The Impact of Dividend Policy on Shareholders' Wealth

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The Impact of Dividend Policy on Shareholders' Wealth:
Evidence from Consumer Cyclical Sector in India
Pacific Business Review International
Volume 9 Issue 7, Jan. 2017
91
Abstract
Dividend policy (DP) is the most important to shareholders because it
can affect the share price and shareholders’ wealth (SW) as well.
Generally, higher dividends increase the market price of the share and
vice versa. Besides higher future dividends may also increase the
market price of share and thereby end up with wealth maximization of
the shareholders. Hence, the objective of the paper is to analyze the
impact of DP on SW of Consumer Cyclical Sector in India. Out of
13firms listed on Bombay Stock Exchange (BSE), 10 firms that have
been paying dividend consecutively for the recent past ten years are
considered for analysis. Besides descriptive statistics, Augmented
Dickey Fuller Test (ADF), Levin, Lin & Chu (LLC) t test, Philip Perron
(PP) Fisher x2 test, Im- Pesaran-Shin W (IPS-W) and Breitung test are
used. To test whether the data are stationary and to satisfy one pre-
condition for co-integration, Johansen Co-integration test is used.
Regression and Chow test are also applied to differentiate the impact
between pre and post financial meltdown periods. The results of the co-
integration test proves that there exists a stationary, long-run co-
integration between DP and SW. Regression result proves that DP has
significant impact on SW and the Chow test result proves that the
impact of DP on SW of Consumer Cyclical Sector has been
significantly affected by the event viz., the financial meltdown in
respect of variable dividend yield (DY) and not for the other selected
variables viz., dividend per share (DPS) and dividend payout (DPO).
Keywords:Dividend policy, Shareholders’ wealth, Financial
meltdown
JEL Classification:G 35, L 25, L 62
Introduction
The principal financial objective of any business enterprise is to
maximize the shareholders’ wealth (SW). The corporate function of
maximizing the SW assumes that managers operate in the best interests
of the shareholders. Therefore, it takes place when the returns to the
shareholders’ on the investment are maximized. In addition, these
returns are made up of capital gains in the form of increase in the share
prices, as well as dividends, which are made possible when the firm
generates adequate distributable profits.
When facing uncertainty, it is not always possible for a firm to achieve
its objectives. Wealth creation in entrepreneurial and established
organizations is a complex and challenging task. Therefore, in an ever-
Sandanam Gejalakshmi
Ph.D Research Scholar
Kanchi Mamunivar Centre for PG Studies
Puducherry
Dr. Ramachandran Azhagaiah
Associate Professor of Commerce
Avvaiyar Govt. College for Women
Karaikal

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Pacific Business Review International
92
changing environment, any organization wishing to Profile of the Study Sector - Consumer Cyclical Sect
maintain a competitive position and to satisfy its India
shareholders’ expectation should be engaged in planning Consumer cyclical sector includes industries such as
carefully every time when there is a need for. automotive, housing, entertainment and retail. The sector
The SW (Azhagaiah and Sabaripriya, 2008) is represented can further be divided into durable and non-durable sectors.
by market price of the firm’s common stock, which in turn, is Durable includes physical goods such as hardware or
the function of the firm’s investment, financing and vehicles, while consumer non-durable represents sector viz.,
dividend decision. The modern approach of financial entertainment or hotel services.
management provides a conceptual and analytical The performance of consumer cyclical sector is highly
framework for decision making, which emphasizes the related to the state of the economy. It represents goods and
effective use of resources to create SW. The optimal services that are not considered necessities, but for luxurious
dividend policy (DP) is one that maximizes the firm’s stock purchases. During contractions or recessions, investors
price; this leads to maximization of SW and thereby ensures have less disposable income to spend on consumer cyclical.
rapid economic growth. When the economy is expanding or booming, the sale of
Therefore, the present study is aimed at to study the long-run these goods rise as retail and leisure spending increase.
co-integration between the DP and the SW, and the impact of Consumer cyclical sector comprises textiles, automobiles,
DP on SW before and after an event viz., the global financial tyres, hotel, tourism and others as shown in figure A.
meltdown.
Figure A
Industries of Consumer Cyclical Sector
Source:http://www.investopedia.com
Review of Literature Priya and Nimalathasan (2013) revealed that dividend
payout had a significant impact on SW. Further, earnings per
Researchers have propounded many theories about a firm’s share (EPS), price earnings ratio (P/E) and market price to
value as well as the SW. There has been a substantial book value (MP_BV) had significant correlation with return
literature on the relationship between the DP and the SW and on assets (ROA); the P/E ratio had significant correlation
the impact of DP on SW. Several studies were made in with return on equity (ROE); EPS and the MP_BV were
respect of determinants of DP as well asSW in the developed significantly correlated with ROE of the selected hotels and
as well as in the developing economics like India. restaurants in Sri Lanka.
Vijaya kumar (2011) revealed that the sales and profit after Kumaresan (2014) found that there was a positive
tax of automobile firms had strong relationship with SW. relationship between return on equity, dividend per share
Devaki and Kamalaveni (2012)revealed that there was a and DP and SW of the firms while there was a negative
positive association between lagged dividend, earnings, relationship between retention ratio and SW of the listed
debt-equity ratio, sales size, age of the firm and institutional firms in hotel and travel sectors of Sri Lanka.
shareholding of the Indian corporate hotels. Ganesh et al.
