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Anwar, S., Singh, S. and Jain, P. K. (2015). Cash Dividend Announcements and Stock Return Volatility: Evidence from India. Procedia Economics and Finance. (30) 38-49.
Archana, G. (2016) Impact of Macro-economic Variables: Dividend Policy of Indian Companies SCMS Journal of Indian Management, 13(2), 111-121.
Arellano, M. and S. Bond. (April 1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58, 277 – 297.
Asghar, M., Shah, Z.A.S., Hamid, K. and Suleman M.T. (2011). Impact of Dividend Policy on Stock Price Risk: Empirical Evidence from Equity Market of Pakistan. Far East Journal of Psychology and Business, 4(1), 45-52.
Habib, Y., Kiani, Z.I. and Khan, M.A. (2012). Dividend Policy and Share Price Volatility: Evidence from Pakistan. Global Journal of Management and Business Research, 12(5), 1-7.
Iqbal, N., Ahmad, N., Ullah, H. and Abbas, A., (2014). Effect of dividend announcement on stock prices in banking industry of Pakistan. International Letters of Social and Humanistic Sciences, 35, 15-25.
Lee, B. S. and Mauck, N. (2016) Dividend initiations, increases and idiosyncratic volatility. Journal of Corporate Finance, 40 (C), 47–60.
Nazir, M.S., Abdullah, Nawaz, M.M (2012). How Dividend Policy Affects Volatility of Stock Prices of Financial Sector Firms of Pakistan, American Journal of Scientific Research,. 61, 132-139.
Shah A. S. and Noreen U. (2016) Stock Price Volatility and Role of Dividend Policy: Empirical Evidence from Pakistan. International Journal of Economics and Financial Issues, 6(2), 461-472.
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Research article

Juxtaposition of Micro and Macro Dynamics of Dividend Policy on Stock Price Volatility in Financial Sector of Pakistan: (Comparative Analysis through Common, Fixed, Random and GMM Effect)

kashif hamid1*,
muhammad usman khurram2,
wasim ghaffar2
1
Institute of Business Management Sciences, University of Agriculture, Faisalabad, Faisalabad, Pakistan
2
LSCS, University of Agriculture Faisalabad, Faisalabad, Pakistan
Journal of Accounting, Finance and Auditing Studies
|
Volume 3, Issue 1, 2017
|
Pages 64-79
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: 03-30-2017
View Full Article|Download PDF

Abstract:

The purpose of this study is to analyze the dividend policy dynamics in context to firm specific and macroeconomic variables with stock price volatility in the financial sector of Pakistan. Panel data is used for the period 2006-2014 to identify the common, fixed, random and GMM effect. It is concluded that dividend payout ratio, market value, interest volatility and inflation volatility have positive significant correlation with price volatility. Common effect model shows that dividend payout and interest volatility has a significant positive impact on the share prices. Whereas fixed effect model is more appropriate and good fit than random effect model and model indicate that dividend payout ratio has significant positive impact and market volatility has significant negative impact on stock prices. GMM results also support the fixed and random effect outcomes with more robustness. This study significantly contributes in dividend policy decisions and elaborates the dynamic roll of micro and macro variables on stock price volatility in financial sector of Pakistan.

Keywords: Dividend policy, Macroeconomic variables, GMM effect, Price volatility
JEL Classification: F37, F40

