples: firms in high leverage industry and firms in low leverage industry. The study repeats the test on these two subsamples. Significant and negative association between cash flow measures, metrics and firm financial performance is still sustained. Moreover, the results of differential coefficients across two sub samples via seemingly unrelated regression (SUR) systems indicated that cash flow measures and metrics are more pronounced in low debt industries.

The paper makes significant contribution to the literature related to cash flow management and working capital management. First, this paper is among few to empirically study, how cash flow measures and metrics are related to firm performance from dynamic stand point especially in the Chinese context. The study sheds light on the role of cash flow management in improving the firm’s financial performance. Second, extant researches on cash flow management focus on the manufacturing industries. Unlike others this paper investigates the relation between cash flow measures, metrics and firm performance in the context of whole Chinese market, which is essential to know how these performance levers contribute to financial performance of other industries also. Third, results highlight the role of cash flow management in improving financial performance by taking firms’ leverage into consideration and declare that low leveraged industries are better off in terms of influence of changes in cash flow measures and metrics on firm performance. Fourth, the present paper uses generalized estimating equations (GEEs) Zeger and Liang [ 27 ] technique which is robust to estimate variances of regression coefficients for data samples that display high correlation between repeated measurements. Finally, to ensure robustness of findings the study uses sensitivity analysis, and in order to control for the potential issue of endogeneity the present study also uses generalized method of moments (GMM) following statistical procedures of Arellano and Bover [ 28 ] and Blundell and Bond [ 29 ].

The remainder of the paper is organized as follows. Section two discusses the role of cash flow management in China. Section three discusses the relevant literature, theoretical framework and development of hypotheses. Section four presents the data and variables of the study. Section five reports the methodology, empirical results and discussions. Section six concludes the paper.

Cash flow management in China

The economy of China has undergone a massive economic growth rates followed by high rates of fixed investment in the past three decades [ 5 , 30 ]. This growth miracle is outcome of highly productive firms and their ability to accrue significant cash flows [ 31 ], despite inadequate financial system. Moreover, although Chinese economy has seen fast growth and development in the past two decades but still the legal environment in China cannot be regarded as conducive [ 32 , 33 ]. As, in the credit market of China government plays a decisive role in credit distribution [ 34 , 35 ], and mostly the credit is granted to companies owned by state or closely held firms [ 34 , 36 ]. The Chinese firms have restricted admittance to the long-standing funds marketplace [ 37 ], therefore, companies held private or non-SOE find difficulty to access credit from financial market relatively to state owned firms. Although by the 1998 leading Chinese banks were authorized to lend credit to privately held firms but still these firms face troublesome to get external finance comparatively to state owned firms [ 32 ]. The prior literature also indorses this and states that with the presence of regulatory discrimination amid privately held and state owned firms, the privately held firms to the extent are often the subject of state predation [ 38 , 39 ].

Given country’s poor financial system, firms in China have managed their growth rates from their internal resources. Working capital management from where firms manage cash flows is the source of financing of the growth by Chinese firms. Accordingly, Ding et al . [ 5 ] mentioned that in their sample of Chinese firms about 66.6% dataset were characterized by a large average ratio of working capital to fixed capital, as it is a source and use of short term credit. Additionally, Dewing [ 40 ] termed working capital as one of the vital elements of the firm along with fixed capital. Moreover, Ding et al . [ 5 ] conclude that in the presence of financial constraints and cash flow shocks still Chinese firms can manage high fixed investment levels which correspond more to working capital than fixed capital. They further state that this all roots to the efficient management of working capital that Chinese firms use in order to mitigate liquidity constraints.

Literature review, theoretical background and hypothesis development

Literature review and theoretical background.

Corporate finance theory states that the main goal of a corporation is to maximize shareholder wealth [ 41 ]. Neoclassical capital theory is based on the proposition put forward by Irving Fisher [ 42 ] that individual consumption decisions can be separated from investment decisions. Fisher’s separation theorem holds true in perfect capital markets, where companies and investors can lend and borrow on the same terms without incurring transaction costs. In such a world, the choice to change income streams by lending and borrowing to meet preferences of consumption means that investors rank income streams according to their present value. Therefore, the value of the company is maximized by choosing the set of investments that generate the largest net present value over returns. When the company pays cash dividends with capital reserves, cash dividends can be maintained at a certain level, and when the ratio of capital reserves to cash dividends is high, accrual income management is low [ 43 ]. Since Gitman’s [ 44 ] seminal work, in which he introduced the concept of cash circulation as a means of managing corporate working capital and its impact on firm liquidity. Richards and Laughlin [ 16 ] then transformed the cash cycle concept into the Cash Conversion Cycle (CCC) theory for analyzing the working capital management efficiency of firms. CCC theory holds that effective working capital management (i.e., shorter cash conversion cycles) will increase a company’s liquidity, all else being equal. Signal theory can illustrate how a company can provide excellent signals to users of financial and non-financial statements [ 45 ]. In addition, this theory can also be used as a reference for investors to see how good or bad a company is as an investment fund. This theory explains the relationship between working capital turnover and profitability.