(2013) found that the economic value added, market value Iqbal et al. (2014)found that the DP, firm size and firm
added, cash flow, and market to book value ratio were growth had significant positive impact on SW of selected
healthier in Ashok Leyland than that of the Tata Motors. manufacturing industries from three sectors viz., textile,
sugar and chemical.

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Volume 9 Issue 7, Jan. 2017
Ashvin (2012) found that there was a linear relationship Specific Objectives
between dividend decision and market price of stock of the Ÿ To study the long-run relationship between dividend per
firm of selected auto sector. Ajanthan (2013) showed that the share, dividend payout as well as dividend yield and
DP was a crucial factor affecting the firm’s performance of shareholders’ wealth of the Consumer Cyclical Sector in
the listed hotels and restaurants in Sri Lanka. India.
The above literature provides a review of impact of DP on Ÿ To estimate the impact of dividend variables along with
SW. The previous studies, by and large, were attempted to finance variables on shareholders’ wealth of the
study the long-run and short-run co-integration between DP Consumer Cyclical Sectorin India.
and SW and the impact of DP on SW. In the present study, an Ÿ To estimate the influence of finance factors on
attempt has been made to estimate the difference in the shareholders’ wealthof the Consumer Cyclical Sector in
impact of DP on SW between pre and post financial India.
meltdown periods. Ÿ To study the difference in the impact of dividend policy
Statement of the Problem on shareholders’ wealth of Consumer Cyclical Sector
Previous researchers have propounded many theories on DP between pre and post financial meltdown periods.
as well as on SW. Thus, the researchers are puzzled by the Hypotheses Developed for the Study
question, “whether SW was affected by DP? for many years. H01:“There is no co-integration between dividend per share
In the literature, there are different views regarding whether and shareholders’ wealth”.
DP affects firm’s share price in the long-run. Some studies H02:“There is no co-integration between dividend payout
showed that the firm’s value was not influenced by DP while and shareholders’ wealth”.
some others showed that DP affected firm’s value (Toby, H03: “There is no co-integration between dividend yield and
2014; and Baker Collins et al.2007). So, the present study shareholders’ wealth”.
has made an attempt to study the difference in the impact of H04:“There is no significant impact of dividend policy on
DP on SW between pre and post financial meltdown periods shareholders’ wealth”.
of the selected firms of Consumer Cyclical Sector in India. H05: “There is no significant difference in the impact of
Research Questions dividend per share on shareholders’ wealth between pre and
The research proposes to seek answers to the following post financial meltdown periods”.
questions: H06: “There is no significant difference in the impact of
Ÿ
Whether long-run relationship exists between dividend dividend payout on shareholders’ wealth between pre and
policy and shareholders’ wealth of listed firms of post financial meltdown periods”.
Consumer Cyclical Sector during the study period. H07:“There is no significant difference in the impact of
Ÿ
How do the dividend variables along with financial dividend yield on shareholders’ wealth between pre and post
variables influence the shareholders’ wealth of financial meltdown periods”.
Consumer Cyclical Sector? Research Methodology
Ÿ
How do finance variables (after removing dividend Data Source and Period of the Study
variables) influence the shareholders’ wealth of the The study used secondary data, which are collected from the
Consumer Cyclical Sector in India? capital market data base called Centre for Monitoring Indian
Ÿ
How does dividend policy impact shareholders’ wealth Economy Private Limited (Prowess CMIE) for a period of
before and after financial meltdown of Consumer 10 years on year to year basis from 2003-04 to 2012-13.
Cyclical Sector in India? Sampling Procedure and Technique
Objectives of the Study The study used multi-stage non-random sampling technique
To study the difference in the impact of dividend policy on and the different stages involved in it are shown in figure B.
shareholders’ wealth between before and after financial
meltdown periods.
Source: Compiled and edited data collected from PROWESS database provided by CMIE

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Table-1- List of Firms Selected for the Study (Based on listed firms in BSE 200) for
the Study Period 2003-04 – 2012-13
Total No.
of Firms
(1)
Dividendnon-paying firms
(2)
Adequate Data
not availablein
the data source
(3)
Totalnumber of firms not
considered for the study
(4)= (2+3)
Ultimate sample firms
selected for the study
(5) =(1) - (4)
13 2 1 3 10
Source: Compiled data collected from PROWESS database provided by CMIE
Table 1 shows the number of firms of Consumer Cyclical statistical methods viz., Augmented Dickey Fuller Test,
sector listed in Bombay stock exchange (13), out of which Johansen Co-integration, Ordinary Least Square method
dividend non-paying firms (2), and firms for which adequate and Chow test are applied for analysis of data using Eviews
data were not in the data source (1) are eliminated, hence the 7 Econometrics software package.
ultimate number of sample firms considered for the study is Ratios used for Analysis
10 only.
The study used two important ratios viz., dividend related
Research Methods ratios and shareholders' wealth related ratios and details of
Besides various dividend variables and finance factors, the ratios used for analysis are shown in table2.
Table2- Dividend Variables (DPS, DPO and DY) used to Estimate the Impact of DP on SW (M
Sl.
No.
Classification of
DividendRatios Variables Description Inference
I Dividend related
ratios
1. Dividend per
share (DPS)
Dividend / Number
of equity shares
outstanding
The DPS reveals how well
earnings support the dividend
payout.
2. Dividend
payout ratio
(DPO)
Dividend per share /
Earnings per share
The DPO provides an idea as to
how well earnings support the
dividend payment. Mature firms
tend to have a higher payout ratio,
while low dividend payout ratio
enables the firm to keep a large
portion of its earnings for its
future growth.
