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                                            Australian Finance & Banking Review; Vol. 4, No. 1; 2020  
                                                                               ISSN 2576-1196   E-ISSN 2576-120X 

           Published by Centre for Research on Islamic Banking & Finance and Business, USA 
 

        37 
 

Impact of Dividend Policy on Financial Performance - A Study 
 

 
 

Pradip Kumar Das PhD 
Associate Professor 

Jagannath Kishore College, Purulia (West Bengal) 
Affiliation under Sidho-Kanho-Birsha University, Purulia, India 

E-mail.: pradip57.prl@rediffmail.com 
 
 
Abstract 
This study is an attempt to evaluate the impact of dividend policy on financial performance of selected companies registered in 
Bombay Stock Exchange. The study based on correlation matrix and panel regression model shows that the selected companies 
do not follow consistent pattern of dividend payments and the association between price earnings ratio and dividend payout 
ratio is low positive. However, there is strong association between return on assets and return on equity. Hausman Test reveals 
that random affect model is appropriate thereby indicating that performance of selected companies have momentous impact on 
dividend policy. Divided policy is still contemplated as one of the complicated areas in corporate finance. The findings from this 
study are worthwhile to be welcomed into account by the board of managers of companies to demonstrate dividend policy for 
the companies. 
 
Keywords: Dividend policy, Performance, Earnings per Share, Price Earnings Ratio, Return on Assets, Return on Equity.  
 
1. Introduction  
Corporate dividend policy decision not only emphasizes important demonstration about the possibilities of business growth but 
also influences other business decisions like investment and finance. Dividend policy shows the opportunity for investment, 
future expansion and growth (Afza & Mirza,2011). It revolves around decision making between  distribution of current return 
and reinvestment of the same for future return (Pandey&Ashvini,2016;Kouser,Luqman,Yaseen and Azeem,2015).Dividend 
policy is a corporate finance decision on transfer of value in the form of dividend from an organization to its shareholders out of 
the profits available for a prescribed period of time(Okafori and Mgbame,2011).Pragmatic management of corporate pertains to 
earnings distribution in the form of dividend to shareholders over time (Ajanthan,2013). Design of dividend policy details the 
segment of company’s earnings as return on shareholders’ investment in the form of cash dividend or stock dividend (Abdul and 
Muhibudeen,2015). Pertinence of dividend policy in the discourse of corporate performance reflects bifurcation yet to be 
resolved (Abdul and Muhibudeen,2015).  

2. Literature Review 

Kolawole, E.et al.(2018)observed in their study favorable or positive impact of dividend payout and retention ratios on EPS in 

the Nigerian oil and gas firms. Kanwal, M. & Hameed, S. (2017) found efficacious influence of dividend payout   on financial 

performance of firm. Thirumagal, P.G. and Vasantha, S. (2018) perceived pessimistic or negative impact of dividend payout on 

shareholders’ wealth for majority of the Indian industries. Significant difference was found in share price between pre and post 

dividend announcements. Velmurugan (2015) noticed association of dividend declaration in Indian fertilizer industry with 

previous year’s dividend, current year’s depreciation and current year’s profit after tax, current year’s sales and previous year’s cash 

flow. Labhane & Mahakud (2016) distinguished that investment opportunity, financial leverage, company size, business risk, 

firm life cycle, profitability, tax and liquidity are major determinants of dividend policy for Indian companies. Das (2017) 

confirmed that leverage not size is an important determinant of dividend of Bombay Stock Exchange companies in India. Abiola 

(2014) observed that among other things both current and past year’s profits fix dividend policy of firm indicating that 

performance of firm in terms of profitability is vital in the pattern of dividend policy. Pandey & Ashvini (2016) propounded 

that dividend policy of firm is governed by factors including debt-equity ratio, earnings, corporate tax, earnings per share, and 

firms’ size. Kurawa & Ishaku(2014)recognized corporate governance as a determinant of dividend policy. Sakinc & 

