Purpose

One main concern and issue affecting earnings quality is the extent to which managers manipulate earnings to mislead stakeholders about the underlying economic performance of the company or to influence contractual outcomes that depend on reported accounting numbers. This study builds on prior research and examines empirically the relationship between board leadership structure and earnings quality of manufacturing firms in Nigeria. The purpose of this paper is to specifically focus on four board structure characteristics: board size, composition, proportion of non-executive directors and CEO duality.

Design/methodology/approach

Data used for this investigation were collected from secondary sources, i.e. annual reports and accounts. The study used the Pooled OLS regression model to examine the effect of the board structure on earnings management for a sample of 45 non-financial listed Nigerian companies (conglomerates, consumer goods and industrial goods firms) for the years 2011 to 2016.

Findings

Based on the analysis, board size and board composition were positive and significant. However, proportion of non-executive directors was negative and significant; while, CEO duality was positive and statistically significant. It was consequently recommended that audit firms should review their audit business model and become more circumspect of their client, e.g. provide fraud assessment and checks for earnings quality. Boards should not just reflect size but rather the skills and expertise of individuals appointed to the board. Furtherance to this, the effectiveness of boards can be improved by committees and sub-committees allocation of duties.

Originality/value

Few studies have addressed this area in the country.

Sun and Rath (2008) posit that the primary role of financial statements is to disclose a company’s financial information to internal and external users in a timely and reliable manner. This was further reiterated by the International Accounting Standard Board (2001) that the objective of a financial report is to provide information about the financial position, performance and change in financial position of an entity that is useful to a wide range of users in making economic decisions. In the preparation of financial reports, managers are free to select accounting and reporting methods (Algharaballi, 2013). This, as in most cases, leads to the selection of reporting methods that could be misleading to the users of such information. This arises from the agency relationship; when shareholders (principals) delegate authority to managers (agents) as the decision makers of the corporations (Ruangviset et al., 2014). Managers (agents) are led to self-seeking behavior, and thereby present the most successful image to the market, by exploiting insufficiencies of accounting rules (Sayari et al., 2013).

Financial reporting system is designed to provide value-relevant financial information for all users (AL-Dhamari and Ismail, 2014), and a sound system of corporate governance is expected to curb the managerial use of opportunistic earnings management activities (Hashim and Devi, 2007). Earnings management may take “the form of creative accounting such as recording anticipated sales as turnover for the present year, or the reduction in the cost of research and development” (Obigbemi et al., 2016).

It may also involve the use of discretionary accruals, the accumulation of accrued expenses in the bid to give a different picture of the financial well-being of the company (Obigbemi et al., 2016). Evidence of such misdeeds was highlighted by reporting scandals that have rocked both the financial and non-financial sectors. Such erosions in earnings quality caused investors to be less confident in the integrity of accounting numbers and thereby unable to make informed investment decisions (AL-Dhamari and Ismail, 2014). Therefore, the literature is filled with issues bothering corporate boards as the main focal point of the corporate governance discuss. Discussions on boards size, proportion of NEDs, CEO duality, women representation, among several others have emerged (Adams et al., 2010; Anderson et al., 2004).

According to Jensen (1993), the board is responsible for decision making and the operation of a company. Boards define the rules for the chief executive officer (CEO/executive director) regarding hiring, firing and compensation plans, and provide high-level advice (Holtz and Sarlo Neto, 2014). According to Vafeas (2000), boards are responsible for monitoring the quality of information contained in financial reports and controlling the behavior of managers to ensure that their actions are aligned with the interests of stakeholders. The ability of managers to effectively monitor the quality of such information is constrained by the structure of the board and effectiveness of internal controls (Alves, 2012). This was succinctly put by Obigbemi et al. (2016) that “the board structure of an organization gives an overview of the standard of such organization, which also influences its public image.” In general, boards are responsible for monitoring, evaluating and disciplining the management of a company (Anderson et al., 2004). The present study therefore evaluates the how board leadership structure of Nigerian manufacturing firms is related to earnings quality of such firms.

Studies have investigated and documented the effect of board leadership structure on accounting information quality (Yasser and Mamun, 2016; Holtz and Sarlo Neto, 2014; Alkdai and Hanefah, 2012; Alves, 2012; Dimitropoulos and Asteriou, 2010; Habib and Azim, 2008; Firth et al., 2007; Ahmed et al., 2006; Vafeas, 2000). The studies however, present mixed findings on the relationship between both. While some document a positive association, others show a negative association. Yasser and Mamun (2016), in the context of Asia-Pacific countries, reveal that board leadership structure was not associated with firm performance and financial reporting quality. Holtz and Sarlo Neto (2014) show that for companies listed on the Brazilian Securities, Commodities and Futures Exchange, the characteristics of board independence and separation of the roles of chairman and executive director positively influence the quality of reported accounting information. AL-Dhamari and Ismail (2014), on a sample of firms in Malaysia, find that the quality of earnings is higher among firms with independent chairmen than firms with non-independent chairmen. They also found inconclusive results for board independence, and that investors do not perceive board size as a good indicator of quality earnings.

Chaharsoughi and Rahman (2013), on a sample of firms listed on the Tehran Stock Exchange (TSE), show that there was an insignificant positive relationship among independent boards of directors, managerial ownership and earnings quality. Subsequent analysis shows an insignificant negative relationship between board size and earnings quality.

However, the literature is scanty on this area of research in Nigeria. Osemene (2012) observed that majority of Nigerian firms are driven by the need to make more and more profits to the detriment of other stakeholders. As such, managers may engage in engage in earnings manipulation to meet or beat analysts forecast. Few studies by Isa and Farouk (2018) and Madugba and Ogbonnaya (2017) focused on money deposit banks, while the study by Eze (2017) focused on six food product firms. Another extensive study was conducted by Obigbemi et al. (2016) on a sample of 137 quoted firms from the period 2003–2010. This was period before the mandatory adoption of International Financial Reporting Standards in the country.

Despite the adoption of several corporate governance codes in Nigeria, such as the Companies and Allied Matters Act (1990) as amended, Financial Reporting Council of Nigeria Act (2011), Bank and other Financial Institution Act (for financial institutions), among others, that serve as guidelines for the preparation of financial reports (Obigbemi et al., 2016). The country has witnessed its fair share of corporate collapses; the study therefore addresses the link between board leadership structure and earnings quality of manufacturing firms in Nigeria.

Based on the above research problem, the main objective of this study is to determine the relationship between board leadership and earnings quality of quoted manufacturing firms in Nigeria. The specific objectives of the study are as follows:

  1. to determine the relationship between board size and earnings quality of manufacturing firms;

  2. to determine the relationship between board composition and earnings quality of manufacturing firms;

  3. to determine the relationship between board independence and earnings quality of manufacturing firms; and

  4. to investigate the relationship between ceo duality and earnings quality of manufacturing firms.

According to the Association of Chartered Certified Accountants, the most prominent group of actors in corporate governance are the company’s directors, who can be either executive or non-executive directors (NEDs). Board characteristics, such as the distinction between the CEO and the chairman, and the percentage of non-executive (outside directors) in the board can be seen as among the internal mechanisms of corporate governance (Mousa et al., 2012). According to Kumar and Singh (2010), the primary role of the board of directors is that of trusteeship to protect and enhance shareholders’ value through strategic supervision. As trustees, they will ensure that the company has clear goals relating to shareholders’ value and its growth. The provide direction, and exercise appropriate control to ensure that the company is managed in a manner that fulfils stakeholders’ aspirations and societal expectations.