The trade-off theory in capital structure is a balance of benefits and sacrifices that may occur due to the use of debt [ 46 ]. The higher the amount a company spends on financing its debt, the greater the risk that they will face financial hardship due to excessive fixed interest payments to debt holders each year and uncertain net income. Higher cash flow uncertainty leads to an increased risk of business collapse [ 19 ]. Companies with high levels of leverage should keep their liquid assets high, as leverage increases the likelihood of financial distress. This theory is used to explain the relationship between leverage and profitability. Pecking order theory explains that companies with high liquidity levels will use more debt funds than companies with low liquidity levels [ 47 ]. Liquidity measures a company’s ability to meet its cash needs to pay short-term debts and fund day-to-day operations as working capital. The better the company’s current ratio, the more the company will gain the trust of creditors so that creditors will not hesitate to lend the company funds used to increase capital, which will benefit the company.

Prevailing working capital management theories argue that firms can improve their competitive position by manipulating cash flow to improve liquidity [ 14 , 15 , 20 , 48 – 50 ]. In addition, the company’s ability to convert materials into cash from sales reflects the company’s ability to effectively generate returns from investments [ 51 ]. It’s better to combine investment spending with cash flow from ongoing operations than to measure and report both discretely [ 52 ]. Three factors directly affect the company’s access to cash: (i) the company’s inability to obtain cash receivables while waiting for the customer to pay for the delivered goods; (ii) the company is unable to obtain cash receivables; (iii) the company is unable to obtain cash receivables. (ii) Cash invested in goods is tied up and unavailable and the goods are inventoried; and (iii) cash may be made to the company if it chooses to delay payment to suppliers for goods or services provided [ 16 ]. While a company’s cash payments and collections are typically managed by the company’s finance department, the three factors that affect cash flow are primarily manipulated by operational decisions [ 53 ].

In the literature, the prevailing view is that the presence of liquidity is not always good for the company and its performance, because sometimes liquidity can be overinvested. Since emerging markets are characterized by imperfect markets, companies maintain internal resources in the form of liquidity to meet their obligations. As in emerging markets, financial markets are inefficient in allocating resources and releasing financial constraints, resulting in underinvestment by financially constrained companies [ 54 ]. In addition, access to capital markets, external financing costs, and availability of internal financing are financial factors on which a company’s investments rely [ 55 ]. Alternatively, the pecking order theory [ 56 ] argues that due to information asymmetry, companies adopt a hierarchical order of financing preferences, so internal financing takes precedence over external financing. A study by Zimon and Tarighi [ 7 ] argue that businesses must use the right working capital strategy to achieve sustainable growth as it optimizes operating costs and maintains financial liquidity. Moreover, asset acquirements affect a company’s output and performance [ 57 ].

The existing literature provides different evidence of the impact of working capital management on firm performance. A study by Sharma and Kumar [ 58 ] examine the relationship between working capital management and corporate performance in Indian firms. Considering a sample of 263 listed companies during the period 2000–2008, they found that CCC had a positive impact on ROA. Similarly, of the 52 Jordanian listed companies in the period 2000–2008, Abuzayed [ 11 ] found a positive impact of CCC on total operating profit and Tobin’s-Q. Similar findings have been reported by companies in China [ 59 ], the Czech Republic [ 60 ], Ghana [ 25 ], Indonesia [ 6 ], Spain [ 61 ], and Visegrad Group countries [ 62 ]. In contrast, few studies reported an inverse correlation between CCC and firm performance in India [ 63 ], Malaysia [ 2 ], and Vietnam [ 64 ]. A negative correlation indicates that a higher CCC leads to lower company performance. A study by Afrifa et al. [ 65 ] did not find any significant relationship between CCC and firm performance. The findings of the relationship between NWC and company performance are not much different from CCC. Companies in European countries [ 66 ], and the United Kingdom [ 67 ] reported positive correlations, and those in Poland reported negative correlations [ 68 ]. Although previous operations management studies have explored the relationship between working capital and firm performance, the results of these studies remain inconclusive, and the study has found positive, curved, and even insignificant relationships. This is mainly since accidental factors make this relationship both complex and special. Therefore, to enhance the beneficial impact of working capital and cash flow on corporate performance, companies must make appropriate investments to promote more objective, informed, and business-specific working capital and cash flow management choices [ 69 ]. Collectively, these mixed pieces of evidence provide sufficient motivation for this study to develop hypotheses based on positive and negative relationships.

The cash flow measures and firm financial performance

The firms’ trade where merchandise sold on credit instead of calling for instantaneous cash imbursement, such transaction generate accounts receivables [ 70 ]. Accounts receivable directly affect the liquidity of the enterprise, and thus the efficiency of the enterprise [ 71 ]. From the stands of a seller, the investment in accounts receivables is a substantial component in the firm’s balance sheet. Firms’ progressive approach towards significant investment in accounts receivables with respect to choice of policies for credit management contributes significantly to enhance firm value [ 72 ]. Firms can utilize cash received from customers by investing in activities which contribute to enhance sales [ 1 ]. Firms can improve liquidity position with capability to collect overheads from customers for supplied goods and services rendered in a timely manner [ 17 ]. However, credit sales is instrumental to increase sales opportunities for firms but may also increase collection risk which can lead to cash flow stresses even to healthy sales growth companies [ 73 ]. Firms offer sales discounts which may not increase sales but may increase payments by customers and improve firms’ cash flow, reduce uncertainty of future cash flows, reduce risk and required rate of return [ 74 ].

