Cash Conversion Cycle and Value of Listed Oil and Gas Companies in Nigeria

Abstract

This study investigated the impact of cash conversion cycle on the value of listed oil and gas companies in Nigeria, with the specific objectives of determining its impact on share price and Tobin’s Q. To achieve this, panel data is applied with the use of descriptive, correlational and explanatory research design. The study hypothesised two research hypotheses and generalised least square regression is used in analysing the collected data that were extracted from the annual reports and accounts of eight listed oil and gas companies in Nigeria for the period 2006-2019. Share price and Tobin’s Q were used as proxies for value of firm. The study found that cash conversion cycle has a negative and significant impact on the value of listed oil and gas companies in Nigeria. In view of this finding, the study recommended, among others, that the cash conversion cycle should be reduced as reasonably possible below 365 days when the economic condition is good and vice versa when the economic situation is hard as this will enhance the value positively. It is further recommended that the individual components of cash conversion cycle, such as inventory, receivables and payables, be studied individually as net off effect exist using cash conversion cycle.

JEL Classification: G31, G32

Keywords

Working capital management cash conversion cycle firm value Tobin’s Q

Introduction

The management of working capital (WC) involves the ability of a firm to manage its inventory, receivables and payables efficiently with the aim of enhancing the value of the firm. The Cash Conversion Cycle (CCC) is considered as a broad measure of WC as this indicates the time between the payment for raw materials purchased and the realisation of cash from the sales of finished goods (Sharma & Kumar, 2011). The CCC is not a ratio but is very vital in determining the working capital management (WCM) of an entity and has commonly been regarded as a better measure of WC because it takes in details of how long inventory remain unsold, how long it takes the firm to recover receivables and how long it takes the firm to settle its current obligations. A lower CCC period is considered better as it indicates lesser investment in current assets which signifies high liquidity, while a higher CCC period implies larger investment in current assets and, therefore, shows greater need of financing the current assets (Sharma & Kumar, 2011). This implies that the lower the CCC period, the better it is for the company, and the higher the CCC period, the worse it is for the company. The CCC is the sum of the Inventory Conversion Period (ICP) and the Account Receivables Collection Period (ARCP) less the Account Payables Payment Period (APPP). Furthermore, Ademola and Kemisola (2014) opined that the ratio of current assets to total assets (CATA) varies from one sector to another. For instance, while more than half of CATA are needed in manufacturing firms, even more is needed in marketing or retail firms. This suggests that the management of WC is very vital for the success of an entity, and thus the need for proper monitoring.

One of the major objectives of a corporate entity is wealth maximisation of its shareholders through maximising the market price of its shares, and an entity that is experiencing an increase in its share price (SP) is considered a good company for investment by investors (Ademola & Kemisola, 2014). The value of a firm is often viewed as the investor’s perception towards a company’s success which is reflected in its SPs, and a company with high SP is an indication of higher value of the company not only on current performance but also on the firm’s future prospect (Hama & Santosa, 2018; Purwohandoko, 2017). This implies that a higher value is an indication of good utilisation of productive resources on the side of the management, which suggests the importance of managing WC well to enhance value. Based on the foregoing, despite how the literatures have suggested CCC to be a better and dynamic measure of WC, there is paucity of literatures on CCC and value in Nigeria within the oil and gas sector and, thus, this study intends to bridge the gap by studying the impact of CCC on the value of listed oil and gas companies in Nigeria. Moreover, there is a mixed finding on the relationship of CCC to value. While some researchers found a negative relationship of CCC to value (Tobias et al., 2020; Vijayakumaran, 2019), others found a positive relationship (Ademola & Kemisola, 2014; Vural et al., 2012).

The choice of oil and gas sector is based on empirical review. While previous studies focused on other sectors within the Nigerian border and beyond, only few focused on the oil and gas sector. For instance, studies by Lai (2012), Vural et al. (2012), Langroudi et al. (2013), and Ademola and Kemisola (2014) focused on sectors such as the manufacturing and airline industries. However, based on the literatures reviewed, there is no work that considers CCC separately on the value of listed oil and gas companies in Nigeria, and this provides empirical evidence that less attention is given to this sector. Moreover, the oil and gas sector is regulated by the Department of Petroleum Resources (DPR) which is different from other sectors like manufacturing, food and beverages that is regulated by National Agency for Food and Drug Administration and Control (NAFDAC) in addition to other regulations. This justifies the need to study the oil and gas sector separately because of the differences in regulations within the business environment in which they operate.