As one of the mechanisms, the board of directors is expected to monitor and control the behavior of managers to ensure they act on the behalf of shareholders and protect shareholders’ investment (Hendry and Kiel, 2004). In addition, the board is accountable to endorse the strategy of the firm; develop directional policy; appoint, supervise and remunerate senior executives; and ensure accountability of the firm to its related parties (Ponnu, 2008). Several board characteristics (e.g. board size, board composition, role duality) have been examined in the literature (Yermack, 1996; John and Senbet, 1998; Pye, 2000; Kiel and Nicholson, 2003).

Empirical research has documented that board size may inform the level of disclosure and transparency in corporation (Majeed et al., 2015). Smaller boards are easier to coordinate; quicker in making decisions; less likely to have free-rider problems; and less likely to oppose innovation (Dimitropoulos and Asteriou, 2010). Smaller boards also facilitate the influential exchange of ideas between the firm and its directors and are less likely to exacerbate the coalition costs among board members (Vafeas, 2000). Board efficiency involves the issue of increases in coalition costs between members and the fact that boards with more members have greater difficulty finding time to discuss and reach consensus on issues pertaining to the company’s organizational structure (Firth et al., 2007). Lipton and Lorsch (1992) suggest that one reason for the lack of meaningful dialogue on boards is their size. According to the authors, when a board has more than ten members, it becomes difficult for everyone to express their opinions and ideas in the limited time available for meetings.

According to Ahmed and Duellman (2007), larger boards can face the problem of “free riding” in the sense that the members of the board depend on each other to monitor management. Jensen (1993) believes that as the number of directors’ increases, the board’s efficiency decreases and internal conflicts can arise.

The perspective that board monitoring is a function of not only the composition of the board as a whole, but also of the structure and composition of the board’s subcommittees is a relatively recent one. Kesner (1988) posits that most crucial board decisions are made at the committee level. Corollary to this, Vance (1983) identified four committees that are vital to corporate decision-making: audit, executive, compensation, nomination committee.

Klein (1998) finds no association between board composition and firm performance; however, the structure of accounting and finance committees impact performance. Similarly, Davidson et al. (1998) find that the composition of a firm’s compensation committee influences the market’s perception of golden parachute adoption. One deduces from these studies that outside directors may be more important in committees that handle agency issues (e.g. compensation and audit committees), while insiders may best use their knowledge of corporate activities on committees that focus on firm-specific issues (e.g. investment and finance committees) (Chen and Wu, 2016).

Board independence refers to the extent to which a board is comprised of non-executive directors who have no relationship with the firm beyond the role of director (Davidson et al., 2005). A non-executive director is defined as a director who is not employed in the company’s business activities and whose role is to provide an outsider’s contribution and oversight to the board of directors (Hanrahan et al., 2001). A non-executive director who is entirely independent from management is expected to offer shareholders the greatest protection in monitoring management (Baysinger and Butler, 1985). Fama and Jensen (1983) posit that the superior monitoring ability of non-executives can be attributed to the incentive to maintain their reputations in the external labor market.

Booth et al. (2002) identify two measures of independence on the board: the percentage of outside directors on the board and whether the CEO also serves as the board chairperson. Furthermore, appointing outside directors to the board appears to be an effective corporate governance mechanism to reduce the agency problem and increase earnings quality (Klein, 2002; Peasnell et al., 2000).

Studies by Beekes et al. (2004), Firth et al. (2007), Ahmed and Duellman (2007), Dimitropoulos and Asteriou (2010), Marra et al. (2011) and Abdoli and Royaee (2012) find that firms with greater board independence are associated with higher quality accounting information.

Literature on corporate governance has argued that the separation between CEO and chairperson positions can improve the efficiency and effectiveness of internal control systems in companies; consequently, corporate value will be affected. When the chairman of the board of directors also takes the role of the CEO, the effectiveness of the board to monitor top management is decreased (Firth et al., 2007). The occupation of the roles of chairman and executive director by the same person can reduce the independence of the board as well as its ability to control managers effectively (Holtz and Sarlo Neto, 2014). One effect could be a decreased dissemination of timely and relevant information to external stakeholders (Gul and Leung, 2004). Segregation of the two roles provides the needed checks and balances of power and authority on management behavior (Chapra and Ahmed, 2002).

However, research findings are mixed. Huafang and Jianguo (2007) and Saleh Al Arussi et al. (2009) found a significant negative association between duality and disclosure. On the other hand, Li et al. (2008) and Said et al. (2009) found an insignificant relationship between duality and disclosure. Previous evidence also identifies several firms with the combined role of CEO and chairman of the boards, yet were very effective, and the capability to keep the top management in check (Haniffa and Cooke, 2002).

According to Omoye and Eriki (2014), earnings management is recognized as attempts by management to influence or manipulate reported earnings by using specific accounting methods or accelerating expense or revenue transactions, or using other methods designed to influence short-term earnings. Relevance and reliability are viewed as two principle qualitative characteristics of earnings numbers. In order to be relevant, among other things, current earnings numbers must be persistent and have predictive values. As for the reliability, earnings information must be representationally faithful and free from errors and bias. Earnings persistence, predictability and informativeness are used to represent earnings quality in this study because the features are important characteristics of relevant and reliable earnings information (AL-Dhamari and Ismail, 2014).

Earnings information should be relevant in helping investors make correct asset pricing and investment decisions (Yuan and Jiang, 2008). However, earnings quality is qualitative in nature and several proxies must be used to measure it. Persistence and predictability are viewed as two important characteristics of earnings numbers that help investors in predicting future earnings and cash flows. Earnings are said to be of high quality when they are persistent. It is argued that the importance of predictive nature of accounting earnings is manifested when taking into consideration, for instance, the use of accounting earnings when evaluating the equity of firms (Velury and Jenkins, 2006). At the other end of the spectrum, earnings informativeness refers to the ability of earnings to influence the expectations of investors with respect to the quality of earnings figures, as reflected in changes in share price (Kormendi and Lipe, 1987).

Healy and Wahlen (1999) defined earnings management as follows:

Earnings management occurs when managers use judgment in the financial reporting and in structuring transactions to alter financial reports to either mislead some stakeholders about the underlying economic performance of the company, or to influence contractual outcomes that depend on reported accounting numbers.

According to Algharaballi (2013), these definitions represent two common views of company management. The first view holds that management needs to exercise judgment in business operations and financial reporting since GAAP clearly requires management to make wise estimates and judgments. The second view is known as that of opportunistic earnings management, i.e. managers base their judgments and decisions on whether they will result in personal private gain.

Scott (2003) defines earnings management as follows: “Given that managers can choose from a set of accounting policies (for example, GAAP), it is natural to expect that they will choose policies so as to maximize their own utility and/or the market value of the firm.” Also, Belkaoui (2006) defines earnings management as the ability to “manipulate” the options available and make the right choices in order to achieve the expected level of profit.

According to Li (2014), the agency theory is highly relevant to the understanding of corporate governance in modern corporations. Jensen and Meckling (1976) define agency relationship in terms of a “contract under which one or more persons the principal(s) engage another person (the agent) to perform some service on their behalf which involves delegating some decision-making authority to the agent.” The agency theory paradigm was first formulated by Ross (1973) in the 1970s. The term was first associated with agency costs by Jensen and Meckling in 1976 (Ross, 1973; Jensen and Meckling, 1976; Shapiro, 2005). Rooted in information economics (Turnbull, 1997), the agency theory addresses the problem that occurs when goals of cooperating parties differ (Ross, 1973; Jensen and Meckling, 1976). “Agency conflict is exacerbated by the problem of information asymmetry” (Lopes, 2008, p. 182).

