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Statutory Payments and Accruals on Financial Performance of Listed Non-Financial Firms in Nigeria

  • Dauda John Dzarsa
  • Dr. Isaac Lambe
  • Assoc. Prof. Orbunde Bemshima
  • 2033-2062
  • Jul 13, 2024
  • Accounting & Finance

Statutory Payments and Accruals on Financial Performance of Listed Non-Financial Firms in Nigeria

Dauda John Dzarsa, Dr. Isaac Lambe, Assoc. Prof. Orbunde Bemshima

Department of Accounting, Faculty of Administration, Bingham University, Nasarawa State

DOI: https://dx.doi.org/10.47772/IJRISS.2024.806154 

Received: 03 June 2024; Accepted: 12 June 2024; Published: 13 July 2024

ABSTRACT

The unresolved controversy regarding the effect of statutory payments and accruals on financial performance continues to challenge the success of listed non-financial firms in Nigeria. Despite the critical importance of these factors, there exists a lack of clarity and understanding surrounding their impact on organisational outcomes.  Given the foregoing, this study examined the effect of statutory payments and total accruals on financial performance of listed non-financial firms in Nigeria. To achieve these objectives, longitudinal research design was employed and the study used twenty (20) listed non-financial firms that had consistently published their audited annual financial reports from 2008 to 2022, and analyzed the data using panel multiple regression technique with the help of E-view 13 statistical tools. The result of the study revealed that financial statutory payment has a significant effect on financial performance of listed non-financial firms in Nigeria. Conversely, total accrual has insignificant effect on financial performance of listed non-financial firms in Nigeria. Thus, this study concluded that statutory payments serve as a veritable tool or determinant of financial performance. Thus, the study recommended that non-financial firms are to prioritize compliance with financial statutory payments, as demonstrated by the significant influence of such payments on financial performance. This entails adhering to regulatory requirements, tax obligations, and other statutory payments to maintain transparency and credibility with stakeholders.

Keywords: Statutory Payments, Total Accruals, Financial Performance, Return on Assets and Firm Size.

INTRODUCTION

Financial performance evaluation serves as a crucial tool for investors, managers, policymakers, and other stakeholders to gauge the health, sustainability, and growth potential of businesses (Arunkumar, 2015). In today’s globalized economy, characterized by rapid technological advancements, fluctuating market conditions, and evolving regulatory landscapes, the financial performance of companies plays an increasingly pivotal role in shaping economic outcomes. Temile et al., (2021), opined that examining financial performance encompasses a broad spectrum of indicators, including profitability, liquidity, solvency, and efficiency, all of which provide insights into the effectiveness of a company’s operations and management strategies. Moreover, financial performance analysis extends beyond mere numerical figures; it entails understanding the underlying drivers and factors influencing a company’s financial outcomes. These factors can range from internal operational efficiencies to external market dynamics, competitive pressures, regulatory compliance, and macroeconomic trends.

Statutory payments and total accruals influence the financial performance of listed non-financial firms in Nigeria. Statutory payments, encompassing taxes, fees, and regulatory obligations, directly impact a company’s bottom line by reducing its net income (Ohiomegwe, et al., 2022). Compliance with statutory payments is crucial for maintaining good standing with regulatory authorities and avoiding legal repercussions, but the financial burden they impose can detract from profitability (Osiorenoya, 2017). Similarly, total accruals, which represent adjustments made in financial statements to match revenues and expenses with the periods they occur, can affect reported earnings. While accruals are essential for matching revenue and expenses accurately, excessive accruals may signal potential manipulation of financial results or poor management of operational costs. This can erode investors’ confidence and undermine the perceived financial stability of the firm (Uwuigbe, et al., 2019). The interplay between statutory payments and total accruals influences various aspects of financial performance, including profitability, liquidity, and shareholder value. High statutory payments and excessive accruals can reduce profitability margins, strain liquidity by tying up cash flows, and ultimately dampen investors returns.

In essence, the nexus between financial statutory payments and total accruals on the financial performance of listed non-financial firms in Nigeria remains largely unexplored in existing literature. Despite the extensive research on financial performance and related factors, no study has specifically examined the combined impact of financial statutory payments and total accruals on the financial performance of listed non-financial firms in the Nigerian context.

While Bababo and Christopher (2023) investigated the relationship between accruals management and the financial performance of listed manufacturing companies in Nigeria separately, the integration of these factors into a comprehensive analysis is lacking.

Against this backdrop, conducting a study on the financial performance of listed non-financial firms in Nigeria becomes imperative, aiming to uncover trends, challenges, and opportunities within the non-financial sectors of the Nigerian economy. Such research contributes to the body of knowledge in finance, economics, and business management, informing policy formulation, investment strategies, and academic discourse in Nigeria and beyond.

The basic hypothesis underlying this study are stated thus;

Ho1: Financial statutory payments have no significant effect on return on assets of non-financial firms in Nigeria

Ho2: Total accruals have no significant effect on return on assets of non-financial firms in Nigeria.

LITERATURE REVIEW

2.1 Conceptual Framework

2.1.1    Statutory Payment

Statutory payments refer to mandatory financial obligations imposed by law upon employers or individuals in compliance with specific regulations or statutes. These payments are legally required and typically involve contributions to social security, healthcare, or pension schemes to ensure the welfare and financial security of employees or citizens (Faboyede et al., 2021). According to Ahmadu (2016), statutory payments serve as a mechanism for governments to address societal needs and promote social welfare by mandating contributions towards essential services or benefits.

In the context of employment, statutory payments may include contributions towards unemployment insurance, workers’ compensation, or statutory sick pay. These payments are designed to safeguard employees against unforeseen circumstances such as illness, injury, or unemployment, providing financial support and security during times of need. Norziaton and Arfa (2022) opined that, statutory payments may encompass taxes levied on individuals or businesses to fund public services and infrastructure, such as income tax or value-added tax (VAT). The financial implications of statutory payments involve the costs incurred by employers or individuals to fulfill their legal obligations as mandated by government regulations or statutes. These payments represent direct expenses that must be budgeted for and managed within organisational or personal financial frameworks.

Financial statutory payments (FSP) refer to obligatory payments that firms are required to make to the government or regulatory authorities as mandated by law. These payments encompass a variety of financial obligations, including taxes, social security contributions, regulatory fees, and other statutory levies. In the context of corporate financial management, FSPs are critical because they directly impact a company’s financial performance, cash flow management, and overall financial health (Ajiboye & Ibrahim, 2022).

The importance of FSPs lies in their regulatory and legal nature, ensuring that firms comply with national financial regulations and contribute to the public treasury. These payments are not discretionary and must be fulfilled within stipulated timelines to avoid penalties, legal consequences, and reputational damage. For instance, in Nigeria, statutory payments include corporate income taxes, value-added taxes (VAT), pension contributions, and industry-specific levies imposed by regulatory bodies such as the Securities and Exchange Commission (SEC) and the Federal Inland Revenue Service (FIRS).

According to Omoolorun and Abilogun (2017) financial statutory regulations are designed to protect the rights of consumers and ensure that financial institutions and service providers adhere to high ethical standards. These regulations establish a system of accountability and oversight and provide a framework of rules and regulations that must be followed by financial institutions and service providers. Financial statutory regulations also help to protect the integrity of financial markets and promote economic stability. For example, the SEC requires publicly traded companies to make timely and accurate financial disclosures and to adhere to specific reporting requirements. This helps to ensure that investors are able to make informed decisions when investing in securities. Financial statutory regulations are also important for promoting consumer protection. For example, the Consumer Financial Protection Bureau (CFPB) is an independent government agency that has the authority to enforce consumer protection laws. The CFPB enforces laws that protect consumers from unfair, deceptive, and abusive practices in the financial services industry. Additionally, the CFPB works to ensure fair access to credit for consumers and to promote financial education. Financial statutory regulations help to ensure that the financial services industry operates in an ethical, transparent, and fair manner. They also help to protect consumers from fraud and abuse and promote financial stability. Financial statutory regulations are essential to the functioning of the financial sector and the protection of consumers. In Nigeria, the concept of financial statutory is based on the various Acts of Parliament that have been put in place to regulate the financial sector. These Acts are designed to ensure the efficient functioning of the Nigerian financial markets and to protect both investors and financial institutions from fraudulent and other illicit activities (Imagbe et al., 2019). For the purpose of these study financial statutory will be measure this study, financial statutory payment will be measured as the natural log of legal fees.

2.1.1 Total Accrual

Total accrual is an accounting technique used to measure and record the total amount of financial resources used for a period of time Zehri and Chouaibi (2013). Total accrual accounting is also referred to as accrual basis accounting and is a key accounting concept used in financial statement preparation. It is often used in tandem with cash-basis accounting, which records only cash transactions. Dzomira, (2014) opined that total accrual accounting is based on the concept of matching revenue and expenses to the period in which they are incurred. This means that revenue and expenses are recorded when they are generated, not when the cash is received or paid out. This approach is in contrast to the cash-basis accounting method, which records transactions only when cash is received or paid out. According to Yulistyawti et al. (2019), total accrual accounting is used to record all events that affect the financial position of a company, regardless of whether the events involve cash transactions or not. This includes transactions such as accounts receivable, accounts payable, inventory, depreciation, amortization, and other accruals. It is important to recognize the difference between accrual and cash-basis accounting because both types of accounting have different effects on a company’s financial statements.

