Pacific B usiness R eview I nternational

A Refereed Monthly International Journal of Management Indexed With Web of Science(ESCI)
ISSN: 0974-438X
Impact factor (SJIF):8.396
RNI No.:RAJENG/2016/70346
Postal Reg. No.: RJ/UD/29-136/2017-2019
Editorial Board

Prof. Mahima Birla
(Editor in Chief)

Dr. Khushbu Agarwal
(Editor)

Dr. Asha Galundia
(Circulation Manager)

Editorial Team

A Refereed Monthly International Journal of Management

A Study on Determinants of Financial Efficiency of Selected Pharmaceutical Companies in India: Panel Data Analysis

Dr. Sanjay J. Bhayani

Dean, Professor and Head

Department of Business Management

Saurashtra University, Rajkot

 

 

Dr. Butalal Ajmera

Associate Professor

Department of Commerce

M.K. Bhavnagar University, Bhavnagar (Gujarat)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Abstract: In today era one of the most difficult problems being faced by finance manager is to increase the financial efficiency. Looking the importance of the issues, researcher has made an attempt to study determinants of financial efficiency. Researcher has taken profitability as a measure of financial efficiency. Many researchers have done work on this topic but most of them belong to other parts of the word, only few researches have been carried out in India. Therefore researcher has taken up this topic. Researcher has selected five pharmaceutical companies with study period of 9 years from 2012-2013 to 2020-2021. Data have been analyzed by using Descriptive statistics, Correlation matrix, unit root test, OPL regression, redundant fixed effect test, fixed effect model and Panel cross section dependency test. Researcher has concluded that Descriptive statistics explains that ROCE, EPS, Growth rate and Tobin’s q are satisfactory. Correlation matrix explains that ROCE has significant relationship with DPS, EV and ROA. Unit root test explain that ROCE, DPR, Lev and ROA are stationary variables whereas DPS, EPS, Growth rate Tobin’s q and firm size are non stationary variables. The adjusted R2 is 0.672 which means that all independent variables have caused 67.20% variance in ROCE. Researcher also concluded that ROA has been the determinants of ROCE and other independent variables have been insignificant to ROCE. 

Keyword: Financial efficiency, Profitability, Panel data analysis, Pharmaceutical industry

 

 

Introduction:

 Financial efficiency increases the wealth of firms and stakeholders. Utilization of resources of the firm depends on the financial efficiency. Wasting resource doe not bring financial efficiency. The main objective of the financial efficiency is to decrease the cost of capital and enhances the earning per share. Shareholders expect that their money should be utilized effective and efficiently in the firm. Finance department has to analyze the available options to raise the funds from the market. Finance manager has to take fund from those sources where cost of fund is minimum. Moreover fund of the shareholders should be utilized for the purpose for which the firm procured fund from investors. Using fund for other purpose other than proposed by the firm then there would be a conflict which is also called agency problem. Financial efficiency is also related to earning capacity of the firm. Earning capacity of the firm brings profitability. Thus profitability is the final measure of financial efficiency. Profitability is measure by accounting ratio called return on capital employed and profitability of determined by many factors like earning per share, return on assets, dividend per share, dividend payout ratio, size of the firm and Tobin’s q. Researcher has selected pharmaceutical companies because during corona pandemic their business have flourished.

 