The agency theory tries to resolve two problems that usually occur when one party (the principal) delegates work to another (agent). The first is the conflict of goals between the principal and agent and the costs associated with the minimization of such discrepancy; and, second is the problem of sharing risk when the risk preference of the principal and agent differs (Eisenhardt, 1989). According to Davis et al. (1997), the agency theory provides “a useful way of explaining relationships where the parties’ interests are at odds and can be brought more into alignment through proper monitoring and a well-planned compensation system.”

Eisenhardt (1989) outlined two streams of the theory which developed over time: the principal–agent, where both act in concert, and the positivist perspective, where they are likely to have conflicting goals. She further explained that the agency problem arises when “(a) the desires or goals of the principal and agent conflict and (b) it is difficult or expensive for the principal to verify what the agent is actually doing.” The agency theory rests on a number of assumptions, including human assumptions of self-interest, bounded rationality and risk aversion; organizational assumptions, of partial goal conflict among participants, efficiency as the effectiveness criterion and information asymmetry between principal and agent; and information assumptions, on information as a valuable commodity.

The information asymmetry problem embedded in the principal–agency relationship may result in moral hazard and adverse selection and precludes cooperative parties from the benefits of sharing risks (Li, 2014). Daily et al. (2003) point to two factors that influence the prominence of the agency theory. First, the theory is a conceptually simple one that reduces the corporation to two participants, managers and shareholders. Second, the notion of human beings as self-interested is a generally accepted idea. The agency theory may be applied to any contractual relationships in which the principal and agent have partly differing goals and risk preferences, for example, compensation, regulation, leadership, impression management, whistle-blowing, vertical integration, merge and acquisition, and transfer pricing (Eisenhardt, 1989). Managers can play an important role in improving the value of a firm. They can reduce the agency cost in a firm by decreasing the information asymmetry, which results in improving the value of a firm (Monks and Minow, 2001).

The agency theory serves as the underlying rationale for corporate law as well as principles and regulations of corporate governance (Li, 2014). The agency theory argues that corporate governance mainly deals with three types of conflicts between: shareholders and managers; controlling shareholders and minority shareholders; and shareholders and non-shareholding stakeholders (Davies, 2000, cited in Li, 2014). Hart (1995) believes that the subject of corporate governance arises when two conditions are combined. First, there is an agency problem, or conflict of interest involving members of the organization. Second, transaction costs are such that this agency problem cannot be dealt with through a contract. Financial markets capture these agency costs as a value loss to shareholder (McColgan, 2001; Ruangviset et al., 2014).

Eze (2017) investigated the relationship between corporate governance mechanisms and earnings management in Nigeria. The sample comprised six firms in the food product sector. The study used secondary data from 2003 to 2014. He employed panel data regression to test the hypotheses. The study found that board meeting and board gender had a negative insignificant relationship, while institutional ownership had significant negative effect. Audit committee meeting was positive and significant at 10 percent.

Obigbemi et al. (2016) explored the relationship between board structure and earnings management in Nigeria. The sample comprised one thirty seven quoted firms from the period 2003–2010. They measured earnings management using the performance matched modified Jones model. Ordinary least squares (OLS) regression technique was used to analyze the data. The results revealed that there is a negative significant relationship between board size, gender and board composition; however, board meeting is positive and significant. The presence of a remuneration committee and CEO duality was positive but not significant.

Abdulmalik et al. (2015) examined the relationship between board monitoring, training and financial reporting quality in Malaysia. The sample comprised top 100 Malaysian firms as identified by the Malaysian Shareholder Watchdog Group from the period 2010–2011. They used feasible GLS (FGLS) regression method to test the hypotheses. The result revealed that the proportion of grey directors is positively and significantly related to both accrual and real earnings management, while the proportion of independent directors is negative and insignificant. Training and outsourcing of internal audit function negatively and significantly affected accrual and real earnings management.

Holtz and Sarlo Neto (2014) investigated the effect of board structure on the quality of accounting information in Brazil. The sample comprised non-financial companies listed on the BM&FBovespa with annual stock market liquidity higher than 0.001, from the period 2008–2011. They used multiple regression technique for analyzing the data. Accounting information relevance and earnings informativeness were used as proxies for the quality of accounting information. The results revealed that board independence and separation of the roles of chairman and executive director had a positive influence on accounting information relevance. Earnings informativeness is positively affected by board independence but negatively affected by larger board size (more than nine members).

Yasser and Mamun (2016) explored the relationship between board-leadership structure and earning management in Asia-Pacific countries. They used panel data from 330 firm years from Australia, Malaysia, Philippines and Pakistan from the period 2011–2013. The results revealed that board leadership structure has no effect on firm performance and financial reporting quality. However, female CEOs had a negative impact on firm performance in Malaysia, the Philippines and Pakistan.

AL-Dhamari and Ismail (2014) investigated the relationship between board characteristics and earnings quality in Malaysia. They used heteroskedasticity-corrected least square regressions on a sample of firms from 2008 to 2009. The study results showed that earnings quality was higher for firms with independent chairmen than firms with non-independent chairmen. However, they reported inconclusive results for board independence. The also concluded that investors do not perceive board size as a good indicator of quality earnings.

Chaharsoughi and Rahman (2013) examined the relationship between independent directors, board size, managerial share ownership and earnings quality. The sample comprised one hundred and fourteen firms listed on the TSE from 2008 to 2010. They found that there was an insignificant positive relationship between independent directors, managerial ownership and earnings quality. Board size was negative and insignificant.

Prior studies conducted in the USA also document mixed evidence on diverse corporate governance attributes. For instance, Klein (2002) examined the relationship between earnings management, board and audit committee independence on a sample of six hundred and seven, publicly traded firms. The results showed that earnings management were less pronounced in firms with audit committees comprising majority of independent directors. She also documented a negative association between abnormal accruals and proportion of independent directors.

The study by Beasley (1996) on a sample of 150 publicly traded sub-divided into 75 fraud and 75 non-fraud firms from the period 1980–1991. He employed a logit cross-sectional regression model and the results showed that the proportion of outside directors is higher for non-fraud firms and lower for fraud firms. Also, as the board size increases the likelihood of financial statement fraud increases.

Using evidence from the UK, Peasnell et al. (2000) compared pre-managed earnings with earnings thresholds (either zero earnings or last year’s reported earnings). The results showed that firms with higher proportion of outside directors have less income-increasing accruals when earnings fall below the threshold. However, when earnings exceed the threshold, there is strong evidence of income-decreasing accruals. They concluded that outside directors were more concerned with constraining income-increasing accruals.

The study adopts an ex post facto research design. An ex post facto design seeks to reveal possible relationships by observing an existing condition or state of affairs and searching back in time for plausible contributing factors (Kerlinger and Rint, 1986); in such cases, the researcher does not have direct control of independent variables because their manifestations have already occurred and are inherently not manipulated.