Literature suggests that firm performance increases with shorter period of day’s sales outstanding [ 15 , 20 , 26 ]. Accordingly, Deloof [ 75 ] by working on Belgians firms find negative relationship between number of days accounts receivables and gross operating income. However, models of trade credit (such as; Emery, [ 21 ]) endorse that higher profits also lead to more accounts receivables as firms with higher profits are rich in cash to lend to customers. In a study by García-Teruel and Martinez- Solano [ 76 ] suggest that managers of firms with fewer external financial resources available generally dependent on short term finance and particularly on trade credit that can create value by shortening the days sales outstanding. Furthermore, Gill et al . [ 10 ] declare that firm can create value and increase profitability by reducing the credit period given to customers. Kroes and Manikas [ 1 ] analyzed manufacturing firms and suggested that decline in days of sales outstanding relates to improvements in firm financial performance and persists to several quarters. According to Moran [ 77 ] suppliers happily offer reasonable sales discounts for early payments which improve their cash flow position, locks the receivables, remove the bad debt risk at early stage, and reduce their day’s sales outstanding significantly which ultimately improve their working capital position. Fig 1 depicts this relationship. In consistent with discussion the following hypothesis is proposed:

• H1a : A decrease (increase) in the duration of accounts receivables turning days increases (decreases) firm financial performance.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0287135.g001

The research has mixed views whether reduction in inventory is beneficial to firm performance or increase in inventory leads to increased performance. Despite high cash flow, inventory level management has been neglected [ 78 ]. In this regard literature has evidenced three themes of relationships: positive relationship, negative relationship or no relationship, and inclusion of moderators and mediators to the relationship of number of day’s inventory and firm performance [ 79 ]. However, the inventory management revolutionized after the launch of lean system with familiarizing just-in-time inventory philosophy by Japanese companies [ 80 , 81 ]. Afterwards, research related to inventory management evidenced that firms which adopted lean system not only improved customer satisfaction but also attained greater level of asset employment that ultimately leads to higher organizational growth, profitability, and market share [ 82 , 83 ]. Moreover, in a JIT context firms experience positive effects on organizational performance due to reduced inventory, and reduction in inventory significantly improves three performance measures such as: profits, firms return on sales, and return on investments [ 84 ]. Additionally, Fullerton and McWatters [ 85 ] found positive influence of reduced inventory on organizational performance which corresponds to JIT context.

However, generally literature considers that better inventory performance such as: higher inventory turns or decreased level of inventory is normally attributed to better firm financial performance [ 86 ]. Moreover, it is a mutual consent by researchers that high level of inventory also signifies demand and supply misalliance and often related to poor operational performance [ 87 , 88 ]. In a study by Elsayed and Wahba [ 79 ] indicated that there is influence of organizational life cycle on the relationship of inventory and organizational performance. Their results indicated that at initial stage though ratio of inventory to sales negatively affects organizational performance, but it put forth significant and positive coefficient on organizational performance at the revival phase or rapid growth phase. Additionally, literature has documented negative influence of reduced inventory on performance. In a study by Obermaier and Donhauser [ 89 ] evidenced that lowest level of inventory leads to poor organizational performance and suggest that moving towards zero inventory case is not always favorable. Fig 1 depicts this relationship. Accordingly the hypothesis is proposed as follows:

• H1b : A decrease (increase) in the duration of inventory turning days increases (decreases) firm financial performance.

According to Deloof [ 75 ] payment delays to suppliers are beneficial to assess the quality of product bought, and can serve as a low-cost and flexible basis of financing for the firm. On the contrary, delaying payments to suppliers may also prove to be costly affair if firm misses the discount for early payments offered [ 90 ], hence firms by reducing days payable outstanding (DPO) likely to enhance firm financial performance [ 76 ]. In line with this, Soenen [ 22 ] states that firms try to collect cash inflows as quickly as possible and delay outflows to possible length. Payment delays enable firms to hold cash for longer duration which ultimately increases firms’ liquidity [ 50 ]. As discussed by Farris and Hutchsion [ 20 ] that firms can improve cash to cash cycle by extending the average accounts payable along with inventory and get interest free financing. A study by Sandoval et al . [ 91 ] speculate that investors are more sensitive to accruals of long-term operating assets than to accruals of long-term operating liabilities because the former is more associated with recurring profits than the latter. Moreover, Fawcett et al . [ 92 ] indorsed that by extending the duration of accounts payable cycle companies can improve their cash to cash cycle. However, longer payment cycles not only harm relationship with suppliers, but may also lead to lower level of services from suppliers [ 93 ].

As discussed by Raghavan and Mishra [ 94 ] firms may be reluctant to produce or order at optimal point followed by cash restraints for fast growing firms where money plays the role of catalyst when demand is significantly high but firms are financially restricted to order less and this situation may mark the harmful effects over the performance of whole supply chain at least on temporary basis until restored. Hence, this situation is favoring that firms encourage and motivate their customers for quicker payments in order to increase cash to cash cycles [ 92 ]. Fig 1 depicts this relationship. Accordingly based on discussion hypothesis is proposed as follows:

• H1c : A decrease (increase) in the duration of accounts payable turning days’ increases (decreases) firm financial performance.