1. Introduction

Generally the main goal of every firm is to maximize the wealth of shareholders by increasing the market value of their share of investment. That’s why each firm endeavors towards satisfying the goal by using two different ways by providing the dividend distribution to its shareholders and secondly by re-investing through dividend reinvestment plan. Dividends are defined as the distribution of incomes between the shareholders in relation to their ownership of shareholders. Dividend is always paid to shareholders on after tax income. Arnold (2008) dividend policy means increasing shareholders wealth by increasing the purchasing power of shareholders. Dividend policy can be one of the most stable and predictable element in all decisions of the firm if firm adopts a regular dividend policy. Many firms started to pay dividends when the business reaches at the level maturity. If there is irregularity in dividend payments due to firm policies or state factors then shareholder may be uncertain about the dividend cash flow that may cause volatility in the price level. The more substantial volatility shows that the chances of profit or loss are high in short term. So that the value of the volatile stock would differ significantly over the time period and it is tougher to forecast the future value of this stock. Dividend policy remained a source of dispute over number of years in empirical and theoretical research, due to its relevancy with the share price volatility. Paying the large amount of dividends decreases risk and therefore its effect on share price and provides as base for substitution for upcoming income. Nowadays, dividend policy has gone away from the scope of solving the frequency of paying the retain earnings (whether annually, semiannually or quarterly) or cash dividend, to include such problems as whether to distribute the cash via specially-designated or share repurchase rather than regular dividends. That’s why, dividend policy reflects how to, balance the priorities of relatively untaxed and highly taxed investors, how to maintain and to improve the value of its stock in market. Setting the dividend payouts with the perspective of long-term growth opportunities increase the financial elasticity and decreased the financial resistances related to increasing the external capital. Hereafter, a fast developing firm, with an incremental of positive NPV projects, should hold a larger number of shares of its operating cash flow than a firm having some profitable investment chances.

The previous researches related to impact of dividend policy or relation of dividend policy with stock price behavior has been performed mostly on Non-financial sector of firms listed on Karachi Stock Exchange (KSE). Few researchers have worked on “impact of dividend policy or announcement on share price value of banking sector in Pakistan.” after financial turmoil.

The purpose of this study is to analyze the dividend policy dynamics in context to firm specific and macroeconomic variables with stock price volatility in the financial sector of Pakistan which is still and unaddressed issue in the available financial literature of Pakistan. This research is identifying the specific differentiation that how key micro firm variables and dividend policy effect the stock price volatility in financial sector of Pakistan and how financial sector dividends are affected by the key macroeconomic policies in a juxtaposed manner.

2. Literature Review

Asghar, Shah, Hamid and Suleman (2011) investigated the effect of dividend policy on stock price risk in the banking sector of Pakistan for the period 2005-2009. The outcome of the study indicates that the degree of relationship of price volatility and dividend yield is somewhat important as compare to the other variables. However price volatility has negative association with the growth in assets.

Nazir, Abdullah and Nawaz (2012) analyzed the impact of dividend policy dynamics on the volatility of stock values for financial firms of Pakistan by taking earning instability, assets growth, company size and debt. Fixed Effect model identified that there is significant negative linkage between dividend yield and price volatility. Moreover price volatility and dividend payout has interconnectivity.

Habib, Kiani and Khan (2012) examined relationship between share price volatility and the dividend policy in Pakistani equity market and concluded that share prices and dividend yield are positively linked but payout ratio is negatively associated.

Iqbal, Ahmad, Ullah and Abbas (2014) examined the consequences of variation in dividend on the stock value. They taken data for three banks and found that dividend has positive relationship with the earning per share and negative behavior with the stock value.

Anwar, Singh and Jain (2015) visualized the reflection of expectations of investors on risk and return and evaluated the effect of cash dividend announcements on stock returns volatility. Results reflects that the decline in risk of the company volatility of stock returns is due to enhanced post cash dividend announcement however no significant results were identified on these grounds.

Lee and Mauck (2016) investigated three dynamics of dividend initiation, increased announcements and idiosyncratic volatility aspects. Dividend signaling rose due to increased levels of idiosyncratic volatility which is related with higher announcements of abnormal returns on initiation of increased dividends. A high idiosyncratic volatility firm is linked with higher positive post event return drift.

Shah and Noreen (2016) examined the relationship between stock price volatility and dividend policy of KSE listed companies of Pakistan. The fifty firms have been selected from non-financial sectors from the period of 2005-2012. This study concludes that there exist significant negative relationship between stock price volatility and dividend policy proxies. Further study established significant positive relationship between the control variables of AG, EV, EPS and the dependent variables of Stock price volatility in KSE, However remaining two control variables of FS and LD have negative relation to stock price volatility.

Archana, G. (2016) studied the affect of macroeconomic forces on dividend policy for the corporate sector of India by analyzing the 319 companies listed on BSE by using the panel data for the period 2005-2015. Inflation, Index of Industrial production, interest rates and wholesale price index variables are used as independent variables and dividend payout ratio used as dependent variables. It is concluded that no macroeconomic variable have significant impact on dividend policy in India.