Review of literature

Bhayani Sanjay (2020) worked on determinants of profitability in Indian cement industry during the study period of 2001 to 2008.  The researcher used secondary data. Statistical tools like descriptive statistics, correlation matrix, multiple regressions. He concluded that liquidity, age of the firm, operating profit ratio, interest rate and inflation rate are the significant determinants of profitability in cement industry in India. Adebayo Sayedoyin Ifeduni and Onyeiwu Charles (2018) studied determinants of profitability of manufacturing firms in Nigeria during the study period of 2011 to 2015. The researchers used statistical techniques like correlation matrix, regression analysis. They concluded that ROE is significantly impacted by leverage. Wherein other model ROA is affected by Size of the firm. Thus ROE and ROA are the two major determinants of the profitability. Maja Pervan, Ivica Pervan & Marijana Ćurak (2019) studied on Determinants of firm profitability in the Croatian manufacturing industry during the study period of 2006 to 2015. The researchers concluded that profitability has been significantly affected by age of the firm, labour cost, GDP growth and inflation. Bhayani Sanjay & Butalal Ajmera (2021) worked on effect of productivity on profitability of selected cement manufacturing companies in India during the study period of 2015-2016 to 2019-2020. Researchers have used panel data analysis to examine the effect of productivity on profitability. Two measures like ROCE and Net profit ratio were taken as the measure of profitability. Researcher concluded that ROCE as measure of profitability has been affected by material productivity ratio, labour productivity ratio and overhead productivity ratio and total productivity ratio. However the effect has not been statistically significant. Net profit ratio has been taken as another measure of profitability which has also been affected significantly by material productivity ratio. Other variables have affected insignificantly on Net profit ratio.  Didik Susilo, Sugeng Wahyudi, and Irene Rini Demi Pangestuti (2020) worked on profitability determinants of manufacturing firms in Indonesia during the study period 2010 to 2017. The researcher has selected 350 manufacturing firms as sample. The study concluded that working capital and firm growth have been positively correlated with profitability. But two variables like capital structure and non-debt tax shield have no effect on profitability. Nicolae Petria,Bogdan Capraru And Iulian Ihnatov (2015) have done a research work titled “Determinants Of Banks’ Profitability: Evidence From Eu27 Banking Systems”. In this study they all have assessed the main and important determinants of banks’ profitability in EU 27 over the period of 7 years starting from 2004 to 2011. They have divided the factors that have influenced the banks’ profitability in two criteria. Bank specific factors as well as industry specific and micro economics factors former was known as internal factors and later was indicated as external factors. They have concluded that the market concentration/competition, management efficiency, Credit and liquidity risk, the diversification of business, and the economic growth have influence on bank profitability, both on ROAA and ROAE. Zeeshan Fareed, Zahid Ali, Farrukh Shahzad, Muhammad Imran Nazir and Assad Ullah (2016) have conducted a research work titled “Determinants Of Profitability: Evidence From Power And Energy Sector”. They have examined the effect of various key determinants of profitability such as firm age, firm size, growth of firm, productivity of firm, and financial leverage on power and energy sector in broader sense. They have collected panel data of 16 firms for the period of 12 years starting from 2001 to 2012.At the end they have concluded that productivity and size of firm both were strongest determinants of profitability that affect the power and energy sector in the selected area.Ioannis Asimakopoulos, Aristeidis Samitas And Theodore Papadogonas (2009) have done a research study titled “Firm-Specific And Economy Wide Determinants Of Firm Profitability”. The purpose of this study was to examine the determinants of profitability for a sample of non-financial firm of Greek firm listed in Athens stock exchange. The time period of this study was 1995-2003. They have employed panel data estimation techniques. The findings of this research work indicated that the profitability of firm was positively affected by sales growth, size and investment as well as negatively affected by leverage and current assets. .Darush Yazdanfar (2013) had conducted a research work titled “Profitability Determinants among Micro Firms: Evidence from Swedish Data”. The main aim of this study was to discover the variable affecting firm profitability for that researcher has collected the data of approximately 87000 sample observations covering 12530 non-financial micro firms which were operating in 4 industry sectors. The findings have indicated that growth, lagged profitability, firm size and productivity influenced profitability positively as well as industry affiliation and age of firm affected it negatively. Enrique Claver, Jose Molina and Juan Tari (2002) have conducted a research work titled “Firm and Industry Effects on Firm Profitability: A Spanish Empirical Analysis”. The time period of this research work was from 1994 to 1998, the main aim of this research work was to examine the relative importance (firm effect) firm resources and (industry effect) industry membership in explaining the profitability of a firm for a set of non-diversified manufacturing companies in Spain. Their results had revealed that more significant industry effect for large- and medium-sized firms compare to small companies. S. M. Imamul Haque and Mohd Atif Afzal (2017) worked on an appraisal of financial performance of the fast-moving consumer goods industry in India during the study period of 2012-2012 to 2015 -2016. The researcher concluded that profitability has been satisfactory during the study period. They also concluded that sales have significant impact on profitability and liquidity. S. Felix Sophia and J. Gayathri (2018) worked on Firm Size and Performance with special reference to Multinational Pharmaceutical Firms during study period 2007 to 2016. The researcher concluded that independent variables like Total forex earnings, Profit intensity, total imports and export intensity have an effect on performance of selected multinational pharmaceutical firms during the study period.