The main objective of the study is to find out the effect of CCC on the value of listed oil and gas companies in Nigeria and to answer the research question of the effect of CCC on the value of listed oil and gas companies in Nigeria. The definitions of various variables are given in Table 1.

Literature Review

The Concept of Cash Conversion Cycle

The CCC reflects the time interval between the expenditure by cash on a firm’s procurement of productive resources and the final recovery of cash receipts from product sales. Kipkemoi et al. (2018) similarly viewed CCC as the time between the purchase of raw material and getting the finished products. This implies that the CCC is the amount of time between the acquisition of production materials and the realisation of cash from the sales of the finished product. Moreover, Tobias et al. (2020) opined that the CCC is the time it takes an entity to convert its investment in inventory and other productive resources into cash. The CCC has often and commonly been regarded as a better measure of WC in an entity. This is because it takes in details on how long inventory is kept outstanding and how long it takes the firm to recover receivables, as well as how long it takes the firm to settle its current obligations.

If a firm chooses to maintain a shorter CCC period, it implies that there is little investment in WC, but where a firm maintains a longer CCC, it suggests that there is large investment in WC (Kipkemoi et al., 2018). By and large, a shorter CCC period is considered better as it indicates lesser investment in current assets which also signifies high liquidity of the firm to easily convert its short-term investment in current assets into cash, while a higher CCC period implies a larger investment in ongoing assets and, therefore, a greater need for financing the current assets (Sharma & Kumar, 2011). Based on the foregoing, it can be suggested that the lower the CCC period, the better it is for the company, and the higher the CCC period, the worse it is for the company. The CCC is the sum of ICP and ARCP less APPP.

Concept of Firm Value

Literally, the word value can simply mean the worth of a business entity. Furthermore, the term value can be viewed from the viewpoint of equity value and enterprise value, which is otherwise known as the firm value. The equity value, also known as the market capitalisation of the company, is given by the SP multiplied by the outstanding number of shares, while the enterprise value indicates the value of the firm as a whole, which is further given as total debts plus the market capitalisation minus cash (Oseifuah & Gyekye, 2017). Furthermore, Thamrin et al. (2018) opined that firm value can be viewed by looking at the development of stock prices in the secondary market and that an increase in SPs is an indication of an increase in value because the value of the firm is actually the share market value, as well as the value of long-term debt or bond.

Empirical Review

Ghaziani et al. (2012) investigated the relationship between the components of WCM with the market valuation and profitability of firms listed on the Tehran Stock Exchange from 2006 to 2010. The study used secondary data which was sourced from the annual reports of the sampled firms. The dependent variables were Tobin’s Q (TQ) and return on assets (ROA), while CCC, current assets to current liabilities (CACL) ratio, CATA ratio, current liabilities to total assets (CLTA) ratio and total debt to total assets’ ratio were employed as the independent variables. The data collected were analysed using descriptive statistics and regression analysis. The study found that CCC has a significant negative relationship with both TQ and ROA.

Nzioka (2012) investigated the effect of cash management and liquidity of firm on SPs of companies listed on the Nairobi Securities Exchange from 2007 to 2011. Secondary source of data was used, which was obtained from NSE library and from other financial intermediaries. SP was used as the dependent variable while current ratio and CCC were used as the independent variable. The data collected were analysed using descriptive statistics, correlation and regression analysis. The study found that there is a negative relationship between SP and CCC. The study by Nzioka (2012) used one dependent variable, SP, against two dependent variables used in this study, SP and TQ. Again, the study was not carried out in Nigeria and therefore, the need to replicate such study in Nigeria. Moreover, there are eight more years to study from 2012 to 2019, which makes this study distinct from the study of Nzioka (2012).

Similarly, Ogundipe et al. (2012) observed the relationship between WCM and firm’s performance, as well as market value in Nigeria from 1995 to 2009. Secondary data for the analysis was employed and specifically sourced from the annual financial reports of sampled non-financial firms. TQ, ROA and return on invested capital (ROI) were employed as the dependent variables in the study while CCC, CACL, CATA, CLTA and leverage were considered the independent variables of the study. Correlation and multiple regression analysis were employed in analysing the data gathered. The study found that a significant negative relationship exists between CCC and TQ, as well as ROA. Conversely, an insignificant negative relationship is found between CACL and CATA on TQ. The study considers TQ and ROA as dependent variables against SP and TQ as used in this study, which are market-based measures of financial performance. Therefore, considering the scope of the study, there is still a need to conduct a different study of data from 2010 to 2019 because of the changes in the business environment.