3. Methodology of Research

Panel data for 21 commercial banks have been taken for the period 2006 to 2014. The below function is used for OLS, Fixed Effect Model, Random Effect Model, Hausman Test and GMM.

Stock Price Volatility = ƒ (DP, DY, EV, GA, INFV, INTV, MV) Common Effect Regression Model:

$\begin{gathered}\ln \left(P V_{i t}\right)=\beta_0+\beta_1 D P_{i t}+\beta_2 \ln \left(D Y_{i t}\right)+\beta_3 E V_{i t}+\beta_4 G A_{i t}+\beta_5 I N F V_{i t}+\beta_6 I N T V_{i t} \\ +\beta_7 M V_{i t}+\varepsilon_{i t}\end{gathered}$
(1)

Whereas,

Micro Variables

Dp = Dividend Payout ratio

Ln(DY) = Natural Log of Dividend Yield EV= Earning Volatility

GA= Growth

Macro Variables

INFV= Inflation Volatility INTV= Interest Rate Volatility MV = Market volatility

Fixed Effect Model:

A Fixed effects model provides a constant slope however it is differentiated from intercepts in comparative to cross-sectional company’s data. This model controls the potential degree of relationship between the unabsorbable individual effects and other regressors.

Pooled regression can be used to transform the model to estimate beta with a natural phenomenon and time-invariant variable can be eliminated through ($\omega \gamma$)

$\boldsymbol{B}_{\omega \gamma}=\left[\sum_1^N \sum_1^T\left(\boldsymbol{m}_{i t}-\overline{\boldsymbol{m}}_{i t}\right)\left(\boldsymbol{m}_{i t}-\overline{\boldsymbol{m}}_{i t}\right)\right]^{-1}\left[\sum_1^N \sum_1^T\left(\boldsymbol{m}_{i t}-\overline{\boldsymbol{m}}_{i t}\right)\left(\boldsymbol{m}_{i t}-\overline{\boldsymbol{m}}_{i t}\right)\right]$
(2)

The fixed effect estimators are given by:

$\widehat{\emptyset}_l=l-\bar{m}_t \widehat{\beta \omega \gamma}$
(3)

It is the best advantage of fixed effect model that the error terms may be relevant to the individual effects of the model. On the other hand if the group effects are unrelated with group means of the regressors, it is better to apply a thriftier parameterization of the panel model.

Random Effect Model:

The random effects model is a regression equation with a random constant term. A specific effect is visualized as an outcome of a random variable. In simple terms, a static random effects model may be explained as below.

${k}_{i t}=\beta {l}_{i t}+{m}_{i t}$
(4)
$m_{i t}=s_i+e_{i t}$
(5)

Where ${\mu}_{i t}$ is independent and identical distribution such that:

$\begin{aligned} & \left(-{E}\left({s}_{{i}}\right)={E}\left({e}_{i t}\right)=\mathbf{0}\right. \\ & \left(-{E}\left({s}_{{i}} {e}_{i t}\right)=\mathbf{0}\right. \\ -\mathrm{E}\left(s_i, s_j\right)= & \left\{\begin{array}{cc}\sigma^2 & i=j \\ 0 & i \neq j\end{array}\right\} \\ & -\mathrm{E}\left({e}_{i t}, {e}_{{j} s}\right)=\left\{\begin{array}{cc}{\sigma}^2 & {i}={j}, {i}={j} \\ 0 & {i} \neq {j}, {i} \neq {j}\end{array}\right\} \\ & -E\left({s}_{{i}}, {l}_{{i t}}\right)=\left({e}_{{i t}}, {l}_{{i t}}\right)=0\end{aligned}$
(6)

The suitable GLS estimator of beta indicates that the random estimator, provided by $\boldsymbol{\beta}_{G L S}$ is consistent.

The Hausman Test

The suitable choice between fixed effect and random effect techniques investigated that either the regressor are related with individual effect or not. Housman statistics is a distance measure in between the fixed and random effect estimators. Therefore we can test actually $\mathrm{H}_{\mathrm{o}}$, that random effects are consistent and efficient, whereas $\mathrm{H}_1$ indicate that random effects are inconsistent.

