Operating Cash Flow Ratio, Operating Profit Margin and Stock Returns of Quoted Manufacturing Companied in Nigeria

materials for additional research in this area. This report can be used by regulatory agencies such as the Securities and Exchanges Commission , the Central Bank of Nigeria , as well as the Manufacturers Association of Nigeria to enhance the framework for regulating Nigerian manufacturing firms. The study's findings will also help regulators and policymakers enact new rules and guidelines pertaining to working capital administration in manufacturing companies. 1.7 Scope and Limitations of the Study The study is limited to Nine (9) quoted manufacturing industries listed on the Nigeria Exchange Group. They are; Cadbury Nigeria Plc, Dangote Sugar Refinery Plc, Champion Brewery Plc, Guinness Nig. Plc, Nestle Nigeria Plc. GlaxoSmithKline Consumer Nig. Plc., Lafarge Africa Plc, Unilever Nigeria Plc, Berger paint Plc. The research spans eleven (11) years, from 2015 to 2025. 2.0 Review of Related Literature 2.1.1 Conceptual Review The conceptual issues are discussed in this section of the study. Overview of Accounting Income Accounting in market-based economies has two important roles to play. Firstly, it affords capital providers (shareholders and creditors) the opportunity to evaluate the return potential of investment opportunities (the ex-ante or valuation role of accounting information) and secondly, accounting numbers allows capital providers to watch the use of their capital once committed (the ex-post or stewardship position of accounting as an information system) (Anne et al., 2020). As required by the local entity's legislation and international legislation, these figures are created and published through financial reporting; thus far, they do not clash with local legislation. 2.1.2 Proxies of Accounting Income The commonly used proxies for accounting income are explained in this section 2.1.3 Operating Cash Flow Ratio An organization's operational cash flow ratio measures the amount of money made or spent during business operations. The term 'cash flows' is employed to explain how funds and other liquid resources enter and exit a business. Both readily accessible money and demand deposits are included in the category of cash (less current overdrafts). The cash flow of a business is an essential component in determining its financial health. Cash flow is generated primarily from operational activities, which are collectively referred to as 'operating cash flow ratio.' According to Wild et al. (2005), the company's profit-related operations are represented by revenue generated from Cash inflows along with net cash outflows from associated operating operations are included in operating activities granting credit to customers, investing in inventory, and obtaining credit from suppliers. Burke et al. (2017) said operating cash flow ratio provides useful information for investors in assessing equity. It provides information and predicts future cash flows and earnings beyond accruals. Cash outflow, which includes cash payments for the purchase of products and services for trade purposes or for the payment of manufacturing expenses associated with the processing and transformation of industrial products, cash expenditures to vendors as well as other service providers, cash payments for employee incentives and salaries, and cash needed for paying for taxes and interest, is another component to operating liquidity ratio data (Kew et al., 2021). OCFR = Operating Cash Flow Current Liabilities Model 2.1 2.1.4 Operating Profit Margin Operating profit margin is the difference between operating income and expenses. It is made up of the value derived from operating costs and gross profit. The benefits of operating revenue components can demonstrate how successful a business is at handling cash receipts, claims Koeswardhana (2020). In this instance, operating profit data pertains to the amount of cash flows that come from the business's operational activities, which are its primary source of income. A standard for comparing the value at the moment of operating revenue in the future can be found in operating income values, which can also give investors information about changes in the business's net assets and financial makeup (such as availability and solvency). Thus, operating profit turns into a crucial factor in forecasting future cash flows. Operating Profit Margin is calculated by this formula OPM = (Operating Income) (Revenue) Model 2.2 2.1.5 Conceptual Overview of Stock Returns A number of factors, including excess market returns, price inflation, the overall profit margin, earnings per share, return on equity, return on assets, book-to-market, along with tiny market value portfolios, and the present ratio (Anjani and Syarif, 2019), rate of interest and fluctuations in exchange rates and liquidity, can affect the movement of stock returns (Ahmad et al., 2020). High returns on each investment are often desired by investors (Jasman and Kasran, 2017). Investors must therefore assess the state of the business before investing in order to predict the anticipated return on stock by examining a number of variables that may impact stock returns (Jasman and Kasran, 2017). Investors use a company's profitability as a benchmark when evaluating it (Sugosha, 2017). One of the key considerations for investors when choosing which investments to make is profitability. When a firm has a high level of profitability, Investors have a greater desire to invest in it since it is thought that the business may make a profit. Put differently, before making an investment, investors will evaluate the information supplied by the company's management to determine whether it is a positive or bad signal (Brigham and Houston, 2007). If the data is a good signal over investors, then the price of the shares will rise (Bertuah and Sakti, 2019), and the growth in the price of the business's shares will generate large returns for investors (Saragih, 2018). 