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Misappropriation of Assets, Improper Expenses Recognition and Financial Performance of Listed Non-Financial Firms in Nigeria

  • Dauda John Dzarsa
  • 675-713
  • May 24, 2024
  • Accounting

Misappropriation of Assets, Improper Expenses Recognition and Financial Performance of Listed Non-Financial Firms in Nigeria

Dauda John Dzarsa

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

DOI: https://dx.doi.org/10.47772/IJRISS.2024.803046S

Received: 16 April 2024; Accepted: 29 April 2024; Published: 24 May 2024

ABSTRACT

The Nigerian business landscape, like many others globally, faces persistent challenges related to corporate misconduct, ethical lapses, and accounting irregularities. These challenges have the potential to undermine not only the financial integrity of individual firms but also the overall health and stability of the nation’s financial markets. Given the foregoing, this study examined the effect of misappropriation of assets and improper expenses recognition on financial performance of listed non-financial firms in Nigeria. To achieve these objectives, longitudinal research design was employed and the study employed 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 statistical tools (E-view 10). The result of the study revealed that both misappropriation of assets and improper expenses recognition had positive and insignificant effect on financial performance of listed non-financial firms in Nigeria. Thus, this study concluded that both misappropriation of assets and improper expenses recognition does not appear to be a primary driver of financial success or failure for the listed non-financial firms under study. The study recommended that listed non-financial firms should encourage a mindset of continuous improvement throughout the organization and seek opportunities to optimize operations and financial performance, even if misappropriation of assets and improper expenses recognition are not a significant issue.

Keywords: Misappropriation of assets, Improper expenses recognition, Financial Performance and Return on Assets.

INTRODUCTION

Financial performance in the context of non-financial firms refers to how effectively and efficiently these companies manage their financial resources to achieve their business objectives. While non-financial firms do not primarily engage in financial services such as banking or insurance, their financial performance is crucial for their sustainability, growth, and competitiveness in the market. The financial performance of listed non-financial firms in Nigeria is pivotal not only for the entities themselves but also for the broader economy, these firms serve as engines of growth, employment, and economic stability, a decline in their financial performance can have far-reaching consequences, affecting job security, access to capital, and the nation’s overall economic well-being (Suffian et al., 2022).

The misappropriation of assets, improper recognition of expenses, and the overall financial performance of listed non-financial firms in Nigeria constitute a multifaceted and critical area of concern in the realm of corporate governance and financial stability (Inekwe, 2021). These issues have garnered significant attention due to their potential to undermine the integrity of financial markets, erode investor confidence, and hinder the sustainable growth of businesses operating within Nigeria’s dynamic economic landscape (Agbaje & Oloruntoba, 2018). Misappropriation of assets encompasses a range of activities, including embezzlement, fraud, and unauthorized diversion of company resources for personal gain. Such acts not only inflict financial harm on the affected firms but also raise fundamental questions about the adequacy of internal controls, ethical standards, and corporate governance practices within these organizations. Addressing this challenge is essential to safeguarding the assets of non-financial firms and ensuring the trust and credibility necessary for attracting investment.

Improper recognition of expenses, on the other hand, has the potential to distort the financial picture presented by these firms. When expenses are inadequately recognized, financial statements may inaccurately depict profitability, solvency, and overall financial health. This can mislead investors, creditors, and other stakeholders, thereby impeding informed decision-making and posing systemic risks to the Nigerian business environment (Omoolorun & Abilogun, 2017). Both misappropriation of assets and improper expenses recognition can significantly undermine the financial performance of listed non-financial firms in Nigeria. These unethical practices not only distort financial position but also damage the reputation and trust that are essential for attracting investors and securing capital for growth. In light of these multifaceted challenges, it is imperative to delve deeper into the intricacies of misappropriation of assets, improper expenses recognition, and financial performance within Nigeria’s non-financial sector.

The basic hypothesis underlying this study are stated thus;

Ho1: Misappropriation of assets has no significant effect on return on assets of non-financial firms in Nigeria

Ho2: Improper expenses recognition has no significant effect on return on assets of non-financial firms in Nigeria.

LITERATURE REVIEW

Conceptual Framework

1. Misappropriation of Assets

Misappropriation of assets is an unethical and illegal practice in which a person or organization takes money or other assets without the proper authorization or ownership (Ibironke, 2019). This type of fraud occurs in many different ways, but it generally involves the unauthorized use of funds, property, or information for personal gain, it can also involve the use of company funds for personal purchases or the theft of company assets. Hussaini et al. (2018) opined that misappropriation of assets can be committed by employees, vendors, customers, or other individuals associated with a business and it can also be perpetrated by outsiders, such as hackers, who gain access to a company’s financial records. It is important for businesses to have proper security measures in place to protect against such fraud. There are several red flags that can indicate the potential for misappropriation of assets, these include unexplained bank account activity, sudden changes in an employee’s lifestyle, or discrepancies in financial reports. It is important for businesses to monitor these indicators and investigate any suspicious activities. Companies also need to ensure they have effective internal controls in place to prevent misappropriation of assets. These include good record keeping, a system of checks and balances, and regular audits. It is also important to have policies in place that make it clear that misappropriation of assets is a serious offense and that those responsible will face serious consequences. According to Suffian et al. (2022) misappropriation of assets can have a detrimental effect on a business in many ways and it can lead to a loss of trust between the company and its customers or vendors, as well as a loss of reputation. Ajiboye and Ibrahim (2022) asserted that misappropriation of assets can also result in criminal charges and hefty fines and companies need to take steps to prevent this activity as much as possible in order to protect their finances, reputation, and customers.

2. Improper Expenses Recognition

Improper expense recognition is a term that refers to the incorrect recording of expenses incurred by a business, also it is a process by which expenses are reported on the company’s financial statements when they should not have been (Omoolorun & Abilogun (2017). Improper expense recognition can occur in a variety of ways, such as understating expenses, overstating expenses, or engaging in improper accounting practices. Understating expenses occurs when a company records expenses on its financial statements for less than what is actually spent, this is done in order to make the company’s financial situation look better than it actually is.

For instance, a company may record a certain expense as ₦100 when it actually cost ₦200. This practice is considered fraudulent and can lead to serious legal and financial consequences. Overstating expenses occurs when a company records expenses on its financial statements for more than what is actually spent. This is done in order to make the company’s financial situation look worse than it actually is. For instance, a company may record a certain expense as ₦200 when it actually cost ₦100, this practice is also considered fraudulent and can lead to serious legal and financial consequences. According to Olukayode (2018), improper accounting practices are those activities that are not in accordance with Generally Accepted Accounting Principles (GAAP), examples of improper accounting practices include failing to report expenses, failing to record expenses correctly, and recording expenses that do not actually exist, these practices can lead to inaccurate financial statements, which can then lead to improper expense recognition. Improper expense recognition can have serious consequences for a company, it can lead to inaccurate financial statements, which can then lead to inaccurate financial reports and also can lead to incorrect tax filings and potentially costly fines and penalties. In addition, Yulistyawti et al. (2019) affirmed that improper expense recognition can lead to a loss of credibility with investors, creditors, and other stakeholders, and improper expense recognition can lead to legal action taken against the company, which can result in significant financial losses. Okunbor and Dabor (2018) opined that it is important for companies to understand the concept of improper expense recognition and take steps to ensure that it does not occur and companies should implement internal controls designed to ensure that all expenses are accurately recorded, and that any discrepancies are addressed in a timely manner, additionally, companies should consult with their accountant or financial advisor to ensure that their financial statements are compliant with GAAP. Finally, companies should ensure that their employees, who are responsible for recording expenses, are properly trained and aware of the importance of accurate expense recording.

3. Financial Performance

Financial performance is a measure of how efficient a firm uses its assets to generate revenue from its operating activities (Aniefor & Onatuyeh, 2020).  It can be said to be a term that is used to measure the financial health and growth of a firm over a period of time. It can also be used to compare different firms in the same industry. There are different measures of financial performance and since there are many stakeholders in a company, each group has its own interest in tracking the financial performance of that company. The trade creditors will be interested in the liquidity of the company, the bond holders will be interested in the solvency of the company, the shareholders will be interested knowing how well their investment will yield return and the management will be interested in knowing how well the firm perform in the market (Ahmadu, 2016). The most comprehensive source of information about a company financial performance is from the financial statement which consists of; income statement, statement of financial performance, statement of cash flow and notes to the account.