The Housman Test uses the below econometric approaches.

$\begin{aligned} & H M=\left(\widehat{\beta e t a}^{F E}-\widehat{\beta e t a}^{\text {RE }}\right)\left[\operatorname{Var}\left(\widehat{\operatorname{\beta eta}}^{F E}\right)-\operatorname{Var}\left(\widehat{{\beta e t a}}^{F E}\right)\right]^{-1}\left(\widehat{{\beta e t a}}^{F E}\right)- \\ & \left(\widehat{\operatorname{\beta eta}}^{F E}\right) \sim x^2({k})\end{aligned}$
(7)

Generalized Methods of Moment Model (GMM)

GMM with instrumental variables circumvent problems with correlations of errors. The variables of Dividends, Earning Volatility, Growth, Inflation volatility and Interest rate volatility are the specified instrumental variables in dynamic panel data analysis. The computational work on dividend policy affects the inconsistency on mislaid variables and endogeneity biases and GMM estimator resolve this panel data issue and counts for the dynamics of dividend policy.

The population moments expressed in below manner with the sample moments based for an arbitrary value δ is

$g n(\delta)=\frac{1}{n} \sum_{t=1}^n g\left(w_t, \delta\right)=\frac{1}{n} \sum_{t=1}^n x t\left(\mathcal{Y}-z_t^t \delta\right)$
(8)

These moment conditions are a set of linear equations along with unknown parameters. The cross-section results of regression are inconsistent estimation and provide biased results which are not reliable because due to constant for all the cross-sections is same. The GMM model permits the dividends to affect the current price volatility; the dependent variable is most likely to be correlated with the firms’ specific effect and as well with the macroeconomic estimation by using OLS which provides inconsistent and biased estimates for the model. To attain the consistent estimations, the model should be the estimation of fixed effect and then we should use the instruments to eliminate the inconsistency which can be aroused from endogeneity of the regressor. Arellano and Bond (1991) also used this approach.

4. Results

Table 1. Descriptive statistics

Ln (PV)

Ln (DY)

DP

EV

GA

INFV

IV

MV

Mean

1.1141

4.95

0.6104

0.1566

8.8976

-2.47

5.87

2.94

Median

1.0088

3.22

0.0002

0.0114

0.1124

0.09434

-0.016

0.04

Std. Dev.

1.4579

5.1407

4.6780

1.4711

93.4344

0.341853

0.197

0.23

Skewness

0.1365

0.2379

13.2547

11.5279

13.1036

-0.264164

1.429

-0.81

Kurtosis

2.7043

1.2838

179.8871

143.041

176.6341

2.427615

4.638

2.92

Jarque-Bera

1.2756

24.9761

251935.5

158627.9

242830.6

4.778195

85.468

21.21

Probability

0.0284

0.0000

0

0

0

0.091712

0

0.00

Observations

189

189

189

189

189

189

189

189

Table 1 indicates the behavior of the data in the given panel for the period 2006-2014. The panel data indicates that that only inflation volatility and market volatility has negative skewness and all other variables have positive skewness and the growth in assets and dividend yield have high level of riskiness in comparison to other variables. Whereas, Jarque-Bera value indicates the normality of the panel data.

Figure 1. Panel graph for the variables

Figure 1 indicates that the graphical depiction of each variable in the panel for the period 2006-2014.

Table 2. Correlation matrix among panel variables

Correlation

Probability

DP

Ln(DY)

EV

GA

INFV

INTV

MV

Ln(PV)

DP

1

Ln(DY)

0.07044

1

p-value

[0.335]

EV

-0.00771

-0.003131

1

p-value

[0.916]

[0.9659]

GA

-0.01175

-0.026963

-0.010650

1

p-value

[0.8725]

[0.7127]

[0.8844]

INFV

0.061164

0.020401

-0.040164

0.064940

1

p-value

[0.4031]

[0.7805]

[0.5832]

[0.3747]

INTV

0.203522

-0.100754

-0.023522

0.174518

0.416727

1

p-value

*[0.0050]

[0.1678]

[0.7480]

**[0.0163]

*[0.0000]

MV

-0.162815

0.187805

0.071363

-0.159704

-0.079858

-0.638701

1

p-value

**[0.025]

*[0.0097]