2.1.6 Stock Returns and the Operating Cash Flow Ratio A single the most crucial elements in evaluating a business's financial health is its cash flow. Operational activities are the main source of cash flow, and these activities are referred to as the 'operating revenue ratio.' The quantity that is produced and used during routine business operations is known as an organization's operational liquidity. It is called 'cash flows' when money and other liquid assets are moved into and out of a business. Both readily accessible money and demand deposits are included in the category of cash (fewer current overdrafts) short-term investments include money market funds, savings deposits, and other cash substitutes. Cash outflow, which includes cash payments for the purchase of products or services for trade purposes or for the reimbursement of production costs associated with the processing and transformation of products for industrial use, cash expenditures to vendors as well as additional service providers, cash payments for employee salaries and incentives, and cash needed for paying for taxes and interest, is another component in operating cash flows ratio data (Kew et al., 2021). Stock Returns and Operating Profit Margin The revenue produced by the company's operations can be ascertained using the net profit margin ratio. Kasmir (2016) explains that Net Profit Margin is a measure of profit by comparing profit after interest and tax compared to sales. Given that a strong business may produce substantial profits from its sales activity, this ratio shows that the bigger the ratio, the better. Because it impacts the viability of any corporate organization, firm profitability is important to stakeholders (Nworie and Mba, 2022). Operating profitability is the extent to which a company increases the effectiveness and efficiency of converting the usage of its assets into profits. The difference between revenue and production costs is referred to as profit over a specific time period. It is the difference between net operating expenses and revenue. But in addition to the profit from the company's regular daily operations, there are additional sources of income and costs like interest and taxes that are typically included in the financial records in order to determine the end of the year profit after taxes. Operating profit is the amount of money a company makes from its activities after deducting taxes and interest. Another name for it is earning before interest and taxes. Administrative costs and cost of the products sold are subtracted from revenues to determine operating profit. The ratio for operational profit to net turnover is used to calculate operating profit margin. 2.2 Theoretical Review The theory upon which this study anchored are discussed in this section of the study. 2.2.1 Free Cash Flow Theory The free cash flow theory, first presented in Michael Jensen in 1986, serves as the basis for this investigation. According to this theory, business free cash flows refer to the surplus cash flows that arise after netting off the necessary funds invested in projects that yield positive net present value returns. These projects are long-term investment projects whose expected present value cash inflows are more than their cash outflows (Jensen, 1986). FCF is an effective metric for assessing the firm's success since it is connected to changes in shareholders' wealth, which is linked to stock return (Maham et al., 2008). FCFs indicate the flow of cash from operations (the ability to meet its operational obligations), financial revenue flow (the ability of the organization to repay debts, sell and repurchase its stock), and leveraging cash flow (the facilitation of quicker enterprise growth through prudent the use of fixed assets), all of which increase investors' confidence in the company. According to Jensen (1986), these are a couple of the factors that lead many investors to believe that higher FCF indicates higher business value, which is reflected in a high return of stock. Conversely, the business's shareholders think that its free cash flow can be a criterion for generating value for them. This is because companies with high beneficial FCF are likely to use the extra money for new, profitable ventures that generate positive net present value , which eventually boosts stock returns. 2.3 Review of Empirical studies Samoei and Tenai (2021) established the effect of operating cash flow ratio on stock return of firms listed in NSE. The study was informed by Free Cash Flow theory. Census survey was adapted to review financial statements for 29 listed non-financial firms at NSE that had consistent data for all the study variables. Secondary data was extracted for 12 years from 2007- 2019 with the aid of a data collection sheet. Explanatory research design which is panel in nature was followed by this study. Both descriptive and inferential statistics were used in data analysis. Panel data regression was used to make inferences and test research hypothesis. Fixed and Random effects methods were used to analyze the balanced panel data using STATA statistical package and Hausman test established that Random effect model was the most ideal method to analyze data in this study. The findings indicated that operating cash flow positively and significantly influenced the stock returns for firms listed at NSE. The study concludes that operating cash flow ratio information affects stock returns. Therefore, the study advocates for firms to increase their levels of operating cash flows through prudent utilization of cash resources since it enhances the stock returns. Olaniyan et