The population of the study is made up of all quoted manufacturing companies on the Nigerian Stock Exchange as at January 1, 2017. The companies are classified under eleven sectors, as follows: agriculture; construction/real estate; consumer goods; financial services; healthcare; industrial goods; information & communications technology; natural resources; oil & gas; services; utilities; and conglomerates. The population consisted of 173 firms under the 11 sectors.

Sampling is the process of selecting a subset of the target population to be its true representative on the study (Mugenda and Mugenda, 2009). The study used the purposive sampling technique and selected the 45 firms in the conglomerates, consumer goods and industrial goods sector. The companies included in the sample are shown in Table I.

Table I

Firms included in the sample for the study

 1. A.G. Leventis Nigeria PlcConglomerates
 2. Chellarams PlcConglomerates
 3. John Holt PlcConglomerates
 4. SCOA Nigeria PlcConglomerates
 5. Transnational Corporation PlcConglomerates
 6. UACN PlcConglomerates
 7. DN Tyre & Rubber PlcConsumer goods
 8. Champion Breweries PlcConsumer goods
 9. Golden Guinea Breweries PlcConsumer goods
10. Guinness Nigeria PlcConsumer goods
11. International Breweries PlcConsumer goods
12. Nigerian Breweries PlcConsumer goods
13. 7-Up Bottling Company PlcConsumer goods
14. Dangote Flour Mills PlcConsumer goods
15. Dangote Sugar Refinery PlcConsumer goods
16. Flour Mills Nigeria PlcConsumer goods
17. Honeywell Flour Mill PlcConsumer goods
18. Multi-Trex Integrated PlcConsumer goods
19. N. Nigeria Flour Mills PlcConsumer goods
20. Union Dicon Salt PlcConsumer goods
21. Cadbury Nigeria PlcConsumer goods
22. Nestle Nigeria PlcConsumer goods
23. Nigerian Enamelware PlcConsumer goods
24. Vitafoam Nigeria PlcConsumer goods
25. P.Z. Cussons Nigeria PlcConsumer goods
26. Unilever Nigeria PlcConsumer goods
27. Mcnichols PlcConsumer goods
28. NASCO Allied Industries PlcConsumer goods
29. African Paints (Nigeria) PlcIndustrial goods
30. Ashaka Cem PlcIndustrial goods
31. Austin Laz & Company PlcIndustrial goods
32. Avon Crowncaps & ContainersIndustrial goods
33. Berger Paints PlcIndustrial goods
34. Beta Glass PlcIndustrial goods
35. CAP PlcIndustrial goods
36. Cement Co. of North.Nig. PlcIndustrial goods
37. Cutix PlcIndustrial goods
38. Dangote Cement PlcIndustrial goods
39. First Aluminium Nigeria PlcIndustrial goods
40. Greif Nigeria PlcIndustrial goods
41. Lafarge Africa PlcIndustrial goods
42. Meyer PlcIndustrial goods
43. Paints and Coatings Manufactures PlcIndustrial goods
44. Portland Paints & Products Nigeria PlcIndustrial goods
45. Premier Paints PlcIndustrial goods

The data for the study were from secondary sources. The secondary data were extracted from the annual reports of the selected manufacturing companies.

There are significant reasons for considering annual reports as a source of data. First, an annual report is considered as a source for most of the information of a firm (Botosan, 1997) because significant issues and concerns of a firm are expressed comprehensively through the annual report (Khan et al., 2009; Abeysekera and Guthrie, 2005). According to Part X1, Chapter 1 of the Companies and Allied Matters Act (1990), companies are required to produce accounts that give true and fair view of the company. Second, annual reports are easily accessible source of information (Unerman, 2000).

The study made use of multiple regression technique in testing the formulated hypotheses. Hair et al. (2006) defined multiple regression technique “as a statistical technique which analyses the relationship between a dependent variable and multiple independent variables by estimating coefficients for the equation on a straight line.”

Model specification:

(1)
(2)
(3)
(4)

where EQ=earnings quality, BS = board size, BC=board composition, PNED=population of non-executive directors, CEOD=CEO-duality, α=constant, µ=error term, technically known as the stochastic disturbance or stochastic error term.

Description of variables for the study:

  1. Board size (BS): this is measured as the total number of directors sitting on the board as at the financial year end.

  2. Board composition (BC): this is measured as the number of sub-committees existing within the board as at the financial year end.

  3. Board independence (BI): this is measured as the number of non-executive directors sitting on the board as at the financial year end; a logarithmic transformation of the figures was also done.

  4. CEO duality (CD): CEO duality occurs when the CEO also holds the position of the Chairman of Board at the same time.

  5. Firm size (FS): firm size was proxied using total assets as at financial year end, a logarithmic transformation of the figures was done. Bachetti (2013) observed that statistical models are sometimes more meaningful and accurate if outcome or predictor variables are transformed, and a common choice for transforming variables is to apply logarithmic transformation. This may be appropriate when the variable only takes on positive values, and results are easier to interpret than with most other types of transformations.

  6. Cash flows from operations (CFO): this is measured as the amount of net cash flows generated from operations as at the financial year end.

Financial information of the selected manufacturing firms over a six-year period from 2011 to 2016 was obtained (subject to its availability); this gave rise to a panel data set of observations. The Statistical Package for Social Sciences was used for the analysis. The information derived can be seen in  Appendix 1. Table II shows the descriptive statistic of the data.

Table II

Descriptive statistics of panel data

nMinimumMaximumMeanSD
CFO233−10,536,074,00010,561,326,04034,529,393,25393,842,772,813
Net income229−7,217,001,000196,678,391,0008,021,768,60323,466,240,757
Total asset232−42,217,00021,043,605,39099,365,227,091247,938,024,866
Board size2344.017.09.0132.4133
Non-executive directors2340.011.04.6922.8359
Board structure2342.06.03.2780.9999
CEO duality2340.01.00.7950.4047
Valid n (listwise)226    

Source: SPSS Ver. 23

The descriptive statistic showed that at minimum, the studied firms had two committees and at maximum six committees.

H1.

There is a positive relationship between board size and earnings quality of selected manufacturing firms.

Pooled OLS results showing the relationship between EQ and BS, CFO, FS for the studied manufacturing firms in Nigeria are shown in Tables III–V.

Table III

Model summary for H1

ModelRR2Adjusted R2SE of the estimate
10.345a0.1190.1083.624957

Note: aPredictors: (Constant), BS, CFO, FMS

Source: SPSS Ver. 23

Table IV

ANOVA output for H1

ModelSum of squaresdfMean squareFSig.
1Regression406.7013135.56710.3170.000b
 Residual3,009.13222913.140  
 Total3,415.834232   

Notes: aDependent variable: earnings quality; bpredictors: (Constant), BS, CFO, FMS

Source: SPSS Ver. 23

Table V

Model coefficients for Equation (1)

ModelUnstandardized coefficientsStandardized coefficients
BSEβTSig.
1(Constant)−5.4461.999 −2.7250.007
 CFO−1.3620.000−0.333−5.2870.000***
 FMS−0.0030.067−0.003−0.0480.962
 BS5.5372.1650.1652.5580.011**

Notes: aDependent variable: earnings quality. **p<0.05; ***p<0.01

Source: SPSS Ver. 23

The result of the multiple regression analysis for H1 is summarized in Tables III–V. Table III shows the coefficient of determination (R2) otherwise known as R2 which is the percentage of response variable variation that is explained by a linear model. The Adjusted R2 value of 0.108 clearly indicates that the model explains approximately 11 percent variation in the dependent variable. The F-statistic (shown in Table IV) which is used to check the statistical significance of the model showed a value of 10.317; p-value<0.05; therefore, the hypothesis that all the regression coefficients are zero is rejected. Table V shows the t-statistic of our variable of interest (BS) is 2.558 (p<0.05), confirming that BS has a positive and statistically significant relationship with EQ; thus, the alternate hypothesis is accepted and null rejected. On the other hand, the control variables of firm size and cash flow from operations in the same table also show that FS is negative but not significant, while CFO is negative and significant. We propose the following hypothesis:

H2.