Gungor(2015)acknowledged ownership structure as a determinant of dividend policy of firms. Yusof and Ismail, (2016) 

suggested that before deciding payment of dividend, the companies board should consider profit, debts, investments and also the 

size of shareholders. Maladjian and El Khoury (2014) studied the effects of size, dividend payments, debt, profitability, 

liquidity, growth and firm’s risk on dividend payout ratios of banks listed on Beirut Stock Market in Lebanese for the period 

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from 2005 to 2011 and observed efficacious relationship between size, risk and lag dividends, and observed pessimistic 

relationship between profitability and opportunity growth with dividend payout policy of firms. Chawla and Chadha (2014)and 

Mbuvi(2015)empirically studied the effect of dividend announcement, dividend payout, tax incentives and excess cash flows on 

shareholders wealth in Nairobi Securities Market and revealed positive influence  of dividend announcement, dividend payouts, 

tax incentives and free cash flows  on shareholders’ wealth. Yegon, C., Cheruiyot, J.&Sang,J.(2014)found pragmatic relationships 

between dividend policies of organizations and firm’s profitability, between dividend policy and investments, and between 

dividend policy and EPS. Masum, A. (2014) watched in the study with the help of Panel data approach positive correlation 

between dividend and stock price considering EPS, ROE, Retention Ratio; while dividend yield and profit after tax have 

negative relation with stock prices. Khan, et al (2016) showed negative relation of dividend payout ratio and leverage with the 

return on equity on the stock listed in PSE; while positive relation between ROA, dividend policy, and growth in sales. Labhane, 

N.B. & Mahakud Jitendra (2016) advocated positive impact of dividend policy on profitability and EPS.   

 
3. Purpose Behind the Study 
Dividend policy is a controversial subject to many researchers. A universe hypothesis that dividend plays a considerable role has 
been examined by many empirical studies and behavioral surveys. A strong perception as to the passion behind dividend yields 
opportunity to more study. Literature suggests dividend provides additional worth to company in the eyes of investors; but it is 
uncertain what financial factors management exercises to support their reasoning behind initiating dividend policy. The study 
searches potential factors influencing dividend decision of a large sample of dividend paying companies. 

  

4. Objective  
The prime objectives of the study are: 

▪ To explore how dividend policy of the selected companies influence their profitability;    

▪ To check how dividend policy of companies and their profitability is associated; 

▪ To analyze the impact of dividend policy on companies ROE; 

▪ To evaluate the impact of dividend policy on companies ROA. 
 

5. Methodology 

The study in essence rests on secondary data. The researcher, being an external analyst, has to depend substantially on current 

literature available in the form of books, journals, articles, research studies, official websites, etc. Editing, classification and 

tabulation of data assembled from these sources have been accomplished as per necessities of the study. Different statistical 

techniques and tools have also been applied for the purpose of the analysis. Calculations have been done approximately. 

Correlation Matrix and Panel Regression Model (Fixed Effect and Random Effect) are used to accomplish the objectives of the 

study. Haussmann Test has been used for the hypothesis. DPR, EPS and PER are used as proxy variables for dividend policy. 

ROA and ROE are applied to proxy for firm’s performance. This paper analyzes the dividend policy of reputed  fifty(50)BSE 

registered Indian companies comprising the group of cement industry, pharmaceutical industry, information technology industry, 

iron and steel industry during the study period from 2012–2013 to 2018-2019.The companies were selected on the basis of 

market capitalization obtained from the official website of BSE. Moreover, ratios have been calculated from the data published 

by the companies in their annual reports.  

 

6. Results and Discussions 
With a view to attaining the results and the conclusions drawn there from for the study, the methods administered are 
Correlation Matrix and Panel Regression Model. Performance indicators of dividend policy and profitability are estimated by 
the following ratios (Table.1). 
 