al. (2022) explored the influence of operating cash flow ratio on firm performance in Nigeria. It specifically examined the influence of total operating expenses on return on asset, debt on equity, equity market value, total income growth rate, and net income growth rate. The study predicated on free cash flow theory, Trade off theory, and pecking order theory. Secondary data were used to carry out the facts of the situation, which were obtained through the annual financial report of the firm, which covered a period of thirty years spanning from 1990-2020. Data were analyzed using descriptive research design to test the level of co- integration among the variables. The study revealed that the variables used in this study are co- integrated in the long-run which led to the Vector Error Correction Model test, which revealed that all variables of operating cash flow ratio incorporated in the model have a positive effect on firm performance both in the short-run and long run. This means that in Nigeria, all independent variables produce the expected positive effect within the periods of study. It was concluded that adequate funds have been injected into the firm from time to time and have been well managed, which enhances the firm's to be more productive. Gunanta et al. (2020) examined the effect of the cash flow statement and Earning per Share on stock prices. The method used in this research is descriptive and verification method, where the data has been obtained from the Indonesia Stock Exchange Office - Bandung. This study also involves a literature search. The data is statistically analysed using SPSS 18.00. The statistical analyses conducted are the classical assumption test, multiple linear regression analysis, analysis of the coefficient of determination, t-test, and F test. This study suggests that EPS has significant influence on stock price in manufacturing companies listed on the Indonesia Stock Exchange for the period from 2008 to 2012. Duru et al. (2023), examined the effect of cash flow on performance of companies in Food and Beverages sub-sector of Nigeria. The study involved a survey of six companies of Food and Beverages companies quoted in the Nigerian Stock Exchange. Data were obtained from the Annual reports and accounts of the selected companies under study. The relevant data were analyzed using the multiple regression technique. The result of the study revealed that operating and financing cash flows have significant positive effect on corporate performance in the Food and Beverages Sector in Nigeria. It was also observed that investing cash flow has significant negative relationship with corporate performance. The researchers recommended that regulatory authority should encourage external auditors of these quoted Food and Beverages Companies to use cash flow ratios in evaluating the performance of a company before forming an independent opinion on the financial statement. Habib (2021) which evaluated present cash flow, consistent profitability and development potential on the stock returns in Australian stock market. The investigation, which relied on a multivariate regression technique, found a positive correlation between the growth prospects and free cash flow of a company and its market value. Adelegan (2023) conducted an empirical investigation of the connection between cash flow and monetary reforms in Nigeria. Over a longer testing period spanning 1984–1997, the researcher employed the ordinary least squares approach to analyze data on a sample of 63 listed enterprises in Nigeria. The empirical data shows a negative correlation between cash flow and corporate performance. Miar (2020) investigates the information content of cash flows financial ratios on the Tehran stock market, and finds that investors take no strong position either way. The data ranged from 1988 to 1994, and he ran it via the ordinary Least Squares method. Results showed a modest but statistically significant relationship between cash flow ratios and ratios from the income statement and the balance sheet, which in turn correlate with stock returns. Dastgir et al. (2011) investigated the relationship between cash flows ratio with stock return of companies. The research results indicated that there was not any significant relationship between operating cash flows ratio and stock returns except in 2003 and in the analysis of mixed data, it was concluded that there was no significant relationship between operating cash flow ratio and stock return using mixed data. In fact, at 5% error level, operating cash flows ratio did not provide necessary information content for determining stock returns. Also in the analysis of cross-sectional data, there was significant relationship between free cash flows and stock return. The calculated results for year 2002 and 2003 showed that there was not any relationship between variables. Ojimba et al. (2021) examined the effects of cash flow ratio on stock returns of consumer goods firms for a period of 2010-2019. The study was anchored on agency theory. Panel data were gotten from the Nigerian Stock Exchange and the data collected were analyzed using multiple regression analysis. The findings revealed Cash flows from operating activities has no significant effect on stock returns of consumer goods firms in Nigeria, cash flow from investing activities does not have significant effect on the stock returns of consumer goods firms in Nigeria, Cash flows from financing activities has no significant effect on stock returns of consumer goods firms in Nigeria, Free cash flow has positive and significant effect on stock returns of consumer goods firms in Nigeria. On the basis of the findings of the study, it was recommended among others that there is need for consumer goods industry to improve on their operating cash flow by making money available for this purpose for the general benefit of the economy. Okoye (2020) ascertained the effect of operating cash flow ratio on earnings management of Nigerian Banks. The study adopted Ex post facto research design. The study used sample of fifteen (15) Nigerian banks from 2010 to 2019. Data for the study was collected from annual reports and accounts of the banks. Regression analysis was used to test the hypothesis with the aid of E-view 9. 