Vural et al. (2012) studied how WCM impacted a firm’s performance in Turkey from 2002 to 2009. The study considered a secondary source of data collection, which is specifically gathered from the annual financial reports of the manufacturing firms listed on the Istanbul Stock Exchange Market. TQ and Gross Operating Profit (GOP) were considered as the dependent variables; average account receivable collection period, payables payments period, inventory turnover days, operating cycle and CCC were used as the independent variables; and size of firm and financial leverage were considered as the control variables for the study. Descriptive statistics, regression, and correlation analysis were employed for the data analysis. The study found that a positive relationship exists between CCC and TQ, while a negative relationship is observed between leverage and TQ. This means that extending the CCC will increase the firm value and lowering leverage will result in an increase in the firm value. Furthermore, the results for firm value (TQ) are insignificant except for CCC and leverage. The study combines both accounting and market-based measure of financial performance against SP and TQ that are considered in this study, which is a market-based approach.

In their study, Langroudi et al. (2013) observed the impact of WCM on firm performance of listed companies on the Tehran Stock Exchange from 2006 to 2011. The data were collected from the annual financial reports of the sampled manufacturing firms. GOP and TQ were used as the dependent variables for the study; CCC, days of account receivable, days of account payables and inventory days were employed as the independent variables; and size, growth, and leverage were considered as the control variables. Descriptive statistics, fixed and random effect, and generalised methods of moments were employed for the data analysis, and the study found that TQ is positively and insignificantly affected by CCC and inventory days. However, TQ is negatively and insignificantly affected by receivable days and payable days. Moreover, CCC and inventory days are significantly and positively related with GOP. The study by Langroudi et al. (2013) combines two dependent variables from both accounting and market-based approach of measuring performance (GOP and TQ) against two dependent variables from market-based measure of performance (SP and TQ) that are used in this study.

Additionally, Ademola and Kemisola (2014) surveyed the effect of WCM on market valuation of listed food and beverage manufacturing companies in Nigeria in 2013. Primary source of data collection was employed in the study; the data was specifically gathered from primary source via a questionnaire administered to the staff of the audit and finance departments of the considered firms. TQ was employed as the dependent variable while debtors’ collection period, ICP, creditors’ payments period, CCC and aggressive investment policy were considered as the independent variables of the study. Pearson product moment correlation and multiple regressions were employed in analysing the data. The result found that WC variables had a positive and significant effect on the value of food and beverages companies in Nigeria. Although, the study by Ademola and Kemisola (2014) is a Nigerian study and considers TQ as well as the independent variables to be used in this study, it uses a primary source of data which is prone to subjectivity against secondary source of data that is considered in this study.

In a similar study, Parvaneh and Chashmi (2014) investigated how financing methods and WCM has an impact on value of firms listed in the Tehran Stock Exchange from 2008 the 2012. The data were specifically gathered from the financial reports of the sampled companies. TQ is considered as the dependent variable; debt financing, issue financing, retained earnings financing, average collection periods, inventory turnover in days and CCC were regarded as the independent variables; and firm size, book value of equity to sales ratio, operating profit to sales and financial leverage were employed as the control variables. A method of regression and descriptive statistics were employed in the data analysis. The study established that all the components of WCM had a significant negative impact on firm value. The study considers TQ and financing methods against SP, TQ and WCM. Moreover, the study is not domiciled in Nigeria.

In another similar study, Arachchi et al. (2017) examined the impact of WCM on the value of firms in Colombo from 2011 and 2015. The data utilised for the study were sourced form secondary means specifically gathered from the published annual reports of the selected samples of 44 companies listed on the Colombo Stock Exchange for the study period. TQ is employed as the dependent variable of the study; CCC, account payables days (APD), inventory days (INVD) and account receivables days (ARD) were considered as the independent variables; and size, leverage, and growth of sales were the control variables. The data obtained were analysed using summary statistics, pooled ordinary least’s square, correlation matrix and fixed effect regression. The study found a statistically negative relationship between CCC, ARD and INVD with TQ, suggesting that managers can increase value for their investors by reducing the number of days of the CCC, ARD and INVD. More so, APD is found to have a statistically positive relationship with TQ. The study by Arachchi et al. (2017) considers one dependent variable against two dependent variables that are used in this study. Moreover, the study is conducted in Colombo and not in Nigeria.

