Liquidity-Profitability Trade-Off: A Panel Study of Listed Non-Financial Firms in Ghana

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/ by/4.0) ABSTRACT This study sought to explore the trade-off between liquidity and the profitability of non-financial firms listed on the Ghana Stock Exchange (GSE). A panel data extracted from the audited and published annual reports of fifteen (15) selected firms for the period 2008 to 2017 was used for the study. In the study, liquidity was surrogated by the Cash Flow Ratio (CFR) and the Cash Ratio (CaR), whilst profitability was proxied by Return on Capital Employed (ROCE). After undertaken some diagnostic and specification tests to address the basic assumptions of the Classical Linear Regression Model (CLRM), the study uncovered that, cash flow ratio had a significantly positive effect on the firms’ profitability as measured by ROCE [β=0.1050416, (p=0.038)<0.05], but the cash ratio had an insignificantly negative influence on the firms’ profitability as measured by ROCE [β= -0.0805403, (p=0.306)>0.05]. It was further discovered that, the cash flow ratio and the cash ratio had a combined significant effect on the firms’ profitability as measured by ROCE [Wald chi2(1)=7.43, (p=0.0244)<0.05]. In order to ensure continuous survival and success, the firms should not play with the issue of liquidity management. The entities are expected to maintain an optimal liquidity level that will be capable of performing the ‘twin’ role of meeting their financial obligations and at the same time maximizing their shareholders’ wealth. This optimal liquidity level could be obtained if the establishments are to meet the standards set by the Ghana Stock Exchange (GSE). Adhering to these standards will help the firms to reduce the cases of financial distress. In other words, the firms should keep an adequate level of liquidity that will not portend their going concern status, and yet allow them to make ample returns on their investments. Thus, the firms should strike a balance (trade-off) between their liquidity and profitability. Also, surplus liquidity and inadequate liquidity are two financial ailments that can simply wear down the firms’ profitability. Therefore, the establishments must embrace liquidity management in their attempt to optimize profitability. This could be attained if the firms lessen the amounts they hold in cash and focus more on investments so that, they could gain higher returns rather than tying them down in idle cash. From the perspective of theory, the outcome of this study is in tandem with that of prior studies by bringing to light the effect of liquidity on firms’ financial performance as measured by ROCE. The firms should therefore inculcate into their decisions the findings of this study so as to meet their operational and expansion needs, as well as the desires of their shareholders.