0. Based on this, the study revealed that operating activities are not statistically significant and have a negative effect on total accruals earnings of Nigerian banks. The study concludes that the importance of risk management activities is aimed at reducing future cash flow. Shakeel (2018), examined the relationship between fundamental analysis and Stock Returns based on the Panel Data Analysis; Evidence from Karachi Stock exchange . The study aimed to investigate the ability of the historical accounting data in predicting future stock returns using fundamental analysis especially in emerging economy i.e. Pakistan. Data were collected for the eleven-year period from 2007 to 2017 for 115 non- financial companies listed on Karachi stock exchange with available ten years consecutive data. This paper utilizes five indicators from multiple areas that is, profitability ratios, liquidity ratios, leverage ratios, and market-based ratios. For analysis, this study used penal data analysis (common effect model, fixed effect model, and random effect model). The results indicate that the fundamental analysis can predict future stock returns in Pakistani listed companies and end up with the implications and future directions. Leo and Harlyn (2022) examined the effect of gross profit, operating profit and net profit on the prediction of future cash flows in telecommunications sub-sector companies listed on the Indonesian stock exchange. This research was conducted focused on the telecommunications sub-sector companies listed on the Indonesia Stock Exchange in 2014-2019. The sampling method used is purposive sampling. Through this sampling method, 5 companies were obtained that could be used as samples with a research period of 6 years. Therefore, in this study, the number of this study was 30 units of analysis. This study uses descriptive statistical analysis, correlation coefficient analysis, coefficient of determination analysis, simple regression analysis, and multiple regression analysis in analyzing all data. Testing the data used in this study is the partial t-significance test, the classical assumption test, and the F simulation test. From the research conducted, the results show that the gross profit and operating profit variables partially have no significant effect on future cash flows while the net income variable shows that partially significant effect on cash flows in the future. The variables of gross profit, operating profit and net profit simultaneously show a significant effect on future cash flows. Gilbert and Ugochukwu (2023) evaluated how selected firms’ costs predict the directionality of operating profits of public listed consumer goods firms in Nigeria. Accordingly, the research intends to determine the effect of selling and distribution costs, cost of inventory and cost of labour on the operating profit ratio of the sampled firms. To achieve these objectives, the study adopts the ex-post facto research design. A total of 13 consumer goods firms was purposively sampled out of a population of 20 consumer goods firms that are listed on the floor of the Nigerian Exchange Group. Secondary data obtained from the 2011-2020 annual reports of the selected firms were analysed using descriptive statistics, correlation analysis and ordinary least square regression technique at 5% level of significance. Findings made showed that cost of inventory is positive, but does not significantly drive the operating profit ratio of public listed consumer firms in Nigeria, cost of labour is positive and significantly drives the operating profit ratio of public listed consumer firms in Nigeria, while selling and distribution costs are negative, but do not significantly drive the operating profit ratio of sampled firms. Based on these findings, the research concludes that when an effective costing system or technique has been established in the firm, there are efficient allocation and utilization of resources, which lead to minimization of costs and maximization of profit. It was therefore recommended that managers of consumer goods companies should strengthen envisaged control procedures to eliminate waste in their selling and distribution costs. 2.4 Gap in Empirical Literature Preliminary literature review indicates that empirical studies undertaken on the effect of accounting income on stock returns of selected Manufacturing Companies in Nigeria have not been exhaustive. More so, in very recent years, many studies explored the influence of accounting income on the organizational performance without examining the effect on the stock returns. Other studies tend to aggregate the components of accounting income, but failed to examine their distinct effect on the stock returns of Manufacturing Companies in Nigeria. Another potential gap is the scarcity of the study on the effect of accounting income on stock returns of Manufacturing Companies in Nigeria. Given a preliminary review, most studies in this area are mostly undertaken outside Nigeria. Also, there is scarcity of longitudinal studies that track the effect of Operating cash flow Ratio and Operating profit margin on stock returns of Manufacturing Companies in Nigeria. Most studies offer cross-sectional analyses, which provide only a snapshot of the relationship. Longitudinal research could offer more robust insight into how accounting income evolve and their long-term effect on performance. 