There is a positive relationship between board composition and earnings quality of selected manufacturing firms.

Pooled OLS results showing the relationship between EQ and BC, CFO, FS for the studied manufacturing firms in Nigeria are shown in Tables VI–VIII.

Table VI

Model summary for H2

ModelRR2Adjusted R2SE of the estimate
10.332a0.1100.0993.643225

Note: aPredictors: (Constant), BC, CFO, FMS

Source: SPSS Ver. 23

Table VII

ANOVA output for H2

ModelSum of squaresdfMean squareFSig.
1Regression376.2963125.4329.4500.000b
 Residual3,039.53822913.273  
 Total3,415.834232   

Notes: aDependent variable: earnings quality; bpredictors: (Constant), BC, CFO, FMS

Source: SPSS Ver. 23

Table VIII

Model coefficients for Equation (2)

ModelUnstandardized coefficientsStandardized coefficients
BSEβTSig.
1(Constant)−2.3401.065 −2.1970.029
 CFO−1.303E-110.000−0.319−5.0790.000***
 FMS0.0170.0660.0160.2550.799
 BC3.8221.8680.1292.0460.042**

Note: aDependent variable: earnings quality. **p<0.05; ***p<0.01

Source: SPSS Ver. 23

The result of the multiple regression analysis for H2 is summarized in Tables VI–VIII. Table VI shows the coefficient of determination (R2) otherwise known as R2 which is the percentage of response variable variation that is explained by a linear model. The Adjusted R2 value of 0.099 clearly indicates that the model explains approximately 9.9 percent variation in the dependent variable. The F-statistic (shown in Table VII) which is used to check the statistical significance of the model showed a value of 9.450; p-value<0.05; therefore, the hypothesis that all the regression coefficients are zero is rejected. Table VIII shows the t-statistic of our variable of interest (BC) is 2.046 (p<0.05), confirming that BC has a positive and statistically significant relationship with EQ; thus, the alternate hypothesis is accepted and null rejected. On the other hand, the control variables of firm size and cash flow from operations in the same table also show that FS had positive non-significant effect, while CFO is negative and significant. We propose the following equation:

H3.

There is a positive relationship between board independence and earnings quality of selected manufacturing firms.

Pooled OLS results showing the relationship between EQ and PNED, CFO, FS for the studied manufacturing firms in Nigeria can be shown in Tables IX–XI.

Table IX

Model summary for H3

ModelRR2Adjusted R2SE of the estimate
10.355a0.1260.1153.610681

Note: aPredictors: (Constant), PNED, CFO, FMS

Source: SPSS Ver. 23

Table X

ANOVA output for H3

ModelSum of squaresdfMean squareFSig.
1Regression430.3573143.45211.0030.000b
 Residual2,985.47622913.037  
 Total3,415.834232   

Notes: aDependent variable: earnings quality; bpredictors: (Constant), PNED, CFO, FMS

Source: SPSS Ver. 23

Table XI

Model coefficients for Equation (3)

ModelUnstandardized coefficientsStandardized coefficients
BSEβTSig.
1(Constant)−1.3910.693 −2.0070.046
 CFO−1.2750.000−0.312−5.0310.000***
 FMS0.0240.0650.0230.3660.715
 PNED−4.0421.3940.179−2.9000.004***

Notes: aDependent variable: earnings quality. **p<0.05; ***p<0.01

Source: SPSS Ver. 23

The result of the multiple regression analysis for H3 is summarized in Tables IX–XI. Table IX shows the coefficient of determination (R2), otherwise known as R2 which is the percentage of response variable variation that is explained by a linear model. The Adjusted R2 value of 0.115 clearly indicates that the model explains approximately 11.5 percent variation in the dependent variable. The F-statistic (shown in Table X) which is used to check the statistical significance of the model showed a value of 11.003; p-value <0.05; therefore, the hypothesis that all the regression coefficients are zero is rejected. Table XI shows the t-statistic of our variable of interest (PNED) is −2.900 (p<0.05), confirming that PNED has a negative and statistically significant relationship with EQ; thus, the alternate hypothesis is rejected and null accepted. On the other hand, the control variables of firm size and cash flow from operations in the same table also show that FS is positive and not significant, while CFO is negative and significant. We propose the following hypothesis:

H4.

There is a positive relationship between CEO-duality and earnings quality of selected manufacturing firms.

Pooled OLS results showing the relationship between EQ and CEO Duality, CFO, FS for the studied manufacturing firms in Nigeria are shown in Tables XII–XIV.

Table XII

Model summary for H4

ModelRR2Adjusted R2SE of the estimate
10.342a0.1170.1093.532457

Note: aPredictors: (Constant), CEO duality, CFO, FMS

Source: SPSS Ver. 23

Table XIII

ANOVA output for H4

ModelSum of squaresdfMean squareFSig.
1Regression377.5783125.8599.4860.000b
 Residual3,038.25622913.267  
 Total3,415.834232   

Notes: aDependent variable: earnings quality; bpredictors: (Constant), CEO Duality, CFO, FMS

Source: SPSS Ver. 23

Table XIV

Model coefficients for Equation (4)

ModelUnstandardized coefficientsStandardized coefficients
BSEβTSig.
1(Constant)−1.2110.707 −1.7130.088
 CFO−1.2440.000−0.304−4.8660.000***
 FMS−0.0140.070−0.013−0.1970.844
 CEO duality1.2900.6230.1362.0700.040**

Notes: aDependent variable: earnings quality. **p<0.05; ***p<0.01

Source: SPSS Ver. 23

The result of the multiple regression analysis for H4 is summarized in Tables XII–XIV. Table XII shows the coefficient of determination (R2), otherwise known as R2 which is the percentage of response variable variation that is explained by a linear model. The Adjusted R2 value of 0.109 clearly indicates that the model explains approximately 11 percent variation in the dependent variable. The F-statistic (shown in Table XIII) which is used to check the statistical significance of the model showed a value of 9.486; p-value<0.05; therefore, the hypothesis that all the regression coefficients are zero is rejected. Table XIV shows the t-statistic of our variable of interest (CEO duality) is 2.070 (p<0.05), confirming that CEO duality had a positive and statistically significant relationship with EQ; thus, the alternate hypothesis is accepted and null rejected. On the other hand, the control variables of firm size and cash flow from operations in the same table also show that FS is negative and not significant, while CFO is negative and significant.

The empirical results revealed a positive and significant effect of board size on earnings quality. Contrary to this, Chaharsoughi and Rahman (2013) in Iran reported a negative insignificant effect of board size. Also, Holtz and Sarlo Neto (2014) in Brazil reported that earnings informativeness is negatively affected by larger board size (more than nine members). However, AL-Dhamari and Ismail (2014) argued that investors do not perceive board size as a good indicator of quality earnings. Beasley (1996) in the USA also found likelihood for financial statement fraud as board size increases.