The cash flow metrics and firm financial performance

As shown by Richards and Laughlin [ 16 ] that firms should collect inflows as quickly as possible and postpone cash outflows as long as possible which is a general view based on the concepts of operating cash cycle (OCC) and cash conversion cycle (CCC). This shows that firms by reducing CCC cycle can make internal operation more efficient that ensures the availability of net cash flows, which in turn depicts a more liquid situation of the firm, or vice versa [ 25 ]. They further said that cash conversion cycle (CCC) is based on accrual accounting and linked to firm valuation. Baños-Caballero et al . [ 95 ] suggested that however, higher level of CCC increases firm sales and ultimately profitability, but may have opportunity cost because firms must forgo other potential investments in order to maintain that level. On the contrary, longer duration of CCC may hinder firms to be profitable because this is how firms’ duration of average accounts receivables and inventory turnover increase which may lead firms towards decline in profitability [ 96 ]. Therefore, cash conversion cycle (CCC) can be reduced by shortening accounts receivables period and inventory turnover with prolonged supplier credit terms which ultimately enable firms to experience higher profitability [ 97 , 98 ]. A shorter duration of CCC helps managers to reduce some unproductive assets’ holdings such as; marketable securities and cash [ 23 ]. Because with low level of CCC firms can conserve the debt capacity of firm which enable to borrow less short term assets in order to fulfill liquidity. Therefore, shorter CCC is beneficial for firms that not only corresponds to higher present value of net cash flows from firm assets but also corresponds to better firm performance [ 60 , 62 ].

Operating cash cycle is a time duration where firm’s cash is engaged in working capital prior cash recovery when customers make payments for sold goods and services rendered [ 16 , 26 ]. Literature endorses that shorter the operating cash cycle better the firm liquidity and financial performance because companies can quickly reassign cash and cultivate from internal sources [ 16 ]. In a study by Kroes and Manikas [ 1 ] find that there is significant negative relationship between changes in OCC with changes in firm financial performance. They further suggested that OCC can be taken by managers as a metric to monitor firm performance and can be used as lever to manipulate in order to improve firm performance. A study by Farshadfar and Monem [ 99 ] also found that when the company’s operating cash cycle is shorter and the company is small, the cash flow component improves earnings forecasting power better than the accrual component. Moreover, Nobanee and Al Hajjar [ 100 ] recommend the optimum operating cycle as a more accurate and complete working capital management measure to maximize the company’s sales, profitability, and market value. Fig 1 depicts this relationship. Hence, based on above discussion the proposed hypotheses are:

• H2a : A decrease (increase) in cash conversion cycle increases (decreases) firm financial performance.
• H2b : A decrease (increase) in operating cash cycle increases (decreases) firm financial performance.

Data and variables

Samples selection.

The data used in this study is taken from China Stock Market and Accounting Research (CSMAR) database. The study includes quarterly panel data of non-financial firms with A-shares listed on Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE). The data comprises on eight quarters ranging from 2018:q2-2020:q1, and four lag effects are included that make data up to twelve quarters. The use of quarterly data ensures greater granularity in the findings of the study as prior studies have mainly used annual data, therefore, this study uses two years plus one year of lagged data which offers exclusively a robust sample period that is instrumental to effective inference [ 1 ]. The main benefit of this method of examining quarterly changes within a company is that the company cannot have any missing data items throughout the sample period. Because any missing data will lead to design errors and imbalance panel data. Therefore, this problem led to now selection of a 12-quarter observation frame (two years plus one year of lagging data) because it delivers a reliable sample period from which effective conclusions can be prepared. Moreover, the data is further refined and maintained from unobserved factors, unbalanced panels, and calculation biases. Moreover, deleted firm-year observation with missing values; excluded all financial firms; as their operating, investing, and financing activities are different from non-final firms [ 75 , 101 ], eliminated firms with traded period less than one year, and excluded all firms with less than zero equity. The data is further winsorized up to one percent tail in order to mitigate potential influence of outliers [ 76 ]. Additionally, the firms’ data with negative values for instance; sales and fixed assets is also removed [ 67 , 101 ]. The final sample left with balanced panel of 20288 firm year observations consists of 2536 groups. The change (Δ) in all dependent and independent variables of the study sample represents variable period t measured as difference between value at the end of current quarter and value of the variable at the end of prior quarter divided by value of the variable at the end of prior quarter.

Dependent variable

The firm’s financial performance is dependent variable in the study and is measured through Tobin’s-q. Tobin’s-q is the ratio of firm’s market value to its assets replacement value and it is widely used indictor for firm performance [ 1 , 102 – 105 ]. Tobin’s-q diminishes most of the shortcomings inherent in accounting profitability ratios as accounting practices influence accounting profit ratios and valuation of capital market applicably integrates firm risk and diminishes any distortion presented by tax laws and accounting settlements [ 106 ]. Moreover, this variable has preference over other accounting measures (such as; ROA) as an indicator of relative firm performance [ 107 ].

Independent variables

Based on established literature [ 1 , 5 , 12 , 75 , 76 ] this study has used three cash flow measures and two composite metrics as independent variables. Each one of them is discussed below.

Accounts receivables turning days (ARTD).

The increasing days of sales outstanding specifies that firm is not handling its working capital efficiently, because it takes longer duration to collect its payments, which signifies that firm may be short of cash to finance its short term obligations due to the longer duration of cash cycle [ 5 ].

Inventory turning days (ITD).

A higher ratio of inventory turnover is a good sign for firm as it signifies that firm is not having too many products in idle condition on shelves [ 5 ].