viewed profitability as the ability of establishments to make returns from all their undertakings. According to the author, profitability indicates how management can make value by efficiently employing all available resources at their disposal. Put simply, profitability is an assessment of management's capacity to generate returns on firms' resources. Low returns therefore suggest that, management did not efficiently put the firms' resources into good use, and investors will be reluctant to invest (Ajanthan, 2013).
According to Peavler (2017), Mueller (2018) and Ally (2017) creditors and investors often use liquidity indicators to measure how well businesses are performing. This is because, creditors are primarily concerned with firms' ability to repay their debts, assuch, they would want to see if there is enough cash and cash equivalents to meet the current portions of their investments. Investors on the other hand, are typically more concerned with the overall health of entities and how they can increase their performance in the future (Peavler, 2017;Mueller, 2018;and Ally, 2017). Enlightening further, Peavler (2017), Mueller (2018) and Ally (2017) indicated that, companies that struggle with liquidity usually have difficulties in growing and increasing their performance. This is because, they do not have shortterm funding available, implying, the firms have failed to efficiently generate revenues from their assets to meet their current obligations (Peavler, 2017;Mueller, 2018;and Ally, 2017).
As postulated by Olagunju, Adeyanju and Olabode (2011), liquidity helps firms to avoid a situation where they will be forced to liquidate with its attendant problems of selling assets at distressed prices and extra fees paid to lawyers, trustees in bankruptcy and liquidators on liquidation. However, Ben-Caleb, Olubukunola and Uwuigbe (2013) and Kesseven (2006) cautioned that, firms' too much focus on liquidity will be at the expense of their profitability. As such, Panigrahi (2013) posited that, liquidity should be well managed by body corporates. To the author, managing liquidity has to do with the avoidance of illiquidity (which is very detrimental as it creates a bad corporate image, makes creditors lose confidence, leads to high-cost of emergency borrowing, result in unnecessary legal battles or even liquidation of firms) and the avoidance of excessive liquidity (which leads to high carrying costs, missed financial opportunities due to inflation, and careless financial decisions that might inversely affect corporate profitability).
The above indicates that, liquidity should neither be too high nor too low as a well monitored minimum level of liquidity at a calculated risk is always good for firms' better performance. This study therefore sought to examine the trade-off between liquidity and the profitability of nonfinancial firms listed on the Ghana Stock Exchange (GSE). Specifically, the study sought to explore the effect of cash flow ratio on the firms' profitability as measured by ROCE; find out the effect of cash ratio on the firms' profitability as measured by ROCE; and to assess the combined effect of cash flow ratio and the cash ratio on the firms' profitability as measured by ROCE. Findings of this study will help management in decisions that relate to the determination of an acceptable liquidity level that will be of benefit to firms. More prominently, this study will add to the existing pool of literature on liquidity and its influence on firms' financial performance. It is hoped that, the outcome of this study will be appreciated by academicians, who may discover useful research gaps that may arouse their interest for further studies. The rest of the study is organised as follows; section two presents literature that supports the topic understudy; whilst section three concentrates on the research methodology and model specification. In the fourth section, empirical results are outlined; whilst the fifth section presents discussions and tests of hypothesis. The sixth section finally presents the study's conclusion and policy implications.

REVIEW OF RELATED LITERATURE
This section first presents theoretical reviews on the link between liquidity and firms' financial performance. Secondly, empirical findings on the interactions between liquidity and firms' financial performance are brought to light. Thirdly, formulated hypothesis that governed the study's conduct are outlined, whilst a conceptual framework showing the connection between the variables understudy is finally presented.

Theoretical Reviews
Liquidity is the capacity of an establishment to defray its short-term financial obligations in a timely manner (Raykov, 2017 Puneet and Parmil (2012) and Garcia and Martinez (2007) viewed liquidity and profitability as dual economic expressions at the tail ends of a thread, where a movement in the direction of one point inevitably means, a drive away from the other. In other words, the two are in a trade-off position.
According to the trade-off hypothesis of liquidity, firms target an ideal level of liquidity to bring into balance the costs and benefits of handling cash (Orshi, 2016). The costs of handling cash comprises of the minimal rate of return on current assets as a result of liquidity premium and possible tax burdens; whilst the benefits of keeping cash are that, firms spare exchange costs to raise reserves and do not ought to settle resources to meet commitments; and firms can utilize liquid resources to fund their undertakings if other means of finance are in shortage (Orshi, 2016

Hypothesis Development
A statistical hypothesis test is a method of making statistical decisions using data; it is sometimes called confirmatory analysis (Hari, 2011). Hypothesis testing tells whether the proof for rejecting a null hypothesis is reliable or not (Schick & Vaughn, 2002). According to Schick and Vaughn (2002), Patricia and Hassan (2006) and Patricia and Nandhini (2013), a statistically significant hypothesis or result is considered not to have occurred by chance. Based on the reviews of prior literature, the following hypothesis were formulated for testing; H1: Cash Flow Ratio (CFR) has a significant effect on the firms' profitability as measured by ROCE. H2: Cash Ratio (CaR) has a significant effect on the firms' profitability as measured by ROCE. H3: Cash Flow Ratio (CFR) and the Cash Ratio (CaR) have a combined significant effect on the firms' profitability as measured by ROCE.