In another similar study, Vijayakumaran (2019) examined the efficiency of WCM on the value of listed Chinese firms from 2004 to 2013. The data was obtained from the Chinese stock market accounting database as well as sino-fin. TQ was employed as the dependent variable while days sales outstanding (DSO), days inventory on-hand (DIO), days payables outstanding (DPO) and net trading cycle (NTC) were considered the independent variables. Furthermore, firm size, leverage, sales growth, liquidity and firm age were considered as the control variables. The study used panel ordinary least square regression, as well as Hausman specification test to decide whether fixed or random effect is more appropriate to the data. The study found that TQ is negatively affected by NTC, DSO, DIO and DPO. The study by Vijayakumaran (2019) is on firm value but considers only TQ as a dependent variable against the two dependent variables (SP and TQ) that are considered in this study. More so, the scope of the study stopped at 2013, while this study extend the scope to 2019 which is additional six years. Moreover, the study was conducted in China not Nigeria and the need to carryout the similar study in Nigeria.

Dayi and Olusoy (2020) investigated the effect of operating ratio on firm the value of European airlines from 2010 to 2016. The data was sourced from the annual report of the top ten sampled European firms. The dependent variable for the study was SP, while assets turnover, account receivable turnover, inventory turnover and financial leverage were used as the independent variables of the study. Descriptive statistics, and fixed and random effect regression were used in analysing the data. The study found that assets turnover and account receivables turnover have a positive effect on SP, while inventory turnover negatively affected SP. The study by Dayi and Olusoy (2020) used one dependent variable against two dependent variables used in this study. Moreover, the study is on the top ten airline industries in Europe while this study is on oil and gas industries in Nigeria, which makes this work distinct.

Khan et al. (2020) assessed the relationship of WC on corporate performance and SP from 2006 to 2011 in Pakistan. The data was sourced from the annual report and accounts of the sampled manufacturing firms and were obtained precisely from their website. Operating profit and average SP were used as the dependent variables of the study while current ratio, inventory turnover in days, average collection period in days, average payment period and CCC were employed as the independent variables of the study. Pearson correlation and univariate regression were used in analysing the data. The study found that average payment period and CCC has a significant negative impact on SP while current ratio, inventory turnover in days and average collection period has a significant positive effect on SP. The study by Khan et al. (2020) used two dependent variables from account and market base approach against the two dependent variables used in this study, both from a market base approach. More so, the study was not carried out in Nigeria and therefore, the need to conduct such study in Nigeria.

Tobias et al. (2020) examined the impact of WCM on firm performance in different phases of a business cycle in Sweden from 2008 to 2018. The study uses financial data on the Swedish market. The dependent variable used were TQ and CCC while debt, firm size and ROA were considered the independent variables. Industry and year were also considered as dummy variables. The data collected was analysed using descriptive statistics and correlation matrix, as well as regression analysis. The study found that CCC is statistically and negatively related with TQ in all the phases of business cycle.

Based on the empirical review, an a priori expectation of the relationship between CCC and value is expected to be negative. However, this does not rule out the fact that such relationship can be positive on value just as other researchers have established (Ademola & Kemisola, 2014; Vural et al. 2012). Furthermore, the research hypotheses that seek to guide the study are as follows:

H₀₁: CCC does not significantly impact the SP of listed oil and gas companies in Nigeria.

H₀₂: CCC does not significantly impact the TQ of listed oil and gas companies in Nigeria.

Theoretical Framework

Different theories have been used by various researchers to underpin studies on this area such as agency theory, trade off theory, stakeholder’s theory and cash conversion theory. However, this study is anchored on agency theory because it is found to be the most relevant theory that underpins the current study. The agency theory was developed by Jensen and Meckling in 1976 as cited in ICAN (2014) who defined agency relationship as a form of contractual arrangement between a company’s owners and its managers, where the owners appoint an agent (the management) to manage the affairs of the company on their behalf. As part of the arrangement, the owners must delegate decision-making to the management. It is the expectation of the owners that the management will perform in the best interest of the owners. Ideally, the contract between the owners and the management should ensure that the managers always act in the best interest of the shareholders. However, it is impossible to arrange such a perfect contract because decisions by the managers (agent) affect their own personal interest as well as, the interest of the owners, where managers give priority to their personal interest over those of the shareholders (ICAN, 2014).

It follows, therefore that the relevancy of the agency theory to WCM can be viewed from the perspectives of the agents who run the daily operations of the firm towards profit generation and, consequently, value creation. This implies that a business entity cannot operate successfully and generate profit on its own without the agents taking critical decisions towards the wealth maximisation objective of the firm. It is, indeed, these short-term decisions that are made in terms of efficiency in managing WC which cover cash, inventory, receivables and payables in consideration for profit maximisation and, consequently, value creation as well as shareholders expectation that the management will take decisions that are in harmony with that of the owners.