Accounts payable turning days (APTD).

A firm with higher days of payable outstanding ratio shows that it takes longer duration to make payments to suppliers which is a sign of poor efficiency of working capital, however longer duration of DPO also signifies that company has good terms with suppliers which is also beneficial [ 5 ].

Cash conversion cycle (CCC).

It is generally considered that lower the CCC cycle better the firm efficiency and able to accomplish its working capital [ 5 ]. Additionally, longer duration of CCC shows more time duration between cash outlay and recovery of cash [ 76 ].

Operating cash cycle does not take into account the payables, and hence comprises of days where cash is detained as inventory prior receipts of payments from customer [ 1 ]. Besides, generally it is considered that firm having shorter OCC is with better liquidity and performance [ 26 ].

Control variables

This study uses firm size and return on assets as control variables. Following Deloof [ 75 ] the study uses firm size by taking natural logarithm of quarterly sales. The firm size has significant impact on market value of firms [ 103 , 108 ]. Study uses quarterly sales instead of total assets as measure for firm size to avoid the potential multicollinearity problem because total asset is denominator for the dependent variable [ 1 ]. Following Baños-Caballero et al . [ 95 ] study controls for return on asset (ROA) which is accounting measure of firms. Return on assets (ROA) is a ratio of earnings before interest and taxes (EBIT) divided by total assets [ 109 ].

Descriptive statistics

Table 1 shows the descriptive statistics of variables of the study. The mean and median value of ARTD is 92.89 and 73.14, respectively. On average, the firms in our sample have relatively higher median value of days of sales outstanding than evidence of Ding et al . [ 5 ], which shows that Chinese firms take longer to collect their payments from customers. The mean and median value of APTD is 105 and 82.25, respectively. The mean and median value of ITD is 166.18 and 107.13, respectively. On average it shows relatively high inventory turnover in our sample firms which signifies that Chinese firms are quite efficient in inventory management and products are not sitting idle in shelves. The mean and median value of CCC is 150.62 and 115.30, respectively. On average the CCC of Chinese firms is relatively high. However, in a study by Hill et al . [ 101 ] indicated that higher CCC also signifies higher firm profitability. The mean and median value of OCC is 250.71 and 206.44, respectively. The firm performance (Tobins-q) has a mean and median value of 2.86 and 2.27. The ROA shows mean and median value of 2.46 and 1.67, respectively. On average the size of Chinese firms is 20.79 with median value of 20.71.

https://doi.org/10.1371/journal.pone.0287135.t001

The Table 2 reports results for correlation matrix. The correlation coefficient between Tobin’s-Q and CCC is significant and negative at 1 percent level which is consistent to the findings of Afrifa [ 67 ]. The correlation between all the measures of cash flows and ROA is significant and negative at 1 percent, consistent with the results of Deloof [ 75 ]. Moreover the correlation between ROA and CCC is also significant and negative at 1 percent, similar evidences find by García-Teruel and Martinez-Solano [ 76 ] for the sample of Spanish firms. Furthermore, the correlation coefficients among all the variables are significantly lower than 0.80 indicating no sign of multicollinearity [ 110 ]. The formal test of variance inflation factor (VIF) for all the independent variables of study were examined to check if there is presence of multicollinearity. The variance inflation factor (VIF) also indicated no multicollinearity among analysis variables with all values below the threshold level of 10 proposed by Field [ 110 ], which shows that multicollinearity may not be the case and data is suitable for further analysis.

https://doi.org/10.1371/journal.pone.0287135.t002

Methodology, empirical analysis and discussion

Effect of cash flow measures on firm financial performance.

Where ΔY it represents Tobin’s-q for industry i and time t. The ΔX 1it is accounts receivable turning days (ΔARTD), and ΔX 1it-1 to ΔX 1it-4 are lags for ΔARTD. The ΔX 2it is inventory turning days (ΔITD), and ΔX 2it-1 to ΔX 2it-4 are lags for ΔITD. The ΔX 3it is accounts payable turning days (ΔAPTD), and ΔX 3it-1 to ΔX 3it-4 are lags for ΔAPTD. The CONTROLS it represent control variables; Size and ROA. The U it is probabilistic term. Study included four lag effects in Eq 6 for cash flow measures to examine how long the impact of changes in cash flow measures on changes in firm performance persists.

Table 3 provides detailed results of GEEs model’s parameters estimation analysis. The dependent variable is firm performance (Tobin’s-q) in all the models columns 2 through 4. H1a , H1b , and H1c posits that changes in measures of cash flow (ΔARTD, ΔITD, and ΔAPTD) changes firm financial performance. The coefficient of accounts receivable turning days (ΔARTD) in model 1 is -0.0068297, which is statistically significant at 0.1% confidence level in the current quarter. It is consistent with the study’s argument that decline in firms’ days of accounts receivables increases firm financial performance. Similar evidences were found by Shin and Soenen [ 13 ], Wilner [ 114 ], Deloof [ 75 ], and Kroes and Manikas [ 1 ]. According to Deloof [ 75 ] the negative relationship between days sales outstanding and firm performance suggests that managers can create value for their shareholders by reducing number of day’s accounts receivables to a reasonable minimum.

https://doi.org/10.1371/journal.pone.0287135.t003

The coefficient of inventory turning days (ΔITD) in model 1 is -0.0003014, which is statistically significant at 0.1% confidence level in the current quarter. These results are consistent with the argument given in hypothesis H1b . Significant number of studies conclude that low inventory period increases liquidity and firm performance [ 75 , 86 , 115 , 116 ]. Moreover, this finding is consistent with literature as firms sound inventory position exhibits better operational and financial performance [ 117 , 118 ].