3. Dividend
yield (DY)
Dividend per share /
Market price per
share
The DY shows how much a firm
pays out as dividend each year
relative to its share price. Higher
dividend yield has been
considered to be desirable for
most investors. A high share price
will lead to low dividend yield and
vice versa.
II
Shareholders'
wealth (SW)
related ratio
1. Market price
per share
(MPS)
Market capitalization
/ Number of equity
shares outstanding
High market price reflects that the
firms are in very good position
and low market price reflects
reverse.
Source: www.scibd.com/essays/finance.php; www.ukessays.com/essays/finance/current -assets-current-
liability.php
Table2 shows the variables used to study the co-integration Besides, the study also used finance variables viz., return on
between DP and SW and to analyze the impact of DP on SW capital employed (R_CE), return on net worth (R_NW),
before and after financial meltdown periods. Market price return on assets (ROA), return on long-term fund (R_LF),
per share (MPS) is considered as proxy response variable for return on equity (ROE), total debt to equity (TD_EQ), total
shareholders’ wealth (SW), while dividend per share (DPS), debt to total assets (TD_TA), total debt to fixed assets
dividend payout (DPO), and dividend yield (DY) are (TD_FA), equity multiplier (EM), proprietary ratio (PR),
considered as predictor dividend variables. total liabilities to net worth (TL_NW), current ratio (CR),

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Volume 9 Issue 7, Jan. 2017
quick ratio (QR), earnings per share (EPS), price earnings 1990 and Osterwald-Lenum, 1992)
ratio (PER), working capital to total assets (WC_TA), • Ordinary Least Square Method of Regression and
current assets to total assets (CA_TA), and net fixed assets to
• Chow test(1960)net worth (NFA_NW) as predictor variables to study the
impact of DP on SW. RelationshipbetweenDP and SW: Analysisand
DiscussionFor the analysis of pooled data for ten years i.e. from 2003-
04to 2012-13 the following research methods are used. Consumer Cyclical Sector
• Descriptive Statistics (Jarque-Bera test) Test of normality
• Augmented Dickey Fuller Test, Levin, Lin & Chu Table 3 shows the mean, standard deviation, skewness and
(LLC) t test (2002), Philip Perron (PP) Fisher test kurtosis along with Jarque Bera test for MPS, DPS, DY,
(1988), Im-Pesaran-Shin W test (IPS-W)(2003) and DPO and EPS of ten firms of Consumer Cyclical sector. As
Breitung test (2000) presented in table 3, the mean of MPS ranges from 56.44
(Ashok Leyland) to 4844.91 (MRF).• Johansen Co-integration test (Johansen and Juselius,
Table 3 - Descriptive and Jarque-Bera Normality Test Statistics for Market Price per
Share and Dividend / Finance Variables for Firms under Consumer Cyclical Sector from 2003-0
Firm Name Mean SD SkewnessKurtosisJarque Bera Test
Value p Level
Market Price per Share (MPS)
Apollo Tyres 145.59 100.46 0.36 1.62 1.01 0.6039
Ashok Leyland 56.44 49.47 2.19 6.55 13.26** 0.0013
Century Textiles &
Industries 383.75 202.42 0.73 3.49 0.99 0.6096
Eicher Motors 697.17 727.84 1.43 3.73 3.62 0.1637
Exide Industries 123.5 44.38 0.04 2.59 0.07 0.9642
Grasim Industries 2026.64 765.48 -0.36 1.92 0.71 0.7011
Indian Hotels Co. 293.72 308.53 1 2.4 1.83 0.4001
MRF 4844.91 3006.44 0.9 2.83 1.37 0.5044
Mahindra & Mahindra 621.45 184.23 -0.52 2.25 0.69 0.7083
Tata Motors 562.94 241.64 0.64 2.44 0.81 0.6664
Dividend Per Share (DPS)
Apollo Tyres 0.5 0.09 2.22 6.73 14.03** 0.0009
Ashok Leyland 1.22 0.42 0.29 2.32 0.33 0.8462
Century Textiles &
Industries 0.42 0.13 -0.48 1.76 1.03 0.5973
Eicher Motors 1.14 0.85 0.89 2.69 1.36 0.5055
Exide Industries 0.79 0.55 0.55 1.55 1.38 0.5008
Grasim Industries 2.33 0.59 -0.14 1.7 0.74 0.6915
Indian Hotels Co. 1.19 0.35 0.93 2.7 1.49 0.4739
MRF 2.5 0.91 2.31 7 15.56** 0.0004
Mahindra & Mahindra 1.63 0.71 0.52 1.57 1.31 0.5184
Tata Motors 1.35 0.46 -0.02 2.13 0.31 0.8552
Dividend Yield (DY)
Apollo Tyres 0.63 0.56 1 2.58 1.73 0.4219
Ashok Leyland 2.96 1.26 -0.9 2.76 1.38 0.5023

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Century Textiles &
Industries 0.13 0.05 -0.12 2.06 0.4 0.8201
Eicher Motors 0.25 0.26 2.24 6.71 14.08** 0.0009
Exide Industries 0.7 0.43 -0.21 1.26 1.33 0.5133
Grasim Industries 0.13 0.04 0.47 1.94 0.84 0.6561
Indian Hotels Co. 0.91 0.62 -0.19 1.3 1.26 0.5326
MRF 0.07 0.04 0.25 1.79 0.71 0.6999
Mahindra & Mahindra 0.26 0.08 0 1.44 1.01 0.6038
Tata Motors 0.25 0.07 0.62 2.1 0.99 0.6111
Dividend Payout (DPO)