RESEARCH METHODOLOGY
Howell (2013) defined research methodology as the general research strategy that outlines the way in which research is to be undertaken and among other things, identifies the methods to be used in it. According to the author, these methods define the means or modes of data collection or, sometimes, how a specific result is to be calculated. This aspect of the study presents the research methodology. The methodology covers the data source, diagnostic and specification tests, measurement of study variables, model specification and estimation and the empirical analysis procedure. Ltd were the firms that were used for the study. These firms were used because, they were actively operational during the study period and all their financial statements were up to date. The firms totaling fifteen (15), represented 36.59% of the total number of listed firms or 53.57% of the total number of non-financial firms listed on the Ghana Stock Exchange (GSE). A balanced panel data extracted from the audited and published annual reports of the firms was employed for the study. The annual reports comprised of the comprehensive income statement, statement of financial position, statement of cash flows, statement of changes in equity and notes to the accounts. These annual reports were obtained from the official website of the Ghana Stock Exchange (GSE). Data from the Ghana Stock Exchange (GSE) was depended upon because, the GSE contains the most comprehensive and reliable data for all its listed firms, and have been updating and validating the annual reports of the firms.

Diagnostic and Specification Tests
Performing linear regressions could not automatically give reliable results on the variables understudy. To bring out reliable outcomes, it was imperative for the researchers to ensure that, some basic assumptions of the Classical Linear Regression Model (CLRM) were met. The basic assumptions that were considered comprised of (1) linearity in parameters, (2) homoscedasticity of the error terms, (3) no perfect multi-collinearity between the explanatory variables, (4) no autocorrelation of the error terms, (5) correct specification of the regression model, and (6) normality of residuals. Since outliers impact on regression results in panel data substantially (Hair, Black, Babin & Anderson, 2013; Kohler & Kreuter, 2008), the regression models were tested for the influence of multivariate outliers in addition to the above five (5) assumptions. Table 1 provides a summary of the various assumptions, the tests conducted and their corresponding outcomes, as well as corrective mechanisms undertaken in case of any violations.

Measurement of Study Variables
The measurements for liquidity and the firms' financial performance were purely accounting based, deduced from the firms' financial statements (thus, the comprehensive income statement, the statement of financial position, the statement of cash flows, the statement of changes in equity and the notes to the accounts). In this study, the firms' profitability was proxied by Return on Capital Employed (ROCE). The ROCE was calculated as the ratio of net income to the firms' capital employed. On the other hand, liquidity was surrogated by the Cash Flow Ratio (CFR) and the Cash Ratio (CaR). The CFR was computed as the ratio of net operating cash flows to the total current liabilities of the firms, whilst the CaR was calculated as the absolute liquid assets divided by the total current liabilities of the firms. Table 2 presents a detailed summary of the study's variables and their measurements; Where; α is the intercept; Yit is k ×1 vector of the response variable representing profitability and proxied by ROCE; Xit is a k ×1 vector of regressors surrogated by the Cash Flow Ratio (CFR) and the Cash Ratio (CR); βit is a k ×1 vector of parameters to be valued; uit denotes the between-entity error term; εit denotes the within-entity error term; i is the number of cross-section (i=1, 2, 3………, N); and t is the time period (t=1, 2, 3……, T). With reference to the proxies of the explained and the explanatory variables, equation (1) can be extended as follows; ROCEit = αi +β1CFRit +β2CaRit + uit + εit (2) Where αi is the intercept; β1 and β2 respectively captures the effect of Cash Flow Ratio (CFR) and the Cash Ratio (CaR) on profitability as proxied by ROCE; uit denotes the betweenentity error term; εit denotes the within-entity error term; i is the number of cross-section; and t is the time period.
It is assumed that CFR and CaR will significantly predict the firms' profitability as measured by ROCE. This implies, the partial slope coefficients are expected to be statistically significantly different from zero. Thus, (β1+β2≠0) or (β1=β2≠0) or (β1, β2 ≠0). This also implies, the firms are expected to be in a trade-off (equilibrium) position where they will be highly liquid and still maximize profitability (Orshi, 2016). Individually, the study projects a positive effect of cash flow ratio and cash ratio (β1, β2>0) on the firms' profitability because, establishments with more promising levels of liquidity are more adaptable in terms of giving immediate funding which may result in improved profitability.