The coefficient of accounts payable turning days (ΔAPTD) in model 1 is -0.0717425, which is statistically significant at 0.1% confidence level in the current quarter. These results are consistent with present study’s argument that decline in accounts payable turning days brings positive improvements in firm performance. The findings of results for APTD present strong evidence that when companies reduce their APTD by taking advantage of early discounts payment from suppliers, firms may have a persistent duration of perpetual firm financial performance improvement. As suggested by Moran [ 77 ] that firms may be more beneficial by taking advantage of early payment discounts than prolonging the cycle because of reduction in purchase price of components and materials by them.

Next, study estimated Eq 6 by dividing the sample into two subsamples based on firm leverage level, which is measured by firms’ debt to assets ratio. The high leverage (low leverage) contains firms in industries where their debt to assets ratio is greater (smaller) than the median value. Model 2 and 3 obtain similar patterns when applied on Eq (6) for high and low leveraged firms. The findings of results for high leverage and low leverage firms still hold as of full sample firms and strongly support hypotheses H1a , H1b , and H1c . Conclusively, the findings of results imply that reduction in three cash flow measures (ARTD, ITD, and APTD) relate to significant positive improvements in financial performance of firms at current quarter.

Effect of cash flow metrics on firm financial performance

Where ΔY it represents Tobin’s-q for industry i and time t. The ΔX it is ΔCCC and from ΔX 1it-1 to ΔX 1it-4 are lags for ΔCCC. The ΔX 2it is OCC and from ΔX 2it-1 to ΔX 2it-4 are lags for ΔOCC. The CONTROLS it shows the control variables; Size and ROA. The U it is probabilistic term. Study includes four lag effects in Eq 7 for cash flow metrics to examine how long the impact of changes in CCC and OCC on changes in firm performance persists.

Table 4 represents results for cash flow metrics (CCC and OCC). H2a and H2b predict that changes in ΔCCC and ΔOCC bring positive changes in the firm financial performance. The coefficient for the cash conversion cycle (ΔCCC) is -0.0382176, which is statistically significant at a 5% confidence level in the current quarter (as shown in Table 4 column 2).

https://doi.org/10.1371/journal.pone.0287135.t004

Next, the study estimated Eq 7 by dividing the sample into two subsamples based on firm leverage level which is measured by firms’ debt to assets ratio. The results in Table 4 Column 3 posit findings for highly leveraged firms. The coefficient for ΔCCC is -0.4038345, which is statistically significant at a 1% confidence level in the current quarter, as shown in Table 4 Column 3. The coefficient for ΔOCC is -0.0572725, which is statistically significant at a 1% confidence level in the current quarter, as shown in Table 4 Column 3. The coefficient for ΔCCC is -0.027272, which is statistically significant at a 0.1% confidence level, as shown in Table 4 column 4 for low-leverage firms at the current quarter.

As predicted by the hypothesis H2a ; the findings of results also show significant negative association of CCC with firm financial performance at current quarter for full sample firms, high leveraged firms, and low leveraged firms. These evidences of results are consistent with existing literature and show that decline in cash conversion cycle brings positive improvements in firm financial performance [ 13 , 23 , 75 , 76 , 96 , 97 , 119 ]. A study by Zeidan and Shapir [ 24 ] finds that reducing the CCC by not affecting the sales and operating margin increases the prices of shares, profits, and free cash flow to equity. Moreover, Prior research view that careful handling of the cash conversion cycle leads firms to significantly higher returns [ 13 , 23 , 75 , 76 , 97 ]. This outcome is consistent with the research by Simon et al. [ 120 ], Soukhakian and Khodakarami [ 121 ], Basyith et al. [ 6 ], Yousaf et al. [ 60 ], and Bashir and Regupathi [ 2 ]. The findings of the results show a significant negative association of OCC with firm financial performance in the current quarter for highly leveraged firms. The findings suggest that change in OCC led to changes in corporate performance provides significant support to the use of OCC as an indicator for managers to monitor performance and as a lever to manipulate to improve the corporate financial performance. The findings show that OCC in the current quarter posits a significant negative relationship with firm financial performance for highly leveraged firms. This evidence is consistent with the empirical findings of Churchill and Mullins [ 26 ].

Difference of coefficients across high leverage and low leverage firms

In addition, in the next section the present study analyzed the difference of coefficients across two groups by dividing sample into two subsamples, high leveraged and low leveraged firms based on their total debt to total assets ratios. In order to check the difference of coefficients across two groups study applied seemingly unrelated regression (SUR) system on Eqs ( 6 ) and ( 7 ) to better isolate the effect of cash flow measures and metrics on firm financial performance. The study computed standard errors for differenced coefficients via the seemingly unrelated regression (SUR) system that combines two groups.