Table 1. Measuring Dividend Policy and Performance (i.e. Profitability) 

Performance 
Indicators 

       Performance Drivers         Performance Measures 

Dividend Policy 

(Independent 

Variable) 

 

Dividend Payout Ratio (DPR) Dividend paid to Equity Shareholders÷ Earnings available to 

Equity Shareholders 

Price Earnings Ratio (PER) Market per Share ÷Earning per Share 

Earnings per Share (EPS) (Profit after Tax –Preference Dividends)÷ 



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Number of Shares held by Equity Shareholders 

Profitability 

(Dependent 

Variable) 

Return on Assets (ROA) Profit after Tax ÷ Total Assets 

Return on Equity (ROE) (Profit after Tax ₋ Preference Dividend)÷Shareholders’ 

Equity 

 

6.1 Use of Correlation Matrix 

Correlation Matrix has been used to demonstrate the strength of relationship within the independent variables and between the 

dependent variables and independent variables. It helps in finding out the pair of variables having the highest correlation. 

Table 2. Correlation Matrix 

  DPR EPS PER 

DPR Pearson Correlation 1 -0.128 .285 

Sig.(2-tailed)  .380 .048 

 N 52 52  52 

EPS Pearson Correlation -0.128 1  .067 

Sig.(2-tailed) .380  .650 

 N 52 52 52 

PER Pearson Correlation .285 .067 1 

Sig.(2-tailed) .048 .650  

 N   52 52 52 

  Note- Correlation significant at 0.05 level 

Interpretation (1): Table-2 evidences that values of coefficient between the independent variables DPR-EPS, DPR-PER and 

PER-EPS are -0.128, 0.285 and 0.650 respectively; each value is less than 0.80. The result demonstrates that there is no 

multicolinearity existence among the independent variables. However, correlation coefficient between the dependent variables  

e.g. ROA and ROE is significantly high at 0.915.  

 

6.2 Use of Panel Regression Model 

Regression Model is employed to establish the fact that how the particular value of dependent variable changes with any change 

in one of the independent variables keeping other independent variables constant. Panel Regression Model has been applied in 

administering multidimensional analysis over a period of time. Two methods are available for panel regression:(1) Fixed Effect 

Model and(2) Random Effect Model. 

(1) Fixed Effect Model: With the assumption that properties of certain variables do not vary across time and may or may not be 

correlated with the individual dependent variables, it is tested to establish that whether fixed effects or random effects are 

necessary to upgrade the model. It can be dissected adopting Hausman Test. 

(2) Random Effect Model: In this model, parameters are random variables and have similarity with hierarchy liner model. It is 
used to examine panel data with the assumption of no fixed effects.  

 

7. Results and Discussions: Model. I: (ROA c DPR PER EPS) 

7.1 Interpretation (2):µ01 

There is no significant impact of DPR, PER and EPS on ROA across the panel. 

The model with random effect is evaluated and then Hausman Test is exercised to ratiocinate about the method to be used for 

testing the hypothesis. If prob. value of Hausman Test is less than 0.05, null hypothesis is rejected indicating appropriateness of 

the Fixed Effect Model for the study. If null hypothesis is accepted, it is suitable to use Random Effect Model. As prob. value 



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(0.113) is higher than 0.05, null hypothesis is accepted extrapolating that Fixed Effect Regression model is inappropriate, rather, 

Random Effect Model is appropriate to use (Table-3).            

Table 3. Correlation Random Effects (HausmanTest) 

Test Cross Section Random Effects 

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob. 

Cross Section Random 5.986 3 0.113 

 

Table-3.1. Cross Section Random Effects Test Comparison 

Variable Fixed Random Var (Diff.) Prob. 

DPR 2.327 2.281 0.386 0.940 

PER 0.225 0.178 0.009 0.630 

EPS 0.084 0.097 0.001 0.472 

     

Variable Coefficient Std. Error t-Statistic Prob. 

