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THE IMPACT OF DIVIDEND POLICY ON SHAREHOLDERS’ WEALTH: EVIDENCE FROM CONSUMER CYCLICAL SECTOR IN INDIA
Sandanam Gejalakshmi Dr. RAMACHANDRAN AZHAGAIAH Ph.D Research Scholar Associate Professor of Commerce Kanchi Mamunivar Centre for PG Studies Avvaiyar Govt. College for Women Puducherry – 605 008, India Karaikal – 609602, India EMail: sankari_sandanam@yahoo.co.in EMail: drrazhagaia@yahoo.co.in
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 test, Im PesaranShin W (IPSW) and Breitung test are used. To test whether the data are stationary and to satisfy one precondition for cointegration, Johansen Cointegration 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 cointegration test proves that there exists a stationary, longrun cointegration 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).
Key words: Dividend policy, Shareholders’ wealth, Financial meltdown JEL Classification: G 35, L 25, L 62
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 everchanging environment, any organization wishing to maintain a competitive position and to satisfy its shareholders’ expectation should be engaged in planning carefully every time when there is a need for. The SW (Azhagaiah and Sabaripriya, 2008) is represented by market price of the firm’s common stock, which in turn, is the function of the firm’s investment, financing and dividend decision. The modern approach of financial management provides a conceptual and analytical framework for decision making, which emphasizes the effective use of resources to create SW. The optimal dividend policy (DP) is one that maximizes the firm’s stock price; this leads to maximization of SW and thereby ensures rapid economic growth. Therefore, the present study is aimed at to study the longrun cointegration between the DP and the SW, and the impact of DP on SW before and after an event viz., the global financial meltdown.
Consumer cyclical sector includes industries such as automotive, housing, entertainment and retail. The sector can further be divided into durable and nondurable sectors. Durable includes physical goods such as hardware or vehicles, while consumer nondurable represents sector viz., entertainment or hotel services. The performance of consumer cyclical sector is highly related to the state of the economy. It represents goods and services that are not considered necessities, but for luxurious purchases. During contractions or recessions, investors have less disposable income to spend on consumer cyclical. When the economy is expanding or booming, the sale of these goods rise as retail and leisure spending increase. Consumer cyclical sector comprises textiles, automobiles, tyres, hotel, tourism and others as shown in figure A. Figure A Industries of Consumer Cyclical Sector
Researchers have propounded many theories about a firm’s value as well as the SW. There has been a substantial literature on the relationship between the DP and the SW and the impact of DP on SW. Several studies were made in respect of determinants of DP as well asSW in the developed as well as in the developing economics like India. Vijaya kumar (2011) revealed that the sales and profit after tax of automobile firms had strong relationship with SW. Devaki and Kamalaveni (2012)revealed that there was a positive association between lagged dividend, earnings, debtequity ratio, sales size, age of the firm and institutional shareholding of the Indian corporate hotels. Ganesh et al. (2013) found that the economic value added, market value added, cash flow, and market to book value ratio were healthier in Ashok Leyland than that of the Tata Motors. Priya and Nimalathasan (2013) revealed that dividend payout had a significant impact on SW. Further, earnings per share (EPS), price earnings ratio (P/E) and market price to book value (MP_BV) had significant correlation with return on assets (ROA); the P/E ratio had significant correlation with return on equity (ROE); EPS and the MP_BV were significantly correlated with ROE of the selected hotels and restaurants in Sri Lanka. Kumaresan (2014) found that there was a positive relationship between return on equity, dividend per share and DP and SW of the firms while there was a negative relationship between retention ratio and SW of the listed firms in hotel and travel sectors of Sri Lanka. Iqbal et al. (2014)found that the DP, firm size and firm growth had significant positive impact on SW of selected manufacturing industries from three sectors viz., textile, sugar and chemical. Ashvin (2012) found that there was a linear relationship between dividend decision and market price of stock of the firm of selected auto sector. Ajanthan (2013) showed that the DP was a crucial factor affecting the firm’s performance of the listed hotels and restaurants in Sri Lanka. The above literature provides a review of impact of DP on SW. The previous studies, by and large, were attempted to study the longrun and shortrun cointegration between DP and SW and the impact of DP on SW. In the present study, an attempt has been made to estimate the difference in the impact of DP on SW between pre and post financial meltdown periods.