One of the benefits of total accrual accounting is that it provides a more accurate picture of a company’s financial position (Imagbe et al., 2019). This is because it includes all financial transactions, regardless of whether they involve cash or not. This helps to create a comprehensive view of the company’s financial position and allows for better decision-making. Total accrual accounting is also beneficial because it helps to provide a better understanding of the company’s finacial health. This is because it records all expenses and revenues, even those that may be deferred or capitalized. By recording all of these expenses and revenues, it is easier to identify areas where the company is making a profit or loss. Total accrual accounting is also beneficial because it is more consistent with Generally Accepted Accounting Principles (GAAP). All financial statements prepared using total accrual accounting must be prepared in accordance with GAAP. This helps to ensure that all financial statements are consistent and accurate. Furthermore, total accrual accounting is beneficial because it helps to provide stability to a company’s financial statements. By recording all of the company’s financial transactions, it helps to smooth out the effects of fluctuations in the market. This helps to ensure that the company’s financial statements remain consistent and up-to-date.

Abiloro and Olorunfemi (2021) asserted that total accrual can be used to assess performance by comparing the total income and expenses incurred over a period of time. This comparison allows the organisation to identify trends and patterns in the data which can be used to determine areas of success and failure. For instance, if total income is increasing but total expenses are staying the same, then this could indicate that the organisation is becoming more efficient and/or that the sales team is doing a better job of selling products or services. On the other hand, if total expenses are increasing while total income remains the same, this could indicate that the organisation is not managing its finances effectively. By using total accrual to assess performance, the organisation can develop plans to improve its financial health.

Accruals in accounting pertain to the acknowledgment of revenues and expenses in financial statements at the time they are incurred, irrespective of when the corresponding cash flows take place (Oh & Penman, 2024). Under this accounting principle, revenues are recognised upon being earned, and expenses are recorded upon being incurred, irrespective of the timing of cash inflows or outflows. Accrual accounting differs from cash accounting in that it records transactions when they occur, regardless of when cash is received or paid.

Accruals are essential for ensuring a more precise depiction of a company’s financial performance and condition. Accrual accounting ensures that revenues and expenses are recorded in the period they are incurred, resulting in a comprehensive and up-to-date representation of a company’s profitability and financial well-being (Goel, 2016). This empowers investors, creditors, and other stakeholders to make well-informed judgements regarding the company’s operations and future prospects. Accruals have many ramifications from a financial standpoint. Accrual accounting facilitates the alignment of revenues and expenses, leading to more seamless and consistent financial reporting in the long run. Implementing this strategy can mitigate fluctuations in reported earnings and offer a more accurate representation of the underlying business performance. Flynn et al. (2016) affirmed that accruals allow organisation to comply with accounting standards including the matching principle and revenue recognition principle, which are crucial for maintaining the dependability and comparability of financial statements.

Ohiomegwe et al., (2022) stated that, accruals might have ramifications for the management of cash flow and the planning of finances. Although accruals accurately represent economic activity in real-time, they may not always correspond directly to actual cash inflows and outflows. Consequently, organisation must diligently oversee their cash flow to guarantee they possess adequate liquidity to fulfil their responsibilities, even if income and expenses are acknowledged at varying intervals. Accruals are a crucial component of accounting that improves the precision, dependability, and significance of financial reporting. They facilitate organisation in presenting a thorough and timely representation of their financial performance and position, which is crucial for stakeholders to make well-informed decisions regarding the company’s operations and future prospects. For this study, total accruals was computed as net profit minus net cash from operating activities.

2.1.3 Financial Performance

Financial performance refers to the efficiency with which a company utilizes its assets to generate revenue within a specified period, serving as a metric for assessing its overall financial health and growth trajectory (Aniefor & Onatuyeh, 2020). Stakeholders, including trade creditors, bondholders, shareholders, and management, each have distinct interests in monitoring a firm’s financial performance (Ahmadu, 2016). While trade creditors prioritize liquidity, bondholders focus on solvency, shareholders seek returns on investment, and management evaluates market performance. Financial statements, comprising income statements, cash flow statements, and notes to accounts, serve as a primary source of information on a company’s financial performance.

Various factors influence a firm’s financial performance, including liquidity, leverage, firm size, and managerial competence (Adeniyi & Aderobaki, 2021). Profitability and liquidity metrics are particularly crucial for stakeholders in assessing a firm’s past performance and current standing (Ogoun & Owata, 2019). Capital structure is intricately linked with financial performance, with measures such as return on investment (ROI), earning per share (EPS), return on assets (ROA), and return on equity (ROE) providing insights into a company’s financial strength and market position (Zeitun & Tian, 2007). Equity holders and debt holders, as key stakeholders, are keenly interested in a company’s financial performance due to the implications for their investments (Adenugba et al., 2016). Equity holders bear higher risk but stand to benefit from share value appreciation and dividends, while debt holders prioritize timely repayment with interest, leveraging the security of company assets. Financial performance evaluation thus serves as a critical tool for stakeholders in assessing the viability and sustainability of their investments in the company.

2.1.4 Return on Assets

Returns on assets (ROA) stands as a pivotal financial measure utilized for assessing a company’s profitability, quantifying the ratio of net income to total assets (Imagbe et al., 2019). This metric signifies how effective a company leverages its assets to generate profits and is typically presented as a percentage derived by dividing net income by total assets. A higher ROA implies superior profitability, indicating that the company is generating more earnings from its asset base (Tukur & Abubakar, 2014). Investors often consider ROA crucial in evaluating a company’s performance and growth prospects.

In management literature focusing on accounting-based metrics, return on assets serves as a frequently employed performance indicator (Weir & Laing, 2001). It assesses the efficiency of asset utilization and communicates to investors the earnings derived by the company from its capital investments (Bonn et al., 2004). The effective deployment of corporate funds is reflected in the return rate on assets. Since managers bear responsibility for business operations and asset utilization, ROA serves as a metric aiding stakeholders in assessing the efficacy of a company’s corporate governance structure in fostering and safeguarding managerial performance (Epps & Cereola, 2008). Studies conducted by Tukur and Abubakar (2014) and Arumona et al. (2017) have effectively employed asset returns in their analyses.

2.1.5    Firm Size

Firm size denotes the magnitude of a business entity or the extent of its operations (Falope & Ajirole, 2019). It is often associated with economies of scale, where larger firms can achieve competitive advantages by reducing production costs and expanding market share. The ability of larger firms to produce goods at lower costs compared to smaller counterparts is a manifestation of economies of scale. Additionally, firm size encompasses the breadth and depth of a company’s capabilities, including its capacity to deliver value to customers. Akinyomi and Olagunju (2013) define firm size as the scale of operations and activities within a commercial organisation. They highlight the significance of size in competitive strategy, emphasizing the role of economies of scale in cost reduction and market expansion. Numerous studies have established a positive relationship between firm size and profitability, underscoring the importance of size in determining business success.

Researchers such as Jasch (2013) support this view by pointing out that larger enterprises, due to their larger market share, often enjoy higher profitability. The competitive environment in which larger firms operate contributes to their profitability. Empirical research in corporate finance underscores the significance of firm size as a fundamental characteristic, with studies revealing the impact of size on various dependent variables across different contexts.

2.2       Empirical Review

Bababo and Christopher (2023) investigated the relationship between accruals management and the financial performance of listed manufacturing companies in Nigeria. In order to achieve the aim of the research, six specific objectives and hypothesis were proposed. Accruals management was measured by discretionary accruals (DACC) and non-discretionary accruals (NDAC) while financial performance was measured by return on assets (ROA); net profit margin (NPM); and return on capital employed (ROCE). Data for the research covered a period of 11 years from 2010 to 2020 for 20 listed manufacturing companies – thus comprising 220) firm years. Analytical employed for the purpose of the research included descriptive statistics, Pearson correlation and panel least square (PLS) regression method as well as ADF unit root test. Findings of the research revealed that there is a negative non-significant relationship between discretionary accruals and return on assets; net profit margin; and return on capital employed. And non-significant negative relationship between non-discretionary accruals and return on assets; net profit margin; and return on capital employed. From the findings, it was concluded that both discretionary and non-discretionary accruals do not make meaningful contribution to financial performance. It is further concluded that the ability to control/manage the flow of accruals is very important to corporate organisation. The researcher thus recommended that manufacturing companies take more interest in controlling/managing their accruals. This is because accrual in its uncontrolled/unmanaged form (non-discretionary accruals) is clearly shown to be harmful to the organisation. Bababo and Christopher (2023) fail to provide substantial evidence linking accruals management to financial performance, rendering their conclusions about the harmful effects of non-discretionary accruals on listed manufacturing companies in Nigeria unconvincing.