Apollo Tyres 7.54 5.71 0.46 1.77 0.98 0.6122
Ashok Leyland 44.25 29.55 1.25 4.42 3.47 0.1767
Century Textiles &
Industries 6.56 13.84 2.65 8.07 22.47** 0
Firm Name Mean SD SkewnessKurtosisJarque Bera Test
Value p Level
Eicher Motors 5.53 5.34 1.8 4.9 6.92* 0.0315
Firm Name Mean SD SkewnessKurtosisJarque Bera Test
Value p Level
Eicher Motors 5.53 5.34 1.8 4.9 6.92* 0.0315
Exide Industries 14.14 9.95 -0.09 1.44 1.02 0.6005
Grasim Industries 1.88 0.65 1.71 4.94 6.47* 0.0394
Indian Hotels Co. 30.48 19.95 0.12 1.39 1.1 0.5775
MRF 0.97 0.62 0.2 1.41 1.13 0.5693
Mahindra & Mahindra 4.59 1.4 0.22 1.39 1.17 0.5582
Tata Motors 6.18 5.68 1.84 5.15 7.55* 0.0229
Earnings Per Share (EPS)
Apollo Tyres 11.67 8.4 0.56 2.04 0.9 0.6387
Ashok Leyland 3.9 3.05 2.13 6.42 12.43** 0.002
Century Textiles &
Industries 18.63 11.24 0.14 1.75 0.68 0.7113
Eicher Motors 23.82 14.33 1.86 5.23 7.81* 0.0201
Exide Industries 7.15 3.41 0.28 1.75 0.77 0.6791
Grasim Industries 137.52 55.46 0 2.37 0.16 0.9213
Indian Hotels Co. 7.45 7.48 1.17 2.77 2.3 0.3166
MRF 420.63 317.61 0.5 1.76 1.07 0.5871
Mahindra & Mahindra 35.81 9.6 -1.04 3.83 2.08 0.3533
Tata Motors 32.25 16.32 -0.19 2.01 0.47 0.7912
Source: Computed from the compiled & edited data from the financial statements of selected
firm's listed-CMIE-prowess package.
** Significant at 1% level; * Significant at 5% level.
From the standard deviation, it is found that the MPS for that the MPS data are approximately symmetric. The JB test
most of the firms is highly dispersed from the central statistics for MPS data is significant for Ashok Leyland
tendency (mean) (standard deviation is high for majority of (13.66 at 1% level) and insignificant for all the other nine
the firms under Consumer Cyclical sector). Out of 10 firms firms. This led to accept the null hypothesis that the data are
with play kurtic, the MPS data are found to be with kurtosis, normally distributed for the MPS.As far as the DPS data are
which are approximately equal to 3 for Exide industries, concerned, the JB test statistics, based on skewness and
Indian hotels, MRF and Tata Motors, which fact first reveals kurtosis, are insignificant for eight firms, however they are

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Volume 9 Issue 7, Jan. 2017
significant for two firms (Apollo tyres and MRF), which motors and is insignificant for the rest of the nine firms,
evidences the presence of normality in the DPS. For DPO, which fact shows that the data are normally distributed.
the JB test result is <critical value of x2 at 5 % level for four Therefore, it is inferred that the MPS, DPS, DPO, DY and
firms and it is insignificant for six firms. For DY, the mean EPS are normally distributed for the firms under Consumer
ranges from 0.07 for MRF to 2.96 for Ashok Leyland. The Cyclical sector.
JB test result is < critical value of x2 at 5% level for Eicher
Unit Root Test
Table 4 - Unit Root Test (Panel) Results for Market Price per Share and
Dividend Variables for firms under Consumer Cyclical Sector
Method
No Intercept No Trend Intercept No Trend Intercept and Trend
Level First Difference Level First Difference Level First Difference
Statistic
p
Value Statistic
p
Value Statistic
p
Value Statistic
p
Value Statistic
p
Value Statistic
p
Value
Market Price per Share (MPS)
Levin, Lin & Chu t (LLC) 0.56 0.7111 -8.9** 0 -3.8** 0.0001 -9.07** 0 -7.24** 0 -23.81** 0
Breitung t-stat 2.88 0.998 -3.97** 0
IPS W-stat -1.44 0.0743 -4.23** 0 -0.11 0.4571 -2.87** 0.002
ADF - Fisher Chi-square 27.16 0.1307 102.76** 0 44.99** 0.0011 61.79** 0 55.96 0.1492 103.85** 0
PP - Fisher Chi-square 23.6 0.2605 89.29** 0 37.69** 0.0097 66.81** 0 59.8 0.0832 159.89** 0
Dividend per Share (DPS)
Levin, Lin & Chu t (LLC) 1.77 0.9616 -7.85** 0 -2.38** 0.0087 -7.81** 0 -3.53** 0.0002 -20.21** 0
Breitung t-stat 5.35 1 -3.6** 0.0002
IPS W-stat -1.12 0.1319 -3.48** 0.0003 0.79 0.7853 -3.62** 0.0001
ADF - Fisher Chi-square 10.38 0.9607 89.6** 0 30.31 0.065 50.47** 0.0002 35.98 0.8555 130.03** 0
PP - Fisher Chi-square 8.77 0.9854 104.45** 0 30.14 0.0677 69.84** 0 49 0.3538 170.99** 0
Dividend Payout (DPO)
Levin, Lin & Chu t (LLC) -0.77 0.2219 -7.66** 0 -1.87* 0.0306 -5.92** 0 -8.53** 0 -12.71** 0
Breitung t-stat 0.18 0.5731 -1.83* 0.034
IPS W-stat 1.17 0.878 -2.19* 0.0143 -0.81 0.2101 -1.62 0.0525
ADF - Fisher Chi-square 21.9 0.3462 84.19** 0 25.82 0.1717 53.24** 0.0001 77.37** 0.0026 91.33** 0.0001
PP - Fisher Chi-square 24.12 0.2374 103.31** 0 35.58* 0.0172 93.69** 0 109.95** 0 146.81** 0