A firm’s financial performance is an estimation of what has been achieved by the firm over a given period of time in monetary terms (Adeniyi & Aderobaki, 2021). The importance of measuring a company’s performance is to obtain vital information for the various investors and stakeholders on its liquidity, solvency, profitability and efficiency. According to Kumarudin et al. (2012), the main factors that influence financial performance of an entity include liquidity, leverage, size of the firm and management’s ability i.e. highly competent managerial staff. Financial performance is the measure of how well a firm can use its assets from its primary business to generate revenues. Ogoun and Owata, (2019) noted that financial performance measures like profitability and liquidity among others provide a valuable tool to stakeholders which aids in evaluating the past financial performance and current position of a firm. Financial performance evaluation are designed to provide answers to a broad range of important questions, some of which include whether the company has enough cash to meet all its obligations, is it generating sufficient volume of sales to justify recent investment. Capital structure is closely linked with financial performance (Zeitun & Tian, 2007).

Financial performance can be measured by variables which involve productivity, profitability, growth or, even, customer satisfaction. These measures are related among each other. Financial measurement is one of the tools which indicate the financial strengths, weaknesses, opportunities and threats. Those measurements are return on investment (ROI), residual income (RI), earning per share (EPS), dividend yield, return on assets (ROA), growth in sales, return on equity (ROE). There are various stakeholders who are interested in a company’s performance due to leverage. These include the equity holders, who are owners of the firm and they carry the highest risk in the business since they are the last to be paid upon winding up of the firm after all the debt holders claims are settled. They gain through the value of their shares appreciating and through pay out of dividends. The debt holders are also interested since they gain through repayment of their principal amount with some interest. Their debt is secured by the company’s assets and are first to be paid in the event that the company winds up or is unable to pay its debtors (Adenugba, et al., 2016).

4. Return on Assets

Returns on assets (ROA) is a key financial metric used to measure a company’s profitability by calculating the ratio of net income to total assets. It is a measure of how efficiently a company is using its assets to generate profits (Imagbe et al., 2019). ROA is expressed as a percentage and can be calculated by dividing a company’s net income by its total assets. A higher ROA indicates that a company is generating more profit from its assets and is therefore more profitable (Tukur & Abubakar, 2014). ROA is an important metric for investors to consider when evaluating a company’s performance and potential for growth.

In the management literature for accounting-based metrics, return on asset is also a metric of performance frequently used (Weir & Laing 2001). It is a metric that assesses the efficacy of the assets used (Bonn, et al., 2004) and demonstrates to investors the earnings produced by the company from its capital asset expenditure (Epps & Cereola 2008). Effective utilization of the funds of a corporation is better expressed by the return rate on its funds. Since managers are responsible for the business activity and utilization of the assets of the organization, return on assets is a metric that helps users to determine how well the corporate governance structure of a company performs in protecting and encouraging the management performance of the company, Epps and Cereola (2008); Tukur and Abubakar (2014); and Arumona et al. (2017) have successfully used asset returns.

5. Firm Size

Firm size refers to the size of the business unit. It may also be defined as the number of operations carried out by a single company (Falope & Ajirole, 2019). Because of the economies of scale phenomena, firm size is most critical to its success. Modern businesses strive to increase their intensity in order to get a competitive advantage over their competitors by lowering production costs and increasing market share. Larger businesses may manufacture things at significantly lower prices than smaller businesses. The volume or collection of a business’s capacity to create and wherewithal, or the amount and diversity of value that a corporation may deliver to its consumers at the same time, is referred to as its size. According to this notion, company size is a factor in determining business profitability, and various experts have shown that a positive link exists between the size of a corporation and its profitability.

According to Akinyomi and Olagunju (2013), firm size refers to the size of the firm and the activities of the commercial organisation. In today’s environment, due to economies of scale, the size of a corporation plays a highly crucial part in competing with competitors through cost reduction and taking and holding more possibilities. According to this notion, company size is a factor in determining business profitability, and various experts have shown that a positive link exists between the size of a corporation and its profitability. According to Akinyomi and Olagunju (2013) company size has been identified as an important element in explaining organisational profitability, and a number of research have attempted to investigate the influence of firm size on profitability. Jasch (2013) agreed, stating that because large enterprises have a larger market share, they may earn more. As a result of these circumstances, large enterprises function in more profitable environments with rivalry. In corporate finance Empirical researchers also regard firm size to be a significant and fundamental firm characteristic, and they detect the size effect – company size matters in affecting the dependent variables in many scenarios.

Empirical Review

Davis (2022) studied the effect of improper expenses recognition on financial performance: an empirical analysis. The study employed an empirical research design, utilizing financial data from publicly traded companies in Nairobi. Multiple regression analysis was conducted with the help of E-view 9 to examine the relationship between improper expenses recognition and financial performance indicators, such as profitability, cash flow, and shareholder value. The findings revealed a significant negative impact of improper expenses recognition on financial performance. Companies involved in the misclassification or manipulation of expenses experience lower profitability, reduced cash flow, and diminished shareholder value. These effects are observed across various industries and company sizes. The study recommended that organizations should prioritize ethical financial practices, strengthen internal controls, and promote a culture of transparency. The study of Davis (2022) lacks robust analysis as a result of the empirical research design adopted.

Temile et al. (2021) examined the manipulation of accounting figures and the financial performance of listed firms in Nigeria between the periods of 2007-2019 (Thirteen years). Ninety (90) firms were drawn as research samples among one hundred and nine (109) listed non-financial firms in Nigeria. The study was done quantitatively and conducted from secondary data obtained from the annual reports of various firms. Descriptive statistics and correlation analysis were used to determine the nature of the relationship between the independent and dependent variables. Given the hypothesis formulated for this research, the method of model estimation employed was a panel regression analysis with the aid of Stata 14 software. The findings revealed that some manipulation techniques such as incorrect asset valuation and timing of assets transaction impact positively on return on assets, thereby justifying the act, albeit unethical. Other techniques such as revenue falsification and understatement of liabilities were seen to negatively impact the return on assets. The study recommends among others, that investors should employ the services of competent financial analysts to scrutinize financial statements of firms they want to invest in. The study of Temile et al., (2021) also consider non-financial companies but the study was limited in terms of scope as it only covers a period of thirteen years. This study will bridge the gap by building on the study of Temile et al., (2021).

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) uses trend analysis and ratio analysis to analysis fraudulent financial reporting using SEC as a case study, but this present study will employ panel multiple regression model to analyse for the effect of financial reporting fraud using listed non-financial firms in Nigeria.

Chinedu, (2020) studied the effect of misappropriation of assets and the financial performance of non-financial firms in Nigeria: the study conducted a longitudinal study by collecting data from 60 non-financial firms listed on the Nigerian Exchange Group over a ten-year period (2009-2019). Asset misappropriation was assessed through forensic accounting techniques, internal audit reports, and external audit findings. Financial performance was evaluated using various financial ratios and market-based measures. The study employed regression analysis to examine the long-term impact of asset misappropriation on financial performance. The research identified a insignificant positive relationship between asset misappropriation and financial performance over the ten-year period. Firms with a history of asset misappropriation experienced a decline in profitability, market capitalization, and investor confidence. The study recommended that independent auditor should consider periodic independent audits to complement internal controls and ensure credibility.

Adeniyi and Olatunji, (2019) investigated the effect of improper expenses recognition on financial performance of listed non-financial firms in Nigeria. This empirical study adopted a quantitative research approach to investigate the impact of improper expenses recognition on the financial performance of non-financial firms listed on the Nigerian Stock Exchange. The study collected financial data from a sample of 80 non-financial firms over a five-year period (2017-2021). Improper expenses recognition was identified by assessing accounting irregularities in the financial statements, specifically focusing on expense manipulation, misclassification, and inappropriate timing. Financial performance was evaluated using key indicators such as net profit margin, return on assets (ROA), and return on equity (ROE). Multiple regression analysis was employed to examine the relationship between improper expenses recognition and financial performance, controlling for other relevant variables. The research findings revealed a statistically significant negative relationship between improper expenses recognition and financial performance indicators. Firms that engaged in improper expenses recognition practices exhibited lower net profit margins, decreased ROA, and reduced ROE over the study period. The study recommended that implement training programs and awareness campaigns to promote ethical behavior and financial reporting integrity within the organization.