Methodology

Research Design and Sampling Technique

The study adopted descriptive, correlational and explanatory research design because the study tries to determine the relationship among WCM variables and value using quantitative data of listed oil and gas companies in Nigeria. A secondary source of data was used which was obtained from annual financial reports of the eight sampled listed oil and gas companies on the Nigerian Stock Exchange (NSE) for the period 2006-2019. The sampled 8 oil and gas companies were taken from the total of 12 listed oil and gas companies within the Nigerian economy as of 31 December 2019. The sample size of this study is drawn from the main and premium board listing classification against the alternative security market (Asem) of the NSE. This technique has been used by Hamid and Ibrahim (2020). The choice of the main and premium board firm classification is because such firms are more developed and bigger than those in the Asem classification, which consists of small- and medium-scale enterprises with high-growth prospects. Moreover, additional criteria are further used in the selection of the sample size for the study to filter some firms in the main and premium board listing classification which includes that the firm should not be listed or delisted during the study period for availability of annual reports and the required data should be available during the study period. Thus, after filtering, the study adopts census sampling technique to study the remaining oil and gas companies because all members of the population become research objects or the working population. This sampling technique has been used by Rabiu (2019).

Table 1.

Variables Definition.

Variable Name	Acronym	Measurement and Source
Share Price	SP	Average quarterly share price (Mohammed, 2017).
Tobin’s Q	TQ	Market capitalisation plus total liabilities divide by Total assets (Arachchi et al., 2017).
Cash Conversion Cycle	CCC	Account receivables collection period plus inventory conversion period minus account payables payment periods (Arachchi et al., 2017).
Leverage	LEV	Total debt divided by total assets (Irom et al., 2018).
Net Profit Margin	NPM	Net profit after tax divided by total revenue (Ibrahim, 2017).
Assets Tangibility	ATAN	Fixed assets divided by total assets (Ibrahim, 2017; Olakunle & Oni, 2014)

Source: Compilation by the authors from various literatures.

Method of Data Analysis

Three techniques were used for analysing the impact of CCC on the value of oil and gas companies listed on the NSE—summary statistics, correlation and generalise least square (GLS) regression. In light of the panel data, three basic regressions were carried out: pooled ordinary least square regression, fixed effect regression and random effect regression. Regression analysis is a statistical technique that seeks to explain relationships among variables. This technique is appropriate especially if the focus of the research work is to seek for relationship between dependent variables and independent variables (Adenugba et al., 2016).

Model Specification

Previous researchers have used two or more models of this nature in carrying out their research on WCM against one model, that is, one dependent variable with two or more independent variables. The two models are adapted based on the empirical literatures (Mohamad & Saad, 2010; Ogundipe, et al., 2012) to include the variables of this study.

Y = β_{0} + β_{1} X_{1} + β_{2} X_{2} + β_{3} X_{3} + β_{n} X_{n} + ϵ

(1)

Where Y = dependent variables, β₀ is a constant, β_1–β_n are the coefficient of the independent variables X₁–X_n and ϵ is the error term. Thus, by substituting both the dependent and the independents variables in Equation (1), the following two models are estimated to examine the hypotheses.

S P_{i t} = β_{0} + β_{1} C C C_{i t} + β_{2} L e v_{i t} + β_{3} A T A N_{i t} + β_{4} N P M_{i t} + + ϵ

(2)

T O_{i t} = β_{0} + β_{1} C C C_{i t} + β_{2} L e v_{i t} + β_{3} A T A N_{i t} + β_{4} N P M_{i t} + + ϵ

(3)

Where:

SP_it = Share price of ith firm at time t.

TQ_it = Tobin’s Q of ith firm at time t.

CCC_it = Cash conversion cycle of ith firm at time t.

LEV_it = Leverage of ith firm at time t.

ATAN_it = Assets tangibility of ith firm at time t.

NPM_it = Net profit margin of ith firm at time t.

i = ith firm.

t = time t.

The Equation (2) examined the relationship that exists between CCC and SP, as well as the control variables, while Equation (3) examined the relationship between CCC and TQ, as well as the control variables. Thus, correlation and GLS regression analysis is used to examine the two models.

Results and Discussion

Summary Statistics

The summary statistics for the variables of the study is presented in Table 2.

Table 2.

Summary Statistics.