Previous researchers have propounded many theories on DP as well as on SW. Thus, the researchers are puzzled by the question, “whether SW was affected by DP? for many years. In the literature, there are different views regarding whether DP affects firm’s share price in the longrun. Some studies showed that the firm’s value was not influenced by DP while some others showed that DP affected firm’s value (Toby, 2014; and Baker Collins et al.2007). So, the present study has made an attempt to study the difference in the impact of DP on SW between pre and post financial meltdown periods of the selected firms of Consumer Cyclical Sector in India.
The research proposes to seek answers to the following questions:
6.1. Specific Objectives
Source: Compiled and edited data collected from PROWESS database provided by CMIE
Source: Compiled data collected from PROWESS database provided by CMIE
Table 1 shows the number of firms of Consumer Cyclical sector listed in Bombay stock exchange (13), out of which dividend nonpaying firms (2), and firms for which adequate data were not in the data source (1) are eliminated, hence the ultimate number of sample firms considered for the study is 10 only. 8.3. Research Methods Besides various dividend variables and finance factors, statistical methods viz., Augmented Dickey Fuller Test, Johansen Cointegration, Ordinary Least Square method and Chow test are applied for analysis of data using Eviews 7 Econometrics software package.
8.4. Ratios used for Analysis The study used two important ratios viz., dividend related ratios and shareholders’ wealth related ratios and details of 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 (MPS)
Table2shows the variables used to study the cointegration between DP and SW and to analyze the impact of DP on SW before and after financial meltdown periods. Market price per share (MPS) is considered as proxy response variable for shareholders’ wealth (SW), while dividend per share (DPS), dividend payout (DPO), and dividend yield (DY) are considered as predictor dividend variables. Besides, the study also used finance variables viz., return on capital employed (R_CE), return on net worth (R_NW), return on assets (ROA), return on longterm fund (R_LF), return on equity (ROE), total debt to equity (TD_EQ), total debt to total assets (TD_TA), total debt to fixed assets (TD_FA), equity multiplier (EM), proprietary ratio (PR), total liabilities to net worth (TL_NW), current ratio (CR), quick ratio (QR), earnings per share (EPS), price earnings ratio (PER), working capital to total assets (WC_TA), current assets to total assets (CA_TA), and net fixed assets to net worth (NFA_NW) as predictor variables to study the impact of DP on SW.
For the analysis of pooled data for ten years i.e. from 200304to 201213 the following research methods are used.
Consumer Cyclical Sector Test of normality Table 3 shows the mean, standard deviation, skewness and kurtosis along with Jarque Bera test for MPS, DPS, DY, DPO and EPS of ten firms of Consumer Cyclical sector. As presented in table 3, the mean of MPS ranges from 56.44 (Ashok Leyland) to 4844.91 (MRF). Table 3  Descriptive and JarqueBera Normality Test Statistics for Market Price per Share and Dividend / Finance Variables for Firms under Consumer Cyclical Sector from 200304 to 201213
Source: Computed from the compiled & edited data from the financial statements of selected firm’s listedCMIEprowess package. ** Significant at 1% level; * Significant at 5% level. From the standard deviation, it is found that the MPS for most of the firms is highly dispersed from the central tendency (mean) (standard deviation is high for majority of the firms under Consumer Cyclical sector). Out of 10 firms with play kurtic, the MPS data are found to be with kurtosis, which are approximately equal to 3 for Exide industries, Indian hotels, MRF and Tata Motors, which fact first reveals that the MPS data are approximately symmetric. The JB test statistics for MPS data is significant for Ashok Leyland (13.66 at 1% level) and insignificant for all the other nine firms. This led to accept the null hypothesis that the data are normally distributed for the MPS.As far as the DPS data are concerned, the JB test statistics, based on skewness and kurtosis, are insignificant for eight firms, however they are significant for two firms (Apollo tyres and MRF), which evidences the presence of normality in the DPS. For DPO, the JB test result is <critical value of at 5 % level for four firms and it is insignificant for six firms. For DY, the mean ranges from 0.07 for MRF to 2.96 for Ashok Leyland. The JB test result is < critical value of at 5% level for Eicher motors and is insignificant for the rest of the nine firms, which fact shows that the data are normally distributed. Therefore, it is inferred that the MPS, DPS, DPO, DY and EPS are normally distributed for the firms under Consumer Cyclical sector. Unit Root TestTable 4  Unit Root Test (Panel) Results for Market Price per Share and Dividend Variables for firms under Consumer Cyclical Sector Note:Levin, Lin &Chu& Breitung tstat  Null: Unit root (assumes common unit root process) IPS (Im, Pesaran & Shin) Wstat, ADF  Fisher Chisquare & PP  Fisher Chisquare  Null: Unit root (assumes individual unit root process) Source: Computed from the compiled & edited data from the financial statements of selected firm’s listedCMIEprowess package. ** Significant at 1% level;* Significant at 5% level.