[0.3292]

**[0.0282]

[0.2747]

*[0.0000]

Ln(PV)

0.549719

-0.006130

-0.008569

-0.008957

0.158766

0.387223

-0.33889

1

p-value

*[0.0000]

[0.9333]

[0.9068]

[0.9026]

**[0.0291]

*[0.0000]

*[0.0000]

*Significance level (p<0.01); ** Significance level (p<0.05); *** Significance level (p<0.1)

Table 2 indicates that dividend payout ratio, market value and interest volatility have positive significant correlation with price volatility at (p < 0.00001) as well as inflation volatility also has positive correlation with price volatility at (p < 0.05). On the other hand dividend yield, earning volatility and growth in assets have no significance relationship with price volatility.

Table 3. Panel least squares

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

0.398732

0.120337

3.313471

0.0011

Ln(DY)

0.138249

0.017018

8.123593

0.000*

DP

0.024025

0.018934

1.268881

0.2061

EV

0.083231

0.058272

1.428322

0.1549

GA

0.000270

0.000933

0.289707

0.7724

INFV

0.312888

0.285421

1.096231

0.2744

IV

1.828464

0.645905

2.830857

0.005**

MV

-0.789379

0.485179

-1.626985

0.1055

R-squared

0.380369

Mean dependent var

1.114138

Adjusted R-squared

0.356405

S.D. dependent var

1.457958

S.E. of regression

1.169637

Akaike info criterion

3.192671

Sum squared resid

247.6174

Schwarz criterion

3.329888

Log likelihood

-293.7074

Hannan-Quinn criter.

3.248261

F-statistic

15.87277

Durbin-Watson stat

0.890276

Prob(F-statistic)

0.000000

Dependent Variable: Ln(PV); Method: Panel Least Squares; *Significance level (p<0.01); ** Significance level (p<0.05); *** Significance level (p<0.1)

Table 3 indicates that only dividend yield and market volatility has positive impact on stock price volatility of commercial banks financial assets. R2 indicates that independent variables explain dependent variable up to 38.03%.

Table 4. Fixed effect model

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

7.951005

0.748753

10.61900

0.0000

DP

1.113265

0.163445

6.811249

*0.0000

Ln(DY)

-3.26E-06

5.71E-06

-0.571046

0.5688

EV

0.241237

0.508939

0.474000

0.6361

GA

-0.008595

0.008114

-1.059309

0.2910

INFV

1.986961

2.334193

0.851241

0.3959

INTV

17.24013

5.292333

3.257566

**0.0014

MV

-10.00263

4.004171

-2.498053

**0.0135

Effects Specification

Cross-section fixed (dummy variables)

R-squared

0.692659

Mean dependent var

8.453864

Adjusted R-squared

0.641117

S.D. dependent var

15.96500

S.E. of regression

9.564131

Akaike info criterion

7.489870

Sum squared resid

14727.09

Schwarz criterion

7.970129

Log likelihood

-679.7927

Hannan-Quinn criter.

7.684435

F-statistic

13.43879

Durbin-Watson stat

2.307346

Dependent Variable: Ln(PV); Method: Panel Least Squares Fixed Effect; *Significance level (p<0.01); ** Significance level (p<0.05); *** Significance level (p<0.1)

The results of Table 4 indicate that the value of standard error of constant is 0.7487, which shows 74% fluctuation in sampling means. The value of dividend payout coefficient is 1.1132 at p<0.01 level which shows that dividend payout has a significant positive impact on share prices but on the other hand the coefficient values of dividend yield and earning volatility are -3.26 and 0.24 respectively and are not significant at any level. The coefficient value of market volatility (MV) is -10.88 at (p< 0.05) level of significance indicate that market price (MV) has negative significant impact on share prices. The value of R2 is 69.6% which shows that explanatory variables are explaining the 69.6% variation in dependent variable which is higher that common effect model.