Dividend Yield (DY)
Levin, Lin & Chu t (LLC) -1.6 0.0544 -8.43** 0 -3.55** 0.0002 -6.69** 0 -10.13** 0 -13.39** 0
Breitung t-stat 1.31 0.9043 -2.26* 0.012
IPS W-stat -1.03 0.1515 -2.81** 0.0025 -1.05 0.1458 -1.56 0.0598
ADF - Fisher Chi-square 24.04 0.2408 88.47** 0 24.9 0.2052 43.99** 0.0015 68.03* 0.019 84.52** 0.0005
PP - Fisher Chi-square 27.85 0.1131 98.55** 0 22.6 0.3088 65.83** 0 104.73** 0 156.2** 0
Note:Levin, Lin &Chu& Breitung t-stat - Null: Unit root (assumes common unit root process)
IPS (Im, Pesaran & Shin) W-stat, ADF - Fisher Chi-square & PP - Fisher Chi-square - Null: Unit root (assumes individual unit root process)
Source: Computed from the compiled & edited data from the financial statements of selected firm's listed-CMIE-prowess package.
** Significant at 1% level;* Significant at 5% level.
Table 4 shows the panel unit root test result for MPS, DPS, found that data are stationary at first differenced with
DY and DPO of firms of under Consumer Cyclical sector. intercept and no trend and also with intercept and trend, so it
From the table it can be inferred that for both the MPS and satisfies one precondition for co-integration test. The DY
the DPS data series, the unit root test statistics are significant shows that the unit root test statistics is significant at first
at first difference based on models without deterministic differenced based on models without deterministic trend (no
trend (no intercept and no trend, with deterministic trend intercept and no trend, and with intercept and trend so the
having only intercept and with intercept and trend). data are co-integrated of order I (1)).
Though, IPS-W test is insignificant at levels and both the Lag Length Selection
IPS-W test and the Breitung t-test are significant when first
The results of the analysis determining the lags for co-differenced, the MPS data series with drift process (with
integration model between MPS and dividend variables viz.,time trend) is considered to be stationary at first differenced
DPS, DPO and DY for Consumer Cyclical sector are shownbecause most of the test statistics are significant. Hence, it is
in table 5. Based on the lag length shown by majority of thefound that the MPS data series are integrated of order 1, i.e.
criterion, two lag is chosen for co-integration test betweenI(1) satisfying one precondition for co-integration test.
MPS and DPS. The chosen lag length for co-integration test
The unit root test statistics for DPO is significant at first is six between MPS and DPO and it is four between MPS and
differenced based on models without deterministic trend (no DY (the lag suggested by FPE and AIC is superior over LR
intercept and no trend, with deterministic trend having only test). Hence, the chosen lag length for co-integration test
intercept and with intercept and trend). Though, IPS-W test between MPS and DPS; MPS and DPO; and MPS and DY
is insignificant at levels and both the IPS-W test and the for Consumer cyclical sector is two, six and four
Breitung t-test are significant when first differenced it is respectively.

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Table 5 - Lag Length Selection Criteria for Co-integration Test for
Market Price per Share with Dividend Variables of Firms under Consumer Cyclical Secto
Lag LogL LR FPE AIC SC HQ
Market Price per Share (MPS) and Dividend per Share (DPS)
0 -412.6 NA 3.50E+06 20.73 20.82 20.76
1 -350.3 115.35 1.90E+05 17.81 18.07 17.91
2 -321.6 50.17* 5.5E+04* 16.58* 17.00* 16.73*
3 -319.4 3.66 6.00E+04 16.67 17.26 16.88
4 -314.4 7.75 5.80E+04 16.62 17.38 16.89
5 -310.7 5.43 5.90E+04 16.63 17.56 16.97
6 -308.7 2.66 6.70E+04 16.73 17.83 17.13
Market Price per Share (MPS) and Dividend Payout (DPO)
0 -546.5 NA 2.80E+09 27.43 27.51 27.46
1 -482.5 118.42 1.40E+08 24.43 24.68 24.52
2 -475.5 12.34 1.20E+08 24.27 24.7 24.43
3 -470.4 8.42 1.10E+08 24.22 24.81 24.43
4 -459.4 17.00* 8.10E+07 23.87 24.63* 24.15
5 -454.6 7.04 7.90E+07 23.83 24.76 24.16
6 -447.6 9.4 6.9E+07* 23.68* 24.78 24.08*
Market Price per Share (MPS) and Dividend Yield (DY)
0 -416.3 NA 4.10E+06 20.91 21 20.94
1 -302.3 210.8 1.70E+04 15.41 15.67 15.51
2 -296.5 10.19 1.60E+04 15.32 15.75 15.48
3 -287.7 14.44 1.20E+04 15.09 15.68 15.3
4 -278.2 14.76* 9.4E+03* 14.81* 15.57* 15.09*
5 -276.8 2 1.10E+04 14.94 15.87 15.28
6 -274.4 3.25 1.20E+04 15.02 16.12 15.42
Source: Computed from the compiled & edited data from the financial statements of selected firms listed-CMIE-prowess package.