Variables	Obs	Mean	Std. Dev.	Min.	Max.
SP	112	77.43	80.92	0.21	303.21
TQ	112	1.63	1.64	0.50	14.42
CCC	112	14.50	52.40	–175.44	190.22
LEV	112	76.26	25.16	0.73	247.85
ATAN	112	30.33	18.75	0.88	84.02
NPM	112	32.09	546.72	–704.24	5,640.10

Source: Extracted by the authors from Stata 14.0 Output

From Table 2, it is obvious that the number of observations under this study is 112 for all the variables. For SP, the minimum and maximum SP is N0.21kobo and N303.21kobo, respectively. The mean SP is N77.43kobo, which implies that the mean average SP of the oil and gas companies in Nigeria is N77.43kobo, while the high standard deviation of N80.92kobo above the mean value indicates that the SP is widely spread from the mean, suggesting that firms in the sample have a large difference in terms of their SPs. Firm value measured using TQ reveals a mean of 1.63 which suggests that, on average, the TQ of oil and gas companies in Nigeria is 163% with a minimum and maximum TQ of 50% and 1,442% respectively. The high standard deviation of 164% for TQ above the mean value of 163% also suggests a wide variation in TQ among the oil and gas companies in Nigeria.

Again, CCC has a mean value of 14.50 days, implying that the average CCC of oil and gas companies in Nigeria is 14.50 days, while the high standard deviation of 52.40 from the mean indicates that the CCC is widely spread among the companies, which implies that the firms in the sample have a large difference in terms of their CCC. The minimum and maximum values for CCC are –175.44 and 190.22 days, respectively, which further suggests that the lowest number of days inventories, receivables and payables can be converted into cash is –175.44 days while the highest number of days of inventories, receivables and payables being converted into cash is 190.22 days.

Leverage has a mean value of 76.26, implying that the average leverage among the firms is 7,626% with a low standard deviation of 2,516% below the mean leverage, suggesting that leverage among the oil and gas companies in Nigeria are not widely spread from the mean value, and thus, suggesting that there is a low variation in terms of leverage among the sampled companies. Moreover, leverage has minimum and maximum values of 0.73 and 247.85, respectively, which further imply that the lowest level of leverage is 73% while the highest level of leverage is 24,785%. Similarly, assets tangibility (ATAN) has a mean value of 30.33—implying that the average ATAN among the sampled companies is 3,033%—as well as a low standard deviation of 1,875% below the mean ATAN, suggesting that there is no wide variation in tangibility of assets among the sampled oil and gas companies from the mean. The minimum and maximum values of ATAN are 0.88 and 84.02, respectively, which suggests that the lowest and highest ATAN of 88% and 8,402% is observed among the companies.

Again, NPM has an average of 32.09, suggesting that the mean net profit margin is 3,209% among the oil and gas companies in Nigeria with a high standard deviation of 54,672% above the mean NPM, suggesting that there is a wide difference in terms of the profitability of the sampled oil and gas companies in Nigeria. The minimum and maximum value of –704.24 and 5,640.10, respectively, is observed, suggesting that the lowest and the highest NPM is –70,424% and 564,010%, respectively, among the sampled oil and gas companies in Nigeria. The minimum value of negative –70,424% is as a result of Japaul Plc performing poorly in the sector in 2018. Moreover, the NPM of 564,010% seems to be alarming and too large for a company to have this ratio of profitability, and this is also a result of Japaul Plc having other income to a tone of N43 billion as result of profit on disposal of property, plant and equipment which outweighs the total revenue of about N1 billion in 2019.

Correlation Matrix

The correlation matrix explains the nature of the relativeness between the dependent variables, independent variables and control variables, as well as the nature of the relationship among the variables themselves. The correlation matrix is presented in Table 3.

Table 3.

Correlation Matrix.

	SP	TQ	CCC	LEV	ATAN	NPM	VIF
SP	1
TQ	0.26	1
CCC	0.05	–0.03	1				1.33
LEV	0.27	–0.32	–0.35	1			1.32
ATAN	0.53	–0.14	0.06	0.33	1		1.18
NPM	0.32	0.07	0.35	–0.16	–0.08	1	1.16

Source: Extracted by authors from Stata 14.0 Output

Table 3 shows the correlation coefficients on the relationship between the dependent variables (SP and TQ) and the independent variable CCC, as well as the control variables (LEV, ATAN and NPM) with each other and among themselves. It further reveals the Variable Inflation Factor (VIF) for the individual explanatory variables. The values of the correlation coefficient range between –1 and 1. The sign of the correlation coefficient indicates the direction of the relationship (positive or negative), with the absolute value of the correlation indicating the strength of the association, the larger values of greater than 0.5 indicating stronger relationships and the lower values of less than 0.5 denoting weaker relationships. It can be observed that the correlation coefficients on the main diagonal are positive with absolute values of 1. This is so because each variable has a perfect positive linear relationship with itself.