Table 5. Random effect model

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

7.842320

1.516038

5.172903

0.0000

DP

1.242134

0.161111

7.709823

*0.0000

Ln(DY)

-2.31E-06

5.59E-06

-0.413072

0.6800

EV

0.207491

0.500689

0.414412

0.6791

GA

-0.009167

0.007985

-1.147912

0.2525

INFV

1.998185

2.334038

0.856107

0.3931

INTV

16.73658

5.289499

3.164114

**0.0018

MV

-9.980960

4.001401

-2.494366

**0.0135

Effects Specification

S.D.

Rho

Cross-section random

6.046405

0.2855

Idiosyncratic random

9.564131

0.7145

Weighted Statistics

R-squared

0.430048

Mean dependent var

3.942901

Adjusted R-Sq

0.408005

S.D. dependent var

12.96992

S.E. of regression

9.979212

Sum squared resid

18024.82

F-statistic

19.51005

Durbin-Watson stat

1.961076

Dependent Variable: Ln(PV); Method: Panel EGLS (Cross-section random effects); *Significance level (p<0.01); ** Significance level (p<0.05); *** Significance level (p<0.1)

The results of Table 5 indicate that the value of standard error of constant is 1.51, which indicate that 15.1% fluctuation in sampling means. The value of dividend payout coefficient is 1.24 at (p<0.01) value of significance which shows that dividend payout has a significant positive impact on the share prices but on the other hand the coefficient values of dividend yield, earning volatility, growth in assets, inflation have no significant impact on price volatility. The coefficient values of interest volatility is 16.7 at p<0.01 level which indicates that interest volatility has positive significant impact on share prices. The coefficient values of market volatility is -9.88 at p<0.01 level which indicate that market price (MV) has negative significant impact on share prices. The value of R2 is 43% which shows that explanatory variables are explaining 43% variation in dependent variables.

Table 6. Housman test

Test Summary

Chi-Sq. Statistic

Chi-Sq. d.f.

Prob.

Cross-section random

0.000000

7

1.0000

* Cross-section test variance is invalid. Housman statistic set to zero.

Cross-section random effects test comparisons:

Variable

Fixed

Random

Var (Diff.)

Prob.

DP

1.113265

1.242134

0.000758

*0.0000

Ln(DY)

-0.000003

-0.000002

0.000000

0.4244

EV

0.241237

0.207491

0.008329

0.7116

GA

-0.008595

-0.009167

0.000002

0.6908

INFV

1.986961

1.998185

0.000723

0.6763

INTV

17.240126

16.736580

0.029987

*0.0036

MV

-10.002629

-9.980960

0.022168

0.8843

Correlated Random Effects - Housman Test; Test cross-section random effects; *Significance level (p<0.01); ** Significance level (p<0.05); *** Significance level (p<0.1)

The result of Table 6 shows that the variation value of dividend payout and interest volatility is 0.000758 and 0.029 respectively, is significant at p<0.01 level. This shows that the fixed effect model is more appropriate and good than random effect model.

Table 7. Panel generalized method of moment

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

7.501730

0.978600

7.665776

*0.0000

DP

1.651214

0.202885

8.138666

*0.0000

Ln(DY)

4.66E-07

6.87E-06

0.067825

0.9460

EV

0.122344

0.629708

0.194287

0.8462

GA

-0.010641

0.010057

-1.058039

0.2914

INFV

2.042018

3.084902

0.661939

0.5089

INTV

15.10940

6.981225

2.164291

**0.0318

MV

-9.887112

5.278700

-1.873020

**0.0627

R-squared

0.396180

Mean dependent var

8.453864

Adjusted R-sqd

0.372828

S.D. dependent var

15.96500

S.E. of regression

12.64335

Sum squared resid

28933.64

Durbin-Watson stat

1.441730

J-statistic

3.99E-29

Instrument rank

8

Dependent Variable: Ln(PV); Method: Panel Generalized Method of Moments; *Significance level (p<0.01); ** Significance level (p<0.05); *** Significance level (p<0.1)

Results of Table 7 indicate that the value of standard error of constant is 0.978600, which shows the 9.78% fluctuation in sampling means. The value of dividend payout coefficient is 1.65 with the 0.00001 value of significance which shows that dividend payout has a significant positive impact on the commercial banks share prices but on the other hand the coefficient values of Interest Volatility and Market Prices (MV) are 15.109 and -9.88 respectively with the (p< 0.05) and (p<0.10) values of significance which indicate that Interest Volatility has positive insignificant impact as well as Market Value has significant negative effect. The value of growth in assets coefficient is -0.010 with the 0.29 value of significance which shows that growth in assets has an insignificant impact. The value of R2 is 39.6% which shows that explanatory variables explaining the 39.6% variation in dependent variable. The value of adjusted R2 is 37.2% which is less than the value of R2 which shows that model is over fit.