*Indicates lag order selected by the criterion
LR : sequential modified LR test statistic (each test at 5% level); FPE: Final prediction error; AIC: Akaike information criterion;
SC : Schwarz information criterion; HQ : Hannan-Quinn information criterion
Co-integration Test
Table-6 - Co-integration Test Results for Market Price per Share and Dividend Variables
of Firms under Consumer Cyclical Sector
Test
Hypothesized
Number of Co-
integration
With No Deterministic Trend Linear Deterministic Trend
With Intercept No Time Trend With Intercept and Time Trend
Eigen-
value Statistic Critical
Value
p
Value
Eigen-
value Statistic Critical
Value
p
Value
Eigen-
value Statistic Critical
Value
p
Value
Market Price per Share (MPS) and Dividend Per Share (DPS)
Trace None@ 0.3292 28** 12.32 0.0001 0.3116 30.88** 15.49 0.0001 0.3123 32.09** 18.4 0.0003
At most
1@ 0.0007 0.05 4.13 0.8575 0.0655 4.74* 3.84 0.0294 0.0806 5.88* 3.84 0.0153
Maximum None@ 0.3292 27.95** 11.22 0 0.3116 26.14** 14.26 0.0004 0.3123 26.21** 17.15 0.0018
Eigenvalue
At most
1@ 0.0007 0.05 4.13 0.8575 0.0655 4.74* 3.84 0.0294 0.0806 5.88* 3.84 0.0153
Market Price per Share (MPS) and Dividend Payout (DPO)
Trace None@ 0.3707 15.35* 12.32 0.0151 0.3405 16.44* 15.49 0.0359 0.3283 16.74 18.4 0.0839
At most
1@ 0.0474 1.46 4.13 0.2666 0.1236 3.96* 3.84 0.0467 0.148 4.80* 3.84 0.0284
Maximum None@ 0.3707 13.89* 11.22 0.0166 0.3405 12.49 14.26 0.0937 0.3283 11.94 17.15 0.244
Eigenvalue
At most
1@ 0.0474 1.46 4.13 0.2666 0.1236 3.96* 3.84 0.0467 0.148 4.80* 3.84 0.0284
Market Price per Share (MPS) and Dividend Yield (DY)
Trace None@ 0.3061 25.91** 12.32 0.0002 0.353 34.69** 15.49 0 0.3578 34.28** 18.4 0.0001
At most
1@ 0.1417 7.64** 4.13 0.0068 0.2277 12.92** 3.84 0.0003 0.2155 12.13** 3.84 0.0005
Maximum None@ 0.3061 18.27** 11.22 0.0025 0.353 21.77** 14.26 0.0027 0.3578 22.14** 17.15 0.0086
Eigenvalue
At most
1@ 0.1417 7.64** 4.13 0.0068 0.2277 12.92** 3.84 0.0003 0.2155 12.13** 3.84 0.0005
Note: p values based on MacKinnon-Haug-Michelis (1999)
Source: Computed from the compiled & edited data from the financial statements of selected firms listed-CMIE-prowess package.
**Significant at 1% level; *Significant at 5% level

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Volume 9 Issue 7, Jan. 2017
The results of co-integration analysis of Consumer Cyclical The DY and the MPS have long-run relationship proved by
sector are shown in table 6.The table reveals that both the trace rank test and maximum eigen value test without
trace and the maximum eigen value test statistics are deterministic trend, with intercept without time trend as well
significant for CE with intercept but without time trend as as with intercept and time trend. The results of trace test and
well as CE with intercept and time trend hypothesized as maximum eigen value without deterministic trend for DY
‘none’. This shows that the DPS and the MPS are co- and MPS show the critical values as 12.32 and 11.22,
integrated when the variables in the model are allowed for statistical values as 25.91and 18.27 respectively; that of for
linear deterministic trend. This has further proved the with intercept and without time trend the critical
existence of long-run relationship with time trend between valuesas15.49 and 14.26,statistical values as34.69and 21.77
DPS and MPS. respectively; and that of for with intercept and time trend the
critical values as 18.40 and 17.15,statistical values as34.28
The results further show that the data series is co-integrated and 22.14 respectively, which are highly significant at 1%
as both the trace test and the maximum eigen-value test level.
reject the null hypothesis of no co-integration, and suggests
that there are two significant co-integrating vectors in the The statistical values of the trace test and maximum eigen
model, which implies that there are two common stochastic value test are >critical values for three situations i.e. without
trends indicating a degree of market integration. The DPS deterministic trend, with intercept without time trend as well
and the MPS have long-run relationship, which and is as with intercept and time trend, hence the null
proved by trace rank test and maximum eigen value test hypothesisH03: “there is no co-integration between
without deterministic trend, with intercept without time dividend yield (DY) and shareholders’ wealth (SW)” is
trend as well as with intercept and time trend. rejected at 1% level. Therefore, the co-integration results
prove that there exists a stationary, long-run relationship
The results of trace test and maximum eigen value test between DY and MPS.