From the correlation result presented in Table 3, CCC, LEV, ATAN and NPM are positively correlated with SP with correlation coefficients of 0.05, 0.27, 0.53 and 0.32, respectively. However, ATAN is strongly correlated with SP and the degree of the association is said to be strong because the absolute value of 0.53 is greater than 0.5, while CCC, LEV and NPM are weakly correlated with SP because the absolute values of the correlation coefficient of 0.05, 0.27 and 0.32 are less than 0.5.

It can also be observed from the correlation result presented in Table 3 that CCC, LEV and ATAN are negatively but weakly correlated with TQ with the absolute values of the correlation coefficient of –0.03, –0.32 and –0.14, respectively. The degree of association is said to be weak because the absolute values of the correlation coefficients are less than 0.5. Furthermore, it can be observed that NPM is positively but weakly correlated with TQ with correlation coefficient of 0.35. Similarly, the correlation is said to be weak with TQ because the absolute value of the correlation coefficient is less than 0.5, tending towards zero.

Analysis of Regression Results and Discussions

The panel data for the study is a balanced panel data for 14 years. The overall and combined effect of the explanatory variables on the explained variables of this study as shown in Table 4 provided the evidence that the two models are fit and appropriate. The prob > F of 0.0000 and prob > chi² of 0.0002 for SP and TQ, respectively, is significant at 5%, which provide the evidence that the two models are fit with the variables of the study. Thus, for both models, it is statistically and highly significant that the explanatory variables explained the dependent variables adequately. Furthermore, the coefficient of determination R² of 45% and 20% for SP and TQ, respectively, explains the variations in the explained variables by the explanatory variables. After carrying out the Multicollinearity test, Heteroscedasticity test, Hausman specification test, LM test and F-test, fixed effect and random effect result is the most preferred result for interpretation for SP and TQ, respectively, based on the underlying assumptions of Hausman specification test, LM test and F-test.

Table 4.

Regression Results.

	Share Price Model 1 Fixed Effect (FE)			Tobin’s Q Model 2 Random Effect (RE)
Variables	Coef.	T	P > \|t\|	Coef.	Z	P > \|z\|
CCC	–0.0018	–5.56	0.000	–0.0037	–2.44	0.015
LEV	0.0009	1.30	0.196	–0.0131	–4.17	0.000
ATAN	–0.0010	–0.74	0.461	–0.0039	–0.75	0.456
NPM	0.0002	7.42	0.000	0.00008	0.60	0.548
_cons	0.2950	4.93	0.000	2.1972	7.52	0.000
Number of observations 112 R² Within 0.4523 Between 0.5541 Overall 0.0518 Prob > F 0.0000 Hettest Prob > chi² 0.0000 Hausman test Prob > chi² 0.0041 F-test Prob > F 0.0000				Number of observations 112 R² Within 0.1995 Between 0.0088 Overall 0.1198 Prob > chi² 0.0002 Hettest Prob > chi² 0.0952 Hausman test Prob > chi² 0.9018 LM test Prob > chibar² 0.0000

Source: Author’s extraction from Stata 14.0 output.

CCC and SP of Oil and Gas Companies in Nigeria

It is evidenced from Table 4 that the CCC is found to be negatively and statistically significant at 5% with SP. The result shows a beta coefficient of –0.0018 which suggests that the lower the CCC period, the higher the value of oil and gas companies in Nigeria. The p-value of 0.000 is found to be statistically significant at 5%. The result is in contrast with null hypothesis 1 of the study, which states that CCC does not significantly impact the SP of listed oil and gas companies in Nigeria. This provides the basis for rejecting the first null hypothesis of the study. This negative coefficient suggests that a day decrease in CCC will result in an increase in the value of oil and gas companies in Nigeria. The result is in agreement with the findings of Nzioko (2012) and Khan et al. (2020) who found a negative and significant impact of CCC on SP. This result is in line with the CCC theory which states that the lower the CCC, the higher the value, and the higher the CCC, the lower the value.