Table 8. Equality of means between series test

Method

df

Value

Probability

Anova F-test

(7, 1504)

17.87120

0.0000

Welch F-test*

(7, 618.77)

11.05410

0.0000

*Test allows for unequal cell variances

Analysis of Variance

Source of Variation

df

Sum of Sq.

Mean Sq.

Between

7

2.96E+11

4.23E+10

Within

1504

3.56E+12

2.36E+09

Total

1511

3.85E+12

2.55E+09

Category Statistics

Variable

Count

Mean

Std. Dev.

Std. Err. of Mean

DP

189

0.610425

4.678004

0.340275

Ln(DY)

189

42295.16

137536.5

10004.31

EV

189

0.156694

1.471120

0.107008

GA

189

8.897648

93.43448

6.796359

INFV

189

-1.23E-17

0.341853

0.024866

INTV

189

-4.04E-19

0.197765

0.014385

MV

189

1.47E-19

0.239926

0.017452

Ln(PV)

189

8.453864

15.96500

1.161283

All

1512

5289.160

50491.07

1298.490

As we have relatively a less no of cross sections with moderate number of observation for each company. The test allows for unequal test for cell variances. However data with large cross sections are more justifiable for the above test.

Figure 2. Orthonormal loadings of factors

Figure 2 indicates the Orthonormal loadings of the factors and their direction behavior in the panel to inspire the price volatility.

Table 9. Summary of the results: juxtaposition of micro and macro dynamics of dividend policy on stock price volatility in financial sector of Pakistan

Correlation

Panel Least Square

Fixed Effect Model

Random Effect Model

Generalized Model of Movement

DP

Sig. positive

Sig. positive

Sig. positive

Sig. positive

Sig. positive

Ln(DY)

Insignificant

Insignificant

Insignificant

Insignificant

Insignificant

EV

Insignificant

Insignificant

Insignificant

Insignificant

Insignificant

GA

Insignificant

Insignificant

Insignificant

Insignificant

Insignificant

INFV

Sig. positive

Sig. positive

Sig. positive

Sig. positive

Sig. positive

INTV

Sig. positive

Sig. positive

Sig. positive

Sig. positive

Sig. positive

MV

Sig. positive

Insignificant

Sig. Negative

Sig. Negative

Sig. Negative

5. Conclusion

Correlation indicates that dividend payout ratio, market value and interest volatility have positive significance correlation with price volatility as well as inflation volatility also has positive correlation with price volatility. On the other hand dividend yield, earning volatility and growth in assets have no significance relationship with price volatility. Common effect model indicates that the dividend payout has a significant positive impact on the commercial banks share prices. Dividend yield and earning volatility has positive insignificant impact on commercial banks share price volatility. The growth in 0assets has an insignificant negative impact on the share price volatility. The inflation volatility has positive insignificant impact on share price volatility and interest volatility has positive significant impact on commercial banks share price volatility. The market price (MV) has negative insignificant impact on commercial banks share prices.

Fixed model indicates that the dividend payout has a significant positive impact on share prices but on the other hand the coefficient values of dividend yield and earning volatility are not significant at any level. The market volatility indicates that MV has negative significant impact on share price volatility. The results are not coherent with Asghar et al. (2011) and Nazir et al. (2012). However this study indicates that price volatility have positive correlation but the result of Habib et al. (2012) and iqbal et al. (2014) have negative significant association with price volatility.

Fixed effect has more explanatory power on dependent variable than common effect model. According to Random effect model shows that dividend payout has a significant positive impact on the share prices but on the other hand the dividend yield, earning volatility, growth in assets, inflation have no significant impact on price volatility. The interest volatility has positive significant impact on share prices. The market volatility has negative significant impact on share prices. Random effect has lesser explanatory power that fixed effect model. Hausman test shows that the variation value of dividend payout and interest volatility is significant, which shows that fixed effect model is more appropriate and good than random effect model. GMM model indicates that dividend payout has a significant positive impact on the commercial banks share prices but on the other hand interest volatility has positive significant impact as well as Market Value has significant negative effect. GMM model has greater robustness than common, fixed and random effect.