Omah (2022) evaluated the impact of financial statement fraud and financial performance of selected food and beverage companies in Nigeria. The proxies for the independent variable (Financial statement fraud) were improper expense recognition, and incorrect asset valuation. The proxy used for the dependent variable (financial performance) was return on assets (ROA). The specific objectives were to ascertain the effect of improper expense recognition on return on assets (ROA) and also to ascertain the effect of incorrect asset valuation on return on assets (ROA). Descriptive research design was adopted for the study while secondary data were collected from the financial reports of the selected firms and website of security and exchange commission. The analysis of covariance (ANCOVA) was used, and STATA II econometric method was used in the analysis of the data. Altman model and operating expenses ratio was adopted in the analysis of the financial reports to create a dummy variable for the selected firms from 2004-2018 and validation of the parameters were ascertained using various statistical techniques such as t-test, co-efficient of determination (R2), F-statistics and Wald £hi-square. The findings revealed that there is a significant relationship between financial statement fraud and financial performance of selected food and beverage companies in Nigeria. The study recommended that professional accounting bodies and appropriate regulators should ensure proper financial statement fraud management system within the organisation as internal control. Omah (2022) provides a superficial analysis of the impact of financial statement fraud on the financial performance of selected food and beverage companies in Nigeria, relying excessively on statistical techniques without adequately addressing the underlying mechanisms or offering practical insights for mitigating fraud in financial reporting.

Norziaton and Arfa (2022) assessed financial statement fraud and firms profitability: Evidence from Malaysian public listed companies. The main objective of this study is to investigate the primary factors that influence the public listed companies in Malaysia to be involved in financial statement fraud. The sample used in this study comprised 40 financial statement fraud companies matched with another 40 non-financial statement fraud companies listed in Bursa Malaysia from 2003 to 2020. This study used the fraud triangle theory to form the research framework and develop the research hypotheses. Four hypotheses based on the elements of fraud, which are the financial target, external pressure, earnings management, and related-party transaction, have been developed and tested. Pool regression analysis was conducted to examine the relationship between the elements of fraud and financial statement fraud. The results indicated that there is a significant relationship between financial targets, earnings management, related-party transaction and financial statement fraud, thus the hypotheses are accepted. However, there is an insignificant relationship between external pressure and financial statement fraud, hence the hypothesis was rejected. Interestingly, it was found that the fraud companies had poorer earnings quality one year before they committed the financial statement fraud. Overall, this study would assist the auditors as it identifies early warning signals or red flags. The study recommended that information obtained from this study could be used by Bursa Malaysia to develop strong regulations and encourage Malaysian public listed companies to enhance anti-fraud policies. Norziaton and Arfa (2022) fail to account for potential biases in their sample selection and the limitations of using pooled regression analysis, which undermines the reliability of their findings

Yene-Chimy and Forzeh (2021) studied the relationship between accrual accounting practice and financial performance in local governments in Cameroon. Using accountability and efficiency as performance proxies, the study employs a mix of qualitative and quantitative research approaches with a triangulated method of data collection. In a population of 374 councils, 50 councils were sampled and clustered according to their statutory council categories. Both descriptive and inferential statistics were used with a multiple regression analysis on panel data to test the relationship between accrual accounting practice and financial performance within council categories. The findings revealed that about 77% of councils (predominantly rural councils) practicing mild accrual accounting scored a higher financial performance. Meanwhile, 23% of councils (mostly city councils) practicing a moderate and sometimes strong accrual accounting had a lower financial performance. The pooled regression analysis showed a 13 % insignificant but positive correlation between financial performance and accrual accounting practice. These findings where then backed up with inferences drawn from interviews, discussions as well as content study of accounting books. It revealed that councils have the latitude to navigate from mild, moderate, and strong accrual accounting practice as the need arose. Accrual accounting practice should be sequenced in ways that councils can focus on mild accrual accounting practice, and it should move to a higher level of moderate and full accrual accounting only when they can afford the expertise and infrastructural resources needed to yield a significant performance result. The use of data from Cameroon limits the generalizability of Yene-Chimy and Forzeh’s (2021) findings to other contexts, such as Nigeria, where the institutional and economic environments may differ significantly. Additionally, the reliance on pooled regression analysis without adequately addressing potential panel data issues, such as autocorrelation and heteroskedasticity, raises concerns about the robustness of their results.

Olatunji and Juwon (2020) investigated relationship between accrual-based earnings, real-based earnings management and firm’s value of listed manufacturing companies in Nigeria. The study adopted descriptive, panel least square regression technique such as pooled, fixed and random effect with various diagnostic evaluation techniques. The result revealed that accrual-based earnings management measured by abnormal discretionary accrual earnings (ADA) was positively related with the firm’s value captured by the return on equity (ROE) of the quoted manufacturing companies and increased it to the turn of 38.31 per cent. On the other hand, the real-based earnings management measured by abnormal cash flow operation activities (ACF) was discovered to be negatively related with the firm’s value captured by return on equity and thus reduced it by 12.25 per cent. The result of the individually selected quoted manufacturing companies showed that accrual-based earnings management captured by abnormal discretionary accrual earnings (ADA) and real-based earnings management influence the return on equity (ROE) a measured of firm value respectively. While, on the other hand, accrual-based earnings management captured by abnormal discretionary accrual earnings (ADA) and real-based earnings management reduced the return on equity (ROE) a measured of firm value in Nigeria. Hence, this study concluded that the practice of earnings management constructively benefits the manipulator of accounts. It can be emphasized that ease in detecting accrual earnings management can make investors to decide whether a company is worthy of their investment. The study recommended that companies should be encouraged or mandated to improve their financial disclosure practices. Olatunji and Juwon (2020) present contradictory findings and fail to provide clear, actionable insights due to methodological weaknesses and inconsistent interpretations of the impact of earnings management on firm value.

Isa and Awalludin (2020) investigated detection of fraudulent financial reporting using ratio analysis. The main objective of this analysis was to examine the uses of financial ratios as a tool for detecting fraud in financial reporting. This study examines the annual reports of companies that have been reprimanded by the Securities Commission from 2000 to 2009 for submitting false or misleading information. Ratio-analysis was performed to see if fraudulent financial reporting were predictable or not. The ratios of leverage, profitability, efficiency, and liquidity with have been tested. This study uses trend analysis to figure out changes of more than 10% which may indicate the possibility of financial mismanagement as a change in the ratio of more than 10% annually can be seen as a sign of financial mismanagement. In conclusion, the findings show that signs of fraudulent financial reporting can be detected much earlier. The study recommended that fraudulent financial reporting may be detected even at a much earlier stage if a thorough investigation has been carried out into the submission of each financial statement-related report. Isa and Awalludin (2020) rely on a simplistic and potentially misleading threshold of a 10% change in financial ratios to detect fraudulent financial reporting, which undermines the study’s credibility and practical applicability.

Uwuigbe et al. (2019) looked into the association which exists amid financial statement fraud and governance among business organisations in Nigeria. A population of 122 non-financial companies registered on Nigeria stock exchange was limited to 20 firms employing the rule of thumb based on stratified and simple random technique for a period of 2012-2016. The method of data analysis is panel regression. The dependent variable, fraud in the financial statement was measured using the Beneish M-score model while the independent variable was measured using audit committee independence, board structure. Findings show that an insignificant association exist amid audit committee independence, the composition of the board and financial statement fraud. The research recommended regarding the reduction of the occurrence of financial statement fraud, less emphasis should be placed on audit committee independence, board composition and independent non-executive directors’ effectiveness. The study employed business organisations in Nigeria and was only limited to twenty listed firms. Meanwhile, this present study will consider using seventy-one listed non-financial firms and for the period of fifteen years.

Hussaini et al. (2018) examined the influence of auditor brand name proxied by the Big4 auditors on financial reporting fraud represented by discretionary accruals (DA). The study employ 88 listed companies in Nigeria through 440 firm-year observations for the period of five years from 2012 to 2016. The data for the study are extracted from the annual reports of the listed companies and Thompson Reuters DataStream. The study adopt accruals model to proxy for financial reporting fraud and multiple regression was used to estimate the model of the study. After controlling for monitoring and firm-specific attributes, the study found that non-Big4 auditors are more likely to detect financial fraud as they might have more excellent knowledge of local markets and better relations with their clients. Consistent with the resource dependence theory, the study found that a high proportion of financial experts on the board reduces the extent of financial reporting fraud, thus leading to better financial reporting quality. The study informs regulators and policymakers on the importance of auditor brand name in curtailing financial reporting fraud in the listed companies of Nigeria. The study recommended that high proportion of financial experts is imperative for enriching board monitoring since it leads to better financial reporting quality. Hussaini et al. (2018) present a questionable conclusion that non-Big4 auditors are more effective at detecting financial fraud without adequately addressing potential biases and limitations associated with local market knowledge and auditor-client relationships, which weakens the overall validity of their findings.

2.3 Theoretical Framework

2.3.1 Institutional Theory

Developed by Meyer and Rowan in 1977, institutional theory posits that organisations are influenced by institutional pressures and norms, shaping their behavior, practices, and structures (Meyer & Rowan, 1977). These pressures emanate from external entities such as regulatory bodies, professional associations, and societal expectations, driving organisations to conform to established institutionalized practices to gain legitimacy and survival. In the context of financial statutory payments, total accruals, and financial performance of listed non-financial firms in Nigeria, institutional theory offers valuable insights into how external forces shape corporate behavior and decision-making. Nigerian firms operate within a regulatory environment characterized by legal requirements, taxation policies, and corporate governance standards set by regulatory bodies like the Securities and Exchange Commission (SEC) and the Financial Reporting Council of Nigeria (FRCN). Compliance with these institutional norms regarding financial reporting practices and statutory payments is crucial for maintaining legitimacy and avoiding penalties or reputational damage.