3.0 Methodology 3.1 Research Design Panel data was used for the study and this was based on ex-post facto research design. The panel data has the characteristics of time series and cross sectional as the data were collected from many companies in many years. The data collected already exist and the study made used of it in its original form. The study employed Ex-post facto, because it used data of past performance of the selected manufacturing organization. 3.2 Population of the Study The research population comprises of thirty-five (35) manufacturing companies listed on the Nigerian Exchange Group as at December 2025. For the purpose of this study, nine (9) manufacturing companies were selected for the study. Table3.1: List of selected Manufacturing Companies in Nigeria Exchange Group i. Champion Brewery Plc ii. Guinness Nig. Plc iii. Nestle Nigeria Plc. iv. GlaxoSmithKline Consumer Nig Plc. v. Lafarge Africa Plc vi. Livestock Feeds Plc. vii. Cadbury Nigeria Plc viii. Cutix Plc ix. Dangote Cement x. Lafarge Africa Plc xi. Meyer Plc xii. BUA Brewery xiii. Premier Paint Plc xiv. Berger Paints Plc xv. First Aluminum Nigeria Plc xvi. Unilever Nigeria Plc xvii. Golden Penny xviii. PZ Cussons xix. International Brewery Plc xx. Enamelware Plc xxi. BUA Cement xxii. May and Baker xxiii. Fidson Plc xxiv. Berger Plc xxv. Dunlop Plc xxvi. Nestle Nigeria Plc xxvii. Betaglass Plc xxviii. Dangote Flour Mills Plc xxix. Vitafoam Nigeria Plc xxx. Breaklines Plc xxxi. Dangote Sugar Plc xxxii. Honeywell Flour Plc xxxiii. International Brewery Plc xxxiv. Ashaka Cement Plc xxxv. McNichols Plc. Source: Researchers’ Compilation, (2026) ΔNI i,t Pri i,t-1 NI i,t Pri i,t-1 NI i,t Pri i,t-1 3.3 Sampling Technique The study utilized simple random sampling technique and nine (9) manufacturing companies were selected from the purpose of the study. The goal of random sampling technique is to ensure that the sample is unbiased and representative of the population, allowing researchers to make inferences about the population based on the sample data. 3.4 Source and Nature of Data The study is a secondary research. Secondary data were obtained from audited annual reports published on the Nigerian Exchange Group and from Fact Books of the nine (9) manufacturing companies selected for the purpose of the study. 3.5 Measurement and Description of variables The variables in the regression model is expected as follows: Operating Cash Flow Ratio : Operating Cash flow Current Liabilities Measures a company’s ability to generate cash from its core operation to pay off it short term debts. Positive Operating Profit Margin Operating Income Revenue Measure a company's ability to generate net profit after tax from its core business activities Positive 3.5 Model Specification The model used was premised on the main objectives and anchored on the sub-objective. The following mathematical model were developed to analyse the effect of accounting income on stock returns of Manufacturing Companies in Nigeria using Earning Per Share , Return on Investment , Operational Cash Flow Ratio , Return on Equity , Return on Asset , and Operating Profit Margin as the explanatory variables to see how it affect Stock Returns (SR) which is the dependent variable. The study adapted Easton and Harris (1991) valuation model. This give the background to the work. It establishes that there is relationship between accounting income and stock returns. This prompted the researcher to adapt the model because it suits the researcher’s research variables. The Easton and Harris (1991) Valuation Model Easton and Harris’s (1991) valuation model expresses stock returns as a function of earnings levels and earnings changes, with both variables deflated by the stock price at the end of the previous year. In statistical notation, the model is as follows: Ret i, t = αo + α1 + α2 + Ɛ i,t Model 3.1 where Ret i,t is the stock return of firm i at time t, measured three months after the fiscal year end (Easton and Harris, 1991), the net income (NI) of firm i at time t, before taxes and extraordinary items (NIi,t) divided by the number of common shares outstanding and deflated by the market price at the end of the previous year (Pri t-1) and the change in the net income of firm i at time t, before taxes and extraordinary items (∆NI i,t) divided by the number of common shares outstanding and deflated by the market price at the end of the previous year (Pri,t-1). Last, ε i,t is an error term that follows a normal distribution with mean zero and standard deviation σε . The Easton and Harris model measures the information content of earnings levels and changes for stock returns and thus can be described as providing evidence on the differential relationship between earnings and prices. The model can be used to assess annual differences in the information content of the accounting variables between the pre- and post-IFRS periods. However, Easton (1999) provides some additional insights on the interpretation of the slope coefficients α1 and α2. Specifically, assuming that the clean surplus relation holds, he argues that slope coefficient α1 is a proxy for the statistical association between the stock price and the book values of equity per share. In addition, slope coefficient α2 measures the statistical association between stock prices and earnings per share. Econometrically the basic model is stated as; SRit = β0 + + β3OCFRit + β6OPMit + e Equation 3.1 This equation can be rewritten as putting the variables SR = f (OCFR, OPM) Model 3.1 Whereas: β0 = Intercept SR = Stock Return OCFR = Operating Cash Flow Ratio OPM = Operating Profit Margin e = Error term t = Time dimension i = individual firm 3.6 Method of Data Analysis The secondary data collected was analyzed using descriptive statistics, correlation analysis, regression and interaction analysis. Multiple regression analysis was used to evaluate the effect of the independent variables with the aid STATA. The result is to reveal the degree of influence and the level of significance. The collected research data were checked for any errors and omissions, coded, defined and then entered into STATA. This study used both descriptive and inferential statistics. A panel data was used to evaluate the hypotheses. 