Table 4 shows the panel unit root test result for MPS, DPS, DY and DPO of firms of under Consumer Cyclical sector. From the table it can be inferred that for both the MPS and the DPS data series, the unit root test statistics are significant at first difference based on models without deterministic trend (no intercept and no trend, with deterministic trend having only intercept and with intercept and trend). Though, IPSW test is insignificant at levels and both the IPSW test and the Breitung ttest are significant when first differenced, the MPS data series with drift process (with time trend) is considered to be stationary at first differenced because most of the test statistics are significant. Hence, it is found that the MPS data series are integrated of order 1, i.e. I(1) satisfying one precondition for cointegration test. The unit root test statistics for DPO is significant at first differenced based on models without deterministic trend (no intercept and no trend, with deterministic trend having only intercept and with intercept and trend). Though, IPSW test is insignificant at levels and both the IPSW test and the Breitung ttest are significant when first differenced it is found that data are stationary at first differenced with intercept and no trend and also with intercept and trend, so it satisfies one precondition for cointegration test. The DY shows that the unit root test statistics is significant at first differenced based on models without deterministic trend (no intercept and no trend, and with intercept and trend so the data are cointegrated of order I (1)). Lag Length SelectionThe results of the analysis determining the lags for cointegration model between MPS and dividend variables viz., DPS, DPO and DY for Consumer Cyclical sector are shown in table 5. Based on the lag length shown by majority of the criterion, two lag is chosen for cointegration test between MPS and DPS. The chosen lag length for cointegration test is six between MPS and DPO and it is four between MPS and DY (the lag suggested by FPE and AIC is superior over LR test). Hence, the chosen lag length for cointegration test between MPS and DPS; MPS and DPO; and MPS and DY for Consumer cyclical sector is two, six and four respectively.
Table 5  Lag Length Selection Criteria for Cointegration Test for Market Price per Share with Dividend Variables of Firms under Consumer Cyclical Sector Source: Computed from the compiled & edited data from the financial statements of selected firms listedCMIEprowess 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 : HannanQuinn information criterion
Cointegration Test Table6  Cointegration Test Results for Market Price per Share and Dividend Variables of Firms under Consumer Cyclical Sector The results of cointegration analysis of Consumer Cyclical sector are shown in table 6.The table reveals that both the trace and the maximum eigen value test statistics are significant for CE with intercept but without time trend as well as CE with intercept and time trend hypothesized as ‘none’. This shows that the DPS and the MPS are cointegrated when the variables in the model are allowed for linear deterministic trend. This has further proved the existence of longrun relationship with time trend between DPS and MPS. The results further show that the data series is cointegrated as both the trace test and the maximum eigenvalue test reject the null hypothesis of no cointegration, and suggests that there are two significant cointegrating vectors in the model, which implies that there are two common stochastic trends indicating a degree of market integration. The DPS and the MPS have longrun relationship, which and is proved by trace rank test and maximum eigen value test without deterministic trend, with intercept without time trend as well as with intercept and time trend. The results of trace test and maximum eigen value test without deterministic trend for DPS and MPS show the critical value as 12.32 and 11.22, statistical value as 28.00and 27.95 respectively; that of for with intercept and without time trend the critical value as15.49 and 14.26,statistical value as30.88and 26.14 respectively; and that of for with intercept and time trend the critical value as 18.40 and 17.15,statistical value as32.09 and26.21respectively,which are highly significant at 1% level. The statistical values of the trace test and maximum eigen value test are >critical values for three situations i.e. without deterministic trend, with intercept without time trend as well as with intercept and time trend hence the null hypothesisH_{0}^{1}: “there is no cointegration between dividend per share (DPS) and shareholders’ wealth (SW)” is rejected at 