Critics of institutional theory argue that it tends to oversimplify organisational behavior by neglecting internal dynamics and agency-driven actions (DiMaggio & Powell, 1983). Additionally, some scholars suggest that institutional pressures may lead to isomorphism, where organisations conform to institutionalized practices without considering their effectiveness or appropriateness for achieving organisational goals (Meyer & Rowan, 1977).

2.3.2 Legal Compliance and Reputation Theory

Reputation theory was significantly developed by Charles Fombrun in the 1990s, particularly with his seminal work “Reputation: Realizing Value from the Corporate Image” published in 1996. Fombrun emphasized the strategic importance of managing corporate reputation as an intangible asset that can enhance a firm’s financial performance. Legal compliance, as a concept, has long been embedded in regulatory and business practice discourse, but its integration with reputation theory has gained prominence as firms increasingly recognize the intertwined nature of legal adherence and corporate image.

In the context of listed non-financial firms in Nigeria, the application of legal compliance and reputation theory is highly pertinent. Nigerian firms operate in a regulatory environment characterized by stringent statutory payment requirements, such as taxes and other governmental levies. Compliance with these statutory obligations is not only a legal necessity but also a crucial component in shaping the firm’s reputation among stakeholders, including investors, customers, and regulatory bodies. Firms that adhere to these requirements and foster a positive reputation are likely to enjoy enhanced investor confidence, customer loyalty, and operational stability, all of which contribute to better financial performance.

One of the primary strengths of legal compliance and reputation theory is its holistic approach. It recognizes that financial performance is not solely a product of internal management efficiencies but is also significantly influenced by external perceptions and regulatory adherence. This theory underscores the importance of a firm’s external environment and its interactions with regulatory bodies and the public. However, a notable weakness of the theory is its potential oversimplification of the relationship between compliance, reputation, and financial performance. While compliance and a good reputation generally contribute to positive financial outcomes, this relationship can be influenced by various other factors such as market conditions, competitive dynamics, and macroeconomic variables. Moreover, the theory might not adequately account for the costs associated with maintaining compliance and a positive reputation, which can be substantial and may impact short-term financial performance. Critics argue that legal compliance and reputation theory may place excessive emphasis on external perceptions and regulatory adherence, potentially at the expense of innovation and risk-taking. They suggest that a strict focus on compliance and reputation management can lead firms to adopt overly conservative strategies, stifling creativity and limiting growth opportunities. Additionally, the relationship between reputation and financial performance is sometimes seen as correlational rather than causal, with critics highlighting that firms with strong financial performance might have more resources to invest in compliance and reputation management, rather than these factors directly leading to financial success.

Despite its criticisms, legal compliance and reputation theory serves as a robust underpinning framework for examining the financial performance of listed non-financial firms in Nigeria. The theory’s emphasis on the interplay between regulatory adherence and reputation aligns well with the operational realities faced by these firms. In a regulatory environment where non-compliance can lead to severe penalties and reputational damage can result in significant financial losses, understanding and applying this theory can provide valuable insights into strategic management practices that enhance financial outcomes. Moreover, it offers a comprehensive perspective that integrates legal, social, and economic dimensions, making it a versatile tool for analyzing the multifaceted factors influencing financial performance.

METHODOLOGY

A correlational panel research design was employed in this study to gather information about the pre-existing nature of the phenomenon under study and to provide the necessary support to provide and describe the nature of the relationships between variables of the study. The total population for this study consists of all the one hundred and six (106) non-financial companies (firms) listed in the Nigerian Exchange Group as at 31st December, 2022. In order to arrive at the sample size, the purposeful sampling technique were employed. The criterion used is that; a firm must be listed before the year 2008, remain in operation during the period of the study (2008 to 2022) and selections were also made on the basis of the non-financial firms found in the Nigeria Exchange Group stratification of the listed companies.

This is to reduce any problem associated with validity and reliability. A total of twenty (20) non-financial firms was selected for sample selection. The study covers a period of 15 years ranging from 2008-2022. Secondary data was collected for the dependent and independent variables were analyzed using descriptive statistics, correlation analysis, panel regression and post regression diagnostic test on variables using statistical package E-view version 13. The model employed by Olatunji and Juwon (2020) was modified and adapted for the study, as indicated below.

Adapted Model

ROE = β0+ β1ADA + β2ACF + є —————————————- (i)

Modified Model

ROA =α0+ β1FSP + β2TAC + β3FSZ + є —————————————- (ii)

Where;

ROA = Returns on Assets

FSP = Financial Statutory Payments

TAC= Total Accrual

FSZ = Firm Size

α0 = Constant or intercept

β1- β 3 = Regression coefficients.

ε = Stochastic error term.

Apriori Expectation

Variable A Prior Expectation Explanation
Financial Statutory Payments Positive Compliance with legal and regulatory obligations is expected to enhance financial performance (measured by ROA).
Total Accrual Mixed Previous studies show conflicting findings: potential negative effects due to earnings management and positive effects due to operating efficiency.
Firm Size Positive Larger firms are anticipated to have better financial performance due to economies of scale, greater market share, and better resource accessibility.

Source: Research’s Compilation

This table summarizes the anticipated relationships between each variable and Return on Assets (ROA) based on existing literature and theoretical expectations.

Table 1: Definition of Variables

Variable Type Measurement Source
Return on Assets (ROA) Dependent Measured by dividing profit after tax over total assets. Ogoun & Owota (2019)
Financial Statutory Payments (FSP) Independent Computed as the natural log of legal fees Okoye & Gbegi (2013)
Total Accruals (TAC) Independent Total Accrual = Net Profit – Net Cash from operating activities Suhaily & Oloruntoba (2018)
Firm size (FSZ) Control Measure as natural log of total Asset Omollo, et al., (2018)

Source: Researcher Computation (2024)

RESULTS AND DISCUSSION

Data Presentation

This section established results of regression analysis on the effect of financial statutory payment and total accruals of listed non-financial firms in Nigeria, using the panel regression analysis technique.

Descriptive Statistics

The study’s data are described using the mean, standard deviation, variance, maximums, minimums, skewness, and kurtosis. Table 4.1 presents the descriptive statistics for the variables of the study below.

Table 2: Descriptive Statistics

  ROA FSP TAC FSZ
 Mean  0.201483  5.867059  17390695  7.152297
 Median  0.200700  5.866180  1940324.  7.048500
 Maximum  0.987700  7.858850  5.180808  9.578000
 Minimum -3.913000  3.684127 -5240846.  4.027000
 Std. Dev.  0.405063  0.735515  59748904  0.965036
 Skewness -3.604995 -0.011361  6.315900 -0.069816
 Kurtosis  37.66239  3.173607  46.63529  2.998907
 Jarque-Bera  15668.31  0.381921  25709.03  0.243727
 Probability  0.000000  0.826165  0.000000  0.885269
 Sum  60.44477  1754.251  5.206509  2145.689
 Sum Sq. Dev.  49.05876  161.2126  1.067888  278.4573
 Observations  300  300  300  300

Source: E-views 13 Output (2024)

Table 2 presents a summary of the descriptive statistics for the variables incorporated in the model. The table indicates that the average return on assets (ROA) is 0.201483, with a standard deviation of 0.405063. The minimum observed ROA value is -3.913000, while the maximum value is 0.987700. The relatively narrow range between the minimum and maximum values suggests a stable performance, as supported by the standard deviation indicating that the data are closely distributed around the mean value.

Financial statutory payment is another attribute metric, as shown in table 2 above, with a mean value of 5.86, a standard deviation of 0.73, and a minimum and maximum value of 3.68 and 7.85, respectively. The standard deviation is statistically different from the mean and the range between the minimum and maximum values is limited, the financial statutory payment appears to have decrease marginally throughout the research period. The data also shows that for the time period, the total accrual (TAC) was 17390695, with a standard deviation of 59748904 and lowest and highest values of -5248904 and 5.180808, respectively. This suggests that the total accrual grew dramatically over the research period. Moreover, the average level of firm size is 7.152297, with a standard deviation of 0.965036. The minimum and maximum values for firm size are 4.027000 and 9.578000, respectively.

The analysis was also fortified by the value of the skewness and kurtosis of all the variables involved in the model. All the distributions are both negatively and positivity skewed. Variables with value of kurtosis less than three are called platykurtic (fat or short-tailed) only firm size qualified for this during the study period. On the other hand, variables whose kurtosis value is greater than three are called leptokurtic (slim or long tailed) and all the variables qualified for this during the study period except for firm size. Jarque-Bera test shows that the residuals are not normally distributed as indicated by the probability values less than 5% in the case of ROA and TAC, while in the case of FSP and FZS the residuals are normally distributed. In summary, the descriptive statistics revealed that ROA and TAC data sets are not normally distributed. This is so because the probability values of the variables are less than 5%.

Correlation Analysis

Table 3 below shows the results of the association between the independent and dependent variables of listed non-financial firms in Nigeria. It contains the Pearson pairwise correlation coefficients of the variables under study. The correlation matrix is presented in Table 4.2 below.

An acceptable correlation is typically considered significant if the absolute value of the correlation coefficient is at least 0.3, indicating a moderate relationship, while a high correlation would generally be above 0.7.