without deterministic trend for DPS and MPS show the
critical value as 12.32 and 11.22, statistical value as Both the trace test and the maximum eigen value test
28.00and 27.95 respectively; that of for with intercept and statistics for the CEs without and with deterministic trend
without time trend the critical value as15.49 and for MPS with DPS, DPO and DY are hypothesized as ‘none’
14.26,statistical value as30.88and 26.14 respectively; and at level shows the presence of a long-run relationship
that of for with intercept and time trend the critical value as between DP and SW(MPS and DPS; MPS and DPO; and
18.40 and 17.15,statistical value as32.09 and 26.21 MPS and DY).
respectively, which are highly significant at 1% level. Results and Discussion of Impact of DP on SW
The statistical values of the trace test and maximum eigen Table 7 is reported with the results of regression for eliciting
value test are >critical values for three situations i.e. without the impact of DP on SW. There are two regressions; first one
deterministic trend, with intercept without time trend as well with dividend variables (DPS, DPO and DY) besides the
as with intercept and time trend hence the null financial factors (P, LEV, OF, LQ, EPS, WF, AQ) and the
hypothesisH01: “there is no co-integration between second one is with financial factors (P, LEV, OF, LQ, EPS,
dividend per share (DPS) and shareholders’ wealth (SW)” is WF, AQ) only.
rejected at 1% level. Therefore, the co-integration results
The significance of the explanatory power of DP on SW,prove that there exists a stationary, long-run relationship
when all the financial factors are held constant, is foundbetween DPS and MPS.
based on F value obtained from comparing R2 values of the
The results of trace test and maximum eigen value test two models using the following formula:
without deterministic trend for DPO and MPS show the
critical value as 12.32 and 11.22, statistical value as
15.35and 13.89 respectively; that of for with intercept and
without time trend the critical value as15.49 and
Where,14.26,statistical value as16.44and 3.96 respectively; and
that of for with intercept and time trend the critical value as R2L = R2 from the larger model (full model)
18.40 and 17.15,statistical value as4.80 each respectively,
R2S = R2 from the smaller model (subset model afterwhich are highly significant at 5% level. The statistical
removing certain predictors)values of the trace test and maximum eigen value are
>critical values for three situations i.e. without deterministic dfL = Row degrees of freedom (or number of predictors)
trend, with intercept without time trend as well as with in the larger model
intercept and time trend hence the null hypothesisH02:
dfS = Row degrees of freedom in the smaller model“there is no co-integration between dividend payout (DPO)
and shareholders’ wealth (SW)” is rejected at 5% level. N =Number of observations

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Table7 Impact of Dividend Policy (after Partialling out the Effect of Financial Performanc
on Shareholders' Wealth of Consumer Cyclical Sector
Consumer Cyclic Sector
Estimators
Full Model with
Dividend Variables
Subset Model after Removing
Dividend Variables
Beta (â) t-value Beta (â) t-value
Intercept 5.322** 17.12 5.964** 11.37
Profitability (P) 0.004 0.01 0.084 0.14
Leverage (LEV) -0.319 -1.42 -0.412 -0.99
Owners Fund (OF) 0.53 1.82 0.439 0.88
Liquidity (LQ) 0.08 0.33 -0.412 -0.91
Earning per Share (EPS) 0.158* 2.26 0.374** 3.19
Working Fund (WF) -0.02 -0.14 0.443 1.74
Asset Quality (AQ) 0.067 0.73 0.187 1.17
Dividend policy (DPS) 0.731** 7.7
R 2 0.8647 0.5162
Adjusted R 2 0.8495 0.4794
F Value 56.90** 14.02**
Degrees of Freedom 10..89 7..92
Significance of the
Change in R2
F Value DF
76.41** 3..89
Source: Computed result from the compiled & edited data from the financial statements of
selected firms listed-CMIE-prowess package.
**Significant at 1% level;*Significant at 5% level.
As per table7, both the full and the subset models of been made to estimate whether there has been any
regressions are fitted significantly. From the observation of significant difference in the impact of DP on SW between
the individual coefficients in both the models, it is seen that pre and post financial meltdown periods using the following
the SW tend to increase with increase in EPS. Regarding the formula:
DP, it is seen that the SW seems to increase at significant
level when there has been a significant increase in the DPS
This is distributed as F with k and n1 + n2 – 2k degrees of(ß = 0.731, t =7.70, p < 0.01). While the full model, with
freedomboth the dividend and the financial factors as predictors, has
the power of explanation to the extent of 86.47 per cent of Where, F is the test statistic
the variation; the subset model, with only financial factors as
RSS p = residual sum of squares for the wholepredictors, explain only to the extent of 51.62 % of the
samplevariation in the SW.