Empirical Analysis Procedure
The researchers will first conduct a multi-collinearity test to find out whether the explanatory variables are extremely correlated to each other or not. This test will help the researchers to choose the input variables that will best suit the study. Secondly, because the Classical Linear Regression Model (CLRM) needs the association between the response and the predictor variables to be linear, the researchers will test for linearity in the study's parameters. Thirdly, data abnormality has a lot of consequences, notably amongst them is that, it poses problems for efficiency-that is, the OLS standard errors are no longer the smallest; and the OLS standard errors can be biased-that is, confidence intervals and significance tests may lead to wrong conclusions. The help unravel these concerns, the test for data normality will be conducted. Additionally, the presence of heteroscedasticity is a foremost concern in the application of linear regression analysis because, it could lead to imprecise or misleading inferences. Due to that, the researchers will @ IJTSRD | Unique Paper ID -IJTSRD25068 | Volume -3 | Issue -4 | May-Jun 2019 Page: 1091 conduct a heteroscedasticity test so as to determine the most suitable regression estimator for the working models. Also, serial or autocorrelation of the errors of working models is consequential because, it makes the OLS estimates to be no longer the Best Linear Unbiased Estimator (BLUE). To help determine the most appropriate regression estimator that could help remedy the issue of serial or autocorrelation in the study's working model, the test for serial correlation will be conducted. Further, observation points that are distant from other observations (outliers) are injurious in regression analysis. This is because, extreme values of observed variables can distort estimates of regression coefficients, which might lead to wrong conclusions or inferences. To help remedy this concerns, the test for outliers in the study's distribution will be conducted. Additionally, one of the key assumptions of the Classical Linear Regression Model (CLRM) is that, a model must be correctly specified. This is because, model misspecification is detrimental as it could yield inaccurate results that might lead to wrong conclusions. In order to come out with the right model that would produce correct estimates resulting in reliable conclusions, the model specification test will be undertaken. After the right model has been specified, the researchers will finally proceed to the multivariate regression analysis. Apart from the linearity test which was accomplished via the R-software package, all the data analysis were conducted through the use of STATA version 15 statistical software package at α=5% (p≤0.05). Figure 2 shows the empirical analysis procedure that was followed to achieve the study's goal.

EMPIRICAL RESULTS
This aspect of the study presents the study's empirical results. The first five parts of the section bring to light, some diagnostic and specification tests on the assumptions of the Classical Linear Regression Model (CLRM). These tests were undertaken in order to avoid the problems associated with model misspecification and the choice of wrong regressors. The diagnostic and specification tests included the test for multi-collinearity, the test for linearity in parameters, the test for data normality, the test for heteroscedasticity, the serial or autocorrelation test, the test for outliers and the model specification test. The eight part of the section presents descriptive analysis on both the input and out variables, whilst the final part presents regression results on the influence of liquidity on the firms' profitability as measured by ROCE.

Test for Multi-Collinearity
As explained by Kenton (2018) Table 4, the test was not statistically significant at the 5% level of significance [(p=0.5721)>0.05]. The study therefore accepted the null hypothesis that, the ROCE linear specification was linear, and rejected the alternative hypothesis that, the ROCE linear specification was nonlinear. The results show that, the ROCE working model was linear and fit enough to be used for the study.