Uwuigbe et al. (2019) looked into the association which exists amid financial statement fraud and governance among business organizations 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 organizations 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.

Ibrahim (2019) examined asset misappropriation and its impact on the financial performance of non-financial firms: Evidence from the Nigerian Exchange Group the study employed a mixed-methods study, combining quantitative and qualitative approaches. Data was collected from 70 non-financial firms listed on the Nigerian Exchange Group for the period 2014-2018. Quantitatively, the study analyzed financial data and employed statistical tools to measure the extent of asset misappropriation and its correlation with financial performance indicators. Qualitatively, in-depth interviews were conducted with internal auditors and senior management to gain insights into the causes and mechanisms of asset misappropriation. The quantitative analysis revealed a negative association between asset misappropriation and financial performance indicators, including profitability and liquidity ratios. Qualitative insights emphasized the role of weak internal controls, unethical behavior, and inadequate oversight in contributing to asset misappropriation. The study recommended that governance enhancement strengthen corporate governance practices to improve oversight and transparency.

Agbaje and Oloruntoba (2018) assessed the impact of financial statement fraud on profitability of some selected Nigerian manufacturing firms covering (2002-2016). The specific objectives focused on to ascertain the effect of incorrect asset valuation on return on assets (ROA) and to ascertain the relationship between improper expense recognition and return on assets (ROA). To achieve these objectives, descriptive research design was used 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 10 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 2002-2016 and validation of the parameters were ascertained using various statistical techniques such as t-test, co-efficient of determination (R2), F-statistics and Wald chi-square. Two hypotheses were formulated and tested using the statistics at 5% level of significance. The findings of the analysis revealed that there is a significant relationship between financial statement fraud and profitability in Nigerian manufacturing industry. It was also revealed that incorrect assets valuation has a significant positive relationship and so also is the improper expense recognition on return on assets (ROA) which serves as a proxy for profitability. The study therefore recommended that pragmatic policy options need to be taken in the manufacturing industry to effectively manage incorrect asset valuation and improper expense recognition in order to enhance manufacturing industry performance in the country and also stemming of financial statement fraud should be adequately inculcated into the internal control system of manufacturing firms for the effective running of the manufacturing industry in Nigeria. The study of Agbaje and Oloruntoba (2018) is limited in terms of scope as it only covers a period of 2002 to 2016. This study will bridge the gap by building on the study of Agbaje and Oloruntoba (2018).

Smith (2018). The impact of asset misappropriation on the financial performance of non-financial firms in Nigeria. The study conducted a quantitative study by collecting data from 80 non-financial firms listed on the Nigerian Exchange Group over a five-year period (2013-2017). Asset misappropriation was measured using a financial anomaly index derived from the firms’ financial statements. Financial performance was evaluated through return on assets (ROA) and return on equity (ROE). Regression analysis was employed to determine the relationship between asset misappropriation and financial performance. The study found a statistically significant negative relationship between asset misappropriation and both ROA and ROE. Firms with higher levels of asset misappropriation experienced lower financial performance. The study recommended that firms should enhance their internal control systems to prevent and detect asset misappropriation.

Suhaily and Rashidah (2014) examined determinants of fraudulent financial reporting: Evidence from Malaysia. This study examines two issues relating to fraudulent financial reporting in Malaysia. The first issue examines factors involved with fraudulent financial reporting practices; i.e. predisposition (i.e. related party transactions, history of prior violations, founders on board), motive (i.e. economic factor, ownership factor, political factor) and opportunity (i.e. poor corporate governance). Then, the second issue looks into the relationship between earnings management and the occurrences of fraudulent financial reporting. The study uses a matched sample of 53 firms that were convicted of issuing fraudulent financial statements during the period from 1996 to 2007. The results shows that firms with fewer related party transactions, higher number of prior violations, and higher proportion of founders on board are more likely to “tip” over the edge into fraudulent financial reporting. Also, the study found that the corporate environment most likely to lead to fraudulent financial reporting is characterized by accounting practices that are already “pushing the envelope” on earnings management. Furthermore, the study found that firms are embroiled in fraudulent financial reporting when non-family and non-foreigners own the company, and when the level of financial distress is high. As expected, the results also show that firms involved in fraudulent financial reporting have significantly poor corporate governance structures whereby the audit quality is lower and outside directors seem overcommitted. However, the study concluded that firm’s political connection factor or the level of board independence play a significant role in the potential for fraudulent financial reporting. The study recommended that regulators need to strengthen the legal regime and the firms’ level of transparency to an acceptable level. The study of Suhaily and Rashidah (2014) was carried out in foreign countries (Malaysia) with different culture, language, legislation and business environments. Meanwhile, this present study will focus on financial reporting fraud in Nigeria.

Theoretical Framework

1. White-Collar Crime Theory (WCCT)

White-Collar Crime theory was propounded by Edwin (1939). White collar crimes include such illegal acts which are characterize by deceit, concealment, or violation of trust and which are not dependent on the application of physical force or violence. Sutherland, 1949 cited in Michael, (2004) defined White collar Crime as crime committed by a person of respectable and high social status in the course of his occupation. He noted that in his time, less than 2 percent of the persons committed to Prison in a year belong to the upper class. He tried to establish a relationship between money, social status, and the likelihood of going to jail for a white-collar crime with a more visible, typical crime. He tried to separate and define the difference between the blue-collar street crimes like burglary, theft, rape, arson and vandalism which are often blamed on psychological, associational and structural factor with white collar crimes committed by criminals who are opportunists who overtime learn that they can take advantage of their circumstances to accumulate financial gains. These criminals are educated, intelligent, affluent individuals who can get a job which allows them unfettered and unmonitored access to often large sum of money. According to the Association of Certified Fraud Examiners (2003), 51% of the criminals of occupational fraud had at least a bachelor’s degree, and 49% of the fraudsters were over 40 years old. Also, managers or executives committed 46% of the frauds based on the Association’s recent study. The fraudster has a strong ego and great confidence that he will not be detected, or believes that he could easily take himself out of trouble if caught. Such confidence or arrogance can affect one’s cost benefit analysis of engaging in fraud. The more confident the person, the lower the estimated cost of fraud will be (Wolfe & Hermanson, 2004).

2. Information Asymmetry Theory

The foundation of this theory was established in the 1970s by three researchers; George Akerlof, Michael Spence and Joseph Stiglitz. The information asymmetry perspective assumes that financial statement disclosures have information content that possesses value to shareholders and stakeholders in providing useful signals. Russ (2005) noted that separation of ownership and control creates an information asymmetry between the managers and shareholders, whereby owners are not “armed” with the information to accurately assess the decisions made by the managers. This, therefore, creates room for unethical managers to take advantage of this information asymmetry and use their positions to further their own agendas rather than those of owners. In other words, the information asymmetry allows the management to disguise the real motives for their actions by hiding or distorting information in such a way as to make their actions appear in the best interest of the shareholders. Therefore, the temptation to artificially drive up stock prices, to invent profits and to hide losses is too great for the management whose jobs depend on the results. More so, in times of major economic difficulties, the management is most often tempted to use and even manipulate accounting figures to improve the performance of the firm in a way that does not accurately reflect the overall picture of the organization (Sabau, 2013).

According to Faboyede et al. (2021) information asymmetry theory explores the idea that one party in a transaction has more, or better, information than the other. This can lead to situations where one party has an advantage over the other. This theory is particularly relevant when discussing fictitious revenue and improper asset valuation, as the party in possession of the information is able to manipulate the data to their advantage. In financial reporting fraud, the perpetrator often has more, or better, information than the investors or shareholders. The perpetrator may be aware of certain financial data that is not available to the public, or they could be manipulating the data to misrepresent the financial performance of the company. This information asymmetry gives the perpetrator the advantage, as they can use the information to their benefit. The use of information asymmetry theory in financial reporting fraud is a useful tool for investigators to uncover the truth behind the fraudulent activity. Through the analysis of the available data, investigators can identify any discrepancies that may be the result of information asymmetry. This can then be used as evidence in court proceedings or to recommend corrective action. Agbaje and Oloruntoba (2018) asserted that the information asymmetry theory is also useful for shareholders and investors, as it can be used to identify any potential red flags that could indicate financial reporting fraud. By keeping an eye out for any information asymmetry, investors can take steps to protect their investments and ensure that their money is not going to be wasted on fraudulent activity.