After the review researcher has found that there is no any such specific study has been conducted to measure the financial efficiency of Indian pharmaceutical industry. So researcher has undertaken this study.

Objectives of the study;

  • To Analyze financial efficiency of the selected pharmaceutical companies
  • To examine the correlation between selected variables of financial effiecncy of selected pharmaceutical companies.
  • To study the determinants of financial efficiency of selected pharmaceutical companies in India
  • To give appropriate suggestions to the policy makers

Methodology of the study: Researcher has selected title “A study on determinants of financial efficiency of selected pharmaceutical companies in India”. Researcher has collected data from annual reports of respective companies. Researcher has used secondary data of nine years from 2012-2013 to 2020-2021. Convenient sampling method was used by researcher.  Besides, annual reports, moneycontro.com, icicidirect.com and capitaline data have been used to collect the data. To interpret and analyze the data, descriptive statistics, Correlation matrix, multiple regression and fixed effect and random effect model were used.  Researcher has used SPSS package and E-views software for Statistical techniques.

Model for financial efficiency measure

 

ROCE

Β0

+β1(DPR)

+β2(DPS)

+β3(EPS)

+β4(EV)

+β5(ROA)

 

+β6(Growth)

+β7(size)

+ β 8(tobin’s q)

+β9(Lev)

+Error term

 

 

Definition of selected variables

 

ROCE

Ratio of EBIT/Capital Employed*100

Dependent variable

DPR

DPS/EPS*100

Independent variable

DPS

Total Dividend paid / number of share

Independent variable

EPS

Net profit/ Shares outstanding

Independent variable

EV

Enterprise value= Market capitalisation+Debt+Prefered stock-cash

Independent variable

ROA

Ratio of Net Profit After Tax/Total Assets

Independent variable

Growth

(Current year’s sales – Last year’s sales)/Last year’s sales *100

Independent variable

Size

Natural logarithm of total assets

Independent variable

Tobin’s q

Total Asset Value of Firm / Total Market Value of Firm

Independent variable

Lev

Financial leverage= EBIT/EBT

Independent variable

 

Descriptive statistics: Researcher has calculated descriptive statistics by using SPSS package.

Table-1 Descriptive statistics

 

N

Minimum

Maximum

Mean

Std. Deviation

Skewness

Kurtosis

ROCE

45

-3.57

48.90

16.461

11.12

.903

1.18

DPR

45

-688.64

100.24

1.609

108.76

-6.037

39.08

DPS

45

1.00

35.00

10.767

9.60

1.079

0.22

EPS

45

-13.66

177.23

42.787

39.64

1.233

1.91

EV

45

3.79

5.18

4.629

0.25

-.731

3.02

ROA

45

-20.44

35.61

11.601

10.54

-.043

1.14

LEV

45

0.00

1.24

0.242

0.29

1.852

3.39

GROWTH

45

-38.28

161.41

63.978

46.91

-.165

-0.75

Tobin’s q

45

0.06

15.46

4.604

3.17

1.827

3.73

SIZE

45

3.39

4.50

4.039

0.29

-.454

-0.23

 

Table No.-1 shows mean of ROCE 16.46 with range of (-3.57) to (48.90). ROCE of the pharmaceutical companies has been very good and shows very good profitability.  Value of the skewness is less than +1 which shows that the distribution is moderate and Kurtosis shows the distribution is leptokurtic.  Average of DPR is 1.61 which is ranged between -688.64 to 100.24. The range shows very high fluctuation. DPR is considered low. Skewness is less than -1.0 which shows that the distribution is left skewed. The kurtosis shows that leptokurtic the distribution of DPR. DPS is ranged between 1.00 to 35.00 with a mean of 10.76. The DPS is quite good. As per skewness, data is right skewed. Whereas kurtosis is less than +1 which also shows data is platykutic distributed. Mean of EPS has been very satisfactory with range of (-13.66) to 177.23. Skewness shows that the data have been right skewed and kurtosis shows that the data have been platykurtic. Enterprise value is also satisfactory with a mean of 4.62. ROA has been only 11.60 percentage which also good and skewness shows that data have been left skewed. Financial leverage has been 0.242 shows less financial risk. Growth in sales has been very good during the study period. Tobin’s q is whether selected firms are overvalued or undervalued. Mean of Tobin’s q is 4.60 which are greater than one which shows that selected firms are overvalued. Size of the firms matter a lot for financial efficiency. Mean of size is 4.03 which are also satisfactory.