Test for Data Normality
As posited by Andersen (2012), data non-normality has two important consequences, (1) it poses problems for efficiency-that is, the OLS standard errors are no longer the smallest, and (2) the OLS standard errors can be biased-that is, confidence intervals and significance tests may lead to wrong conclusions. The Shapiro and Wilk (1965) test for data normality was employed for this study. The Shapiro-Wilk test, tests the null hypothesis that, a sample X1……..Xn came from a normally distributed population (Shapiro & Wilk, 1965;Field, 2009;and Razali & Wah, 2011). In other words, if the p-value is less than the chosen alpha (α) level, then the null hypothesis is rejected and there is evidence that, the data tested is not normally distributed (Shapiro & Wilk, 1965;Field, 2009;and Razali & Wah, 2011). On the other hand, if the p-value is greater than the chosen alpha (α) level, then the null hypothesis that, the data came from a normally distributed population cannot be rejected (Shapiro & Wilk, 1965;Field, 2009; and Razali & Wah, 2011). As displayed in Table 5, the z-value of ROCE was statistically significant at α=5% [(p=0.00000)<0.05). The study therefore failed to accept the null hypothesis that, the data values of ROCE came from a normally distributed population and concluded that, the data values of ROCE were not normally distributed.
The z-value of CFR was also significant at the 5% significance level [(p=0.00000)<0.05). The study therefore failed to accept the null hypothesis that, the data values of CFR came from a normally distributed population and concluded that, the data values of CFR were not normally distributed. Finally, a z-value of 9.310 with a probability of 0.00000 for CaR indicated the variable's significance at the 95% confidence interval. The study therefore failed to accept the null hypothesis that, the data values of CaR came from a normally distributed population and concluded that, the data values of CaR were not normally distributed. From the study's results, all the data values of ROCE, CFR and CaR were not normally distributed at α=5% (p<0.05). Hence, a more generalised and robust regression estimator was viewed as ideal for all the data values of the study because, such estimators remedy the issue of data abnormality in classical linear regression analysis.

Test for Heteroscedasticity
According to Greene (2012), Ginker and Lieberman (2017), Giles (2013) and Gujarati and Porter (2009), heteroscedasticity refers to the circumstance in which the variability of a variable is uneven across the range of values of a second variable that predicts it. The Breusch and Pagan (1979) and Cook and Weisberg (1983) test for heteroscedasticity, which tests the null hypothesis of homoscedasticity or the absence of heteroscedasticity in linear regression models, was adopted for this study.

Test for Serial Correlation
Verbeek (2012), Colberg and Höfling (2011), Dunn (2005) and Baum (2006) explained serial or autocorrelation as the mathematical representation of the degree of resemblance between a given time series or a cross-section and a lagged version of itself over successive time intervals. In the presence of serial correlation the OLS estimates are no longer the BLUE making room for wrong conclusions or tests of hypothesis (Wooldridge, 2015;Gujarati & Porter, 2009). The Durbin-Watson test for serial or autocorrelation was adopted for this study. The test, tests the null hypothesis that, the errors are serially uncorrelated as against the alternative hypothesis that, the errors are serially correlated (Durbin & Watson, 1950;Durbin & Watson, 1951). The test reports a d-statistic with a value from 0 to 4 where; 2 is no autocorrelation, 0 to <2 is positive autocorrelation and >2 to 4 is negative autocorrelation (Durbin & Watson, 1950;Durbin & Watson, 1951).  Table  8. Based on the results, it was concluded that, the study's data sample suffered from outlier effects. An estimator that was more robust to outlier effects was therefore viewed as appropriate for data values of this nature.  Gujarati & Porter, 2009). However, the consequences of model misspecification in regression analysis could be severe in terms of the adverse effects on the sampling properties of both estimators and tests (DeBenedictis & Giles, 1996). To avoid these severe consequences, the researchers undertook thorough model specification tests. The Durbin-Wu-Hausman test with the null hypothesis of the random effects model being preferable to that of the fixed effects model (Durbin, 1954;Wu, 1973;Hausman, 1978;and Greene, 2012), was adopted to make a choice for the ROCE working model. .05]. The study therefore failed to reject the null hypothesis that, the random effects model was preferred over the fixed effects model and concluded that, the Robust Random effects GLS regression estimator was the best fit for the ROCE working model.