Table 3: Correlation Matrix

Correlation
Probability ROA FSP TAC FSZ
ROA 1.000000
—–
FSP -0.142497 1.000000
0.0138 —–
TAC -0.071271 0.503746 1.000000
0.2199 0.0000 —–
FSZ 0.045087 -0.134835 0.007504 1.000000
0.4381 0.0199 0.8974 —–

Source: E-views 13 Output (2024)

The correlation results presented in Table 3 illustrate the relationship between the dependent variable, Return on Assets (ROA), and the independent variables: Financial Statutory Payments (FSP), Total Accrual (TAC), and Firm Size (FSZ). Financial Statutory Payments exhibit a negative and weak correlation (-0.142497) with ROA, indicating that higher levels of statutory payments are associated with lower returns on assets. Similarly, Total Accrual demonstrates a negative and weak correlation (-0.071271) with ROA, suggesting that greater accruals are associated with lower returns on assets, albeit to a lesser extent. In contrast, Firm Size displays a positive and weak correlation (0.045087) with ROA, indicating that larger firms tend to have slightly higher returns on assets. Overall, these correlation results suggest that financial statutory payments and total accruals may exert negative influences on ROA, while firm size may have a modest positive impact, although the relationships are relatively weak in magnitude. Moreover, the analysis reveals that the associations between and within the variables under study are weak, indicating the absence of significant multicollinearity.

Multicollinearity Test (VIF)

To ensure the robustness of the measurements, multicollinearity tests were conducted using the Variance Inflation Factor (VIF) as the evaluation criterion. Multicollinearity arises when one or more independent variables exert a significant influence on others, violating the assumptions of the linear regression model and potentially compromising the validity of the analysis outcomes. Conducting multicollinearity tests is essential to determine if there is a strong inter-correlation among independent variables that could lead to erroneous results.

Table 4: Multicollinearity Test (VIF)

Coefficient Uncentered Centered
Variable Variance VIF VIF
C  158.23367  9.02627  NA
FSP  591.53893  7.95491  1.926372
TAC 637.94632  9.64993  1.987645
FSZ  88.974334  9.39419  1.988245

Source: E-View 13 Output (2024)

*Decision rule: uncentered VIF less than 10 indicates the absence of multi-collinearity, while VIF intermediate over 10 is a sign of multi-collinearity. As noted above, the law of multicollinearity test rule uses a variance inflation factor that VIF centered below indicates a lack of multi-collinearity, while VIF intermediate over 10 indicates the presence of multi-collinearity. Table 4 above shows the absence of multicollinearity between independent variables, as all independent variables (FSP, TAC and FSZ) have less than 10 VIF centres.

Heteroskedasticity

In order to validate the panel regression results, the Heteroskedasticity test was conducted as a robustness check. Heteroskedasticity happens when the standard errors of a variable, monitored over a specific amount of time, are non-constant. Heteroskedasticity is a violation of the assumptions for linear regression modeling, and so it can impact the validity of the result from any analysis while heteroskedasticity does not cause bias in the coefficient estimates, it does make them less precise; lower precision increases the likelihood that the coefficient estimates are further from the correct population value.

Hypothesis

Ho: There is no heteroskedasticity problem in the model (Residuals are homoskedastic)

Hi: There is heteroskedasticity problem in the model

Decision Rule:

Reject H0 if the Prob. value is less than 0.05 (5% level of significant). Otherwise, do not reject H0.

Table 5: Heteroskedasticity Test

Value df Probability
Likelihood ratio  101.6732  20  0.0711
LR test summary:
Value Df
Restricted LogL -178.7938  296
Unrestricted LogL -948.8374  296

Source: E-views 13 Output (2024)

Based on the above rule of thumb, the Heteroskedasticity Test, Prob. value is 0.0711, greater than 0.05; thus, the study affirmed that the regression model is free from Heteroskedasticity problem

Hausman Test

The Hausman test is a test for model specification in panel data analysis and this test is employed to choose between fixed effects model and the random effects model. Due to the panel nature of the data set utilized in this study, the test basically checked if the error terms were correlated with the regressors. Thus, the decision rule for the Hausman specification test is stated thus; at 5% Level of significance:

Decision Rule:

Reject H0 if the Prob > F is less than 0.05. Otherwise, do not reject H0.

Hypothesis

H0: Random effect is most appropriate for the Panel Regression analysis

H1: Fixed effect is not appropriate for the Panel Regression analysis

Table 6: Hausman Test

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 1.418968 3 0.7011

Source: E-views 13 Output (2024)

The result of the Hausman test appended in table 5 above does not provide sufficient evidence to reject this null hypothesis at 5% level of significance as can be seen that the probability value (0.7011) of the test is greater than the critical value of 0.05. Therefore, the study upholds that difference in coefficients is not systematic and hence, the random effect model is the most appropriate models for the study. It is imperative therefore, to proceed to another test which is the Langranger Multiplier test, which will indicate the appropriateness or otherwise of using the pooled effect model or the random effect model.

Breusch-Pagan and Lagranger Multiplier Test

In panel data analysis, the Lagranger multiplier test is used to select between pooled and random effects models. 

Hypothesis

H0: Pooled effect is not appropriate for the Panel Regression analysis

H1: Random effect is most appropriate for the Panel Regression analysis

Decision Rule: if the p-value is less than 0.05 the decision rule is to reject the null hypothesis which states that pooled effect is most appropriate for the Panel Regression analysis (meaning that the preferred model is random effects). Similarly, if the p-value is greater than 0.05 the decision rule is to accept the null hypothesis which states that pooled effect is most appropriate for the Panel Regression analysis (meaning that the random effect model is to be rejected).

Table 7: Breusch-Pagan and Lagranger Multiplier Test

Test Statistic d.f. Prob.
Breusch-Pagan LM 266.1187 190 0.0002

Source: E-View 13 Output (2024)

Based on the probability value of the Breusch-Pagan Langranger Multiplier Test at probability value of 0.0002, the null hypothesis is rejected, thus random effect is most appropriate when compared to pooled effect.

Test of Research Hypotheses

In panel regression analysis, the ultimate goal is the estimation of the relationship between dependent and independent variables. This goal can be achieved through the estimation of the coefficients of each independent variable in the model. The sign of coefficients of independent variables indicates their relationship with dependent variable, while the magnitude of the coefficients implies the responses of dependent variables to independent variables.

Decision Rule: The decision rule for accepting or rejecting the null hypothesis for any of these tests was based on the Probability Value (PV) and the Probability (F-statistic). If the PV is less than 5% or 0.05 (that is, if PV < 0.05), it implies that the regressor in question is statistically significant at 5% level; and if the PV is more than 5% or 0.05 (that is, if PV > 0.05), it is categorized as not significant at that level. This implies that the level of significance for the study is at 5% (for the two-tailed test). Thus, the decision rule for accepting or rejecting the null hypothesis is based on both the Probability Value (PV) and the Probability (F-statistic)”.

Table 9: Panel Regression Result (Random Effect)

Variable Coefficient Std. Error t-Statistic Prob.
C 0.691748 0.340728 2.030204 0.0432
FSP -0.071454 0.040210 -2.777013 0.0466
TAC -1.715310 4.768210 -0.359068 0.7198
FSZ 0.009465 0.030623 0.309078 0.7575
Effects Specification
S.D. Rho
Cross-section random 0.156156 0.1454
Idiosyncratic random 0.378572 0.8546
Weighted Statistics
R-squared 0.417076     Mean dependent var 0.107540
Adjusted R-squared 0.397046     S.D. dependent var 0.378903
S.E. of regression 0.377564     Sum squared resid 41.91104
F-statistic 1.702518     Durbin-Watson stat 1.889295
Prob(F-statistic) 0.166580

Source: E-View 13 Output (2024)

The table 9 established that, a unit increase in financial statutory payment (FSP) and Total Accrual (TAC) on the average holding other independent variables constant will lead to a -0.071454 and -1.715310 unit decrease in return on assets (ROA) of the listed non-financial firms in Nigeria. Besides, a unit increase in firm size (FSZ) and on the average holding other independent variables constant will lead to a 0.009465 unit increase in performance (ROA) of the listed non-financial firms in Nigeria respectively. However, based on the probability value, financial statutory payment (FSP) was negatively but statistically significant in explaining the variation in the return on assets (ROA) of the listed non-financial firms in Nigeria; total accruals (TAC) was negatively and statistically insignificant in explaining the variation in the return on assets (ROA) of the listed non-financial firms in Nigeria; Firm Size (FSZ) was positively and statistically insignificant in explaining the variation in the return on assets (ROA) of the listed non-financial firms in Nigeria.

Furthermore, the R2 value is 0.47; it indicates the prediction capability of the independent variables. This indicates that 41% changes in the financial statutory payment and total accruals were explained by the changes in the return on assets (ROA) of the listed non-financial firms in Nigeria. Also, that about 59% other factors that could bring about changes in the model were not included. Furthermore, the value of 41% of the R2 shows an optimum relationship between the financial statutory payment, total accruals and financial performance of listed non-financial firms in Nigeria.

More so, the study established that the HO1 which stated that financial statutory payment has no significant effect on return on assets (ROA) of the listed non-financial firms in Nigeria is rejected; this is because the p-value of 0.0466 is less than 0.05. Conversely, the HO2 which stated that total accrual has no significant effect on return on assets (ROA) of the listed non-financial firms in Nigeria is accepted; this is because the p-value of 0.7198 is greater than 0.05. Thus, the study affirmed that financial statutory payment serves as essential tool that can influence financial performance of listed non-financial firms in Nigeria. This is because; the Prob. (F-statistic) is 0.166580, greater than 0.05.  Also, the Durbin-Watson stat of 1.8 shows that the regression model is free from auto-correlation.