The second hypothesis revealed a positive and significant effect of board composition on earnings quality. Prior studies have shown that most decisions originate at the committee level (Kesner, 1988). The finding is contrary to the study by Eze (2017) in Nigeria, which found that board meeting had a negative insignificant effect on earnings management. However, Obigbemi et al. (2016) revealed that there is a negative significant relationship between board composition and earnings management.

The results revealed that proportion of non-executive directors had a negative and statistically significant effect on earnings quality. This is consistent with the study by Abdulmalik et al. (2015) in Malaysia; when they reported a negative and insignificant effect between the proportion of independent directors and earnings management. Klein (2002) in the USA also documented a negative association between abnormal accruals and proportion of independent directors. Contrary to this, Holtz and Sarlo Neto (2014) in Brazil showed that board independence has a positive influence on accounting information relevance. While, Beasley (1996) in the USA showed that the proportion of outside directors is higher for non-fraud firms and lower for fraud firms.

The empirical results showed that CEO duality is positive and statistically significant. Similar to this, the study by Holtz and Sarlo Neto (2014) in Brazil showed that CEO duality had a positive influence on relevance of accounting information. However, AL-Dhamari and Ismail (2014) in Malaysia showed that earnings quality was higher for firms with independent chairmen than firms with non-independent chairmen.

The study provides empirical evidence on the relationship between earnings quality and board leadership (i.e. board size, board composition and board independence and population of non-executive directors) for manufacturing companies in Nigeria. The study documents mixed findings on the attributes, while some are in support of prior studies others refute such. The board of directors is considered the main pillar in the internal corporate governance structure, and plays a pivotal role in monitoring and enforcement. The board supervises and monitors the CEO; thus, can prevent or mitigate opportunistic behavior by the CEO. If the board enjoys more independence, it plays the role mentioned above more efficiently. Furthermore, public companies are subject to the scrutiny of more external monitoring and capital market regulations. The study confirms a form of relationship between board leadership structure and earnings quality. Firm size was not significant in all four models, while cash flows from operations was found significant. Scholars have argued that cash flow information may be more reliable as they are not prepared on an accrual basis like the income statement items.

Based on the findings of this study, the following recommendations are given:

  1. Shareholders should ensure that BOD of manufacturing firms should not just reflect size but rather the skills and expertise of individuals appointed to the board. Furtherance to this, the effectiveness of boards can be improved by committees and sub-committees allocation of duties. Diligently undertaking a task may be much easier at the committee level than a convergence of the entire board members.

  2. The appointment of independent non-executive directors to create more room for board independence is strongly encouraged. Independent boards are more apt at mitigating the CEO from manipulation of earnings, as they are often more conscious of their reputation. Independent directors strengthen the corporate governance structure of a firm and can reduce the agency problem. Therefore, regulators should consider this issue in corporate governance rules and regulations for manufacturing firms. Audit firms should review their audit business model and become more circumspect of their client.

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Table AI 

Table AI

Board leadership data

Name of companyYearBoard sizeNon-executive directorsBoard structureCEO duality
A.G. Leventis20168531
 20158531
 20148231
 20138531
 20128521
 20118431
Ashaka Cem2016141161
 2015131161
 2014141161
 2013131061
 2012121151
 2011121151
Beta Glass20169841
 20159841
 20149841
 20139841
 20129841
 20119841
Cadbury Nigeria Plc20167551
 20157551
 20147551
 20137551
 20127551
 20117551
CAP plc20166441
 20156441
 20146441
 20136441
 20126441
 20116441
Chams20168431
 20158431
 20148431
 20137431
 20127431
 20117431
Chellarams Plc20165230
 20155230
 20145230
 20135230
 20125230
 20115230
Dangote Cement20169441
 20159441
 20149441
 20139441
 20129441
 20119441
Dangote Sugar20169731
 20159731
 20149731
 20139731
 20129731
 20119731
Transcorp20169741
 20159741
 20149741
 20139741
 20129741
 20119741
Arbico PLC20167041
 20157041
 20147031
 20137031
 20127031
 20117031
Berger Paints Nig PLC20169721
 20159721
 20149721
 20139721
 20129721
 20119721
PZ Cussons201612231
 201512231
 201412231
 201312231
 201212231
 201112231
Champion Breweries20169021
 20159021
 20149021
 20139021
 20129021
 20119021
Dangote Flour Mills201610731
 201510731
 201410731
 201310731
 201210731
 201110731
First Aluminium Nig PLC20166321
 20156321
 20146321
 20136321
 20126321
 20116321
Flour Mills of Nigeria201614041
 201514041
 201414041
 201314041
 201214041
 201114041
GSK20169761
 20159761
 20149761
 20139761
 20129761
 20119761
Guinness Nigeria PLC201614841
 201514841
 201414841
 201312841
 201212841
 201112841
Honeywell Flour Mills20168621
 20158621
 20148621
 20138621
 20128621
 20118621
Julius Berger201612841
 201512841
 201412841
 201312841
 201212841
 201112841
John Holt201612831
 201512831
 201412831
 201312831
 201211831
 201110731
Livestock Feeds20165020
 20155020
 20145020
 20135020
 20125020
 20114020
Neimeth Int. Pharm.201611830
 201511830
 201411830
 201311830
 201211830
 201111830
Nestle Nig PLC20168231
 20158231
 20148231
 20138231
 20128231
 20118231
Nigerian Breweries201617641
 201517641
 201417641
 201313641
 201215641
 201115641
SCOA20169221
 20159221
 20149221
 20139221
 20129221
 20119221
UACN20168531
 20158531
 20148531
 20138531
 20128531
 20118531
Vitafoam201611630
 201511630
 201411630
 201311630
 201211630
 201111630
Unilever Nig PLC20168451
 20158451
 20148451
 20138451
 20128451
 20118451
Union Dicon Salt20168540
 20158540
 20148540
 20138540
 20128540
 20118540
7-UP Bottling Coy. Plc201610831
 201510831
 201410831
 201310831
 201210831
 201110831
Nig. Enamelware Plc20167430
 20157430
 20147430
 20137430
 20127430
 20117430
Multi-Trex intgrt. Prdt.20167330
 20157330
 20147330
 20137330
 20127330
 20117330
NASCO Plc201610731
 201510731
 201410731
 201310731
 201210731
 201110731
PS Mandrides Plc20167020
 20157020
 20147020
 20137020
 20127020
 20117020
UTC Nig. Plc20167021
 20157021
 20147021
 20137021
 20127021
 20117021
Premier Paints Plc20169631
 20159631
 20149631
 20139631
 20129631
 20119631
International Brew. Plc20168531
 20158531
 20148531
 20138531
 20128531
 20118531