Descriptive Analysis of Study Variables
As shown in Table 10, non-financial firms listed on the Ghana Stock Exchange (GSE) had a mean ROCE of 0.1945633, a standard deviation of 1.09571 and a variance of 1.20058. This indicates that, the data values of ROCE deviated from both sides of the mean by 1.09571, implying, the ROCE data values were a bit widely dispersed from the mean. The maximum and minimum values of ROCE were 12.8951 and -1.5666 respectively, leading to a range of 14.4617. The ROCE distribution was positively skewed with a coefficient of 10.44939. This shows that, the right tail of the ROCE distribution was longer than that of the left tail. Put simply, a large portion of the ROCE distribution fell on the left side of the normal curve. The kurtosis coefficient of 122.057 implies, the ROCE distribution was of abnormal shape. The CFR of the firms had a mean figure of 0.3265207, a maximum figure of 4.4039 and a minimum figure of -1.6939, resulting in a range of 6.0978. The average CFR value of 0.3265207 depicts that, for the period 2008-2017, the firms were not able to generate more cash than what was needed to pay off their current liabilities when they fell due. Put simply, the firms' immediate obligations could not be met by the resources raised from their operations over the period. However, there could be many interpretations for the mean value because, not all low operating cash flow ratios are indications of poor financial health. For instance, the firms might have invested their cash flows into projects that could render greater rewards in the future. The figures 0.7158448 and 0.5124337 being the standard deviation and the variance of CFR respectively indicate that, the data values of CFR were not too dispersed or deviated from the average. The operating cash flow ratio had a skewness value of 2.787994, which is an indication that, the CFR distribution was highly positively skewed or skewed to the right.  (2002), regression analysis is a set of statistical processes for estimating the relationships among variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed (Tofallis, 2009;Aldrich, 2005;Mogull, 2004;and Fotheringham, Brunsdon & Charlton, 2002). In order to assess the effect of liquidity on the firms' profitability, the ROCE of the sampled firms was regressed on CFR and CaR. The results are displayed in Table  11 as follows; ]. This means, on the average when all other variables were held stationary, a unit increase in CaR did not have any material increase in ROCE. The overall R-squared (R 2 ) value of 0.0021depicts that, the explanatory variable accounted for only 0.21% of the variations in ROCE, whilst the unexplained variations [99.79% (100-0.21)] were accounted for by other inherent variabilities. The overall R 2 value was statistically significant at α=5%. This is substantiated by the Wald chi2(1) value of 7.43 which was significant at the 95% confidence interval [(p=0.0244)<0.05). The significance of the R 2 coefficient is an indication that, the ROCE model satisfied the overall goodness of fit test at α=5%. The Wald chi2(1) value been significant also portrays that, CFR and CaR had a combined significant effect on the firms' profitability as measured by ROCE. Fitting the coefficients into the ROCE working model, the final model became; ROCE=0.1866939+0.1050416CFRit-0.0805403CaRit (3)

DISCUSSIONS AND TESTS OF HYPOTHESIS
This aspect discusses the study's major findings. The discussions are done in relation to the review of relevant literature and are arranged in the order of; the effect of Cash Flow Ratio (CFR) on the firms' profitability as measured by ROCE; the effect of Cash Ratio (CaR) on the firms' profitability as measured by ROCE; and the effect of Cash Flow Ratio (CFR) and Cash Ratio (CaR) on the firms' profitability as measured by ROCE. Each subdivision concludes with a test of a formulated hypothesis.