DISCUSSION OF FINDING

The findings of the study reveal important insights into the relationship between financial statutory payments, total accruals, and the financial performance of listed non-financial firms in Nigeria. Firstly, the rejection of Hypothesis one (HO1) suggests that financial statutory payments have a significant effect on return on assets (ROA) for these firms. This implies that adherence to statutory payment obligations plays a crucial role in influencing financial performance. The significance of this finding underscores the importance of regulatory compliance and financial transparency in enhancing the overall performance of non-financial firms in Nigeria. It suggests that firms that prioritize meeting their statutory payment obligations are likely to achieve better financial outcomes, possibly through improved stakeholder trust and reduced regulatory risks. This study is in tandem with the study of Olatunji and Juwon (2020) which state that adherence to statutory financial obligations and transparent financial practices positively impact firm performance. Their study indicated that such compliance enhances stakeholder trust and reduces regulatory risks, ultimately contributing to better financial outcomes for firms.

Conversely, the acceptance of Hypothesis two (HO2) indicates that total accruals do not have a significant effect on return on assets (ROA) for listed non-financial firms in Nigeria. While accruals are commonly used as a measure of earnings management and operational efficiency, the non-significant relationship with financial performance in this context suggests that the impact of accruals on ROA may be limited or influenced by other factors. These findings disagree with the findings of Uwuigbe et al., (2019) and Norziation and Arfa (2022). Overall, the study’s findings have significant implications for accounting practices in Nigeria. The confirmation of the influence of financial statutory payments on financial performance highlights the importance of robust financial reporting practices and compliance with regulatory requirements. It emphasizes the need for firms to prioritize transparency and accountability in financial management to enhance their competitiveness and sustainability in the Nigerian market (Uwuigbe et al., (2019). Additionally, the non-significant relationship between total accruals and financial performance suggests that while accruals may be useful for internal decision-making, they may not directly impact financial performance in the context of listed non-financial firms in Nigeria. This underscores the importance of adopting a nuanced approach to financial analysis and decision-making, considering multiple factors beyond accruals alone.

CONCLUSION AND RECOMMENDATION 

The findings of this study shed light on the relationship between financial statutory payments, total accruals, and the financial performance of listed non-financial firms in Nigeria. The results indicate that financial statutory payments significantly influence financial performance, highlighting the importance of regulatory compliance and transparency in enhancing financial performance. Conversely, total accruals do not exhibit a significant effect on financial performance, suggesting that their impact on financial performance may be limited in this context. These findings underscore the importance of robust financial reporting practices and adherence to regulatory requirements for improving the financial performance of non-financial firms in Nigeria.

Based on the study’s findings, the following recommendations are proposed to enhance the efficient financial performance of listed non-financial firms on the Nigeria Exchange Group;

  1. Firstly, it is crucial for firms to prioritize compliance with financial statutory payments, as demonstrated by the significant influence of such payments on financial performance. This entails adhering to regulatory requirements, tax obligations, and other statutory payments to maintain transparency and credibility with stakeholders.
  2. Secondly, while total accruals may not directly impact financial performance in this context, firms should still focus on prudent financial management practices to minimize unnecessary accruals and ensure accurate representation of financial performance. This includes regular monitoring of accrual levels, adopting conservative accounting policies, and maintaining transparency in financial reporting.

These actions are essential for promoting trust among stakeholders and optimizing the financial health and sustainability of listed non-financial firms.