Source: Annual report of sampled companies

Table AII 

Table AII

Earnings quality data

Name of companyYearCFONet incomeAverage assetEarnings quality
A.G. Leventis20160022,501,905,0000
 20150−176,986,00035,011,872,500−0.00506
 2014475,770,000211,813,00032,374,085,000−0.00815
 20131,665,820,000356,357,00033,031,595,500−0.03964
 2012641,126,000314,870,00032,495,698,500−0.01004
 2011769,514,000727,363,00010,551,653,500−0.00399
Ashaka Cem201600453,012,397,0000
 201557,867,963,00026,998,273,000532,385,026,500−0.05798
 201457,816,725,00034,660,666,000220,362,950,000−0.10508
 201336,939,298,0002,616,387,000101,037,000,000−0.33971
 20123,315,218,0002,784,554,00098,874,451,000−0.00537
 20118,734,442,0002,728,857,00032,605,917,500−0.18419
Beta Glass2016 027,171,069,0000
 20154,842,441,0001,991,127,00040,513,921,500−0.07038
 20144,341,369,0002,390,223,00040,630,674,500−0.04802
 2013928,427,0001,467,344,00036,039,807,5000.014953
 20122,735,475,0001,328,580,00029,249,873,500−0.0481
 20114,382,800,0001,545,780,00039,707,396,000−0.07145
Cadbury Nigeria Plc2016739,315,000−672,822,00043,765,305,500−0.03227
 20153,781,283,0001,153,295,00029,780,661,500−0.08824
 20144,382,800,0001,512,687,00050,958,703,500−0.05632
 201330,261,902,0005,498,851,00061,397,727,000−0.40332
 201226,829,844,0005,511,000,00052,548,319,500−0.4057
 201105,053,000,0002,120,681,845,0000.002383
CAP plc2016042,976,212,0002,887,646,269,5000.014883
 2015036,297,592,0002,662,910,377,0000.013631
 2014038,404,784,000875,623,700,5000.04386
 20131,448,652,0001,416,795,0004,393,308,000−0.00725
 2012913,532,0001,115,554,0004,505,047,0000.044843
 2011933,155,0001,078,276,000764,579,573,0000.00019
Chams2016447,711,00020,773,000,0001,058,342,000,0000.019205
 2015179,948,00010,157,000,000892,634,500,0000.011177
 201407,440,000,000287,831,255,0000.025848
 2013418,404,000188,464,00014,076,770,500−0.01633
 2012−243,973,00087,539,00012,062,064,5000.027484
 2011146,239,000−1,236,982,0003,851,684,000−0.35912
Chellarams Plc20160015,415,668,0000
 2015−455,259,00090,407,00023,123,502,0000.023598
 2014−380,290,00090,407,00023,123,502,0000.020356
 20134,163,044,00090,407,00022,467,312,000−0.18127
 2012−2,650,343,000251,162,00017,797,438,0000.163029
 20110228,232,0006,736,757,5000.033879
Dangote Cement2016278,594,000186,624,0001,874,897,000−0.04905
 2015299,517,000181,323,000843,758,746,500−0.00014
 20140196,678,391,0001,264,804,912,5000.155501
 2013231,541,819,000196,678,391,0001,079,802,370,500−0.03229
 2012180,268,299,000142,714,089,000863,681,245,500−0.04348
 2011164,109,364,000125,909,831,000350,450,316,500−0.109
Dangote Sugar2016010,856,673,000124,739,815,5000.087035
 201510,655,421,00010,856,673,000124,739,815,5000.001613
 2014010,856,673,000124,739,815,5000.087035
 201392,297,062,00010,856,673,000124,631,388,500−0.65345
 20121,056,132,604,00010,735,450,000114,340,446,000−9.14285
 201199,974,586,0007,244,056,000207,162,722,500−0.44762
Transcorp201603,304,260,000256,133,043,0000.012901
 201503,304,260,000256,133,043,0000.012901
 20147,731,243,0003,304,260,000234,842,094,000−0.01885
 2013−2,535,529,0006,957,902,000150,336,408,5000.063148
 20124,012,332,0002,710,701,00099,274,186,000−0.01311
 20113,712,795,0004,666,217,00030,736,042,5000.03102
Arbico PLC2016004,532,183,0000
 2015119,437,000271,234,0006,723,544,5000.022577
 2014768,523,000−252,993,0004,709,694,500−0.2169
 2013252,627,00099,242,0003,794,377,000−0.04042
 2012480,182,000−48,305,0001,276,946,500−0.41387
 20110000
Berger Paints Nig PLC2016003,536,641,0000
 20150251,346,0005,304,961,5000.047379
 20140251,346,0005,304,961,5000.047379
 2013311,797,000251,346,0004,616,435,500−0.01309
 2012275,445,000192,009,0004,099,092,500−0.02035
 2011296,518,000227,816,00072,303,252,500−0.00095
PZ Cussons201605,082,747,000106,448,602,5000.047748
 20154,298,160,0005,082,747,00059,968,003,5000.013083
 201467,822,932,0005,082,747,00084,538,988,000−0.74214
 201366,021,901,0005,321,187,000100,555,007,000−0.60366
 201269,615,755,0002,538,846,000101,129,927,500−0.66327
 201160,180,918,0005,697,066,00044,055,645,500−1.23671
Champion Breweries20160−754,523,00014,388,571,500−0.05244
 20150−754,523,00014,388,571,500−0.05244
 20144,056,906,000−754,523,00013,933,906,500−0.3453
 20133,411,284,000−1,178,025,00011,368,058,000−0.4037
 20123,122,035,000−1,336,690,00010,470,962,000−0.42582
 20113,616,868,000−1,825,759,0003,535,681,000−1.53934
Dangote Flour Mills20160 70,965,735,0000
 20150−7,217,001,000101,360,529,500−0.0712
 20140−7,217,001,00098,816,493,000−0.07303
 201337,177,420,000−7,217,001,000111,325,550,000−0.39878
 201233,656,472,000−1,854,490,00082,647,955,500−0.42967
 201166,165,622,000115,704,00021,727,298,000−3.03995
First Aluminium Nig PLC2016008,476,056,0000
 2015029,807,00012,714,084,0000.002344
 2014770,123,00029,807,00012,808,821,000−0.0578
 2013872,165,00097,123,00013,151,663,500−0.05893
 201225,6662,000−1,014,720,00014,290,232,500−0.08897
 2011784,557,000−325,044,0004,928,549,500−0.22514
Flour Mills of Nigeria20160 297,249,445,0000
 2015555,099,0005,367,875,000445,874,167,5000.010794
 201432,677,481,00005,367,875,000428,762,714,500−0.74961
 2013294,401,519,0007,539,810,000372,926,365,000−0.76922
 2012249,891,595,0008,376,656,000279,690,549,500−0.86351
 2011229,346,736,0009,450,204,000109,820,011,500−2.00234
GSK20161,010,812,0002,378,145,00045,424,252,5000.030101
 20154,942,350,000873,134,00043,657,724,500−0.09321
 20141,352,052,0001,848,842,00040,210,097,0000.012355
 20134,841,758,0002,919,170,00034,899,552,500−0.05509
 20123,725,370,0002,823,526,00028,836,516,500−0.03127
 20115,079,202,0002,294,988,000141,298,351,000−0.0197
Guinness Nigeria PLC201609,573,480,000198,492,409,5000.048231
 201532,538,985,0009,573,480,000198,492,409,500−0.1157
 201499,628,640,0009,573,480,000187,224,757,500−0.481
 2013110,599,812,00011,863,726,000163,064,482,500−0.6055
 2012111,616,989,00014,671,195,000143,494,910,000−0.6756
 2011105,735,191,00017,927,934,000109,944,351,000−0.79865
Honeywell Flour Mills201603,351,564,00095,745,658,5000.035005