3.6.1 First Order Econometric Tests The statistical criteria that was used to test the first-order hypotheses include: The economic apriori expectation evaluated the parameter in terms of their meeting the standard economic theory expectations. Statistical tests are done to evaluate reliability of the estimated parameter in accordance with statistical theory and expectation. The statistical test that were carried out included: 1 The t-test: this is used to test the significance of the individual parameters of the regression ΔNI i,t Pri i,t-1 model. The decision to accept null hypothesis is based on the value of the test statistics from the data at hand. A high t – statistic indicates that the independent variable is significance in explaining the variation in the dependent variable. In this study, we will use a significance level of 0.05. 2 The f-test: this measure the overall significance of the model. The null hypothesis for the F-test is that all the regression coefficient is equal to zero, implying that the independent variables do not have any significance influence on the dependent variable. This was carried out to ascertain whether, an individual regression co-efficient is statistically significant. If the calculated F-statistic is greater than the critical value of the F-distribution, the null hypothesis is rejected, and it conclude that at least one independent variable has a significant influence on dependent variable. Conversely, if the calculated F-statistic is less than the critical value of the F-distribution, the null hypothesis is accepted, and it conclude that none of independent variable has a significant influence on dependent variable. 3 Co-efficient of Determination (R2): The goodness of fit test was carried out using the square of the correlation co-efficient. It shows or explains the percentage in total variation of the endogenous variable being explained by the change in the explanatory variables. It measures the extent to which the explanatory variables are responsive for judging the explanatory power of the regression. 3.6.2 Second Order Econometric Tests The test was performed on the regression result in order to evaluate it according to the classical assumptions of Ordinal Least Square . These tests are discussed briefly below: 1. Test for multi-collinearity: This was used to test the linear collinearity among the explanatory variables and correlation matrix would be employed in this test. Multi-collinearity was used to test the linear collinearity among the explanatory variables, whether the independent variables are actually independent or they are linked to each. 2. Unit Root Test: the purpose of the test is to check the stationarity of the time series data in order to avoid having a spurious regression result. To check for stationarity, the Augmented Dickey-Fuller test was utilized. 3. Auto-correlation test: This was used to test if the errors corresponding to different observation are uncorrelated, testing for the randomness of error term. The Durbin-Watson (DW) method would be employed for this test, since according to Koutsoyannis (1997) D.W, provides estimates which have properties and are more efficient for all sample of all sizes. 4. Heteroscedasticity test: This was used to know whether error term of the explanatory variables of the estimated model have equal variance. 5. Normality test: This was used to know whether the error term of the estimated model is normally distributed. 6. Heteroscedasticity Test: Breusch-Pagan-Godfrey: Heteroscedasticity refers to situation where the variance of the residuals is unequal over a range of measured values. This is used to know whether error term of the explanatory variables of the estimated model have equal variance. 4.0 Results and Findings This section presents all the results of the empirical analysis with their interpretations. Data used for this analysis are time series that cover the period 2015-2025. The empirical results were generated using E-views 13 econometric software. 4.1 Data Analysis The descriptive test summarizes the descriptive statistics of the model variable testing the appropriateness and normality of the variables as modeled. Summary result of the descriptive test is presented on Table 4.1 Table 4.1: Descriptive Statistics Source: Researchers’ Computation using E-views 13.0 (2026) Table 4.1 shows that SRI, OCFR, and OPM had mean values of 1.038699, 48.39432, and 6.234757 respectively. This indicates the central or average values for these variables used in the study. In terms of the level of variability and dispersion in the distribution of these variables, the standard deviations obtained for the variables were 0.468159, 50.80855, and 11.22653 respectively. This indicates varying levels of variability in the distribution with OCFR indicating high variations in the distributions. Similarly, from the skewness values obtained, SR and OCFR, showed positive skewness values meaning they were all skewed to the right. This indicates that the mean values of these variables were greater than their median and mode. However, OPM showed negative skewness values meaning they were skewed to the left. The Kurtosis values obtained for the variables were showed in the table respectively. Since the values of the kurtosis are greater than three (3), it indicates a mesokurtic distribution, hence