C 11.548 3.527 3.275 0.003 

DPR 2.327 2.711 0.859 0.397 

PER 0.225 0.170 1.325 0.194 

EPS 0.084 0.035 2.443 0.019 

 

Table-3.2. Effects Specification 

Cross Section Fixed (Dummy Variables) 

R-Squared 0.836 Mean Dependent Variable 19.466 

Adjusted R-Squared 0.783 S.D. Dependent Variable 7.318 

S.E. of Regression 3.416 Akaike Info Criterion 5.514 

Sum Squared Resid. 431.635 Schwarz  Criterion 6.011 

Log Likelihood -124.836 Hannan Quinn Criterion 5.703 

F Statistic 15.658 Durbin Watson Statistic 2.315 

Prob.(F-Statistic) 0.000   

 

Other prerequisite conditions also support the model and its result. Probability value of the model being less than 0.05 

(0.0000) suggests that inference drawn from the model is correct and the result justified. This also recommends that there is no 

potency of Type I and Type II errors. R-Square  value is 0.83 prescribing that variation in the dependent variable is explained by 

the independent variable with a percentage value of 83% and the rest 17% may be due to the appearance of other factors having 

indeterminate behavior and approach. Durbin-Watson(D-W) statistics deal with the problem of autocorrelation and stationary 

alike. D-W statistic value is between 2 and 3 (considered acceptable under the lenient approach). Hence, there is no problem of 

autocorrelation and adaptability in the model(Table-4). 

Table-4. Random Effect Model 

Variable Coefficient Std. Error t-Statistic Prob. 

C 11.428 3.204 3.568 0.001 

DPR 2.327 2.639 0.865 0.392 



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PER 0.225 0.139 1.278 0.208 

EPS 0.084 0.029 3.328 0.002 

 

Table-4.1. Effects Specification 

 S.D. Rho 

Cross Section Random 4.753 0.659 

Idiosyncratic Random 3.416 0.341 

 

Table-4.2. Weighted Statistics 

R-Squared 0.186 Mean Dependent Variable 5.956 

Adjusted R-Squared 0.133 S.D. Dependent Variable 3.785 

S.E. of Regression 3.525 Sum Squared Resid. 571.450 

F-Statistic 3.500 Durban Watson Statistics 1.632 

Prob.(F-Statistic) 0.023   

 

Table-4.3. Unweighted Statistics 

R- Squared 0.287 Mean Dependent Variable 19.465 

Sum-Squared Resid. 1871.364 Durban Watson Statistics 0.564 

 

8. Results and Discussions: Model. II (ROE c DPR PER EPS) 

8.1 Interpretation (3):µ02 

There is no significant impact of PER, PER and EPS on ROA across the panel. 

Prob. value of Hausman Test (0.488) is greater than 0.05. Thus, null hypothesis is accepted indicating significant result. It can 

be inferred that Fixed Effect Regression Model is inappropriate and Random Effect Model is appropriate to use(Table-5).  

Table-5. Correlated Random Effects(HausmanTest) 

Test Summary Chi-Sq Statistic Chi-Sq d.f. Prob. 

Cross- Section Random 2.432 3 0.488 

Table-5.1.  Cross-Section Random Effects Test Comparison 

Variable Fixed Random Variable (Diff) Prob. 

DPR 1.817 2.161 0.992 0.730 

PER 0.087 0.068 0.026 0.905 

EPS 0.078 0.098 0.001 0.515 

                             

Table-5.2.  Cross-Section Random Effects Test Equation 

 

Variable Coefficient Std. Error t-Statistic Prob. 

C 22.665 6.550 3.461 0.002 

DPR 1.817 5.033 0.361 0.721 



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PER 0.087 0.313 0.275 0.785 

EPS 0.078 0.064 1.224 0.30 

                                  

Table-5.3. Effects Specification 

 

Cross-Section Fixed (Dummy Variable) 

R-Squared 0.801 Mean Dependent Variable 28.456  

Adjusted R-Squared 0.735 S.D. Dependent Variable 12.351  

S.E. of Regression 6.343 Akaike Info Criterion 6.752  

Sum-Squared Resid 1490.338 Schwarz Criterion 7.24 91  

Log Likelihood -156.797 Humann-Quinn Criterion 6.940  

F-Statistic 12.40 Durbin-Watson Statistic 2.45  

Prob. (F-Statistic) 0.000    

                                    