Table-2 Correlation Matrix

 

ROCE

DPR

DPS

EPS

EV

ROA

LEV

GROWTH

Tobin's Q

SIZE

ROCE

1

                 

DPR

0.246

1

               

DPS

.334*

0.189

1

             

EPS

.491**

0.193

.757**

1

           

EV

-.300*

-0.053

-0.206

-0.065

1

         

ROA

.799**

0.213

.432**

.582**

-.371*

1

       

LEV

-0.049

0.077

.456**

0.041

-0.26

0.041

1

     

GROWTH

0.146

-0.177

-.299*

-0.181

0.218

0.153

-0.239

1

   

TOBIN'S Q

-0.081

-0.098

-0.228

-0.277

0.15

-0.212

-0.056

-0.099

1

 

SIZE

-0.239

0.01

-0.101

0.064

.639**

-0.263

-.335*

.327*

-.318*

1

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

c. Listwise N=45

 

Table-2 shows correlation between dependent variable and independent variables. Independent variables like DPS and EPS have been positively correlated with statistically significance.  EV and ROA have been negatively correlated but statistically both variables have been insignificant. DPR has positive and insignificant correlation with ROCE. Tobin’s q and size of the firms have negative correlation. EPS and DPS have positive and significant correlation. ROA and DPS have also positive and significant correlation. Financial leverage and DPS have been significantly correlated. Growth and DPS have significant and negative correlation. EPS and ROA have significant correlation. Size and enterprise value have been significantly correlated. Size and financial leverage have been negatively but significantly correlated. And last but not least size and Tobin’s q are negatively correlated.

Table No. 3. Results of Unit Root Test: It is assumed that all independent variables must be having stationary before running variables into regression model. Researcher has used Levin, Lin & Chu, Breitung-t state, IPS W stat, ADF and Fisher chi-square test in two ways (1) individual intercept and (2) Trend and intercept 

Hypothesis of the unit root test

Ho: All selected variables are non- stationary

Ha: All selected variables are stationary

Level of significance: 5%

Table No. 3. Results of Unit Root Test

 

P.  value

Variables

Model form

LLC

Breitung

IPS

ADF

PP

Inference

ROCE

Individual

Intercept

0.0038

---

0.2293

0.1757

0.4819

Stationary

 

Trend and

Intercept

0.0001

0.3430

0.4020

0.2067

0.0305

Stationary

DPR

Individual

Intercept

0.0000

-----

0.0018

0.0013

0.0001

Stationary

 

Trend and

Intercept

0.0006

0.2200

0.1866

0.0281

0.0007

Stationary

DPS

Individual

Intercept

0.8864

------

0.9491

0.6916

0.5871

Non Stationary

 

Trend and

Intercept

0.6088

0.9994

0.7926

0.8247

0.3741

Non Stationary

EPS

Individual

Intercept

0.1061

-------

0.3774

0.2791

0.1936

Non Stationary

 

Trend and

Intercept

0.0000

0.2857

0.2536

0.0591

0.0013

Non Stationary

EV

Individual

Intercept

0.0000

----------

0.1503

0.1015

0.1445

Stationary

 

Trend and

Intercept

0.0163

0.1466

0.5128

0.5073

0.1832

Stationary

Growth

Individual

Intercept

0.0022

 

0.3533

0.3702

0.0091

Non Stationary

 

Trend and

Intercept

0.0000

0.1006

0.5424

0.5789

0.2625

Non Stationary

LEV.

Individual

Intercept

0.0000

 

0.0000

0.0006

0.0000

Stationary

 

Trend and

Intercept

0.0000

0.2133

0.0113

0.0004

0.0000

Stationary

TOBIN_S_Q

Individual

Intercept

0.1005

 

0.2671

0.2554

0.0309

Non Stationary

 

Trend and

Intercept

0.2322

 

0.4862

0.5798

 

0.6494

 

0.1621

 

Non Stationary

ROA

Individual

Intercept

0.0261

 

0.4510

0.5015

0.4736

Stationary

 

Trend and

Intercept

0.0000

0.1453

0.0093

0.0002

0.0006

Stationary

SIZE

Individual

Intercept

0.0089

 

0.7924

0.8708

0.2136

Stationary

 

Trend and

Intercept

0.0014

0.2086

0.4632

0.3592

0.1389

Stationary

 

Table No-3 shows that Null hypothesis of unit root is rejected for selected variables like ROCE, DPR, EV, Lev and ROA at 5% level of significance.  Researcher also found conflicting result in some of the variables like DPS, EPS, Growth, TOBIN_S_Q and SIZE.