The Table 5 reports results for differential impact of cash flow measures and metrics on firm performance across high leverage and low leverage industries. The study finds that the estimated coefficients for differences are positive and statistically significant. These findings of results imply that low leveraged industries are better off in terms of changes in cash flow measures and metrics that bring more positive changes in low debt industries financial performance. Since, low cash conversion cycle (CCC) conserves the debt capacity of the firm as in this situation firms need less short term borrowing to provide liquidity [ 97 ]. Therefore, lower cash conversion cycle (CCC) lessens the requirement for lines of credit and contributes to the firms’ debt capacity [ 23 ]. Due to high financial distress and higher likelihood of bankruptcy high leverage firms are more bounded by financial constraints which may hinder them to take valuable investments and, thus, harm their profitability [ 122 ]. This also suggests that firms with low leverage are high value firms and maintain lower duration of cash conversion cycle (CCC) at low levels that counts to higher profitability which ultimately leads to higher retained earnings and reduce the need for debt.

https://doi.org/10.1371/journal.pone.0287135.t005

Test of endogeneity effect and sensitivity analysis

Where ΔY it represents firm performance, ΔY it-1 is first lag of dependent variable firm performance. All the independent variables (cash flow measures and metrics) are denoted with ΔX it . CONTROLS it represents control variables and λ t shows time fixed effects, Ƞ i represents industry fixed effects, and ɛ it represents unobserved heterogeneity factors.

Table 6 represents estimated results obtained using Eq (8) . The findings of study observes significant negative association between cash flow measures, metrics and firm financial performance in the full sample, high leverage and low leverage subsamples, indicating that firms’ changes in cash flow measures and metrics bring significant positive improvements in financial performance. Overall, the results still hold after study considers the endogeneity problem, supporting the hypotheses of the study.

https://doi.org/10.1371/journal.pone.0287135.t006

Where ΔY it represents firm performance. All the independent variables (cash flow measures and metrics) are denoted with ΔX it-1 , and CONTROLS it represents control variables. D t shows time fixed effect, D i represents industry fixed effects, and ɛ it represents unobserved heterogeneity factors.

Table 7 represents estimated results of sensitivity analysis regression. The study finds that estimated coefficients of cash flow measures (ΔARTD t-1 , ΔITD t-1 , ΔAPTD t-1 ) and cash flow metrics (ΔCCC t-1 , ΔOCC t-1 ) are negative and significant, indicating that changes in previous period’s cash flow measures (ΔARTD t-1 , ΔITD t-1 , ΔAPTD t-1 ) and cash flow metrics (ΔCCC t-1 , ΔOCC t-1 ) bring significant positive changes in firm financial performance. The study finds similar results to the previously reported findings for alternative subsamples of high leverage and low leverage firms. Overall, the sensitivity analysis results still hold in consistent with the primary analysis results and ensure robustness of main analysis results of the study.

https://doi.org/10.1371/journal.pone.0287135.t007

Practical, managerial, and regulatory implications

This study provides significant practical, managerial, and regulatory implications for cash flow management and working capital management decisions in the corporate sector to improve performance. Most studies on cash flow management have focused on its relationship to profitability from the perspective of manufacturing companies. This research focuses on cash flow management by linking the leverage of non-financial firms in the Chinese context, a fundamental issue of corporate cash flow management and working capital investment that has not been studied much in the emerging markets scenario. Practically study suggests that a decline in cash flow measures and metrics positively enhances a company’s financial performance. Moreover, the paper determines that low-leverage industries perform healthier to cash flow measures and metrics changes. The study also reveals that companies in low-debt industries experience more positive improvements in their financial performance relative to high-debt industry companies. Therefore, the findings of this paper suggest that highly leveraged companies may be less conducive to improving corporate performance in industries where competitors’ leverage is relatively low.

Thus, from managers’ and policymakers’ points of view, the analysis found that changes in cash flow measures (ARTD, ITD, and APTD) and metrics (CCC and OCC) have led to significant positive improvements in the company’s financial performance. These positive changes in the CCC mean that changes in the accounts payable cycle appear to mitigate the combined impact of changes in the accounts receivable and inventory cycles. For managers, this finding suggests that reducing CCC simply by lowering APTD can translate into improvements in company performance. These findings provide rich insights and practical implications for managers and policymakers to use CCC as an operational tool to improve company performance. Therefore, managers and policymakers must actively evaluate the company’s policies regarding cash flow management, working capital management, corporate leverage, and capital budgeting policy before capitalizing on these companies.

Conclusion, limitations, and future implications

Cash flow management is the central issue of company operational strategies that affect a firm’s operational decisions and financial position. Firms’ effective policy of cash flow management is achievable through efficient management of working capital, which is possible through shorter days of accounts receivables, giving discounts on prompt payments, offering cash incentives, reducing inventory turning days through sound inventory management policies, shortening days of accounts payable by achieving rebate on early outlays. Likewise, inventory turnover may lead to a significant positive relationship with organizational performance symbolized by return on assets, cash flow margins, and return on sales in the JIT context. Moreover, high-performance firms may have a lengthier duration of days of accounts payables, which ensures the presence of liquidity. Many firms invest a large portion of their cash in working capital, which suggests that efficient working capital management significantly impacts corporate profitability.