Requisite conditions support the use of Random Effect Model and its result. Probability value of the model being less 

than 0.05 (0.0000) suggests that the inference drawn from the model is correct and the result is justified. It indicates also 

chances of Type I and Type II errors. R-Square value is 0.80 prescribing that variation in the dependent variable is demonstrated 

by the independent variable with a percentage value of 80 % and the rest 20% may be due to presence of other factors having 

uncertain behavior and movement. D-W. statistics deal with the problem of  auto correlation and adaptability alike. D-W. 

statistics value varies between 2 and 3 (considered acceptable under the lenient approach) signifying no problem of auto 

correction and adaptability in the model (Table-6).  

 

Table-6. Random Effect Model 

Variable Coefficient Std. Error t-Statistic Prob.  

C 21.730 6.442 3.371 0.002  

DPR 2.161 4.935 0.435 0.664  

PER 0.065 0.271 0.245 0.805  

EPS 0.095 0.055 1.740 0.089  

 

Table-6.1. Effects Specification 

 S.D. Rho. 

Cross-Section Random 10.495 0.733 

Idiosyncratic Random 6.345 0.268 

 

Table-6.2. Weighted Statistics 

R-Squared 0.065 Mean Dependent Variable 7.425 

Adjusted R-Squared 0.004 S.D. Dependent Variable 6.322 

S.E. of Regression 6.304 Sum-Squared Resid. 1828.720 

F-Statistic 1.084 Durbin-Watson Statistic 1.915 

Prob.(F-Statistic) 0.000   



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Table-6.3. Unweighted Statistics 

R-Squared 0.144 Mean Dependent Variable 28.453 

Sum-Squared Resid. 6386.842 Durbin-Watson Statistic 0.614 

 

9. Conclusion 

Different dividend ratios selected for the study depicts that sample companies do not have any consistent pattern of dividend 

payment and trend is skewed in character. Correlation Matrix also exhibits a very low positive association between PER and 

DPR at 5% level of significance. Similarly, association between ROA-EPS and ROE-EPS is low positive and significant at 1%. 

However, there is strong positive association between ROE-ROA at 1% level of significance. Results of both the two regression 

models exercised in this study are expressive thereby demonstrating suitability of the Random Effect Regression Model. It can 

be deduced that performance of the selected companies has considerable impact on dividend policy. 

 

10. Implication of The Study 

Dividend policy and its benchmark of disbursement is a key concern in finance. The study is rejuvenating for managers 

fascinated in profit planning and investment and will also accommodate the researchers to build more perception on dividend 

policy which is still an intricate subject in corporate finance. However, this study is a venture to build germane dividend policy 

model in future.  

 

11. Research Comment 

Despite pros and cons to dividend policy, the analysis appears to designate that income through dividend is a seductive 

determinant to investors both for the confirmation of return as well as the management claims. Dividend policy is under 

management deliberation. A trade-off exists between management’s decision as to the implementation and extent of the payout 

in dividend policy vs. reinvestment. Although there is compact information about dividend policy making process, decisions do 

not appear to be made delicately. Factors like tax, external financing costs, transaction costs, earnings stability, liquidity position, 

investment decision, etc. also play decisive role in management’s dividend policy. 

 
12. Future Research 

Further research can be persuaded to establish the cognition of dividend policy and its execution on market-based strategy. Data 

for several periods can also be exercised for distinguishing payout variation over time and the level of materiality for each 

determinant. Insertion of more variables like float, PE ratio projections, operating margins, DE ratio, and others may also yield 

expressive result. The present analysis may be lengthened to study the impact of ownership distinctiveness in case of other 

developing resources for future research. Impact of board structure on dividend policy is a beguiling effort to research. This is 

waiting for future research. 

 

Acknowledgement 

The paper is devoted to ALMIGHTY GOD who always shows HIS BLESSINGS in all walks of my life. 

 

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Abbreviations 

▪ Earnings per Share: EPS; 

▪ Price Earnings Ratio: PER; 

▪ Return on Assets: ROA; 

▪ Return on Equity: ROE. 
 
 
 
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