Table No. – 4 Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

.820a

.672

.588

7.13641

1.433

a. Predictors: (Constant), SIZE, DPR, EPS, LEV, GROWTH, Tobin’s q , EV, ROA, DPS

b. Dependent Variable: ROCE

 

Table No.-4 show model summary of multiple regression model. R square 0.675 which means that independent variable combine caused the effect of 67.5% on dependent variable.

Table No-5 ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

3656.377

9

406.264

7.977

.000b

Residual

1782.492

35

50.928

 

 

Total

5438.870

44

 

 

 

a. Dependent Variable: ROCE

b. Predictors: (Constant), SIZE, DPR, EPS, LEV, GROWTH, tobin's q , EV, ROA, DPS

 

Table No.-5 shows ANOVA test of multiple regression. The F test shows significant result which means that model is fit.

 

 

 

 

 

Table No-- 6 Coefficients

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

27.739

26.904

 

1.031

.310

DPR

.012

.011

.114

1.099

.279

DPS

.022

.230

.019

.096

.924

EPS

.035

.057

.124

.605

.549

EV

-2.368

6.652

-.053

-.356

.724

ROA

.697

.171

.661

4.077

.000

LEV

-4.068

5.170

-.106

-.787

.437

GROWTH

.027

.031

.116

.895

.377

tobin's q

.342

.432

.097

.791

.434

SIZE

-3.091

6.571

-.081

-.470

.641

a. Dependent Variable: ROCE

Table no.5 shows coefficient of dependent and independent variables. The result shows that ROA has significant and positive effect on ROCE. Enterprise value and financial leverage and size of the firm have been negative factors for ROCE with insignificant effect. DPR, DPS, EPS, ROA, Growth and Tobin’s q have positively affected to ROCE but their effect has been insignificant to the ROCE.

1 Graph

 

Residuals vs. Dependent

Figure 2

Residuals vs. Predicted

Figure 3

 

 

To decide whether fixed effect of POLS model is best, Redundant Fixed Effects Tests has been performed and the result is significant and hence we use fixed effect model.

 

Table- 7    Redundant Fixed Effects Tests

 

Equation: Untitled

 

 

Test cross-section fixed effects

 

Effects Test

Statistic  

d.f. 

Prob. 

Cross-section F

6.338364

(4,31)

0.0008

Cross-section Chi-square

26.894537

4

0.0000

 

Table No-7 shows that redundant fixed effect cross section F is 0.0008 which is less than 0.05 which shows that the result is significant and fixed effect model is suitable over pooled OLS model

Table- 8  Fixed effect model

Cross-section fixed effects test equation:

Dependent Variable: ROCE

 

 

Method: Panel Least Squares

 

Date: 11/01/18   Time: 00:58

 

Sample: 2013 2021

 

 

Periods included: 9

 

 

Cross-sections included: 5

 

 

Total panel (balanced) observations: 45

 

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

ROCE

27.73893

26.90443

1.031017

0.3096

DPR

0.011632

0.010586

1.098803

0.2794

DPS

0.022084

0.230060

0.095994

0.9241

EPS

0.034660

0.057287

0.605027

0.5491

EV

-2.368272

6.652034

-0.356022

0.7240

GROWTH

0.027399

0.030611

0.895083

0.3769

LEV

-4.067811

5.169957

-0.786817

0.4367

TOBIN_S_Q

0.341835

0.431957

0.791364

0.4341

ROA

0.696975

0.170945

4.077201

0.0002

SIZE

-3.091227

6.571368

-0.470408

0.6410

Root MSE

6.293722

    R-squared

0.672268

Mean dependent var

16.46111

    Adjusted R-squared

0.587994

S.D. dependent var

11.11803

    S.E. of regression

7.136410

Akaike info criterion

6.961427

    Sum squared resid

1782.492

Schwarz criterion

7.362907

    Log likelihood

-146.6321

Hannan-Quinn criter.