This study is anchored on the information asymmetry theory this is because misappropriation of assets and improper expenses recognition often involves a deliberate manipulation of financial information by a company’s management or insiders to make the firm appear more profitable than it actually is. This manipulation typically leads to a discrepancy between the information presented in financial statements and the underlying economic reality. Information asymmetry arises when company insiders possess knowledge of the revenue manipulation while external investors are unaware. This knowledge gap can lead to external investors making decisions based on inaccurate or incomplete information, leading to market inefficiencies and potentially harming investor interests.

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 10. The model employed by Smith (2022) and Inekwe (2021) was modified and adopted for the study, as indicated below.

ROA =α0+ β1MA + β2IER + β3FSZ + є —————————————- (i)

Where;

ROA = Returns on Assets

MA = Misappropriation of Assets

IER= Improper Expenses Recognition  

FSZ = Firm Size

α0 = Constant or intercept

β1- β 3 = Regression coefficients.

ε = Stochastic error term.

Apriori Expectation

The Apriori expectation is that the effect of misappropriation of assets and improper expenses on financial performance should be negative. Mathematically, the apriori expectation is stated as follows: β1 and β2 < 0. This means that all the independent variables are negatively related to the observed variables.

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)
Misappropriation of Assets (MA) Independent (Reconciled Asset Balance − Adjusted financial statement Balance) + (financial statement − Last Audit Balance). Suffian et al., (2022)
Improper Expenses Recognition (IER) Independent Measured by dividing operating expenses over total revenue. Agbaje & Oloruntoba (2018)
Firm size (FSZ) Control Measure as natural log of total Asset Omollo, et al., (2018)

Source: Researcher Computation (2023)

RESULT AND DISCUSSION

Descriptive Statistics

To gain an initial understanding of the dataset utilized in this study, the study conducted an initial analysis using descriptive statistics. This preliminary examination provided valuable insights into the data’s inherent patterns, which are subsequently presented in Table 2 as summary statistics.

Table 2: Descriptive Analysis Result

ROA MA IER FSZ
 Mean  0.201483  3.996942  0.172687  7.152297
 Median  0.200700  3.343058  0.174560  7.048500
 Maximum  0.987700  8.99E+08  0.736719  9.578000
 Minimum -3.913000 -2228492.  0.005632  4.027000
 Std. Dev.  0.405063  1.56E+08  0.113032  0.965036
 Skewness -3.604995  2.406640  1.188595 -0.069816
 Kurtosis  37.66239  9.059902  6.338625  2.998907
 Jarque-Bera  15668.31  748.6260  209.9681  0.243727
 Probability  0.000000  0.000000  0.000000  0.885269
 Sum  60.44477  3.00E+10  51.80616  2145.689
 Sum Sq. Dev.  49.05876  7.32E+18  3.820065  278.4573
 Observations  300  300  300  300

Source: E-View 10 Output (2023)

Table 2 revealed the summary of descriptive statistics of the variables included in the model. Table The table shows that the mean of return on assets (ROA) is 0.201483, the standard deviation is 0.405063, the lowest value is -3.913000, and the highest value is 0.987700. Given that the range between the lowest and maximum is not as broad, it shows a stable performance, as the standard deviation demonstrated that the data are not widely distributed from the mean value.

Misappropriation of assets is another attribute metric, as shown in table 2 above, with a mean value of 3.996942, a standard deviation of 1.56, and a minimum and maximum value of -2.228492 and 8.99, respectively. Because the standard deviation is not statistically different from the mean and the range between the minimum and maximum values is limited, the misappropriation of assets appears to have improved marginally throughout the research period. The data also shows that for the time period, the average improper expenses recognition (IER) was 0.172687, with a standard deviation of 0.113032 and lowest and highest values of 0.005632 and 0.736719, respectively. This suggests that the value of improper expenses recognition 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, MA and IER, while in the case of FZS the residuals are normally distributed. In summary, the descriptive statistics revealed that ROA, MA and IER data sets are not normally distributed. This is so because the probability values of the variables are less than 5%.

Correlation Analysis

Table 3 presents correlation values between dependent and independent variables and the correlation among the independent variables themselves. These values are generated from Pearson Correlation output. The table contains correlation matrix showing the Pearson correlation coefficients between the dependent and independent variables and among the independent variables of the study.  Table 3 shows the correlation between the dependent variable, ROA and the independent variables of ROA, MA, IER and FSZ among the independent variables themselves on the other hand. Generally, a high correlation is expected between dependent and independent variables while a low correlation is expected among independent variables. According to Gujarati (2004), a correlation coefficient between two independent variables of 0.80 is considered excessive, and thus certain measures are required to correct that anomaly in the data.

Table 3: Correlation Analysis Result

Correlation
Probability ROA MA IER FSZ
ROA 1.000000
—–
MA 0.032829 1.000000
0.5711 —–
IER 0.020784 -0.034525 1.000000
0.7200 0.5514 —–
FSZ 0.043355 -0.147135 -0.078784 1.000000
0.4544 0.0107 0.1735 —–

Source: E-View 10 Output (2023)

From the table, it can be seen that all the correlation coefficients among the independent variables are below 0.80. This point to the absence of possible multicollinearity among the independent variables and the correlation between the variables shows that there is a mix of both positive and negative correlation among the dependent and independent variables. There exist positive insignificant and 3%, 2% and 4% correlation between return on assets and misappropriate of assets, improper expenses recognition and firm size respectively indicating that the higher the firm size the higher the return on assets of the selected firms under study. Furthermore, it is notable from the analysis that other association between and within the variables of studies are weak, thus, signifies absence of possible multicollinearity.

Multicollinearity Test (VIF)

Multicollinearity is a statistical concept where several independent variables in a model are correlated. Multicollinearity occurs when one or more independent variants have a stronger influence on others and this condition is a violation of the linear regression model, that so it may affect the validity of the outcome in any analysis.

Multicollinearity tests are performed to test whether there is a strong correlation between independent variables that may result in misleading results. In Table 3, the coefficient for the highest correlation is -0.147135 (between MA and FSZ). Therefore, the low degree of correlation between independent variables indicates that multicollinearity is not a problem in the sample database. However, collinearity diagnostics tests were performed using the variance inflation factor (VIF) to further confirm the absence of multicollinearity problem between independent mutations. The results of the collinearity diagnostic test are presented in Table 4.3 below:

Table 4: Multicollinearity Test (VIF)

Coefficient Uncentered Centered
Variable Variance VIF VIF
C  639.01342  18.09563  NA
MA  79.838493  9.26539  1.881052
IER  147.26945  7.99326  1.946704
FSZ  261.04628  7.76748  1.966431

Source: E-View 10 Output (2023)

*Decision rule: Centered VIF less than 10 indicates the absence of multi-collinearity, while VIF Uncentered over 10 is a sign of multi-collinearity. Table 4 above shows the absence of multicollinearity between

independent variables, as all independent variables (MA, IER and FSZ) have value less than 10.

Heteroskedasticity Test

A heteroskedasticity test was performed as a diagnostic check to verify the robustness of the estimates. Heterogeneous variance occurs when the standard error of the variable being monitored is not constant over time. Heteroscedasticity violates linear regression modelling assumptions and can affect the validity of analytical results. On the other hand, heteroscedasticity does not cause any bias in the coefficient estimates, but it reduces the precision, and less precise coefficients are more likely to be estimated. The estimates are far from the correct population values that have been removed.

*Decision Rule: At 5% level of Significance

While heteroskedasticity is assumed to be absent in the test’s null hypothesis, it is assumed to be present in the alternative hypothesis. In the event that the P value is less than 5% level of significance, the null hypothesis must be rejected.