The Effect of Cash Flow Ratio (CFR) on the Firms'
Profitability as Measured by ROCE From the findings, CFR had a significantly positive influence on ROCE at the 5% level of significance [β=0.1050416, (p=0.038) <0.05]. This finding supported that of Ofoegbu, Duru and Onodugo (2016) whose study on pharmaceutical companies in Nigeria, provided evidence of liquidity having a significantly positive effect on the firms' financial performance. The finding also supported that of Swagatika and Ajaya (2018) whose research on manufacturing establishments in India, found liquidity as asignificantly positive determinant of the firms' profitability. The finding was further consistent with that of Shaheen, Muhammad, Muhammad, Mudasar and Muhammad (2015) whose study on the Pakistani sugar sector, uncovered liquidity as a significantly positive predictor of the firms' profitability. The finding was again consistent with the prior expectation of the study that β1>0. The finding was however not consistent with that of Ashutosh and Gurpreet (2018) whose research on sugar mills in Punjab, India, found liquidity as an insignificant determinant of the profitability of private sugar mills in the Punjab sugar industry. The finding was also not consistent with that of Bilal, Khan, Tufail and Ul Sehar (2013) whose panel study on 31 insurance firms in Pakistan, discovered liquidity as an insignificant determinant of the firms' profitability. The finding finally conflicted that of Rizwan (2016) whose research on 64 non-financial firms listed on the Karachi Stock Exchange (KSE), established liquidity as an insignificant predictor of the firms' financial performance.

Test of Hypothesis
From the study's findings, cash flow ratio had a significantly positive impact on the firms' ROCE at α=5% [β=0.1050416, (p=0.038)<0.05]. The study therefore failed to accept the null hypothesis that the cash flow ratio had no significant influence on the firms' profitability as measured by ROCE, and concluded that the cash flow ratio had a significantly  (8) life insurance companies in Tunisia, uncovered liquidity as an immaterial explanator of the firms' financial performance. The finding was also in line with that of Pratheepan (2014) whose research on 55 manufacturing companies listed on the Colombo Stock Exchange in Sri Lanka, found liquidity as an insignificant predictor of the firms' profitability. The finding further supported that of Batchimeg (2017) whose study on 100 Joint Stock Companies (JSC) listed on the Mongolian Stock Exchange (MSE), discovered liquidity as an insignificant determinant of the firms' financial performance. The finding was however not consistent with that of Ali, Mahmoud, Fadi and Mohammad (2018) whose panel study on listed industrial and service firms in Jordan, established liquidity as a significantly positive influencer of the firms' financial performance.
The finding was also not consistent with that of Hamidah and Muhammad (2018) whose research on 21 companies in Malaysia, found liquidity as a significantly positive predictor of the firms' financial performance. The finding was further inconsistent with that of Maja, Ivica and Marijana (2017) whose dynamic panel study on 956 firms operating in the Croatian food industry, discovered liquidity as a significantly adverse determinant of the firms' performance. The finding was finally inconsistent with the priori expectation of the study that β2>0.

Test of Hypothesis
From the study's findings, cash ratio had an insignificantly negative effect on the firms' ROCE at α=5% [β= -0.0805403, (p=0.306)>0.05]. The study therefore failed to reject the null hypothesis that cash ratio had no significant influence on the firms' profitability as measured by ROCE, and concluded that cash ratio had an insignificantly adverse effect on the firms' profitability as measured by ROCE.

The Effect of Cash Flow Ratio (CFR) and the Cash
Ratio (CaR) on the Firms' Profitability as Measured by ROCE It was finally discovered from the study that, cash flow ratio and the cash ratio had a combined significant influence on the firms' profitability as measured by ROCE [Wald chi2(1)=7.43, (p=0.0244)<0.05]. This finding is consistent with that of Mehmet and Mehmet (2018) whose research on 10 quoted energy firms in Turkey, found liquidity as a significantly positive determinant of the firms' profitability. The finding is also consistent with that of Isik (2017) whose panel study on 153 real sector firms listed on the Borsa Istanbul Stock Exchange, disclosed liquidity as a significant determinant of the firms' profitability. The finding is further consistent with that of Kanga and Achoki (2017) whose research on agricultural companies listed on the Nairobi Securities Exchange (NSE), uncovered liquidity as a significantly positive predictor of the firms' financial performance. The finding is also in tandem with the priori expectation of the study that (β1, β2>0) or (β1+β2≠0). The finding is however not consistent with that of Mohammed, Muhammad and Imran (2015) whose research on 99 nonfinancial firms listed on the Saudi Stock Exchange (Tadawul), found liquidity as an insignificant explanator of the firms' profitability. The finding is also inconsistent with that of Gonga and Sasaka (2017) whose study on 55 licensed insurance firms in Nairobi County, discovered liquidity as an insignificant predictor of the firms' financial performance. The finding finally contrasts that of Doğan and Topal (2016) whose pooled OLS regression estimates on 136 Turkish manufacturing firms listed on the Borsa Istanbul Stock Exchange, found liquidity to be weakly related to the firms' financial performance.