References
Anwar, S., Singh, S. and Jain, P. K. (2015). Cash Dividend Announcements and Stock Return Volatility: Evidence from India. Procedia Economics and Finance. (30) 38-49.
Archana, G. (2016) Impact of Macro-economic Variables: Dividend Policy of Indian Companies SCMS Journal of Indian Management, 13(2), 111-121.
Arellano, M. and S. Bond. (April 1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58, 277 – 297.
Asghar, M., Shah, Z.A.S., Hamid, K. and Suleman M.T. (2011). Impact of Dividend Policy on Stock Price Risk: Empirical Evidence from Equity Market of Pakistan. Far East Journal of Psychology and Business, 4(1), 45-52.
Habib, Y., Kiani, Z.I. and Khan, M.A. (2012). Dividend Policy and Share Price Volatility: Evidence from Pakistan. Global Journal of Management and Business Research, 12(5), 1-7.
Iqbal, N., Ahmad, N., Ullah, H. and Abbas, A., (2014). Effect of dividend announcement on stock prices in banking industry of Pakistan. International Letters of Social and Humanistic Sciences, 35, 15-25.
Lee, B. S. and Mauck, N. (2016) Dividend initiations, increases and idiosyncratic volatility. Journal of Corporate Finance, 40 (C), 47–60.
Nazir, M.S., Abdullah, Nawaz, M.M (2012). How Dividend Policy Affects Volatility of Stock Prices of Financial Sector Firms of Pakistan, American Journal of Scientific Research,. 61, 132-139.
Shah A. S. and Noreen U. (2016) Stock Price Volatility and Role of Dividend Policy: Empirical Evidence from Pakistan. International Journal of Economics and Financial Issues, 6(2), 461-472.

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Hamid, K., Khurram, M. U., & Ghaffar, W. (2017). Juxtaposition of Micro and Macro Dynamics of Dividend Policy on Stock Price Volatility in Financial Sector of Pakistan: (Comparative Analysis through Common, Fixed, Random and GMM Effect). J. Account. Fin. Audit. Stud., 3(1), 64-79. https://doi.org/10.56578/jafas030105
K. Hamid, M. U. Khurram, and W. Ghaffar, "Juxtaposition of Micro and Macro Dynamics of Dividend Policy on Stock Price Volatility in Financial Sector of Pakistan: (Comparative Analysis through Common, Fixed, Random and GMM Effect)," J. Account. Fin. Audit. Stud., vol. 3, no. 1, pp. 64-79, 2017. https://doi.org/10.56578/jafas030105
@research-article{Hamid2017JuxtapositionOM,
title={Juxtaposition of Micro and Macro Dynamics of Dividend Policy on Stock Price Volatility in Financial Sector of Pakistan: (Comparative Analysis through Common, Fixed, Random and GMM Effect)},
author={Kashif Hamid and Muhammad Usman Khurram and Wasim Ghaffar},
journal={Journal of Accounting, Finance and Auditing Studies},
year={2017},
page={64-79},
doi={https://doi.org/10.56578/jafas030105}
}
Kashif Hamid, et al. "Juxtaposition of Micro and Macro Dynamics of Dividend Policy on Stock Price Volatility in Financial Sector of Pakistan: (Comparative Analysis through Common, Fixed, Random and GMM Effect)." Journal of Accounting, Finance and Auditing Studies, v 3, pp 64-79. doi: https://doi.org/10.56578/jafas030105
Kashif Hamid, Muhammad Usman Khurram and Wasim Ghaffar. "Juxtaposition of Micro and Macro Dynamics of Dividend Policy on Stock Price Volatility in Financial Sector of Pakistan: (Comparative Analysis through Common, Fixed, Random and GMM Effect)." Journal of Accounting, Finance and Auditing Studies, 3, (2017): 64-79. doi: https://doi.org/10.56578/jafas030105
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