*Decision Rule: At 5% level of Significance

Hypothesis

H0: The Error Variances are all Equal (Homoskedastic)

H1: The Error Variances are not Equal (Heteroskedasticity)

Table 5: Heteroskedasticity Test

Value df Probability
Likelihood ratio  248.8360  20  0.0942
LR test summary:
Value df
Restricted LogL -153.4455  296
Unrestricted LogL -29.02747  296

Source: E-View 10 Output (2023)

Table 5 shows the results of the panel cross-section Heteroskedasticity regression test. The decision rule for the panel cross-section Heteroskedasticity test is stated thus:

The null hypothesis of the test states that there is no Heteroskedasticity, while the alternate hypothesis states that there is Heteroskedasticity. The null hypothesis is not to be accepted if the P value is greater than 5% level of significance. From the result in table 5 above with a ratio value of 248.8360 and a corresponding probability value of 0.0942 which is greater than 5%, the study therefore posits that, there is no reason to reject the null hypothesis. Consequently, based on the diagnostic probability 0.0942 the null hypothesis is not rejected, thus there is homoskedasticity, indicating that residuals are homoskedastic and as such the samples give a true reflection of the population.

Hausman Test

The Hausmann specification test is a model specification test used in panel data analysis to select between fixed and random effects models. Because the datasets utilised in this investigation were panel, both fixed and random effects regressions were performed. A Hausmann specification test was then used to choose between the fixed-effects and random-effects regression models. This test determined if the error term was connected to the regressor. As a result, the decision rule for the Hausmann specification test is presented at a 5% level of significance:

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

H1: Fixed effect is more appropriate for the Panel Regression analysis

As previously stated, if the p-value is less than 0.05, the null hypothesis is rejected. According to the null hypothesis, fixed effects are best suited for panel regression analysis (that is, the preferred model is the random effects). Similarly, if the p-value is less than 0.05, the null hypothesis is rejected. As a result, fixed effects are best suited for panel regression analysis (meaning we reject the random effects model)

Table 6: Hausman Specification Test.

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.
Cross-section random 1.975790 3 0.5774

Source: E-View 10 Output (2023)

The result of the Hausman test appended in the table above does not provide sufficient evidence to reject this null hypothesis at 5% level of significance as can be seen that the probability value (0.5774) 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. Because the dataset was a panel, both pooled and random effects regression analyses were done. The optimum model among the pooled-effects and random-effects regression models was then determined using a Breusch-Pagan Lagrangian multiplier test. At a 5% significance level, the decision rule for the Breusch-Pagan Lagrangian multiplier test is provide:

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

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

As previously stated, 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 Langranger Multiplier Test

Test Statistic d.f. Prob.
Breusch-Pagan LM 283.2350 190 0.0000

Source: E-View 10 Output (2023)

Based on the probability value of the Breusch-Pagan Langranger Multiplier Test at probability value of 0.0000, the null hypothesis is rejected, thus random effect is most appropriate when compared to pooled effect.

Test of Research Hypotheses

Table 8: Random Effect Regression Result

Dependent Variable: ROA
Method: Panel EGLS (Cross-section random effects)
Date: 09/25/23   Time: 15:39
Sample: 2008 2022
Periods included: 15
Cross-sections included: 20
Total panel (balanced) observations: 300
Swamy and Arora estimator of component variances
Variable Coefficient Std. Error t-Statistic Prob.
C 0.165260 0.228730 0.722511 0.4706
MA 2.11E-10 1.87E-10 1.126290 0.2610
IER 0.090931 0.211564 0.429807 0.6676
FSZ -7.55E-05 0.030654 -0.002464 0.9980
Effects Specification
S.D. Rho
Cross-section random 0.161001 0.1529
Idiosyncratic random 0.378988 0.8471
Weighted Statistics
R-squared 0.464799     Mean dependent var 0.104646
Adjusted R-squared 0.445587     S.D. dependent var 0.377335
S.E. of regression 0.378331     Sum squared resid 42.36787
F-statistic 0.475830     Durbin-Watson stat 1.853216
Prob(F-statistic) 0.699344

Source: E-View 10 Output (2023)

Table 9 revealed and analyses the panel random regression results of the explained variable proxied by ROA as well as the explanatory variables MA, IER and FSZ. Between the R2 and the adjusted R2, there is a range of values 46% and 44% respectively. The variation in the dependent variable (ROA) as a result of change in the independent variables is explained by the R2 of 46%. Therefore, it can be concluded that the independent variables have a combined predictive power of influencing on the financial performance of listed non-financial firms in Nigeria, with the remaining 54% been explained by other factors not included in the model. Furthermore, the regression results as presented above reveals an intercept of (0.165260) which is positive. This simply implies that when other variable are held constants, the financial performance of listed non-financial firms increases by 0.165260. The result of the constant is statistically insignificant, as indicated by a P-value of 0.4706. In terms of residual test, the model is free from serial correlation as revealed by the Durbin-Watson statistic of 1.85 is within the acceptable range of 1.5 to 2.0 for a sample of at least 50 observations

Table 9 described that the coefficient of the variable MA was 2.11 with a p-value of 0.2610 (>0.05). It can be deduced that misappropriation of assets has a positive and insignificant effect on the financial performance of listed non-financial firms which provide support for the null hypothesis.

Also, the second hypothesis revealed that the coefficient of the variable IER was 0.09 with a p-value of 0.6676 (<0.05). It can be deduced that improper expenses recognition has a positive and significant effect on the financial performance of listed non-financial firms which provide support for the null hypothesis. Finally, it is evidence from the control variable that firm size has a positive and statistical insignificant effect on the financial performance of listed non-financial firms in Nigeria.

DISCUSSION OF FINDINGS

The study empirically assesses the effect of misappropriation of assets and improper assets recognition on financial performance of listed non-financial firms in Nigeria. The result of the analysis as recorded above revealed that misappropriation of assets has positive and insignificant effect on financial performance of listed non-financial firms. This implies that while there is a relationship between misappropriation of assets and financial performance, it is not strong enough or statistically significant to draw a meaningful conclusion. In other words, the misappropriation of assets does not appear to be a major driver of financial performance for these listed non-financial firms. This study is in tandem with the study of Chinedu (2020) while it disagrees with the study of Smith (2022) and Ibrahim (2019).

It is evidenced also from the second hypothesis that a positive and insignificant effect exists between improper assets recognition and financial performance of listed non-financial firms. The research outcome congruent with the apriori expectation. The statement implies that, while improper assets recognition may be an accounting issue that needs to be corrected for accuracy and compliance reasons, it does not necessarily distort the overall picture of a firm’s financial performance in the study area. If improper assets recognition had a significant effect on financial performance, it could mislead investors and creditors and affect their decisions. This study concord with the study of Isa and Awalludin (2020) but disagrees with the study of Adeniyi and Olatunde (2019) and Davis (2022).

CONCLUSION AND RECOMMENDATIONS

The study evaluated the effect of misappropriation of assets and improper assets recognition on financial performance of listed non-financial firms in Nigeria. Based on the study findings reached through the study objectives guided by the study hypotheses, the following conclusion were made; the study affirmed that misappropriation of assets and improper expenses recognition has no significant effect on financial performance of listed non-financial firms. In essence, the statement that misappropriation of assets and improper expenses recognition has no significant effect on a firm’s financial performance implies that, based on the available data and analysis, this misconduct does not appear to be a primary driver of financial success or failure for the firm. However, this conclusion should be interpreted cautiously and periodically reevaluated in light of changing circumstances and business conditions. Therefore, based on the findings of this study, the following recommendations are made for efficient financial performance of listed non-financial firms on the Nigeria Exchange Group;

  1. Listed non-financial firms should encourage a mindset of continuous improvement throughout the organization and seek opportunities to optimize operations and financial performance, even if misappropriation is not a significant issue.
  2. While improper assets recognition may not have a substantial effect on financial performance, it is crucial to maintain strong internal controls to ensure accurate financial reporting and compliance with accounting standards. It is recommended that firms continue to invest in and improve their internal control systems to detect and prevent errors in assets recognition.