7.111095

    F-statistic

7.977171

Durbin-Watson stat

1.435898

    Prob(F-statistic)

0.000003

Table No-8 shows that the result of fixed effect model explains that ROA has significant effect on ROCE but other independent variables like Enterprise value and firm size have negative but insignificant effect on ROCE. DPR, DPS, EPS, Growth and Tobin’s q have positive but insignificant effect. Thus ROA is the only determinant of ROCE.

 

Panel cross section dependency test:

Table No - 9 Residual Cross-Section Dependence Test

Null hypothesis: No cross-section dependence (correlation) in

Periods included: 9

 

Cross-sections included: 5

 

Total panel observations: 45

Cross-section effects were removed during estimation

Test

Statistic  

d.f.  

Prob.  

Breusch-Pagan LM

12.63046

10

0.2451

Pesaran scaled LM

0.588189

 

0.5564

Bias-corrected scaled LM

0.275689

 

0.7828

Pesaran CD

2.610997

 

0.0090

 

 

This test is used to know panel cross sectional dependence among residual at the time of number of cross sectional units are large. Presence of cross sectional dependence has a grave effect on the model building which may lead to inefficient estimator and invalid result. In the model null hypothesis is that there is no sufficient evidence of cross sectional dependency and alternative hypothesis is that there is a sufficient evidence of cross sectional dependency. The result of the test shows that p value is 0.05 which is greater that the probability value. Hence Pesaran CD has smaller value than LM which means that we failed to reject null hypothesis.

 Multiple indicators multiple cause models: The researcher has also used MIMIC model to estimate the effect of indicator on predictors’ variables.

Table-10 Predictor coefficients

Predictor

Estimate

Std. Error

z-value

p

Lower

Upper

ROCE

0.136

0.027

5.085

< .001

0.083

0.188

Indicator coefficients

Indicator

Estimate

Std. Error

z-value

p

Lower

Upper

DPR

14.428

9.174

1.573

0.116

-3.552

32.409

DPS

2.571

0.806

3.19

0.001

0.991

4.151

EPS

13.561

3.213

4.221

< .001

7.264

19.858

EV

-0.052

0.021

-2.505

0.012

-0.093

-0.011

ROA

5.534

0.957

5.782

< .001

3.658

7.41

LEV

0.01

0.025

0.404

0.686

-0.039

0.059

GROWTH

2.645

4.206

0.629

0.529

-5.599

10.889

tobinsq

-0.373

0.271

-1.375

0.169

-0.904

0.159

SIZE

-0.043

0.025

-1.739

0.082

-0.091

0.005

Figure 4

Table No. 10 shows MIMIC model which indicates that EPS and ROA are significant to ROCE. Other variables like DPR, DPS, EV, LEV, Growth, Tobin’s Q and firm size have insignificant effect on ROCE.

Conclusion:  Researcher has analyzed secondary data of selected companies of Pharma sectorTo study the determinants of financial efficiency, researcher has identified profitability ratio which such as ROCE, DPR,DPS, EPS, EV, ROA, Lev, growth rate, Tobin’s q and firm’s size. Financial efficiency is measured through return on capital employed and other ratios are taken as independent variables. Researcher has calculated descriptive statistics, correlation matrix, Unit root test, OLS regression mode, Redundant Fixed Effects, fixed effect model; Residual Cross-Section Dependence Test. Descriptive statistics explains that ROCE, EPS, Growth rate and Tobin’s q are satisfactory. Correlation matrix explains that ROCE has significant relationship with DPS, EV and ROA. Unit root test explain that ROCE, DPR, Lev and ROA are stationary variables whereas DPS, EPS, Growth rate Tobin’s q and firm size are non stationary variables.  The Result of the regression explains that only ROA is the determinant of financial efficiency. The adjusted R2 is 0.672 which means that all independent variables have caused 67.20% variance in ROCE. F test shows that the model is fit. The result of Redundant Fixed Effects Tests significant which means that fixed effect model is appropriate over OLS regression. The fixed effect model explains that adjusted R square is 0.6720 which means that independent variable caused 67.20% fluctuations in ROCE. Researcher has also used panel cross section dependency test which results significant and there is no sufficient evidence of cross sections dependency in model.

 

Reference

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