the presence of outliers in the data for the variables. SR OCFR OPM Mean 1.038699 48.39432 6.234757 Median 1.016129 37.27562 6.508240 Maximum 3.273322 359.7834 29.20514 Minimum 0.195779 -20.46371 -52.74914 Std. Dev. 0.468159 50.80855 11.22653 Skewness 1.864935 2.796873 -1.712104 Kurtosis 8.837431 15.84077 10.12249 Jarque-Bera 197.9486 809.2234 257.6274 Probability 0.000000 0.000000 0.000000 Sum 102.8312 4791.037 617.2410 Sum Sq. Dev. 21.47893 252987.8 12351.43 Observations 99 99 99 Finally, based on the Jarque-Bera probability values obtained, all variables (SR, OCFR, OPM) indicated normality in their distribution, given that their Jarque-Bera probability was greater than 0.05. Multicollinearity Test In evaluating multicollinearity, the study examines the correlation coefficients using correlation matrix and the variance inflation factor . For correlation matrix, multicollinearity occurs when the magnitude of the correlation coefficient exceeds .80 (Kim, 2019; Benjamin, et al., 2023). Table 4.2: Correlation Matrix SR OCFR OPM SR 1.000000 0.021118 0.090971 OCFR 0.021118 1.000000 0.043006 OPM 0.090971 0.043006 1.000000 Source: Researchers’ Computation using E-views 13.0 (2026) From the correlation matrix result on Table 4.2, there is no problem of multicollinearity, given that the magnitude of the correlation coefficient does not exceed 0.80. Table 4.3: Variance Inflation Factor Variance Inflation Factors Date: 06/17/26 Time: 03:32 Sample: 1 99 Included observations: 99 Coefficient Uncentered Centered Variable Variance VIF VIF OPM 5.34E-05 4.101162 3.126908 C 0.007078 3.320662 NA Source: Researchers’ Computation using E-views 13.0 (2026) For Variance Inflation Factor , multicollinearity occurs when the VIF exceed 10. (Marcoulides and Raykov, 2019). The test conducted in table 4.2 indicate the absence of violations of the assumption of multicollinearity on all the variables used in the study (given that Centred VIF values is less than 10). 4.2.1 Heteroscedasticity Test: Breusch-Pagan-Godfrey Heteroscedasticity refers to situation where the variance of the residuals is unequal over a range of measured values. This is used to know whether error term of the explanatory variables of the estimated model have equal variance. The result revealed there is no heteroscedasticity, given that the Prob (F-Statistic) and Prob (Chi-Square) are both 0. Table 4.4: Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 1.312286 Prob. F(6,92) 0.2596 Obs*R-squared 7.804835 Prob. Chi-Square(6) 0.2528 Scaled explained SS 24.95107 Prob. Chi-Square(6) 0.0003 SSource: Researchers’ Computation using E-views 13.0 (2026) Given that the probability value associated with the Chi-Squares is significantly greater than 0.05 levels, we accept the null hypothesis that there is no heteroscedasticity in the model. 4.2. Testing of Hypotheses HO3: There is no significant influence of Operational Cash Flow Ratio on stock returns of selected Manufacturing Companies in Nigeria. Table 4.7: OLS Analysis showing the influence of OCFR on stock returns of selected Manufacturing Companies in Nigeria. Dependent Variable: SR Method: Least Squares Date: 06/17/26 Time: 05:38 Sample: 1 99 Included observations: 99 Variable Coefficient Std. Error t-Statistic Prob. C 1.029283 0.065457 15.72448 0.0000 OCFR 0.000195 0.000935 0.208032 0.8356 R-squared 0.000446 Mean dependent var 1.038699 Adjusted R-squared -0.009859 S.D. dependent var 0.468159 S.E. of regression 0.470461 Akaike info criterion 1.349787 Sum squared resid 21.46935 Schwarz criterion 1.402214 Log likelihood -64.81448 Hannan-Quinn criter. 1.370999 F-statistic 0.043277 Durbin-Watson stat 2.022362 Prob(F-statistic) 0.835640 Source: Researchers’ Computation using E-views 13.0 (2025) The regression line can be written as follows: SR = 1.029283 + 0.000195 OCFR + e The equation implies that if the independent variable were held constant, SR will grow on an average rate of 1.029283. Furthermore, the results indicated that OCFR exhibited positive relationship with the SR with a coefficient of 0.000195. This means that if other factors remain unchanged, a one-unit increase in OCFR will lead to a one-unit increase in SR by 0.000195. The statistical significance of the above relationships was given by the p-value associated with each of the variables. Since this study t-test is based on the 95% level of confidence, a variable is said to have significant effect if its p-value is less than or equal to 0.05. Therefore, with the p-value of 0.8356, OCFR is said to have an insignificant effect on SR, given that the p-values is greater 0.05. The R-squared value of 0.000446 indicates that about 0.000446 variation in SR is accounted for by the independent variable of this study. In order to test the hypothesis, the researcher relied on the p-value of the F-statistic. The result shows the p-value of 0.8356 which implies that the independent variable has an insignificant effect on the dependent variable. Therefore, the null hypothesis which states that there is no significant influence of OCFR on stock returns of selected Manufacturing Companies in Nigeria, is accept. HO2: There is no significant influence of Operating Profit Margin on stock returns of selected Manufacturing Companies in Nigeria. Table 4.6: OLS Analysis showing the influence of OPM on stock returns of selected Manufacturing Companies in Nigeria Source: Researchers’ Computation using E-views 13.0 (2026) The regression line can be written as follows: SR = 1.015047 + 0.003794 OPM + e The equation implies that if the independent variable were held constant, SR will grow on an average rate of 1.015047. Furthermore, the results indicated that OPM exhibited positive relationship with the SR with a coefficient of 0.003794. This means that if other factors remain unchanged, a one-unit increase in OPM will