This paper offers a strong insight and findings on cash flow management and firm financial performance by examining the Chinese full sample firms, high debt, and low debt firms to investigate the impact of changes in cash flow measures and metrics on firm performance. Using the exclusive cash flow measures and metrics data, study finds that decline in cash flow measures and metrics bring significant positive changes in firm financial performance. Moreover, study finds that low leveraged industries are better off in terms of changes in cash flow measures and metrics that bring more positive improvements in low debt industries firms’ financial performance relatively to high debt industries firms. This paper also demonstrates that, following firms’ leverage, high-leveraged firms may be less advantageous to enhance firm performance in industries where rivals are relatively low-leveraged.

The results of the study are consistent with the argument that changes in cash flow measure (ARTD, ITD and APTD) and metrics (CCC and OCC) bring significant positive improvements in firm financial performance. These findings furnish a great amount of insight and practical implication for manager to utilize CCC as operating tool in order to enhance firm performance. Firms by actively monitoring and controlling levers such as; ARTD, ITD, APTD, CCC, OCC can enhance financial performance. The findings of results are robust to different measures and metrics of cash flow and firm financial performance, following sensitivity analysis and endogeneity test still main results hold and ensures the robustness of primary analysis.

Study limitations and directions for future research

This research is of great significance to the studies on the relationship between cash flow management and enterprise performance in the Chinese market environment. However, the study did not consider some aspects that need consideration in future studies. This study uses Tobin Q to measure a company’s performance. However, it is also possible to include other company performance indicators that are important in the strategic impact of studies and may provide significant insights. The lack of data availability is a major constraint due to companies’ exits and entry into the sample period. This paper uses secondary data; however, studies can also use primary data to understand and gain appropriate knowledge of corporate cash flow management by combining archived and survey data to improve the robustness and significance of research findings in the context of emerging markets. This study focuses on the financial performance of firms. However, future studies can also use non-financial performance as a consequence variable.

Future extensions of this work may examine whether a company’s cash flow management policies in other areas of the supply chain have a similar relationship to company performance.

In addition, further inquiries that explore the directional association amid inventory and performance changes may extend the understanding of the cash flow management role in a company’s success. In addition, there is a need to explore more the impact of cash flow and working capital investment on firm performance by taking the market imperfections within the framework of emerging economies. Finally, the evidence of this research from the fastest emergent economy of the world may also use other transition economies to generalize for a widespread population group. Finally, studies in the future can consider linking product market competition with the cash flow measures, metrics, and firm performance relationship.

Supporting information

S1 appendix..

https://doi.org/10.1371/journal.pone.0287135.s001

Acknowledgments

The authors wish to thank anonymous referees for all value comments. The authors are responsible for any remaining errors.

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Factors Affecting Financial Performance of Firms: An Exploration of the Existing Research Works

• First Online: 02 July 2022

Cite this chapter

• Sumit Kumar Maji 4 ,
• Arindam Laha 5 &
• Debasish Sur 6  

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This chapter highlights the existing research works carried out in India and abroad by the scholars exploring the microeconomic, macroeconomic and industry-specific factors affecting the firm-level performance. Comprehensive review of the existing literature on the effect of these factors on the efficiency, profitability and stock prices was accomplished. The research gap in the existing literature was identified in this chapter by using Evidence Gap Map. The chapter also outlines the objectives of the study in the perspective of such research gap.

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Banking sector is a notable exception in this regard. As a matter of fact, there are many empirical studies on the banking sector where the researchers have attempted to explain the profitability of banks of different countries on the basis of the various company-specific, industry-specific and macroeconomic factors (Ali et al., 2011 ; Goddard et al., 2004 ; Kosmidou et al., 2007 ; Ramya & Mahesha, 2012 ; Sufian & Habibullah, 2010a ; Vejzagic & Zarafat, 2014 ; Vong & Chan, 2009 ; Williams, 2003 ; Zang & Daly, 2013 ). It has been observed that the profitability of banks and other firms is not only influenced by internal factors but also by the macroeconomic factors (Raza et al., 2013 ).

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Maji, S.K., Laha, A., Sur, D. (2022). Factors Affecting Financial Performance of Firms: An Exploration of the Existing Research Works. In: Indian Manufacturing Sector in Post-Reform Period. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-19-2666-2_3

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Trade Wars and the Optimal Design of Monetary Rules

Monetary rules may have a large effect on the outcome of trade wars if central banks target the CPI inflation rate or more generally changes in the relative price of traded goods. We lay out a two-country open-economy model with sticky prices where countries engage in trade wars. In the presence of monopoly pricing markups, we show that the final level of tariffs and welfare losses from trade wars critically depend on the design of monetary policy. If central banks adopt a fixed nominal exchange rate or even better target the CPI inflation rate, trade wars are much less intense than those under PPI inflation targeting. We further show that an optimally delegated monetary rule that internalizes the formation of non-cooperative trade policy can actually completely eliminate a trade war, and even act to partly offset the welfare cost of monopoly markups.

Devereux thanks SSHRC for research funding. Auray and Eyquem acknowledge the financial support of Projets Generique ANR 2015, Grant Number ANR-15-CE33-0001-01. Finally we acknowledge the financial support of the Europlace Institute of Finance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

The Centre for Business Taxation is an independent research centre of the University of Oxford, based at the Saïd Business School. The Centre receives financial support from a number of sources, including currently around 30 companies. A full list of current and past corporate donors to the Centre for Business Taxation, as well as a statement about the independence of the centre from its donors, is available at http://www.sbs.ox.ac.uk/ideas-impact/tax/about/funding.

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