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 APPENDIX

Data Presentation

FIRM ID YEAR ROA FSP TAC FSZ
11 Plc 1 2008 0.042 5.453  0,136,524 5.431
11 Plc 1 2009 0.213 5.590 8,211,048 6.519
11 Plc 1 2010 0.231 5.635 9,940,155 6.026
11 Plc 1 2011 0.234 6.376 10,737,302 6.622
11 Plc 1 2012 0.205 6.415 10,991,054 7.431
11 Plc 1 2013 0.319 6.513 15,704,295 7.526
11 Plc 1 2014 0.129 6.290 15,273,383 7.610
11 Plc 1 2015 0.090 6.319 16,593,619 7.692
11 Plc 1 2016 0.132 6.245 16,640,488 7.733
11 Plc 1 2017 0.101 6.253 12,980,124 7.790
11 Plc 1 2018 0.132 5.533 544,573 7.873
11 Plc 1 2019 0.117 5.626 494,238 7.849
11 Plc 1 2020 0.097 5.624 538,914 7.960
11 Plc 1 2021 0.073 5.624 589,717 7.969
11 Plc 1 2022 0.080 5.635 594,900 7.640
Academy Press Plc 2 2008 0.518 5.631 514,604 4.028
Academy Press Plc 2 2009 0.086 5.586 379,907 5.839
Academy Press Plc 2 2010 0.067 5.461 514,152 6.032
Academy Press Plc 2 2011 0.080 5.510 485,838 6.116
Academy Press Plc 2 2012 0.026 5.492 509,567 6.374
Academy Press Plc 2 2013 -0.026 5.337 1,430,355 6.451
Academy Press Plc 2 2014 0.030 5.600 516,659 6.550
Academy Press Plc 2 2015 0.018 5.328 298,054 6.579
Academy Press Plc 2 2016 -0.016 5.141 (96,865) 6.572
Academy Press Plc 2 2017 0.298 5.553 (399,400) 6.547
Academy Press Plc 2 2018 0.450 5.420 (821,915) 6.474
Academy Press Plc 2 2019 0.296 5.954 (122,448) 6.437
Academy Press Plc 2 2020 0.195 4.988 144,814 6.425
Academy Press Plc 2 2021 0.291 4.845 130,317 6.418
Academy Press Plc 2 2022 0.090 4.900 133,493 6.721
Afromedia Plc 3 2008 0.155 5.090 226,520 5.022
Afromedia Plc 3 2009 0.253 5.117 215,953 5.682
Afromedia Plc 3 2010 0.296 5.117 279,470 5.018
Afromedia Plc 3 2011 0.200 5.196 307,693 5.036
Afromedia Plc 3 2012 0.178 5.261 333,724 6.944
Afromedia Plc 3 2013 0.175 5.370 342,607 6.639
Afromedia Plc 3 2014 0.228 4.862 340,104 6.623
Afromedia Plc 3 2015 0.362 4.872  336,297 6.557
Afromedia Plc 3 2016 0.321 4.796  306,883 6.362
Afromedia Plc 3 2017 0.296 5.394  267,546 6.333
Afromedia Plc 3 2018 0.245 6.644  8,726,240 6.264
Afromedia Plc 3 2019 0.059 6.144  10,145,132 6.333
Afromedia Plc 3 2020 0.073 6.158  12,627,606 6.324
Afromedia Plc 3 2021 0.057 6.250  18,464,929 6.320
Afromedia Plc 3 2022 0.090 6.384  18,361,430 6.592
Aluminium Extrusion Indus 4 2008 0.029 6.412  20,583,914 5.027
Aluminium Extrusion Indus 4 2009 0.047 6.387  24,115,939 5.217
Aluminium Extrusion Indus 4 2010 -0.069 6.414  11,329,033 5.519
Aluminium Extrusion Indus 4 2011 0.005 6.381  11,281,717 5.878
Aluminium Extrusion Indus 4 2012 -0.161 6.254  12,130,548 6.089
Aluminium Extrusion Indus 4 2013 0.062 5.821  1,320,692 6.206
Aluminium Extrusion Indus 4 2014 -0.225 5.865  1,747,807 6.227
Aluminium Extrusion Indus 4 2015 0.128 6.010  1,617,678 6.244
Aluminium Extrusion Indus 4 2016 0.080 6.035  1,480,418 6.265
Aluminium Extrusion Indus 4 2017 0.177 5.990  1,633,583 6.582
Aluminium Extrusion Indus 4 2018 0.244 5.964  1,211,481 6.354
Aluminium Extrusion Indus 4 2019 0.274 5.970 1,617,983 6.398
Aluminium Extrusion Indus 4 2020 0.315 5.892 1,373,412 6.394
Aluminium Extrusion Indus 4 2021 0.300 5.904  1,711,212 6.409
Aluminium Extrusion Indus 4 2022 0.263 5.889  1,223,268 6.620
Ardova Plc (Forte Oil) 5 2008 0.240 5.512  1,130,470 5.203
Ardova Plc (Forte Oil) 5 2009 0.143 5.537  1,162,592 5.093
Ardova Plc (Forte Oil) 5 2010 -0.185 5.618  1,172,160 5.938
Ardova Plc (Forte Oil) 5 2011 0.086 5.659  1,186,780 6.546
Ardova Plc (Forte Oil) 5 2012 -0.105 5.634   898,537 7.655
Ardova Plc (Forte Oil) 5 2013 0.124 5.655  850,004 7.629
Ardova Plc (Forte Oil) 5 2014 0.036 5.616 1,098,099 8.020
Ardova Plc (Forte Oil) 5 2015 0.066 5.663  1,444,051 8.144
Ardova Plc (Forte Oil) 5 2016 0.200 5.740  1,404,757 8.085
Ardova Plc (Forte Oil) 5 2017 0.220 5.663  1,514,371 8.148
Ardova Plc (Forte Oil) 5 2018 0.122 4.607   969,205 8.168
Ardova Plc (Forte Oil) 5 2019 0.128 4.726  977,052 8.151
Ardova Plc (Forte Oil) 5 2020 0.323 5.639  1,068,562 7.672
Ardova Plc (Forte Oil) 5 2021 0.471 5.687  1,346,870 7.812
Ardova Plc (Forte Oil) 5 2022 0.396 5.797  1,378,568 8.193
Associated Bus Company 6 2008 0.638 5.753  1,111,631 5.092
Associated Bus Company 6 2009 0.260 5.770  1,273,077 6.111
Associated Bus Company 6 2010 0.180 5.668 1,480,361 6.409
Associated Bus Company 6 2011 0.264 5.769  1,664,324 6.501
Associated Bus Company 6 2012 0.201 5.856  1,419,078 6.705
Associated Bus Company 6 2013 0.264 6.237 3,420,872 6.698
Associated Bus Company 6 2014 0.233 6.285 3,120,701 6.751
Associated Bus Company 6 2015 0.024 6.346  3,225,915 6.809
Associated Bus Company 6 2016 0.251 6.351 4,448,652 6.777
Associated Bus Company 6 2017 0.017 6.393  3,705,877 6.636
Associated Bus Company 6 2018 0.008 6.440  3,945,815 6.650
Associated Bus Company 6 2019 -0.119 6.316   5,247,863 6.660
Associated Bus Company 6 2020 -0.118 6.498 6,380,639 6.711
Associated Bus Company 6 2021 -0.068 6.478  7,810,998 6.769
Associated Bus Company 6 2022 -0.059 6.500  5,903,982 6.872
B.O.C Gases Nig 7 2008 -0.034 6.717 11,159,197 4.027
B.O.C Gases Nig 7 2009 -0.041 6.689  11,097,299 5.918
B.O.C Gases Nig 7 2010 0.956 6.727 13,100,096 5.317
B.O.C Gases Nig 7 2011 0.333 6.711  7,930,457 6.052
B.O.C Gases Nig 7 2012 0.663 6.739   8,930,227 6.350
B.O.C Gases Nig 7 2013 -0.621 6.756   6,860,403 6.423
B.O.C Gases Nig 7 2014 -0.609 6.702  7,435,134 6.460
B.O.C Gases Nig 7 2015 -3.913 6.726 7,956,066 6.534
B.O.C Gases Nig 7 2016 0.246 6.735  8,325,598 6.507
B.O.C Gases Nig 7 2017 -0.769 6.765   5,899,045 6.560
B.O.C Gases Nig 7 2018 -0.202 6.253   1,393,772 6.628
B.O.C Gases Nig 7 2019 -0.142 6.175  1,387,195 6.652
B.O.C Gases Nig 7 2020 0.356 5.996  1,209,653 6.702
B.O.C Gases Nig 7 2021 0.264 5.751   1,318,286 6.734
B.O.C Gases Nig 7 2022 0.150 5.774 1,337,933 7.045
Berger Paints Nig 8 2008 0.230 5.687  1,591,229 6.023
Berger Paints Nig 8 2009 -0.459 6.290 1,503,168 6.251
Berger Paints Nig 8 2010 0.123 6.251 1,107,704 6.304
Berger Paints Nig 8 2011 0.137 6.011  1,056,119 6.393
Berger Paints Nig 8 2012 0.304 5.693   (357,937) 6.427
Berger Paints Nig 8 2013 0.686 5.542   (378,007) 6.463
Berger Paints Nig 8 2014 0.842 5.632   (466,382) 6.549
Berger Paints Nig 8 2015 0.465 5.579   25,935 6.561
Berger Paints Nig 8 2016 0.788 5.828  639,932 6.591
Berger Paints Nig 8 2017 0.425 5.900  999,698 6.613
Berger Paints Nig 8 2018 0.709 5.940   1,067,053 6.635
Berger Paints Nig 8 2019 0.988 6.010  1,386,621 6.657
Berger Paints Nig 8 2020 0.566 6.000 1,191,092 6.705
Berger Paints Nig 8 2021 0.031 5.942  64,282,380 6.697
Berger Paints Nig 8 2022 0.858 6.020   1,940,324 6.781
Beta Glass Company 9 2008 0.263 5.710   686,786 6.092
Beta Glass Company 9 2009 0.784 5.572   2,301,735 6.637
Beta Glass Company 9 2010 0.090 5.641 1,849,786 6.032
Beta Glass Company 9 2011 0.905 5.519 1,550,360 7.281
Beta Glass Company 9 2012 0.619 5.738  388,957 7.256
Beta Glass Company 9 2013 0.159 5.513  693,457 7.351
Beta Glass Company 9 2014 0.562 5.561 742,993 7.434
Beta Glass Company 9 2015 0.535 5.586  785,534 7.430
Beta Glass Company 9 2016 0.905 5.706 1,008,344 7.434
Beta Glass Company 9 2017 0.332 5.669 759,515 7.521
Beta Glass Company 9 2018 0.343 5.889 2,524,383 7.582
Beta Glass Company 9 2019 0.824 5.970 3,016,977 7.664
Beta Glass Company 9 2020 0.383 5.955 3,029,670 7.717
Beta Glass Company 9 2021 0.290 6.007 2,638,601 7.732
Beta Glass Company 9 2022 0.392 6.033 2,069,430 7.892
Cadbury Nig 10 2008 0.754 6.030 3,851,343 6.027
Cadbury Nig 10 2009 0.808 5.914 3,425,629 6.843
Cadbury Nig 10 2010 0.956 5.860 1,815,066 6.682
Cadbury Nig 10 2011 0.333 5.897  527,284 6.834
Cadbury Nig 10 2012 0.666 5.898 641,863 7.527
Cadbury Nig 10 2013 0.621 5.603 1,541,240 7.604
Cadbury Nig 10 2014 0.093 5.574 2,599,498 7.635
Cadbury Nig 10 2015 -0.913 5.653 3,155,104 7.460
Cadbury Nig 10 2016 0.246 5.772  3,597,686 7.454
Cadbury Nig 10 2017 -0.791 5.781 3,587,965 7.453
Cadbury Nig 10 2018 0.202 5.764 3,312,483 7.454
Cadbury Nig 10 2019 0.123 5.741 3,249,965 7.440
Cadbury Nig 10 2020 0.566 5.734 3,729,973 7.459
Cadbury Nig 10 2021 0.031 5.862 3,972,962 7.521
Cadbury Nig 10 2022 0.858 6.009 3,742,192 7.610
Champion Breweries 11 2008 0.263 5.881 1,941,233 6.574