 20155,602,147,0003,351,564,00095,745,658,500−0.02351
 201451,732,741,0003,351,564,00087,352,697,500−0.55386
 201342,865,862,0002,843,520,00072,658,819,000−0.55083
 201235,369,071,0002,702,431,00051,607,647,000−0.63298
 201131,565,227,0002,492,397,000241,830,060,500−0.12022
Julius Berger201607,853,340,000340,891,885,5000.023038
 201507,853,340,000340,891,885,5000.023038
 201407,853,340,000340,891,885,5000.023038
 201315,922,650,0007,853,340,000292,664,792,500−0.02757
 201231,548,838,0008,012,694,000258,882,126,000−0.09091
 201119,881,569,0004,874,513,00094,986,522,000−0.15799
John Holt20160591,000,00015,456,000,0000.038238
 20150591,000,00015,456,000,0000.038238
 2014145,000,000591,000,00014,410,000,0000.030951
 20132,971,000,00093,000,00016,560,000,000−0.17379
 2012−1,862,000,000424,000,00017,532,500,0000.130386
 20111,527,000,000−1,565,000,0005,783,500,000−0.53462
Livestock Feeds2016003,670,604,0000
 20150210,746,0005,505,906,0000.038276
 20140210,746,0005,505,906,0000.038276
 2013−175,817,000210,746,0003,907,622,0000.098925
 2012−34,174,000144,102,0002,595,405,0000.068689
 2011−11,729,00097,682,0003,562,110,5000.030715
Neimeth Int. Pharm.2016160,380,000 4,173,732,000−0.03843
 201594,824,000−228,535,0004,173,732,000−0.07747
 201489,515,000−228,535,0004,282,323,000−0.07427
 201396,845,000130,578,0002,599,948,5000.012974
 2012159,023,00001,731,613,500−0.09184
 201100577,204,5000
Nestle Nig PLC2016014,904,000,000133,450,000,0000.111682
 201542,913,138,00014,904,000,000200,175,000,000−0.13992
 201476,961,000,00014,904,000,000187,167,000,000−0.33156
 201381,928,000,00010,445,000,000186,450,000,000−0.38339
 201281,264,000,00011,060,000,000177,205,500,000−0.39617
 201173,966,000,0009,804,000,000248,227,200,000−0.25848
Nigerian Breweries2016042,520,253,000292,889,697,0000.145175
 201572,673,843,00042,520,253,000447,878,586,500−0.06733
 2014223,852,222,00042,520,253,000524,291,365,500−0.34586
 2013225,533,169,00043,080,349,000428,472,021,000−0.42582
 2012214,631,499,00038,042,714,000362,518,010,500−0.48712
 2011173,021,048,00038,050,756,000126,955,156,000−1.06313
SCOA20161,792,952,0002,945,00013,796,483,000−0.12974
 2015458,210,000179,477,00013,866,306,000−0.0201
 2014690,557,000179,477,00012,434,572,000−0.0411
 2013485,966,000110,738,00010,980,870,000−0.03417
 2012−44,763,00073,406,0009,609,300,5000.012297
 2011−160,082,000101,266,0003,035,991,5000.086083
UACN2016010,944,795,000130,360,660,0000.083958
 20158,432,638,00010,944,795,000195,540,990,0000.012847
 20142,339,231,00010,944,795,000190,195,824,0000.045246
 20137,408,670,0009,902,858,000185,483,340,0000.013447
 20129,489,345,0007,102,951,000183,081,419,500−0.01303
 2011−5,438,823,0003,407,685,00074,142,180,5000.119318
Vitafoam2016−1,925,426,000−32,032,00019,522,239,5000.096987
 2015661,883,0003,500,00016,385,815,500−0.04018
 2014901,147,0003,000,00014,941,557,000−0.06011
 201315,928,510,000410,313,00015,239,180,000−1.01831
 201213,978,187,000501,594,00014,422,101,500−0.93444
 201114,001,930,000518,850,0004,646,385,500−2.90184
Unilever Nig PLC20160045,736,255,0000
 201515,773,000,0002,412,343,00068,604,382,500−0.19475
 201453,341,966,0002,412,343,00066,622,241,500−0.76445
 201355,279,690,0004,724,429,00058,374,681,000−0.86605
 201249,950,185,0005,597,613,00050,528,770,000−0.87777
 201149,209,536,0005,491,076,00016,139,979,000−2.70871
Union Dicon Salt20160000
 2015153,224,000093,945,000−1.631
 2014343,000−87,616,000133,399,500−0.65937
 2013−1,711,00011,844,000121,636,836,5000.000111
 201211,963,000−20,415,00060,754,594,500−0.00053
 2011−210,000−325,044,00067,775,502,500−0.00479
7-UP Bottling Coy. Plc201616,984,343,000−2,897,369,000100,329,480,500−0.19816
 201511,631,223,000410,319,00089,078,796,500−0.12597
 2014−10,536,074,0006,434,601,00079,301,774,5000.214001
 2013−9,740,888,00042,976,212,000375,361,869,0000.140443
 20122,072,320,00037,497,651,000410,539,588,0000.08629
 20111,076,658,00015,378,322,000117,850,598,0000.121354
Nig. Enamelware Plc20160 6,886,614,0000
 2015−1,652,580,000 10,329,921,0000.15998
 201455,754,309,0003,351,564,0003,443,307,000−15.2187
 201302,843,520,0000–
 2012349,676,784,0002,702,431,00014,778,273,000−23.4787
 2011235,701,196,0002,492,397,0007,389,136,500−31.561
Multi-Trex intgrt. Prdt.2016000–
 201502,105,646,0000–
 201405,367,875,0000–
 2013349,676,784,0007,539,810,0000–
 2012253,633,629,0008,376,656,000122,975,593,000−1.99435
 2011114,389,432,0009,450,204,00061,487,796,500−1.70667
NASCO Plc201602,105,646,0000–
 20154,007,770,0002,105,646,0000–
 201409,573,480,0001,100,793,0008.696894
 201316,338,823,00011,863,726,0001,354,977,500−3.30271
 201214,479,781,00014,671,195,0001,240,102,5000.154353
 201114,520,780,00017,927,934,000418,906,0008.133457
PS Mandrides Plc201607,853,340,0000–
 201507,853,340,000122,975,593,0000.063861
 2014114,389,432,0000183,081,419,500−0.6248
 2013114,389,432,0007,853,340,00071,761,734,500−1.48458
 20128,057,546,0008,012,694,00023,987,244,500−0.00187
 20116,071,983,0004,874,513,0009,252,391,500−0.12942
UTC Nig. Plc20160210,746,0001,100,793,0000.191449
 20150210,746,0001,651,189,5000.127633
 2014001,651,189,5000
 201316,338,823,000210,746,0001,354,977,500−11.9028
 201214,479,781,000144,102,0001,240,102,500−11.5601
 201114,520,780,00097,682,000418,906,000−34.4304
Premier Paints Plc201600341,289,0000
 201525,656,000−29,497,00018,675,427,500−0.00295
 201412,904,000591,000,0009,532,779,5000.060643
 20132,751,00093,000,0001,240,987,0000.072724
 2012−22,311,000424,000,00019,055,179,5000.023422
 20110−1,565,000,00042,734,497,500−0.03662
International Brew. Plc201602,652,748,00046,912,643,0000.056547
 20153,151,232,0001,946,490,000147,414,068,000−0.00817
 201499,628,640,0002,105,500,000187,224,757,500−0.52089
 2013110,599,812,0002,327,342,000163,064,482,500−0.66399
 2012111,616,989,0002,327,342,000143,494,910,000−0.76163
 2011105,735,191,000−2,172,888,00046,113,912,000−2.34003

Source: Annual report of sampled companies

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

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