1% level. Therefore, the cointegration results prove that there exists a stationary, longrun relationship between DPS and MPS. The results of trace test and maximum eigen value test 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 14.26,statistical value as16.44and 3.96 respectively; and that of for with intercept and time trend the critical value as 18.40 and 17.15,statistical value as4.80 each respectively, which are highly significant at 5% level. The statistical values of the trace test and maximum eigen value are >critical values for three situations i.e. without deterministic trend, with intercept without time trend as well as with intercept and time trend hence the null hypothesisH_{0}^{2}: “there is no cointegration between dividend payout (DPO) and shareholders’ wealth (SW)” is rejected at 5% level. The DY and the MPS have longrun relationship proved by trace rank test and maximum eigen value test without deterministic trend, with intercept without time trend as well as with intercept and time trend. The results of trace test and maximum eigen value without deterministic trend for DY and MPS show the critical values as 12.32 and 11.22, statistical values as 25.91and 18.27 respectively; that of for with intercept and without time trend the critical valuesas15.49 and 14.26,statistical values as34.69and 21.77 respectively; and that of for with intercept and time trend the critical values as 18.40 and 17.15,statistical values as34.28 and 22.14 respectively, which are highly significant at 1% level. The statistical values of the trace test and maximum eigen value test are >critical values for three situations i.e. without deterministic trend, with intercept without time trend as well as with intercept and time trend, hence the null hypothesisH_{0}^{3}: “there is no cointegration between dividend yield (DY) and shareholders’ wealth (SW)” is rejected at 1% level. Therefore, the cointegration results prove that there exists a stationary, longrun relationship between DY and MPS. Both the trace test and the maximum eigen value test statistics for the CEs without and with deterministic trend for MPS with DPS, DPO and DY are hypothesized as ‘none’ at level shows the presence of a longrun relationship between DP and SW(MPS and DPS; MPS and DPO; and MPS and DY). 10.Results and Discussion of Impact of DP on SW
Table 7 is reported with the results of regression for eliciting the impact of DP on SW. There are two regressions; first one with dividend variables (DPS, DPO and DY) besides the financial factors (P, LEV, OF, LQ, EPS, WF, AQ) and the second one is with financial factors (P, LEV, OF, LQ, EPS, WF, AQ) only. The significance of the explanatory power of DP on SW, when all the financial factors are held constant, is found based on F value obtained from comparing R^{2} values of the two models using the following formula: Where, R^{2}L = R^{2} from the larger model (full model) R^{2}S = R^{2} from the smaller model (subset model after removing certain predictors) df_{L }= Row degrees of freedom (or number of predictors) in the larger model df_{S }= Row degrees of freedom in the smaller model N = Number of observations
Table7 Impact of Dividend Policy (after Partialling out the Effect of Financial Performance) on Shareholders’ Wealth of Consumer Cyclical Sector
Source: Computed result from the compiled & edited data from the financial statements of selected firms listedCMIEprowess package. **Significant at 1% level;*Significant at 5% level. As per table7, both the full and the subset models of regressions are fitted significantly. From the observation of the individual coefficients in both the models, it is seen that the SW tend to increase with increase in EPS. Regarding the DP, it is seen that the SW seems to increase at significant level when there has been a significant increase in the DPS (β = 0.731, t =7.70, p < 0.01). While the full model, with both the dividend and the financial factors as predictors, has the power of explanation to the extent of 86.47 per cent of the variation; the subset model, with only financial factors as predictors, explain only to the extent of 51.62 % of the variation in the SW. The additional variance in the dependent variable (SW) explained by the dividend variables is 37.01 per cent (R^{2}L – R^{2}S). Further, the additional variance in presence of dividend variables is highly significant at 1% level (F value = 76.41, p < 0.01). Therefore, it is found that the DP (DPS), as an explanatory variable, has unique influence (impact) in creating additional wealth to the shareholders of firms. Therefore, H_{0}^{4}: “there is no significant impact of dividend policy (DP) on shareholders’ wealth (SW)” is rejected at 1% level.