CCC and TQ of Oil and Gas Companies in Nigeria

Table 4 shows that CCC is also found to be negatively and statistically significant at 5% with TQ. The result shows a beta coefficient of –0.0037 which suggests that the lower the CCC period, the higher the value of oil and gas companies in Nigeria. The p-value of 0.015 is found to be statistically significant at 5%. The result is also in contrast with null hypothesis 2 of the study, which states that CCC does not significantly impact the TQ of listed oil and gas companies in Nigeria. This provides the basis for rejecting the second null hypothesis of the study. This negative coefficient suggests that a day decrease in CCC will result in an increase in the value of oil and gas companies in Nigeria. The result is in agreement with the findings of Ogundipe et al. (2012), Parvaneh and Chashmi (2014), Arachchi et al. (2017) and Tobias et al. (2020) who found a negative effect of CCC on TQ. This result is in line with the CCC theory which states that the lower the CCC, the higher the value, and the higher the CCC, the lower the value. The result also contrasts the findings of Vural et al. (2012) and Ademola and Kemisola (2014) that establishes a positive and statistically significant impact of CCC on TQ.

Control Variables and Value of Oil and Gas Companies in Nigeria

Table 4 indicates that leverage is found to be positively and statistically insignificant at 5% when measured by SP, implying that leverage does not significantly impact value when measured by SP. However, when leverage is measured by TQ, it was found to be negatively and statistically significant at 5% which suggests that leverage significantly impacts value as measured by TQ. The result indicates a beta coefficient of 0.0009 and –0.0131 for SP and TQ, respectively, with a P > |t| of 0.196 and 0.000 for SP and TQ, respectively. The negative impact suggests that there is poor debt utilisation in this sector, which implies that borrowed funds are not channelled into profitable business ventures. This finding is consistent with the findings of Vural et al. (2012), Langroudi et al. (2013) and Arachchi et al. (2017), which Arachchi et al. (2017) established a negative relationship between leverage and value of firm, and it is inconsistent with the findings of Mohamad and Saad (2010), Ogundipe et al. (2012) and Ibrahim (2017), which found a positive relationship between leverage and value.

Moreover, asset tangibility is found to be negatively and statistically insignificant at 5% with both SP and TQ. The result shows beta coefficients of –0.0010 and –0.0039 with SP and TQ, respectively, as well as p-values of 0.461 and 0.456 for SP and TQ, respectively. The negative impact of ATAN suggests that tangible assets in the oil and gas sector in Nigeria are either idle, underutilised or been used as collateral for securing external financing (loan). The result is consistent with the findings of Nurein and Din (2017) and Musah and Agyemang (2019) which found a negative relationship between ATAN and the value of firms. The result further contrasts the findings of Ibrahim (2017) and Igbinovia and Ogbeide (2019) that established a positive relationship with value.

Finally, NPM in this study is found to be positively and statistically significant with SP at 5% and insignificant with TQ at 5%. The positive effect suggests that the higher the profit, the higher the value of oil and gas companies in Nigeria. The result shows a beta coefficient of 0.0002 and 0.00008 for SP and TQ, respectively, with p-value of 0.000 and 0.548 for SP and TQ, respectively. This finding is consistent with findings of Igbinovia and Ogbeide (2019) and Rabiu (2019) which established a positive relationship between profitability and value. The positive impact is in agreement with the literature expectations and contrary to the finding of Purwohandoko (2017) that found a negative relationship in profitability and value.

Conclusion and Policy Implication

Considering the current study which examines the impact of CCC on value of listed oil and gas companies in Nigeria and having found out that CCC has a significant negative impact on SP and TQ, respectively, it can be concluded that there is an efficient management of the components of CCC (ICP, ARCP and APPP) in the oil and gas sector in Nigeria. Based on the findings of the study, having a lower CCC is important in WCM on value creation since it has a significant impact on SP and TQ, respectively. It is therefore recommended that management should work towards reducing the number of days it takes to convert the CCC components into cash, as this will enhance the value of oil and gas companies in Nigeria.

In addition, it is recommended that the management should treat each component of CCC individually by reducing the ICP and receivables collection period in line with the current economic realities while extending settlement for creditors as long as practicably possible to enhance value since a balancing-off effect exists on the CCC. However, while trying to achieve this, management should beware of too much investment in WC as it would tie up funds in WC which will not yield return. Similarly, too little investment in WC may result in stock out, which would cause customer dissatisfaction due to unsatisfied demand because of little investment in WC. Thus, it is recommended that a balance should be maintained between too much and too little investment in WC.

Footnotes

Declaration of Conflicting Interests

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iDs

Mohammed Ibrahim

Bula Yila Dengel

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