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APPENDIX

Data Presentation

FIRM CODE YEAR ROA MA IER FSZ
11 Plc 1 2008 0.042       33,650,852 0.216 5.431
11 Plc 1 2009 0.213       54,356,361 0.208 6.519
11 Plc 1 2010 0.231       43,698,056 0.209 6.026
11 Plc 1 2011 0.234       41,855,597 0.271 6.622
11 Plc 1 2012 0.205       39,169,330 0.255 7.431
11 Plc 1 2013 0.319       40,465,910 0.206 7.526
11 Plc 1 2014 0.129       41,027,271 0.175 7.610
11 Plc 1 2015 0.090       43,439,018 0.179 7.692
11 Plc 1 2016 0.132       47,692,034 0.162 7.733
11 Plc 1 2017 0.101       57,774,357 0.142 7.790
11 Plc 1 2018 0.132        (1,479,711) 0.459 7.873
11 Plc 1 2019 0.117        (1,240,852) 0.328 7.849
11 Plc 1 2020 0.097       11,058,334 0.218 7.960
11 Plc 1 2021 0.073       17,527,937 0.241 7.969
11 Plc 1 2022 0.080       15,954,356 0.125 7.640
Academy Press Plc 2 2008 0.518       17,114,310 0.170 4.028
Academy Press Plc 2 2009 0.086       15,853,244 0.313 5.839
Academy Press Plc 2 2010 0.067       17,617,231 0.248 6.032
Academy Press Plc 2 2011 0.080       19,570,304 0.224 6.116
Academy Press Plc 2 2012 0.026       23,142,503 0.182 6.374
Academy Press Plc 2 2013 -0.026     140,022,253 0.084 6.451
Academy Press Plc 2 2014 0.030     154,467,306 0.104 6.550
Academy Press Plc 2 2015 0.018     167,722,696 0.069 6.579
Academy Press Plc 2 2016 -0.016     185,717,245 0.051 6.572
Academy Press Plc 2 2017 0.298     264,377,422 0.035 6.547
Academy Press Plc 2 2018 0.450     333,094,631 0.024 6.474
Academy Press Plc 2 2019 0.296     311,534,777 0.044 6.437
Academy Press Plc 2 2020 0.195     340,314,880 0.043 6.425
Academy Press Plc 2 2021 0.291     415,954,103 0.040 6.418
Academy Press Plc 2 2022 0.090     501,873,154 0.039 6.721
Afromedia Plc 3 2008 0.155     443,494,583 0.164 5.022
Afromedia Plc 3 2009 0.253     533,984,898 0.180 5.682
Afromedia Plc 3 2010 0.296     640,760,184 0.175 5.018
Afromedia Plc 3 2011 0.200     740,941,867 0.130 5.036
Afromedia Plc 3 2012 0.178     832,606,591 0.110 6.944
Afromedia Plc 3 2013 0.175     899,065,537 0.102 6.639
Afromedia Plc 3 2014 0.228     120,869,185 0.124 6.623
Afromedia Plc 3 2015 0.362     189,157,726 0.133 6.557
Afromedia Plc 3 2016 0.321     181,824,267 0.138 6.362
Afromedia Plc 3 2017 0.296     169,426,592 0.121 6.333
Afromedia Plc 3 2018 0.245         5,040,595 0.006 6.264
Afromedia Plc 3 2019 0.059         5,454,279 0.051 6.333
Afromedia Plc 3 2020 0.073         5,274,072 0.045 6.324
Afromedia Plc 3 2021 0.057         3,104,476 0.032 6.320
Afromedia Plc 3 2022 0.090         2,793,457 0.485 6.592
Aluminium Extrusion Indus 4 2008 0.029         5,560,788 0.351 5.027
Aluminium Extrusion Indus 4 2009 0.047         7,030,913 0.162 5.217
Aluminium Extrusion Indus 4 2010 -0.069         6,111,928 0.070 5.519
Aluminium Extrusion Indus 4 2011 0.005       11,325,614 0.062 5.878
Aluminium Extrusion Indus 4 2012 -0.161       10,222,960 0.067 6.089
Aluminium Extrusion Indus 4 2013 0.062     217,347,825 0.233 6.206
Aluminium Extrusion Indus 4 2014 -0.225     253,668,598 0.242 6.227
Aluminium Extrusion Indus 4 2015 0.128       55,627,406 0.279 6.244
Aluminium Extrusion Indus 4 2016 0.080       59,368,103 0.278 6.265
Aluminium Extrusion Indus 4 2017 0.177       87,253,137 0.209 6.582
Aluminium Extrusion Indus 4 2018 0.244     204,062,484 0.179 6.354
Aluminium Extrusion Indus 4 2019 0.274     223,765,068 0.204 6.398
Aluminium Extrusion Indus 4 2020 0.315     150,462,243 0.271 6.394
Aluminium Extrusion Indus 4 2021 0.300     147,400,702 0.245 6.409
Aluminium Extrusion Indus 4 2022 0.263     177,116,243 0.255 6.620
Ardova Plc (Forte Oil) 5 2008 0.240     319,532,847 0.252 5.203
Ardova Plc (Forte Oil) 5 2009 0.143     362,913,698 0.258 5.093
Ardova Plc (Forte Oil) 5 2010 -0.185     511,439,433 0.264 5.938
Ardova Plc (Forte Oil) 5 2011 0.086     513,775,503 0.274 6.546
Ardova Plc (Forte Oil) 5 2012 -0.105     561,842,234 0.265 7.655
Ardova Plc (Forte Oil) 5 2013 0.124     593,886,866 0.257 7.629
Ardova Plc (Forte Oil) 5 2014 0.036     574,811,669 0.280 8.020
Ardova Plc (Forte Oil) 5 2015 0.066     566,173,140 0.300 8.144
Ardova Plc (Forte Oil) 5 2016 0.200     613,113,872 0.266 8.085
Ardova Plc (Forte Oil) 5 2017 0.220     667,706,031 0.282 8.148
Ardova Plc (Forte Oil) 5 2018 0.122     261,415,160 0.040 8.168
Ardova Plc (Forte Oil) 5 2019 0.128     326,158,695 0.058 8.151
Ardova Plc (Forte Oil) 5 2020 0.323     329,468,281 0.060 7.672
Ardova Plc (Forte Oil) 5 2021 0.471     330,603,088 0.069 7.812
Ardova Plc (Forte Oil) 5 2022 0.396     343,966,532 0.057 8.193
Associated Bus Company 6 2008 0.638     461,877,902 0.045 5.092
Associated Bus Company 6 2009 0.260     483,295,607 0.044 6.111
Associated Bus Company 6 2010 0.180     470,536,967 0.051 6.409
Associated Bus Company 6 2011 0.264     473,166,521 0.058 6.501
Associated Bus Company 6 2012 0.201     488,373,808 0.048 6.705
Associated Bus Company 6 2013 0.264     157,557,871 0.182 6.698
Associated Bus Company 6 2014 0.233     184,108,607 0.294 6.751
Associated Bus Company 6 2015 0.024     189,071,550 0.329 6.809
Associated Bus Company 6 2016 0.251     178,382,907 0.338 6.777
Associated Bus Company 6 2017 0.017     176,637,163 0.372 6.636
Associated Bus Company 6 2018 0.008     190,904,951 0.310 6.650
Associated Bus Company 6 2019 -0.119     247,560,747 0.251 6.660
Associated Bus Company 6 2020 -0.118     255,336,821 0.250 6.711
Associated Bus Company 6 2021 -0.068     204,604,903 0.303 6.769
Associated Bus Company 6 2022 -0.059     244,948,438 0.228 6.872
B.O.C Gases Nig 7 2008 -0.034         7,449,772 0.246 4.027
B.O.C Gases Nig 7 2009 -0.041       13,038,228 0.242 5.918
B.O.C Gases Nig 7 2010 0.956       15,439,025 0.231 5.317
B.O.C Gases Nig 7 2011 0.333       15,734,211 0.250 6.052
B.O.C Gases Nig 7 2012 0.663       17,810,370 0.247 6.350
B.O.C Gases Nig 7 2013 -0.621       17,628,229 0.187 6.423
B.O.C Gases Nig 7 2014 -0.609       20,465,841 0.188 6.460
B.O.C Gases Nig 7 2015 -3.913       19,865,295 0.222 6.534
B.O.C Gases Nig 7 2016 0.246       31,945,956 0.224 6.507
B.O.C Gases Nig 7 2017 -0.769       46,479,821 0.130 6.560