RSS1= residual sum of squares for the first group (beforeThe additional variance in the dependent variable (SW)
financial meltdown)explained by the dividend variables is 37.01 per cent (R2L –
R2S). Further, the additional variance in presence of RSS2 = residual sum of squares for the second group (after
dividend variables is highly significant at 1% level (F value financial meltdown)
= 76.41, p < 0.01). Therefore, it is found that the DP (DPS),
N = number of observationsas an explanatory variable, has unique influence (impact) in
creating additional wealth to the shareholders of firms. K = number of regressors (including the intercept term) in
Therefore, H04: “there is no significant impact of dividend each unrestricted sub-sample
policy (DP) on shareholders’ wealth (SW)” is rejected at 1%
2K = number of regressors in both the unrestricted sub-level.
sample regressions (whole sample)
Difference in the Impact of DP on SW between Pre and
Post Financial Meltdown Periods N1 = number of observations for before financial meltdown
period
To test whether there is any significant difference in the
N2 = number of observations for after financial meltdownimpact of DP on SW between pre and post financial
periodmeltdown periods, Chow test has been used and the results
are shown in table 8.By applying Chow test, an attempt has

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Volume 9 Issue 7, Jan. 2017
Difference in the Impact of DP on SW in Pre and Post-periods, i.e. the impact of DP (DPS and DPO) on SW is
FinancialmeltdownPeriodsfor ConsumerCyclical unaffected by the financial meltdown. Hence, H05:“there is
Sector no significant difference in the impact of dividend per
share(DPS) on shareholders’ wealth (SW) between pre and
The results of the chow test (vide table 8) reveals that the F post-financial meltdown periods” andH06:“there is no
value for DPS (1.09) and DPO (2.15) are not significant and significant difference in the impact of dividend
are higher than the 5% level. This shows that there is no payout(DPO) on shareholders’ wealth (SW) between pre
significant difference in the impact of DP (DPS and DPO) on and post-financial meltdown periods” are accepted.
SW (MPS) between pre and post financial meltdown
Table 8 - Results of Chow Test for the difference in the Impact of DP on SW between Pr
and Post Financial Meltdown Periods for Consumer Cyclical Sector
Variables
Pooled Regression Regression for Pre
Period 2003–07
Regression for Post
Period 2009-13(2003-07, 2009-13)
Beta (â) t-value Beta (â) t- value Beta (â) t-value
MPS = f (DPS, DPS_1)
Intercept -900.38** -3.06 -392.04* -2.05
-
1229.29** -2.6
DPS 422.93 1.59 0.32 0 594.62 1.56
DPS_1 1070.52** 3.94 1199.12** 4.6 1062.70** 2.8
R2 0.4488 0.6575 0.4369
Adjusted R2 0.4345 0.6321 0.4129
F Value 31.35** 25.92** 18.23**
DF 2..77 2..27 2..47
CHOW Test F Value DF p Value
1.09 NS 3..74 0.3748
MPS = f (DPO, DPO_1)
Intercept 1450.20** 6.04 887.18** 4.76 1873.49** 4.98
DPO -21.19 -1.47 -20.34 -0.92 -25.89 -1.4
DPO_1 -11.7 -0.76 2.13 0.07 -15.92 -0.84
R2 0.1031 0.0883 0.1433
Adjusted R2 0.0798 0.0208 0.1068
F Value 4.42* 1.31 NS 3.93*
DF 2..77 2..27 2..47
CHOW Test F Value DF p Value
2.15 NS 74 0.069
MPS = f (DY, DY_1)
Intercept 1424.92** 6 866.65** 4.86 1884.07** 5.01
DY -494.72 -1.11 -230.31 -0.91 -1265.05 -1.14
DY_1 -94.47 -0.21 -37.17 -0.12 395.54 0.38
R2 0.0986 0.0849 0.1468
Adjusted R2 0.0852 0.0172 0.1105
F Value 4.21* 1.25 NS 4.04*
DF 2..77 2..27 2..47
CHOW Test F Value DF p Value
2.48* 74 0.0393
**Significant at 1% level; *Significant at 5% level; NS – Not significant.
Source: Computed from the compiled & edited data from the financial statements of selected firms listed-CMIE-
prowess package.
However, the F value of DY (2.48) is significant at 5% level. Concluding Remarks
Hence, H07:“there is no significant difference in the impact The study attempts to answer the question: Is there any
of dividend yield (DY) on shareholders’ wealth (SW) significant difference in the impact of DP on SW due to
between pre and post financial meltdown periods” is financial meltdown particularly the consumer cyclical
rejected at 5% level i.e. the impact of DY on SW is affected sector. The main objective of the study is to shed light on the
by the financial meltdown. Hence, it is concluded that the stated question. To test the relationship between DP and SW,
impact of DP on SW is significantly affected by the financial and to estimate the impact of DP on SW before and after
meltdown event only for the variable DY and not for the financial melt down periods,10 firms from Consumer
variables DPS and DPO.

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Pacific Business Review International
Cyclical Sector are considered with one pre condition that proxy for measuring the shareholders’ wealth (SW). Further
the firms should have consistent track record in paying studies may be conducted using the response variable viz.,
dividend over the period. The response variable viz., market Economic value added (EVA) and Market value added
price per share (MPS) is considered as proxy for SW and the (MVA) to measure the SW.
dividend variables viz., DPS, DPO, and DY are considered The study has used research tools like Johansen co-
as proxies of predictor variable (DP). The study used integration test, multiple regressions and Chow test for
Johansen co-integration, factor analysis, regression and analyzing the co-integration between DP and SW.
chow test to study the impact of DP on SW. Therefore, inclusion of some more appropriate methods of
The overall result of the study reveals that the trace test and analysis viz., Block Exogeneity Wald test (1943), Bai-
maximum eigen value test statistics for the CEs without and Perron test (2003) and Variance decomposition for analysis
with deterministic trend for MPS with DPS, DPO and DY may add to exploring new and further inference in the area of
hypothesized as ‘at most 1’ are not significant at level, hence research.
it leads to accept null hypothesis that there is at most one co- References
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