To test whether there is any significant difference in the impact of DP on SW between pre and post financial meltdown periods, Chow test has been used and the results are shown in table 8.By applying Chow test, an attempt has been made to estimate whether there has been any significant difference in the impact of DP on SW between pre and post financial meltdown periods using the following formula: This is distributed as F with k and n_{1} + n_{2} – 2k degrees of freedom Where, F is the test statistic RSS _{p = }residual sum of squares for the whole sample_{ } RSS_{1 = }residual sum of squares for the first group (before financial meltdown) RSS_{2 = }residual sum of squares for the second group (after financial meltdown) N _{= }number of observations K _{=} number of regressors (including the intercept term) in each unrestricted subsample 2K _{=} number of regressors in both the unrestricted subsample regressions (whole sample) N_{1 = }number of observations for before financial meltdown period N_{2 = }number of observations for after financial meltdown period Difference in the Impact of DP on SW in Pre and PostFinancial meltdown Periods for Consumer Cyclical SectorThe results of the chow test (vide table 8) reveals that the F value for DPS (1.09) and DPO (2.15) are not significant and are higher than the 5% level. This shows that there is no significant difference in the impact of DP (DPS and DPO) on SW (MPS) between pre and post financial meltdown periods, i.e. the impact of DP (DPS and DPO) on SW is unaffected by the financial meltdown. Hence, H_{0}^{5}:“there is no significant difference in the impact of dividend per share(DPS) on shareholders’ wealth (SW) between pre and postfinancial meltdown periods” andH_{0}^{6}:“there is no significant difference in the impact of dividend payout(DPO) on shareholders’ wealth (SW) between pre and postfinancial meltdown periods” are accepted. Table 8  Results of Chow Test for the difference in the Impact of DP on SW between Pre and Post Financial Meltdown Periods for Consumer Cyclical Sector
However, the F value of DY (2.48) is significant at 5% level. Hence, H_{0}^{7}:“there is no significant difference in the impact of dividend yield (DY) on shareholders’ wealth (SW) between pre and post financial meltdown periods” is rejected at 5% level i.e. the impact of DY on SW is affected by the financial meltdown. Hence, it is concluded that the impact of DP on SW is significantly affected by the financial meltdown event only for the variable DY and not for the variables DPS and DPO.
The study attempts to answer the question: Is there any significant difference in the impact of DP on SW due to financial meltdown particularly the consumer cyclical sector. The main objective of the study is to shed light on the stated question. To test the relationship between DP and SW, and to estimate the impact of DP on SW before and after financial melt down periods,10 firms from Consumer Cyclical Sector are considered with one pre condition that the firms should have consistent track record in paying dividend over the period. The response variable viz., market price per share (MPS) is considered as proxy for SW and the dividend variables viz., DPS, DPO, and DY are considered as proxies of predictor variable (DP). The study used Johansen cointegration, factor analysis, regression and chow test to study the impact of DP on SW. The overall result of the study reveals that the trace test and maximum eigen value test statistics for the CEs without and with deterministic trend for MPS with DPS, DPO and DY hypothesized as ‘at most 1’ are not significant at level, hence it leads to accept null hypothesis that there is at most one cointegration equation for MPS with each one of the dividend variables, implying that the MPS and dividend variables are cointegrated. So, there is a longrun relationship between DP and SW of the selected firms. The finding of the study (the longrun relationship between DP and SW) corroborates with the findings of the previous research studies viz., Chidinma et al. (2013), Dewet and Mpinda (2013), Haque Collins et al. (2013), Mokaya et al. (2013), Oladele (2013), Salman (2013),Kumaresan (2014) and Toby (2014). The financial factors viz., profitability (P), leverage (LEV), owners’ fund (OF), liquidity (LQ), working fund (WF), asset quality (AQ) and dividend variables viz., earnings per share (EPS), market price per share (MPS), and dividend per share (DPS),which are used to estimate the impact of DP on SW show that the DP has influence (impact) in creating additional wealth to the shareholders of the selected firms. The overall conclusion of the analysis of the impact of DP on SW corroborates with the findings of the previous research studies viz., Arindam and Samanta (2012), Atiyet (2012), Gul Collins et al. (2012), Zafar et al. (2012), Onwumere et al. (2012), Altroudi and Milhem (2013), and Bawa and Kaur (2013). The study proves that there is a significant difference in the impact of DY (DY as a proxy of DP) on MPS (MPS as a proxy of SW), hence it can be concluded that the impact of DP on SW of firms of Consumer Cyclical sector in India is significantly affected by the financial meltdown event.
The study is based on secondary data collected from the Centre for Monitoring Indian Economy Private Limited (Prowess CMIE). Therefore, the quality of the study depends upon the accuracy, reliability, and quality of secondary data source. The analysis has produced some meaningful inferences and results, and one avenue for future research is to extend the investigation to the other sectors and across sectors. The present study has used market price per share (MPS) as a proxy for measuring the shareholders’ wealth (SW). Further studies may be conducted using the response variable viz., Economic value added (EVA) and Market value added (MVA) to measure the SW. The study has used research tools like Johansen cointegration test, multiple regressions and Chow test for analyzing the cointegration between DP and SW. Therefore, inclusion of some more appropriate methods of analysis viz., Block Exogeneity Wald test (1943), BaiPerron test (2003) and Variance decomposition for analysis may add to exploring new and further inference in the area of research. References Ajanthan, A. 2013. The relationship between dividend payout and firm profitability: A study of listed hotels and restaurants in Sri Lanka. International Journal of Scientific and Research Publications 3(6): 16.
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