B.O.C Gases Nig 7 2018 -0.202       20,033,429 0.207 6.628
B.O.C Gases Nig 7 2019 -0.142       21,019,335 0.218 6.652
B.O.C Gases Nig 7 2020 0.356       20,730,229 0.223 6.702
B.O.C Gases Nig 7 2021 0.264       25,488,705 0.222 6.734
B.O.C Gases Nig 7 2022 0.150       35,064,678 0.204 7.045
Berger Paints Nig 8 2008 0.230       47,000,050 0.185 6.023
Berger Paints Nig 8 2009 -0.459       46,584,126 0.200 6.251
Berger Paints Nig 8 2010 0.123       51,603,320 0.197 6.304
Berger Paints Nig 8 2011 0.137       59,719,121 0.191 6.393
Berger Paints Nig 8 2012 0.304       58,123,060 0.170 6.427
Berger Paints Nig 8 2013 0.686       93,530,572 0.157 6.463
Berger Paints Nig 8 2014 0.842     104,202,536 0.167 6.549
Berger Paints Nig 8 2015 0.465     103,655,795 0.737 6.561
Berger Paints Nig 8 2016 0.788       98,543,630 0.720 6.591
Berger Paints Nig 8 2017 0.425       92,461,890 0.157 6.613
Berger Paints Nig 8 2018 0.709     109,351,471 0.149 6.635
Berger Paints Nig 8 2019 0.988     109,957,055 0.227 6.657
Berger Paints Nig 8 2020 0.566       98,710,613 0.237 6.705
Berger Paints Nig 8 2021 0.031       74,305,989 0.281 6.697
Berger Paints Nig 8 2022 0.858       93,231,496 0.235 6.781
Beta Glass Company 9 2008 0.263         2,685,921 0.078 6.092
Beta Glass Company 9 2009 0.784         3,461,296 0.083 6.637
Beta Glass Company 9 2010 0.090         4,411,757 0.058 6.032
Beta Glass Company 9 2011 0.905         2,996,641 0.080 7.281
Beta Glass Company 9 2012 0.619         3,250,394 0.062 7.256
Beta Glass Company 9 2013 0.159         3,184,840 0.056 7.351
Beta Glass Company 9 2014 0.562         3,091,520 0.044 7.434
Beta Glass Company 9 2015 0.535         2,372,645 0.210 7.430
Beta Glass Company 9 2016 0.905         2,188,573 0.121 7.434
Beta Glass Company 9 2017 0.332         1,787,313 0.441 7.521
Beta Glass Company 9 2018 0.343     144,286,055 0.207 7.582
Beta Glass Company 9 2019 0.824     171,060,560 0.218 7.664
Beta Glass Company 9 2020 0.383     164,237,350 0.223 7.717
Beta Glass Company 9 2021 0.290     180,958,904 0.222 7.732
Beta Glass Company 9 2022 0.392     208,388,975 0.204 7.892
Cadbury Nig 10 2008 0.754     225,406,035 0.185 6.027
Cadbury Nig 10 2009 0.808     255,562,934 0.200 6.843
Cadbury Nig 10 2010 0.956     284,615,058 0.197 6.682
Cadbury Nig 10 2011 0.333     314,694,006 0.191 6.834
Cadbury Nig 10 2012 0.666       61,513,012 0.170 7.527
Cadbury Nig 10 2013 0.621        (1,036,676) 0.216 7.604
Cadbury Nig 10 2014 0.093           (983,879) 0.208 7.635
Cadbury Nig 10 2015 -0.913        (1,163,407) 0.209 7.460
Cadbury Nig 10 2016 0.246        (1,096,704) 0.271 7.454
Cadbury Nig 10 2017 -0.791           (356,502) 0.255 7.453
Cadbury Nig 10 2018 0.202           (920,064) 0.206 7.454
Cadbury Nig 10 2019 0.123        (2,228,492) 0.175 7.440
Cadbury Nig 10 2020 0.566        (2,194,913) 0.179 7.459
Cadbury Nig 10 2021 0.031           (699,746) 0.162 7.521
Cadbury Nig 10 2022 0.858         1,185,335 0.142 7.610
Champion Breweries 11 2008 0.263       20,039,356 0.459 6.574
Champion Breweries 11 2009 0.784       24,577,724 0.328 6.792
Champion Breweries 11 2010 0.090       12,749,451 0.218 6.039
Champion Breweries 11 2011 0.905       12,285,297 0.241 6.033
Champion Breweries 11 2012 0.619       11,056,733 0.125 6.843
Champion Breweries 11 2013 0.159       11,742,791 0.170 6.832
Champion Breweries 11 2014 0.562       12,676,146 0.313 6.961
Champion Breweries 11 2015 0.535       13,566,235 0.248 6.982
Champion Breweries 11 2016 0.905       13,549,523 0.224 7.014
Champion Breweries 11 2017 0.332       13,636,354 0.182 6.998
Champion Breweries 11 2018 0.343         3,430,000 0.084 7.004
Champion Breweries 11 2019 0.824         4,608,386 0.104 7.021
Champion Breweries 11 2020 0.639         5,870,431 0.069 7.041
Champion Breweries 11 2021 0.222         7,121,637 0.051 7.056
Champion Breweries 11 2022 0.267         7,670,880 0.035 7.893
Chellarams 12 2008 0.259         8,135,460 0.024 6.043
Chellarams 12 2009 0.326         7,935,532 0.044 6.632
Chellarams 12 2010 0.109         8,031,796 0.043 6.503
Chellarams 12 2011 0.233         8,042,994 0.040 7.010
Chellarams 12 2012 0.024         9,219,643 0.039 7.037
Chellarams 12 2013 0.251       46,269,159 0.164 7.169
Chellarams 12 2014 0.017       53,817,512 0.180 7.188
Chellarams 12 2015 0.008       58,526,202 0.175 7.225
Chellarams 12 2016 0.436       66,386,057 0.130 7.265
Chellarams 12 2017 0.235       74,584,750 0.110 7.141
Chellarams 12 2018 0.120       99,207,358 0.102 7.126
Chellarams 12 2019 0.543     107,180,126 0.124 7.120
Chellarams 12 2020 0.234     118,082,942 0.133 7.102
Chellarams 12 2021 0.119     125,302,900 0.138 6.980
Chellarams 12 2022 0.118     129,830,169 0.121 6.356
Chemical & Allied Product 13 2008 0.068       17,253,299 0.006 5.382
Chemical & Allied Product 13 2009 0.059       18,950,996 0.051 6.002
Chemical & Allied Product 13 2010 0.034       22,194,742 0.045 6.049
Chemical & Allied Product 13 2011 0.041       26,045,438 0.032 6.392
Chemical & Allied Product 13 2012 0.956       30,153,349 0.485 6.487
Chemical & Allied Product 13 2013 0.333       32,577,873 0.351 6.459
Chemical & Allied Product 13 2014 0.663       33,315,264 0.162 6.482
Chemical & Allied Product 13 2015 -0.621       34,641,998 0.070 6.489
Chemical & Allied Product 13 2016 -0.609       33,732,002 0.062 6.533
Chemical & Allied Product 13 2017 -0.913       30,686,788 0.067 6.692
Chemical & Allied Product 13 2018 0.246         1,353,145 0.233 6.700
Chemical & Allied Product 13 2019 -0.769         1,605,717 0.242 6.800
Chemical & Allied Product 13 2020 0.183         1,773,912 0.279 6.830
Chemical & Allied Product 13 2021 0.115         1,480,063 0.278 6.931
Chemical & Allied Product 13 2022 0.053         1,250,937 0.209 7.321
Conoil 14 2008 0.013         1,239,578 0.179 6.043
Conoil 14 2009 0.031         1,174,262 0.204 6.873
Conoil 14 2010 0.074         1,150,712 0.271 7.245
Conoil 14 2011 0.249         2,768,993 0.245 7.691
Conoil 14 2012 0.267         2,787,771 0.255 7.791
Conoil 14 2013 0.290         9,834,229 0.252 7.920