Champion Breweries 11 2009 0.784 5.886 2,636,675 6.792
Champion Breweries 11 2010 0.090 5.877 9,772,810 6.039
Champion Breweries 11 2011 0.905 5.854 5,918,891 6.033
Champion Breweries 11 2012 0.619 5.882 5,983,199 6.843
Champion Breweries 11 2013 0.159 5.897 6,360,638 6.832
Champion Breweries 11 2014 0.562 6.089 16,509,895 6.961
Champion Breweries 11 2015 0.535 6.158 10,712,240 6.982
Champion Breweries 11 2016 0.905 6.226 11,714,967 7.014
Champion Breweries 11 2017 0.332 6.139  12,962,902 6.998
Champion Breweries 11 2018 0.343 6.319   15,017,327 7.004
Champion Breweries 11 2019 0.824 6.346 16,163,716 7.021
Champion Breweries 11 2020 0.639 6.335 17,038,518 7.041
Champion Breweries 11 2021 0.222 6.342 13,789,472 7.056
Champion Breweries 11 2022 0.267 6.339 11,537,758 7.893
Chellarams 12 2008 0.259 6.359 14,140,549 6.043
Chellarams 12 2009 0.326 6.305 13,049,372 6.632
Chellarams 12 2010 0.109 6.256  12,770,903 6.503
Chellarams 12 2011 0.233 6.320 13,439,254 7.010
Chellarams 12 2012 0.024 6.346 9,818,385 7.037
Chellarams 12 2013 0.251 4.672  364,055 7.169
Chellarams 12 2014 0.017 4.766 473,693 7.188
Chellarams 12 2015 0.008 4.809 602,695 7.225
Chellarams 12 2016 0.436 4.940  824,623 7.265
Chellarams 12 2017 0.235 5.009 85,187 7.141
Chellarams 12 2018 0.120 5.087 691,940 7.126
Chellarams 12 2019 0.543 4.996  450,153 7.120
Chellarams 12 2020 0.234 5.333  483,092 7.102
Chellarams 12 2021 0.119 5.595 1,417,701 6.980
Chellarams 12 2022 0.118 5.506  (5,240,846) 6.356
Chemical & Allied Product 13 2008 0.068 5.103 452,708 5.382
Chemical & Allied Product 13 2009 0.059 4.973 390,527 6.002
Chemical & Allied Product 13 2010 0.034 5.081 53,656 6.049
Chemical & Allied Product 13 2011 0.041 5.129 651,535 6.392
Chemical & Allied Product 13 2012 0.956 5.191 639,008 6.487
Chemical & Allied Product 13 2013 0.333 5.255 33,352 6.459
Chemical & Allied Product 13 2014 0.663 5.359 1,005,646 6.482
Chemical & Allied Product 13 2015 -0.621 5.460 1,520,689 6.489
Chemical & Allied Product 13 2016 -0.609 5.599 1,644,787 6.533
Chemical & Allied Product 13 2017 -0.913 5.571 1,441,969 6.692
Chemical & Allied Product 13 2018 0.246 6.874 143,698,035 6.700
Chemical & Allied Product 13 2019 -0.769 7.046 180,149,728 6.800
Chemical & Allied Product 13 2020 0.183 7.184 243,660,152 6.830
Chemical & Allied Product 13 2021 0.115 7.306 248,581,163 6.931
Chemical & Allied Product 13 2022 0.053 7.486 289,917,000 7.321
Conoil 14 2008 0.013 7.660 291,287,000 6.043
Conoil 14 2009 0.031 7.699 454,292,000 6.873
Conoil 14 2010 0.074 7.758 517,902,000 7.245
Conoil 14 2011 0.249 7.798 511,682,000 7.691
Conoil 14 2012 0.267 7.859 #VALUE! 7.791
Conoil 14 2013 0.290 6.344 13,597,719 7.920
Conoil 14 2014 0.322 6.387 23,228,510 7.916
Conoil 14 2015 0.430 6.554 24,598,474 7.937
Conoil 14 2016 0.402 6.760 18,628,010 7.841
Conoil 14 2017 0.445 6.663 20,728,480 7.844
Conoil 14 2018 0.364 6.719 22,988,581 7.798
Conoil 14 2019 0.258 6.793 50,988,094 7.785
Conoil 14 2020 0.494 6.840 39,685,360 7.803
Conoil 14 2021 0.293 6.428 38,285,230 7.689
Conoil 14 2022 0.886 6.503 53,746,448 8.932
Dangote Cement 15 2008 0.654 5.613  2,440,166 7.039
Dangote Cement 15 2009 0.373 5.611 3,045,273 8.374
Dangote Cement 15 2010 0.558 5.672 2,884,448 8.032
Dangote Cement 15 2011 0.511 5.740 1,291,455 8.472
Dangote Cement 15 2012 0.055 5.781  3,080,000 8.721
Dangote Cement 15 2013 -0.300 5.866 8,568,372 8.828
Dangote Cement 15 2014 0.175 5.743 6,337,277 8.926
Dangote Cement 15 2015 0.209 5.902 4,642,446 8.993
Dangote Cement 15 2016 0.203 5.947 4,950,207 9.046
Dangote Cement 15 2017 0.158 6.004 5,507,893 9.184
Dangote Cement 15 2018 0.052 5.496 756,243 9.222
Dangote Cement 15 2019 0.143 5.655 938,832 9.229
Dangote Cement 15 2020 0.093 5.685 1,044,316 9.241
Dangote Cement 15 2021 0.365 5.712 1,543,468 9.306
Dangote Cement 15 2022 -0.098 5.779 2,472,339 9.578
Flour Mills of Nigeria 16 2008 -0.240 5.926  2,903,919 6.058
Flour Mills of Nigeria 16 2009 0.969 5.940 1,716,948 7.053
Flour Mills of Nigeria 16 2010 0.051 6.009 1,624,634 7.043
Flour Mills of Nigeria 16 2011 0.682 6.027 2,025,603 8.032
Flour Mills of Nigeria 16 2012 0.357 5.976 1,146,203 8.213
Flour Mills of Nigeria 16 2013 0.691 4.300 89,257 8.367
Flour Mills of Nigeria 16 2014 0.340 4.267 71,078 8.448
Flour Mills of Nigeria 16 2015 0.456 4.309   81,121 8.473
Flour Mills of Nigeria 16 2016 0.409 4.301 60,547 8.535
Flour Mills of Nigeria 16 2017 0.343 4.291 65,252 8.538
Flour Mills of Nigeria 16 2018 0.307 4.353  48,281 8.684
Flour Mills of Nigeria 16 2019 0.577 4.290  36,477 8.611
Flour Mills of Nigeria 16 2020 0.327 4.241  42,425 8.620
Flour Mills of Nigeria 16 2021 0.272 4.215 25,008 8.636
Flour Mills of Nigeria 16 2022 0.747 4.351   98,807 8.026
Glaxosmithkline Nig 17 2008 0.825 5.954 4,053,438 7.048
Glaxosmithkline Nig 17 2009 0.195 5.874  4,073,993 5.392
Glaxosmithkline Nig 17 2010 0.139 5.834 5,113,933 7.083
Glaxosmithkline Nig 17 2011 0.603 5.812  5,439,589 7.049
Glaxosmithkline Nig 17 2012 0.269 5.786   4,351,864 7.254
Glaxosmithkline Nig 17 2013 0.173 5.734 4,055,363 7.338
Glaxosmithkline Nig 17 2014 0.300 5.781  7,155,167 7.419
Glaxosmithkline Nig 17 2015 0.145 5.872  6,319,684 7.447
Glaxosmithkline Nig 17 2016 0.080 6.012 5,875,557 7.496
Glaxosmithkline Nig 17 2017 0.063 6.185 8,581,340 7.450
Glaxosmithkline Nig 17 2018 0.064 6.943 40,185,298 7.423
Glaxosmithkline Nig 17 2019 0.335 7.003 39,565,812 7.196
Glaxosmithkline Nig 17 2020 0.143 7.183  38,010,122 7.272
Glaxosmithkline Nig 17 2021 0.168 7.147 43,656,993 7.375
Glaxosmithkline Nig 17 2022 -0.183 7.116  35,366,960 7.693
Guinness Nig 18 2008 -0.279 7.251 37,624,722 6.048
Guinness Nig 18 2009 0.024 7.265  66,689,068 6.038
Guinness Nig 18 2010 -0.179 7.316 68,775,057 7.832
Guinness Nig 18 2011 0.321 7.404  53,347,557 7.736
Guinness Nig 18 2012 -0.080 #NUM! 65,787,177 7.965
Guinness Nig 18 2013 -0.489 4.984 4,479 8.025
Guinness Nig 18 2014 -0.001 4.902  (253,402) 8.083
Guinness Nig 18 2015 0.842 4.899  (95,183) 8.122
Guinness Nig 18 2016 0.188 4.393  (261,409) 8.087
Guinness Nig 18 2017 0.087 4.549   (59,333) 8.137
Guinness Nig 18 2018 0.257 4.624  (58,208) 8.164
Guinness Nig 18 2019 0.133 4.548  (250,962) 8.185
Guinness Nig 18 2020 0.157 4.987  (213,666) 8.206
Guinness Nig 18 2021 0.202 5.067  (546,151) 8.159
Guinness Nig 18 2022 -0.110 4.683  (297,172) 8.937
Lafarge Cement Wapco Nig 19 2008 0.028 6.192 8,987,868 7.261
Lafarge Cement Wapco Nig 19 2009 0.842 6.316  10,227,698 7.392
Lafarge Cement Wapco Nig 19 2010 -0.783 6.359 11,602,050 7.942
Lafarge Cement Wapco Nig 19 2011 0.688 6.460 10,735,015 8.016
Lafarge Cement Wapco Nig 19 2012 -0.436 6.188  10,326,243 8.183
Lafarge Cement Wapco Nig 19 2013 -0.404 6.239  8,966,411 8.182
Lafarge Cement Wapco Nig 19 2014 0.484 6.225  4,479,568 8.207
Lafarge Cement Wapco Nig 19 2015 -0.304 6.218  6,756,778 8.486
Lafarge Cement Wapco Nig 19 2016 0.239 6.048   6,052,300 8.656
Lafarge Cement Wapco Nig 19 2017 -0.064 6.202 5,914,756 8.701
Lafarge Cement Wapco Nig 19 2018 0.086 4.795 160,451 8.762
Lafarge Cement Wapco Nig 19 2019 0.250 4.676  129,553 8.733
Lafarge Cement Wapco Nig 19 2020 0.321 4.724  142,389 8.696
Lafarge Cement Wapco Nig 19 2021 0.415 4.722 137,901 8.705
Lafarge Cement Wapco Nig 19 2022 0.104 4.765   149,343 8.944
Total Nigeria 20 2008 0.286 4.810 166,152 6.840
Total Nigeria 20 2009 0.162 4.855  255,336 6.048
Total Nigeria 20 2010 0.021 4.857  (114,676) 7.632
Total Nigeria 20 2011 0.007 4.809  (178,842) 7.696
Total Nigeria 20 2012 0.017 3.684 1 7.769
Total Nigeria 20 2013 0.009 6.852 55,043,605 7.881
Total Nigeria 20 2014 -0.047 6.916  55,183,201 7.900
Total Nigeria 20 2015 0.216 6.965  6,078,434 7.980
Total Nigeria 20 2016 -0.057 6.979 51,333,214 7.922
Total Nigeria 20 2017 0.104 7.105  55,891,520 8.136
Total Nigeria 20 2018 0.120 7.091 41,810,413 8.033
Total Nigeria 20 2019 0.097 7.062  48,315,304 8.122
Total Nigeria 20 2020 0.021 6.982 48,625,405 8.126
Total Nigeria 20 2021 0.063 6.943 40,129,228 8.157
Total Nigeria 20 2022 0.426 7.018 33,330,061 8.823

Source: Author’s Compilation from Annual Report (2008-2022).

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