Test of Hypothesis
From the study's findings, cash flow ratio and the cash ratio had a combined significant influence on the firms' profitability as measured by ROCE [Wald chi2(1)=7.43, (p=0.0244)<0.05]. The study therefore failed to accept the null hypothesis that cash flow ratio and the cash ratio had no joint significant influence on the firms' profitability as measured by ROCE, and concluded that, the cash flow ratio and the cash ratio had a combined significant influence on the firms' profitability as measured by ROCE.

Regression Accepted
H2: Cash ratio has a significant effect on the firms' profitability as measured by ROCE.

Regression Rejected
H3: Cash flow ratio and the cash ratio have a combined significant effect on the firms' profitability as measured by ROCE.

CONCLUSION AND POLICY IMPLICATIONS
This study sought to explore the trade-off between liquidity and the profitability of non-financial firms listed on the Ghana Stock Exchange (GSE). Specifically, the study sought to examine the effect of cash flow ratio on the firms' profitability as measured by ROCE; find out the effect of cash ratio on the firms' profitability as measured by ROCE; and to determine the combined effect of cash flow ratio and the cash ratio on the firms' profitability as measured by ROCE. After undertaken some diagnostic and specification tests to address the basic assumptions of the Classical Linear Regression Model (CLRM), the study uncovered that cash flow ratio had a significantly positive effect on the firms' profitability as measured by ROCE [β=0.1050416, (p=0.038)<0.05], but the cash ratio had an insignificantly negative influence on the firms' profitability as measured by ROCE [β= -0.0805403, (p=0.306)>0.05]. It was further discovered that the cash flow ratio and the cash ratio had a combined significant effect on the firms' profitability as measured by ROCE [Wald chi2(1)=7.43, (p=0.0244)<0.05].
The beta (β) value for CFR as stated above implies, a unit increase in liquidity led to a 0.1050416 increase in profitability. On the contrary, the coefficient for CaR though immaterial indicates that, a unit increase in liquidity proxied by the CaR could lead to a 0.0805403 decrease in the firms' profitability. The underlying issue here is that, the firms do not have to forgo liquidity in order to become profitable. What is required of them is that, they have to strike a balance between the extent at which they can lose liquidity to earn their desired profits, which is the ultimate trade-off between the firms' liquidity and their profitability. In order to ensure continuous survival and success, the firms should not play with the issue of liquidity management. The entities are expected to maintain an optimal liquidity level that will be capable of performing the 'twin' role of meeting their financial obligations and at the same time maximizing their shareholders' wealth. This optimal liquidity level could be obtained if the establishments are to meet the standards set by the Ghana Stock Exchange (GSE). Adhering to these standards will help the firms to reduce the cases of financial distress. In other words, the firms should keep an adequate level of liquidity that will not portend their going concern status, and yet allow them to make ample returns on their investments. Thus, the firms should strike a balance (tradeoff) between their liquidity and profitability. Also, surplus liquidity and inadequate liquidity are two financial ailments that can simply wear down the firms' profitability. Therefore, the establishments must embrace liquidity management in their attempt to optimize profitability. This could be attained if the firms lessen the amounts they hold in cash and focus more on investments so that, they could gain higher returns rather than tying them down in idle cash. From the perspective of theory, the outcome of this study is in tandem with that of prior studies by bringing to light the effect of liquidity on firms' financial performance as measured by return on capital employed. The firms should therefore inculcate into their decisions the findings of this study so as to meet their operational and expansion needs, as well as the desires of their shareholders.