lead to a one-unit increase in SR by 0.003794. The statistical significance of the above relationships was given by the p-value associated with each of the variables. Since this study t-test is based on the 95% level of confidence, a variable is said to have significant effect if its p-value is less than or equal to 0.05. Therefore, with the p-value of 0.3705, OPM is said to have an insignificant effect on SR, given that the p- values is greater 0.05. The R-squared value of 0.008276 indicates that about 0.008276 variation in SR is accounted for by the independent variable of this study. In order to test the hypothesis, the researcher relied on the p-value of the F-statistic. The result shows the p-value of 0.3705 which implies that Dependent Variable: SR Method: Least Squares Date: 06/17/26 Time: 06:01 Sample: 1 99 Included observations: 99 Variable Coefficient Std. Error t-Statistic Prob. C 1.015047 0.053938 18.81881 0.0000 OPM 0.003794 0.004217 0.899688 0.3705 R-squared 0.008276 Mean dependent var 1.038699 Adjusted R-squared -0.001948 S.D. dependent var 0.468159 S.E. of regression 0.468615 Akaike info criterion 1.341923 Sum squared resid 21.30117 Schwarz criterion 1.394350 Log likelihood -64.42521 Hannan-Quinn criter. 1.363135 F-statistic 0.809438 Durbin-Watson stat 2.041766 Prob(F-statistic) 0.370515 the independent variable has an insignificant effect on the dependent variable. Therefore, the null hypothesis which states that there is no significant influence of OPM on stock returns of selected Manufacturing Companies in Nigeria, is accept. 4.3 Discussion of Findings The result of the first hypothesis indicates that about 0.000446 variation in SR is accounted for by the independent variable of this study. In order to test the hypothesis, the researcher relied on the p-value of the F-statistic. The result shows the p-value of 0.8356 which implies that the independent variable has an insignificant effect on the dependent variable. Therefore, the null hypothesis which states that there is no significant influence of OCFR on stock returns of selected Manufacturing Companies in Nigeria is accepted. The findings is in contrast with prior studies conducted by Samoei and Tenai, 2021); Olaniyan et al., 2022). The result of the second hypothesis indicates that about 0.008276 variation in SR is accounted for by the independent variable of this study. In order to test the hypothesis, the researcher relied on the p-value of the F-statistic. The result shows the p-value of 0.3705 which implies that the independent variable has an insignificant effect on the dependent variable. Therefore, the null hypothesis which states that there is no significant influence of OPM on stock returns of selected Manufacturing Companies in Nigeria, is accept. The finding is in contrast with prior studies conducted by Samoei and Tenai (2021); Olaniyan et al (2022). 5.0 Summary and Conclusion 5.1 Summary of major findings The followings are the summary of the findings: i. Operating cash flow ratio has a positive and insignificant effect on stock returns of selected Manufacturing Companies in Nigeria ii. Operating profit margin has a positive and insignificant effect on stock returns of selected Manufacturing Companies in Nigeria 5.2 Conclusion The main objective of the study was to examine the effect of accounting income on stock of selected manufacturing companies in Nigeria. Ex-post-facto research design was adopted for the study. The data for the study were obtained from secondary source (that is from the published financial statement of Consumer goods manufacturing companies) for the period of eleven years (2013 to 2023). Ordinary least square regression, with the aid of E-views 13, was used in analysing the data and testing the stated hypotheses. 5.3 Business Implications of the Findings In line with the findings of the study, the following business implications were made: i. The insignificant effect of OCFB and OPM on stock returns suggests that investors in the Nigerian manufacturing sector may prioritize other factors, such as market trends and macroeconomic conditions, over financial statements. This implies that businesses must complement their financial disclosures with other value-driven strategies to attract investors. ii. Companies should focus on improving financial reporting quality and transparency to enhance investor confidence. The findings indicate that financial statements alone may not be enough to drive stock prices, possibly due to concerns about earnings management or information asymmetry. Strengthening corporate governance and adopting high-quality financial reporting standards can improve the credibility of accounting information. 5.4 Recommendations In view of the finding that OCFR has a positive and insignificant influence on SR, i. Investors should be educated on the importance of financial statement analysis and how to integrate accounting income with other financial and economic indicators. ii. Taking in to account the finding that OPM has a positive and insignificant influence on SR, Manufacturing companies should focus on improving their Operating Profit Margin by reducing operating costs, improving pricing strategies, and enhancing operational efficiency. 5.5 Suggestions for further studies Further studies should analyze how macroeconomic variables (e.g., inflation, exchange rates, interest rates, and GDP growth) and industry-specific factors (e.g., competition, government policies, and production costs) affect the stock returns of manufacturing firms. 5.6 Contribution to Knowledge This study contributes to the body o/f knowledge in accounting and finance by providing empirical evidence on the relationship between accounting income and stock returns in the Nigerian manufacturing sector. 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