Conoil 14 2014 0.322         9,347,922 0.258 7.916
Conoil 14 2015 0.430         7,478,808 0.264 7.937
Conoil 14 2016 0.402         8,003,253 0.274 7.841
Conoil 14 2017 0.445       11,689,943 0.265 7.844
Conoil 14 2018 0.364       75,908,375 0.257 7.798
Conoil 14 2019 0.258       82,789,543 0.280 7.785
Conoil 14 2020 0.494       66,528,350 0.300 7.803
Conoil 14 2021 0.293       62,129,120 0.266 7.689
Conoil 14 2022 0.886       65,939,051 0.282 8.932
Dangote Cement 15 2008 0.654       97,525,226 0.040 7.039
Dangote Cement 15 2009 0.373     112,359,185 0.058 8.374
Dangote Cement 15 2010 0.558     171,964,263 0.060 8.032
Dangote Cement 15 2011 0.511     172,321,503 0.069 8.472
Dangote Cement 15 2012 0.055     165,805,542 0.057 8.721
Dangote Cement 15 2013 -0.300     178,150,934 0.045 8.828
Dangote Cement 15 2014 0.175     166,644,184 0.044 8.926
Dangote Cement 15 2015 0.209     167,564,562 0.051 8.993
Dangote Cement 15 2016 0.203     161,150,877 0.058 9.046
Dangote Cement 15 2017 0.158     172,139,303 0.048 9.184
Dangote Cement 15 2018 0.052       80,016,501 0.182 9.222
Dangote Cement 15 2019 0.143       93,523,520 0.294 9.229
Dangote Cement 15 2020 0.093       98,943,111 0.172 9.241
Dangote Cement 15 2021 0.365       96,653,666 0.269 9.306
Dangote Cement 15 2022 -0.098     100,244,139 0.168 9.578
Flour Mills of Nigeria 16 2008 -0.240       18,115,699 0.094 6.058
Flour Mills of Nigeria 16 2009 0.969       11,446,296 0.027 7.053
Flour Mills of Nigeria 16 2010 0.051       13,829,273 0.026 7.043
Flour Mills of Nigeria 16 2011 0.682       14,636,890 0.065 8.032
Flour Mills of Nigeria 16 2012 0.357       15,987,794 0.079 8.213
Flour Mills of Nigeria 16 2013 0.691       40,352,504 0.069 8.367
Flour Mills of Nigeria 16 2014 0.340       46,039,111 0.033 8.448
Flour Mills of Nigeria 16 2015 0.456       45,061,717 0.390 8.473
Flour Mills of Nigeria 16 2016 0.409       48,341,376 0.256 8.535
Flour Mills of Nigeria 16 2017 0.343       41,660,605 0.127 8.538
Flour Mills of Nigeria 16 2018 0.307       42,943,015 0.011 8.684
Flour Mills of Nigeria 16 2019 0.577       87,588,174 0.069 8.611
Flour Mills of Nigeria 16 2020 0.327       89,060,462 0.064 8.620
Flour Mills of Nigeria 16 2021 0.272       73,038,140 0.033 8.636
Flour Mills of Nigeria 16 2022 0.747       74,286,575 0.021 8.026
Glaxosmithkline Nig 17 2008 0.825         3,228,064 0.020 7.048
Glaxosmithkline Nig 17 2009 0.195         3,267,313 0.047 5.392
Glaxosmithkline Nig 17 2010 0.139         3,747,004 0.233 7.083
Glaxosmithkline Nig 17 2011 0.603         3,802,832 0.252 7.049
Glaxosmithkline Nig 17 2012 0.269         4,299,252 0.203 7.254
Glaxosmithkline Nig 17 2013 0.173         4,463,206 0.191 7.338
Glaxosmithkline Nig 17 2014 0.300         4,822,994 0.190 7.419
Glaxosmithkline Nig 17 2015 0.145         5,932,044 0.381 7.447
Glaxosmithkline Nig 17 2016 0.080         8,687,013 0.241 7.496
Glaxosmithkline Nig 17 2017 0.063       12,401,122 0.204 7.450
Glaxosmithkline Nig 17 2018 0.064         6,577,579 0.250 7.423
Glaxosmithkline Nig 17 2019 0.335         6,890,626 0.175 7.196
Glaxosmithkline Nig 17 2020 0.143         6,307,306 0.053 7.272
Glaxosmithkline Nig 17 2021 0.168         7,088,233 0.124 7.375
Glaxosmithkline Nig 17 2022 -0.183         8,046,227 0.183 7.693
Guinness Nig 18 2008 -0.279       11,535,212 0.232 6.048
Guinness Nig 18 2009 0.024       11,893,480 0.185 6.038
Guinness Nig 18 2010 -0.179       11,089,285 0.105 7.832
Guinness Nig 18 2011 0.321       12,719,820 0.123 7.736
Guinness Nig 18 2012 -0.080       14,630,680 0.058 7.965
Guinness Nig 18 2013 -0.489       26,584,929 0.046 8.025
Guinness Nig 18 2014 -0.001       26,584,929 0.044 8.083
Guinness Nig 18 2015 0.842       27,607,313 0.235 8.122
Guinness Nig 18 2016 0.188       26,584,929 0.140 8.087
Guinness Nig 18 2017 0.087       33,792,289 0.132 8.137
Guinness Nig 18 2018 0.257       34,076,230 0.135 8.164
Guinness Nig 18 2019 0.133       33,750,379 0.158 8.185
Guinness Nig 18 2020 0.157       33,816,582 0.013 8.206
Guinness Nig 18 2021 0.202       22,625,192 0.052 8.159
Guinness Nig 18 2022 -0.110       23,045,466 0.028 8.937
Lafarge Cement Wapco Nig 19 2008 0.028         1,137,213 0.023 7.261
Lafarge Cement Wapco Nig 19 2009 0.842         1,183,938 0.025 7.392
Lafarge Cement Wapco Nig 19 2010 -0.783         1,241,581 0.569 7.942
Lafarge Cement Wapco Nig 19 2011 0.688         1,305,603 0.505 8.016
Lafarge Cement Wapco Nig 19 2012 -0.436         1,410,567 0.323 8.183
Lafarge Cement Wapco Nig 19 2013 -0.404         1,427,112 0.149 8.182
Lafarge Cement Wapco Nig 19 2014 0.484         1,423,779 0.263 8.207
Lafarge Cement Wapco Nig 19 2015 -0.304         1,182,145 0.093 8.486
Lafarge Cement Wapco Nig 19 2016 0.239            831,339 0.110 8.656
Lafarge Cement Wapco Nig 19 2017 -0.064            555,806 0.112 8.701
Lafarge Cement Wapco Nig 19 2018 0.086       34,185,562 0.064 8.762
Lafarge Cement Wapco Nig 19 2019 0.250       40,594,801 0.020 8.733
Lafarge Cement Wapco Nig 19 2020 0.321       35,939,643 0.259 8.696
Lafarge Cement Wapco Nig 19 2021 0.415       38,007,074 0.383 8.705
Lafarge Cement Wapco Nig 19 2022 0.104       30,878,075 0.247 8.944
Total Nigeria 20 2008 0.286       44,878,177 0.221 6.840
Total Nigeria 20 2009 0.162       50,220,486 0.171 6.048
Total Nigeria 20 2010 0.021       45,557,630 0.185 7.632
Total Nigeria 20 2011 0.007       29,296,984 0.116 7.696
Total Nigeria 20 2012 0.017       21,386,797 0.096 7.769
Total Nigeria 20 2013 0.009         1,093,934 0.047 7.881
Total Nigeria 20 2014 -0.047         1,082,120 0.075 7.900
Total Nigeria 20 2015 0.216         1,169,736 0.111 7.980
Total Nigeria 20 2016 -0.057         1,162,549 0.094 7.922
Total Nigeria 20 2017 0.104            837,439 0.105 8.136
Total Nigeria 20 2018 0.120            921,213 0.025 8.033
Total Nigeria 20 2019 0.097         2,044,814 0.104 8.122
Total Nigeria 20 2020 0.021         2,195,713 0.091 8.126
Total Nigeria 20 2021 0.063         1,494,380 0.061 8.157
Total Nigeria 20 2022 0.426         1,374,220 0.126 8.823

Source: NGX Fact-Book

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